Videos

Syntax	Description
shiny	Search all fields
title:shiny	Title only
author:"jane doe"	Author name
topic:visualization	Topic
duration:30..60	Duration in minutes
date:>2025-01-01	After a date
topic:visualization OR topic:community	Either value
topic:visualization AND topic:community	Both values
NOT topic:visualization	Exclude matches
(a OR b) AND c	Group with parens

YOU can contribute to base R! | Ella Kaye | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Ella Kaye, Senior Research Software Engineer at the University of Birmingham and a fellow of the Software Sustainability Institute, who walks through how to contribute to base R by live-fixing a bug in the stem() function. Ella demonstrates the full workflow of contributing to base R: setting up the R dev environment using GitHub Codespaces, checking out the R source code, building R from source, finding and fixing a bug in C code, running tests, and creating a patch file. She shows that contributing to base R is accessible even if you don’t know C, and highlights the welcoming R Contributor Working Group community with upcoming R Dev Days events where novice contributors can get hands-on help. Hosting crew from Posit: Libby Heeren and Isabella Velasquez Ella Kaye’s GitHub: https://github.com/EllaKaye Ella Kaye’s Bluesky: https://bsky.app/profile/ellakaye.co.uk Ella Kaye’s LinkedIn: https://linkedin.com/in/ellakaye Ella Kaye’s Mastodon: https://fosstodon.org/@ellakaye Ella Kaye’s Website: https://ellakaye.co.uk Resources mentioned in the video and chat: R Contributor Website: https://contributor.r-project.org/ R Contributors Slack: https://contributor.r-project.org/slack.html Penguins in base R paper (Ella’s work): https://zenodo.org/records/14902740 All of Ella’s Data Science Lab links: https://github.com/r-devel/data-science-lab Ella’s current role at R Core: https://blog.r-project.org/2025/12/17/rsmf-enabling-the-next-generation-of-contributors-to-r/index.html R Dev Day at useR! 2026: https://user2026.r-project.org/additional/r-dev-day.html ohwhaley package by Fonti Kar: https://fontikar.github.io/ohwhaley/ MonoLisa font (with ligatures): https://www.monolisa.dev/ basepenguins package by Ella: https://ellakaye.github.io/basepenguins CS50 Harvard course (includes learning C): https://cs50.harvard.edu/x ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! 00:00 Introduction 02:27 “What’s base R here and what’s tidyverse?” 06:02 “What type of pipe is that?” 07:20 Starting the live demo to fix a base R bug 08:24 Spinning up a code space on GitHub 11:33 Demoing the bug in the stem function in Positron 12:35 “I’m wondering if the BaseR team has a help wanted slash good first issue space similar to what rOpenSci has.” 13:39 “Is there anything especially interesting about the base R code base like Easter eggs or fun tidbits?” 16:00 Checking out the R source code from subversion 19:53 “Is SVN scary to use?” 22:27 “Can a future DS lab be on how to set up dev containers for things like teaching R or to make it easier for people to run a Shiny app, etcetera?” 23:45 Debugging the stem function source code in Positron 28:29 “Wait, what is unit testing?” 30:51 Looking at Bugzilla to find bug discussions 35:38 Editing the C source code and rebuilding R 39:57 Creating a patch file 41:01 “Where did we get the bug number from?” 43:42 “How did you go about adding penguins then?” 46:02 “Have people considered contributing to base R and if not why not?” 46:36 “How many people are currently developing BaseR?” 53:14 “I haven’t thought about contributing to base R because I thought of it as static software unlike R packages” 56:02 “Is there a similar process for contributing bug reports or fixes in our tools?” 56:46 “Is useR in person only?”

Same model 🤖 different magic 🪄🎩

A simplification? sure but this is the gist of it, I promise! #datascience #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf #positshorts #polars #datavalidation #validation #pydantic #rstats #rprogramming #rlang #rstudio #sql

Positron rlang rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Working with Larger than Memory Data in R

R/Medicine 2026 Workshop As datasets continue to grow in size and complexity, R users increasingly encounter data that exceeds their system’s memory capacity. This hands-on workshop provides practical strategies for efficiently analyzing larger-than-memory datasets using modern open source tools, with a focus on DuckDB and Apache Arrow—all while maintaining familiar tidyverse workflows. Participants will learn when and why to move beyond traditional in-memory data frames, and how to choose the right tool for their specific data challenges. Through a combination of presentation and hands-on exercises, we’ll explore how DuckDB enables SQL-based analytics on large datasets without loading them entirely into memory, and how Arrow provides a high-performance columnar data format for efficient data interchange and processing. We’ll also introduce duckplyr, which brings DuckDB’s performance optimizations directly to your existing dplyr code with minimal syntax changes. The workshop covers essential workflows including reading and querying large CSV and Parquet files, performing aggregations and joins on data that won’t fit in RAM, and leveraging duckplyr to accelerate familiar tidyverse operations on larger datasets. Participants will gain practical experience through real-world examples and learn decision frameworks for selecting appropriate tools based on data size, query patterns, and performance requirements—all without abandoning the tidyverse syntax they already know. Attendees should have basic familiarity with R and the tidyverse. By the end of this 3-hour session, participants will be equipped with reproducible techniques to confidently tackle larger datasets in their own research and data analysis workflows. Resources R/Medicine: https://rconsortium.github.io/RMedicine_website/ R Consortium: https://www.r-consortium.org/

Compliance Without Friction: Mastering the Persistent Analysis Lifecycle

Explore how Positron, Positron Assistant, and Databot (AI) help data scientists quickly analyze, document and refactor legacy code, then deploy the revised app in a click to Posit Connect. With reproducibility via Package Manager and governance via granular access controls, Posit delivers a rapid, traceable deployment cycle that closes the loop with stakeholders, elegantly and efficiently. What you’ll see in action: Integrated Development Environment: A Positron session in Posit Workbench, designed to support reproducible analyses, scalable models, and collaborative workflows. Legacy Refactoring: Using Positron and AI-powered Databot to document and refactor old code in minutes. Traceable Deployment: Deploy the app from Positron in Workbench to Posit Connect with granular access controls and one-click publishing. Guaranteed Reproducibility: Leveraging Posit Package Manager to ensure your models are auditable and stable for years to come

Positron RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

The Three-Click Rule for Executive Dashboards | Jason Frederick | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! This week’s guest was Jason Frederick, Corporate Treasurer (Interim) at Texas Capital! Some topics covered in this week’s Hangout were building dashboards for banking and finance (really, we got deep into the dashboard zone!), deploying Shiny applications on first AWS and then leveling up to Posit Connect, dashboard design principles and stakeholder communication (these go hand in hand), and using AI tools to accelerate development work. One community member asked: “For someone who’s getting into dashboards, what are tips and tricks that you wish you knew when you started?” Jason answered: The first thing I would do when asked to create a dashboard is step back and get out a piece of paper or whiteboard to draw it out visually. You need to understand what the stakeholder wants, though they often don’t always know. It’s your job to interpret what they’re saying. Then figure out your data source and access, and always recommend separating the data processing from the dashboard itself. Write a script that generates and summarizes your data to create an intermediate file, then have your dashboard read from that file. This allows the dashboard to run independently when you update data. For the visual design, pick the right architecture, keep your pages simple, and remember the three-click rule: executives need to see information and gather it from visual cues very quickly, in about 3 clicks or less. (Paraphrasing Jason a little here of course.) Resources mentioned in the video and chat: Big Book of R Finance Section → https://www.bigbookofr.com/chapters/finance Dask for Parallel Computing → https://www.dask.org/ CARFAX Customer Story with Tiger Tang → https://posit.co/about/customer-stories/carfax Posit Fraud Detection and Claims Analytics Demo → https://posit.co/product-demo/fraud-detection-and-claims-analytics Hangout Episode with Jay Sewell on Dashboard Management → https://posit.co/data-science-hangout/52-jay-sewall TidyTuesday Dataset Repository → https://github.com/rfordatascience/tidytuesday Gregory Power’s Public Dashboard Example → https://data.carync.gov/page/cassi-autonomous-shuttle-bond-park/ Hangout Episode with Libby and Rachael on Community building → https://youtu.be/lc6ad15gjeo Ted Laderas’ Databot TidyTuesday Project → https://github.com/laderast/astronomy-pod Credit Card Fraud Prediction Project → https://github.com/InfiniteCuriosity/InfiniteCuriosity.github.io/blob/main/_posts/2026-03-31-Predicting-credit-card-fraud.md Rachel K Collier Ableton Live Music Production Course (lots of music production talk in the chat this week!) → https://maryspender.teachable.com/p/how-to-produce-music-in-ableton-live Posit Connect Cloud → https://connect.posit.cloud/ PositConf 2026 Registration → https://conf.posit.co/2026/ Introducing Posit AI for Positron and RStudio → https://youtu.be/4er6aua7zyI Making a Business Case for Posit Tools → https://posit.co/champions/business-case ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 13:00 “What are interesting problems specific to banking or finance-related data science?” 16:22 “How can I prepare myself to land an entry-level data role?” 23:15 “How do you convince leadership of the value of using and paying for a tool like Connect or Connect Cloud?” 27:01 “For someone getting into dashboards, what are tips and tricks that you wish you knew when you started?” 35:47 “How do you strike the balance of what you put into a dashboard and what you don’t? What do you consider pertinent information and what do you consider fluff?” 41:00 “What advice can you give to stay up to date with data science developments as a manager who is no longer as close to the code?” 45:03 “How and from who do you acquire feedback for your dashboards? What kind of questions do you ask to get the feedback you want?” 46:44 “How would you recommend people get domain experience without being part of a team working in that specific domain, like fraud and compliance?”

New and updated open source packages from last month

This is just one month ‍ #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf #positshorts #polars #datavalidation #validation #pydantic #rstats #rprogramming #rlang #rstudio #sql

Positron rlang rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Demonstrating Value as the First Data Scientist | Hansel Palencia | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! This week’s guest was Hansel Palencia, Manager of Data Science at DaVita International! Some topics covered in this week’s Hangout were clinical data workflows and data governance, building data science teams from scratch in healthcare, predictive modeling for patient outcomes, and university major transitions from pre-med ambitions to discovering data science. One community member asked: What does the data show predicts healthy kidneys? (DaVita is a company centered on providing dialysis, a treatment for kidney failure) Hansel answered: Exercise and water are the two biggest things that you can do to prevent kidney disease. Exercise helps manage things like hypertension and obesity, which are major causes of kidney disease. Water and fluid intake are also key. Diet and exercise are your major influencers of whether you have kidney disease or not. Resources mentioned in the video and chat: Patchwork R Package → https://patchwork.data-imaginist.com/ Microsoft365R Package → https://github.com/Azure/Microsoft365R R/Medicine Conference → https://rconsortium.github.io/RMedicine_website/ David Robinson - Voices in the Code (Kidney Transplant Algorithms) → https://rconsortium.github.io/RMedicine_website/#david-robinson Why We Sleep by Matthew Walker → https://www.simonandschuster.com/books/Why-We-Sleep/Matthew-Walker/9781501144325 R Survival Package → https://cran.r-project.org/web/packages/survival/index.html Emily Zabor’s Survival Analysis Course → https://www.emilyzabor.com/survival-analysis-in-r.html Posit Workflow Demo (Using Posit AI in RStudio and Positron) → https://pos.it/workflow-demos Posit Conference Registration → https://conf.posit.co/2026/ Seattle Children’s Hospital Sr Data Analyst Position → https://careers.seattlechildrens.org/us/en/job/R261971/Sr-Data-Analyst-Strategic-Sourcing ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 09:24 “Can you give us an example of a problem you face in data science and the types of data you work with?” 16:04 “How did you convince your team to use Shiny over Excel?” 24:04 “Does this work satisfy your pre-med desires, and how did you get into data science?” 29:33 “What does a normal day look like for you?” 35:00 “What does the data show predicts healthy kidneys?” 37:09 “How do you measure subjective metrics like degree of pain?” 41:21 “Does a history of kidney disease also potentially predict other health conditions later on?” 43:42 “Are there any R packages out there that you feel like people are sleeping on and that deserve more press?” 46:04 “Is there a piece of career advice, something that you wish you had known going into your data science journey?”

Wake up babe, new IDE for Jupyter notebooks just dropped

Love the variables pane #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf #positshorts #polars #datavalidation #validation #pydantic

Positron rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

How to use the native pipe in R

It can be done! Anonymously…#datascience #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf #positshorts #polars #datavalidation #validation #pydantic #tidyverse #rstats #r #dplyr #databases #sql #rlang

dplyr Positron rlang rstudio tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Building Reproducible LLM Workflows | Leslie Emery | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! This week’s guest was Leslie Emery, Senior Principal Data Scientist at Bristol-Myers Squibb! Some topics covered in this week’s Hangout were data harmonization in Pharma, using LLMs for reproducible data workflows (you read that right!), transitioning from academia to industry, and building custom Shiny apps for internal stakeholders. One community member asked: “Given your role at BMS, what are some of the challenging aspects and rewards of your role?” Leslie answered: The most rewarding thing is working on something that someone else is specifically asking for and waiting for, and they’ll be excited to use it the moment it’s ready. Unlike academia where you might work for months or years and publish a paper without hearing feedback, in industry you get immediate feedback helping colleagues who need analysis-ready datasets. The challenging aspect is that BMS is an enormous organization, so everything requires collaboration. She had to learn that instead of trying to learn every single thing herself (the academia mindset), she should look for colleagues who already have expertise and collaborate with them. Resources mentioned in the video and chat: OHDSI Data Standardization → https://www.ohdsi.org/data-standardization/ CDISC Standards → https://www.cdisc.org/ FHIR Healthcare Data Standard → https://fhir.org/ tidyCDISC R Package → https://github.com/Biogen-Inc/tidyCDISC ADaM Standard → https://www.cdisc.org/standards/foundational/adam XKCD Standards Comic → https://xkcd.com/927/ Book of OHDSI → https://ohdsi.github.io/TheBookOfOhdsi/ Posit Connect Cloud → https://connect.posit.cloud/ Posit Data Science Lab → https://pos.it/dslab Positron IDE → https://posit.co/products/ide/positron Plumber for R APIs → https://www.rplumber.io/ Plumber2 → https://plumber2.posit.co/ Data-as-a-Product Talk (Clara Amorosi, BMS) → https://youtu.be/RrBdbbinS1E?si=gS3JCp0dqFFUVeZx PCDC Data Dictionaries → https://commons.cri.uchicago.edu/data-dictionaries/ Proteomic Data Commons → https://proteomic.datacommons.cancer.gov/pdc/ Cancer Data Aggregator → https://datacommons.cancer.gov/ Ravelry Knitting Community → https://www.ravelry.com/ LibraryThing Book Tracking → https://www.librarything.com/index.php Climate Change Data Visualization in Knitting → https://www.smithsonianmag.com/smart-news/knitting-enthusiasts-are-using-their-craft-visualize-climate-change-180974231/ Quinn Daedal Fabric Visualizations → https://quinndaedal.com/blog/2023/12/21/locked-loom-1-gideon-the-ninth/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 2:08 “What do you like to do for fun?” 3:23 “What types of data do you see and problems do you solve?” 9:10 “What are some challenging aspects and rewards of your role at the intersection of multiple domains?” 12:25 “What LLMs have you looked into for data harmonization?” 14:52 “Did you knit your sweater?” 15:18 “What sci-fi books do you recommend, and do you think AI could help or hurt with data harmonization?” 20:11 “Does BMS use data curation like CDISC or OHDSI?” 23:08 “How do you get your company to put resources into changing computational infrastructure?” 26:17 “What tools does your team use?” 29:16 “How often do you have to say no to harmonizing incompatible datasets?” 32:40 “Have you figured out how to use ontologies and data dictionaries in your harmonization flow?” 38:08 “Can you share advice for getting into work at the intersection of technical knowledge and data science?” 44:02 “Are you incorporating skills or MCP for providing necessary context?” 45:23 “Is there a way to measure levels of harmonization or dissonance in data, like in music?” 47:39 “Do you still have long-term projects? How do you motivate yourself through them?” 50:54 “Do you have a Ravelry account for tracking knitting projects?”

LLM agent skills for coding and development

Which Agent skill is missing from your life? #datascience posit-dev skills repo: posit-dev/skills on GitHub protestor comic: xkcd coding picture: Photo by Christopher Gower on Unsplash writing picture: Photo by Unseen Studio on Unsplash laptop photo: Photo by John on Unsplash #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf #positshorts #polars #datavalidation #validation #pydantic #ai #agent #skills

Positron rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Claude Code without typing anything 🤷🏻‍♂️ | Joey Marshall | Data Science Lab

The Data Science Lab is a live weekly call every Tuesday at 12pm US Eastern. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Joey Marshall, who walks through how he uses Claude Code for data science workflows and building interactive data applications. Joey shows us how he uses Claude Code, Wispr Flow, cmux, and the 1Password CLI to do data science in his everyday. He uses a TidyTuesday dataset about frog call observations in Australia to build an interactive web app called “Frog Finder” - complete with a cottagecore aesthetic, radius-based species search, and deployment to Vercel - all in about 30 minutes total (we ask Joey a lot of questions the rest of the time)! The dataset is publicly available, so you can try this at home, too! Hosting crew from Posit: Libby Heeren, Isabella Velasquez Joey Marshall’s LinkedIn: http://linkedin.com/in/joeymarshall Resources mentioned in the video and chat: Frog Finder App (built live during the session): https://frog-finder-iota.vercel.app/ Australian Frogs TidyTuesday Dataset: https://github.com/rfordatascience/tidytuesday/tree/master/data/2024/2024-08-27 Claude Code: https://www.anthropic.com/claude/code wispr flow for dictation: https://wisprflow.ai/ cmux Terminal: https://github.com/aca/cmux 1Password CLI: https://developer.1password.com/docs/cli Vercel: https://vercel.com/ Positron IDE: https://positron.posit.co/ Simon Couch’s LLM Evaluation Blog: https://simonpcouch.com/blog/ Anthropic Course Marketplace: https://www.anthropic.com/courses Posit Data Science Hangout every Thursday: https://pos.it/dsh ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps of Questions / Topics: 00:00 Introduction 02:30 “How long have you been using LLMs for your data workflows?” 04:00 “For someone who’s bearish on AI, what benefit does using something like Claude Code bring?” 07:00 Starting the Claude Code demo with the frogs dataset 08:00 Setting up terminal environment and cmux 10:00 Using 1Password CLI for secrets management 12:17 Asking Claude to explore the Australian frogs dataset 13:00 Managing secrets with 1Password vs plain text environment files 17:22 “Have you done any analysis of how different generative AIs perform for the kind of work you’re doing?” 18:00 Using Opus vs Sonnet vs Haiku models in Claude Code 19:20 “With your Claude prompts, what components are you intentionally saying versus what did you feel free to be more liberal about?” 21:00 “How does the computer know you’re talking to the AI versus just talking?” 22:00 Reviewing Claude’s three project proposals for the frog data 24:30 Going into planning mode and setting up deployment to GitHub and Vercel 26:00 “Do voice-to-text things require a lot of hardware to work fine?” 28:00 Choosing a cottagecore aesthetic and color scheme 28:30 “Do you have advice on setting up agents that are working on the same folder?” 34:00 “Is there an equivalent to the CLI 1Password functionality in Apple passwords on Mac?” 35:00 Reviewing the implementation plan from Claude’s planning mode 36:30 “What does the four percent context mean?” 41:00 Skills in Claude Code and system prompts 41:30 Frog Finder app is live and deployed 42:30 Viewing the deployed Frog Finder app in the browser 45:30 “Can you switch back and forth between models during the same session?” 46:30 “What resources would you recommend to learn all of this stuff?” 48:30 Verifying where Claude got the frog images (Wikipedia) 49:00 Asking Claude to use frog emojis instead of dots on the map 51:00 “How accurately did Claude pull the images from Wikipedia?” 52:30 Frog emojis are live on the map

Simon Couch

Building healthy data communities | Rachael Dempsey & Libby Heeren | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! This week’s guests were Rachael Dempsey, Customer Marketing Lead at Posit, and Libby Heeren, Data Community Manager at Posit! Some topics covered in this week’s Hangout were community building strategies, return on investment for community events, career transitions into data science and community management, and advice for starting internal communities. One community member asked: “What are challenges that you face being a community builder, and what are tips you would give to folks trying to build community as well?” Libby answered: The biggest challenge is that people don’t willingly volunteer to do things when you ask a group for volunteers. The most important tip is to individually ask people to contribute rather than making broad requests. You need someone who is consistently inviting, welcoming, and scheduling. Find a balance between structure (consistent schedule, same time and place) and flexibility (don’t over-prescribe the meeting content). Make it convenient for yourself as the organizer since you’ll be the one who always shows up. Resources mentioned in the video and chat: Posit Conference Registration → https://posit.co/conference/ Data Science Lab → https://pos.it/dslab mirai R Package → https://github.com/shikokuchuo/mirai Pinterest Customer Story → https://posit.co/about/customer-stories/pinterest/ Rachael’s conf talk about learning about community from growing up in her mom’s bar → https://www.youtube.com/watch?v=s0ZU4FDstfY Rachael’s 2022 Community Conf Talk as a Blog → https://rachaeldempsey.quarto.pub/community-corner-blog/posts/post-with-code/ Meetup Burnout Article by Ted Laderas → https://laderast.github.io/meetup_burnout/ “The New Gold Standard” Book (Leadership) → https://www.amazon.com/New-Gold-Standard-Leadership-Ritz-Carlton/dp/1480595829 “Unreasonable Hospitality” Book → https://www.unreasonablehospitality.com/ Hangout Episode with JD Long about Empathy When Integrating with Other Tools → https://www.youtube.com/watch?v=nrukV58ZXBg ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 06:20 “How do you describe to Posit leadership the value of Data Science Hangout and the return on investment?” 11:24 “What’s your career arc through data science and into teaching and community building?” 16:08 “What does a day in the life look like for you?” 20:13 “What are challenges that you face being a community builder, and what tips would you give to folks trying to build community?” 24:42 “What are the first steps for starting a data science hangout in Vietnam, and does Posit support regional organizers?” 32:56 “What skills or backgrounds or qualities do you look for in people?” 37:21 “How did you manage to put together such a positive and authentic community? Did you ever have issues with toxic community members?” 43:08 “What features do you wish you had on Zoom that would make hosting easier, and do you get nervous before hosting?” 47:30 “What goes into the behind-the-scenes of organizing the Hangout?” 51:23 “What’s your favorite piece of career advice?”

dbplyr: dplyr for databases

No SQL, no problem #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf #positshorts #polars #datavalidation #validation #pydantic #tidyverse #rstats #r #dplyr #databases #sql

dbplyr dplyr Positron rstudio tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Free and open-source tools for Python users

Interested in learning more? Let us know and we’re on it 🫡 #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf #positshorts #polars #datavalidation #validation #pydantic

Positron rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

What are the lifecycle badges on tidyverse packages?

What does your style guide look like? #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf #positshorts #quarto #tidyverse #rstats #rstudio

Positron Quarto rstudio tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Building Posit Assistant: An AI Agent for Data Science and Analysis

How do you build an AI agent specifically for the complex, iterative nature of data science and analysis? The Test Set podcast host Michael Chow talks to four Posit engineers about how they built Posit Assistant and what they learned in the process. Featuring: 0:00 Introduction 0:39 Simon Couch: Thinking 02:29 George Stagg: Terminal User Interface 03:31 Sara Altman: Predictive Modeling 05:14 Winston Chang: Image Data Extraction

George Stagg, Michael Chow, Sara Altman, Simon Couch, Winston Chang

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Plotnine: Grammar of Graphics in Python

Make beautiful plots with Python #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf #positshorts #quarto #ggplot2 #dataviz #datavisualisation Plotnine: https://plotnine.org Music: These Times by Blue Dot Sessions https://app.sessions.blue/browse/track/374609

ggplot2 plotnine Positron Quarto rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

What is literate programming?

The best way of writing data reports #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf #positshorts #quarto

Positron Quarto rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Ethical concerns, LLMs, and sensitive data in Legal | Tim Golden | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! This week’s guest was Tim Golden, Director of Data Science Analytics at Morrison & Foerster (or, MoFo) LLP! Some topics covered in this week’s Hangout were how AI and LLMs are reshaping the legal profession, building and maintaining a library of internal R packages to stay DRY (don’t repeat yourself!), navigating sensitive data and ethical walls while championing AI adoption at a law firm, and career advice on active listening and communicating technical ideas clearly. Resources mentioned in the video and chat: Morrison & Foerster (MoFo) → https://mofo.com/ Legal Tech Research Hub → https://www.legaltechnologyhub.com/ ILTA (International Legal Technology Association) → https://www.iltanet.org/home ellmer (R package for working with LLMs) → https://ellmer.tidyverse.org/ R Packages (book by Hadley Wickham & Jenny Bryan) → https://r-pkgs.org/ Adam Neely (from all the AI and music discussion!) → https://www.youtube.com/@AdamNeely Gödel, Escher, Bach by Douglas Hofstadter → https://bookshop.org/p/books/godel-escher-bach-an-eternal-golden-braid-douglas-r-hofstadter/fe2a529a5600a0c4?ean=9780465026562 Slideology by Nancy Duarte (this is her full website, but go look at her books) → https://www.duarte.com/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 08:12 “What does a normal day look like for you, and what software do you use?” 10:23 “With AI being the current fad in data science, how is that impacting your work in law and the legal profession as a whole, especially with copyright and so forth?” 13:36 “Which legal CRMs are popular these days for data use cases? How are you using your tools to figure out which cases are best for the firm?” 25:28 “I’m doing some work with legal document analysis pipelines, including with LLMs/agents, and I’m not at all connected with anyone else doing this. Where do you all hang out?” 27:53 “How did you get into the legal industry? When you hire a new data scientist or analyst, do you look specifically for people with this experience/domain knowledge?” 31:28 “How does your experience in the legal industry with sensitive data compare, and how do you champion AI adoption when privacy concerns are the immediate pushback?” 35:59 “I liked your point about creating a package early in a project if it’ll be reused. Do you have a standard playbook for building those, especially smaller or niche ones?” 40:08 “As a musician, do you have any hot takes on the rise of AI in music production?” 43:04 “Have you noticed a shift in the types of clients and the work you do for them since AI has ingrained itself more in everyone’s lives?” 45:01 “What kinds of things did you focus on prior to AI becoming as pervasive as it is, and how do you keep those skills strong?” 48:55 “Is there a piece of career advice that maybe you wish you had known when you were first getting started, or that you find is helpful to give out to people now?”

Hadley Wickham, Jenny Bryan

Easiest way to get started on a Python project?!

If we do say so ourselves #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf #positshorts

Positron rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

How to contribute to TidyTuesday by curating a dataset | Jon Harmon | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Jon Harmon, Executive Director at DSLC.io (the Data Science Learning Community), who walks through how to curate and submit a #TidyTuesday dataset, step by step. Ever wondered how all the amazing real world datasets provided weekly by the TidyTuesday project got there? Jon explains the curation process, and how you can help! #TidyTuesday datasets are used by students (including self-learners) to learn about data visualization and other aspects of data science. It’s fun to find and prep interesting datasets, but Jon wants to see what else is out there in the community, waiting to be shared! Get ready to curate! Hosting crew from Posit: Libby Heeren, Isabella Velasquez Jon’s GitHub: https://github.com/jonthegeek Jon’s Bluesky: https://bsky.app/profile/jonthegeek.com Jon’s LinkedIn: https://www.linkedin.com/in/jonthegeek/ Jon’s Mastodon: https://fosstodon.org/@jonthegeek TidyTuesday: https://tidytues.day DSLC website: https://DSLC.io Resources mentioned in the video and chat: Data Science Learning Community: https://dslc.io/ Official TidyTuesday repo: https://github.com/rfordatascience/tidytuesday Lisa’s vegetable garden data: https://github.com/rfordatascience/tidytuesday/blob/main/data/2024/2024-05-28/readme.md BLS data: https://github.com/rfordatascience/tidytuesday/blob/2f7730b4c51eec7dec09efc6b48a9583b3661736/data/2021/2021-02-23/readme.md Pokemon data: https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-04-01/readme.md Joe and Hadley’s keynote from 2025, which is mentioned: https://www.youtube.com/watch?v=tSFHQWGyRzo R package for TidyTuesday, including curation functions: https://github.com/dslc-io/tidytuesdayR Curating a dataset instructions: https://dslc-io.github.io/tidytuesdayR/articles/curating.html Bluesky feed where you can follow the TidyTuesday hashtag: https://bsky.app/profile/libbyheeren.bsky.social/feed/aaajcyvxadl56 Nicola Rennie’s BOOK about dataviz with TidyTuesday: https://nrennie.rbind.io/art-of-viz/ DSLC has a book club going through the book at DSLC.io right now: https://dslc.club/ttcookbook We also mention word-wrapping and soft-wrapping so that code wraps and stays visible on-screen! In Positron, you can use “editor.wordWrap”: “on”, or just search the settings UI for “wordwrap”, and in RStudio, you’re looking for Global Tools, Code, Editing, Soft-wrap source files ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps of Questions / Topics: 00:00 Introduction 03:26 Navigating the Tidy Tuesday website 06:27 What makes a good Tidy Tuesday dataset? 10:51 “Do TidyTuesday datasets get updated after they’re published?” 12:04 “How does it work if you use official data that can change?” 12:56 “Can you talk a little bit about why you created this curation process?” 14:48 Following the TidyTuesdayR curating instructions 18:34 Finding a dataset to curate via GitHub issues 20:34 Running tt_clean and setting up the cleaning script 31:35 Saving the datasets and generating data dictionaries 32:53 Editing and formatting the data dictionaries 37:04 Writing the dataset description and introduction 42:19 Saving a screenshot and adding alt text 46:12 Filling out the metadata 48:54 Running tt_submit to create a GitHub pull request 52:12 “How does one confirm a dataset has not been used before?” 53:49 Reviewing the submitted pull request on GitHub

Clinical data science when your patients are kids | Nikolay Braykov | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! This week’s guest was Nikolay Braykov, Manager of Advanced Analytics & Outcomes at Children’s Healthcare of Atlanta! Some topics covered in this week’s Hangout were the unique challenges of working with pediatric healthcare data, building clinical decision support tools using machine learning and LLMs, using Shiny and Posit Connect to communicate model outputs to clinical stakeholders, and statistical process control charts for healthcare quality improvement. Resources mentioned in the video and chat: HDAA (Healthcare Data & Analytics Association) → https://hdaa.memberclicks.net/ Trust, but Verify: Lessons from Deploying LLMs in a Large Health System → https://www.youtube.com/watch?v=HYQaZTLb2Co SHAP vs. LIME for Model Explanation (Medium) → https://medium.com/cmotions/opening-the-black-box-of-machine-learning-models-shap-vs-lime-for-model-explanation-d7bf545ce15f ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 00:52 “Could you introduce yourself, tell us about what you do, and something you like to do for fun?” 04:54 “Can you talk about the tools your team uses? Do you use notebooks, scripts? Are you deploying things?” 11:26 “How are you accounting for systemic bias in AI and ML tools trained on biased clinical data?” 13:34 “Can you speak to the differences between working with data about children versus data about adults?” 15:53 “Does your team use SAS at all? And how does that tie into the open source work?” 17:01 “Do you use tidymodels tools like parsnip?” 18:47 “Do you have access to internal LLMs you can use with PHI? And what does your data landscape look like?” 24:15 “What explainable ML outputs have you used to get stakeholder buy-in, and what’s been your most impactful project?” 28:19 “Clinicians often want a straightforward answer, but ML methods produce complex results. How do you balance that?” 29:59 “What’s your favorite project you’ve worked on at the pediatric hospital?” 35:12 “Clinicians at my hospital don’t understand the difference between prediction models and explanation models. What’s your experience with that?” 39:15 “Do you have a piece of career advice you’d give your younger self?” 41:51 “Are you building models in-house or mostly integrating vendor solutions like Epic’s AI?” 43:52 “How do you get buy-in for Posit Connect when there is free open-source Shiny?” 46:15 “Can you speak to transitioning into data science from a public health background?” 50:49 “Why do you tend to favor Python over R for deploying models?” 52:21 “How has the rise of agentic AI coding played into your day-to-day workflow, if at all?”

Five data validation libraries for Python Polars

Which has your favorite logo? #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf #positshorts #polars #datavalidation #validation #pydantic Rich’s validation roundup: https://posit-dev.github.io/pointblank/blog/validation-libs-2025/

pointblank Positron rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

What’s new in dplyr 1.2.0

Which one is your favorite? #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf #positshorts

dplyr Positron rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Not if, but how, AI will fail you

Where is AI making decisions for you right now? AI compounds, automates, and scales bias. It will create structural, symbolic, and ambient failures we haven’t imagined yet. We must acknowledge, and prepare, for this new reality. Lara Gast reflects on the inevitability and value of failure in both human and AI contexts in her posit::conf(2025) talk, Failure (and Mistakes). #AI #Failure #Data #DataScience

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Behind the scenes of tidyverse development: new dplyr functions | Davis Vaughan | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Davis Vaughan, Positron and tidyverse dev, who walks through the newest functions in dplyr, the problems they solve, how decisions are made to add or replace functions, and how community-driven development drives the tidyverse (and how wife-driven development drives Davis!). Hosting crew from Posit: Libby Heeren, Isabella Velasquez, Davis Vaughan Davis’s Bluesky: https://bsky.app/profile/davisvaughan.bsky.social Davis’s LinkedIn: https://www.linkedin.com/in/davis-vaughan/ Davis’s GitHub: https://github.com/DavisVaughan Resources mentioned in the video and chat: Tidyverse (R package collection) → https://www.tidyverse.org dplyr (R package) → https://dplyr.tidyverse.org tidyr (R package) → https://tidyr.tidyverse.org Air (R formatter) → https://github.com/DavisVaughan/air Tidy-ups (Tidyverse GitHub Repo) → https://github.com/tidyverse/tidyups Python Enhancement Proposals (PEPs) → https://peps.python.org dbplyr (R package) → https://dbplyr.tidyverse.org Lifecycle meanings → clock (R package) → https://clock.r-lib.org Advanced R (Book by Hadley Wickham) → https://adv-r.hadley.nz duckplyr (R package) → https://duckplyr.tidyverse.org R devel notes about %notin% being added to R natively soon → https://stat.ethz.ch/R-manual/R-devel/doc/html/NEWS.html ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps of Questions / Topics: 00:00 Introduction 02:01 Demo of why we needed the new recode_values function and what it does 08:49 “What all was going on behind the scenes that came together for you to say… it’s definitely time to take action?” 10:19 Demo showing how to thumbs-up an issue on GitHub 11:55 “Could you show us how packages like dplyr are created” 12:31 “So if we have a data dictionary, we could use recode_values with across() to apply the right values to the right question?” 14:07 “Why dplyr 1.2 as opposed to 2.0?” 15:15 “Can you talk a little bit about what a tidyup means?” 19:08 Demo of filter_out 23:51 “I didn’t catch that anonymous formula notation in the last shared screen. What was that for?” 24:32 “What’s the difference between a comma and an and?” 26:17 “Did Libby say wife-driven development?!” (She did) 29:08 “Thoughts on incorporating the power of dplyr into Snowflake pipelines for data transformations.” 30:58 Explaining the anonymous notation and what.x means 33:48 “Can we touch on deprecation?” and the difference between deprecating and superseding 39:03 “Is there an internal rule of thumb on how long before something goes from experimental to stable?” 40:10 “Why would we need a replace_values?” (and replace_values demo) 40:35 “How often do you have these wife-driven coding moments?” 41:17 “What exactly is a gist?” (feel free to roast Libby for how she says gist) 46:58 “Is there a list of packages or functions that are going to be archived so I can get a little bit more lead time if this ever happens again?” 49:02 “Could coalesce() be used on multiple columns?” 50:42 “Why do I keep seeing this dot dot dot as an argument option and what does that mean?” 54:22 “Any chance of duckplyr going mainstream and included in dplyr?”

Davis Vaughan, Hadley Wickham

Analyzing a TidyTuesday dataset with Posit Assistant in RStudio | Hadley Wickham | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Hadley Wickham who uses Posit’s new Posit Assistant (not to be confused with Positron Assistant or Databot in Positron) in RStudio to analyze the week’s TidyTuesday dataset. Hosting crew from Posit: Libby Heeren, Isabella Velasquez, Hadley Wickham Hadley’s Blueskey: https://bsky.app/profile/hadley.nz Hadley’s LinkedIn: https://www.linkedin.com/in/hadleywickham/ Hadley’s GitHub: https://github.com/hadley Hadley’s website: https://hadley.nz/ Resources mentioned in the video and chat: Posit AI / Posit Assistant waitlist → https://posit.co/products/ai/ Tidy Tuesday Dataset → https://github.com/rfordatascience/tidytuesday/blob/main/data/2026/2026-02-17/readme.md#agricultural-production-statistics-in-new-zealand nanoparquet (R package) → https://nanoparquet.r-lib.org/ Databot → https://positron.posit.co/databot.html wbstats (R package for WorldBank API) → https://github.com/gshs-ornl/wbstats World Bank NZ data → https://data.worldbank.org/indicator/SP.POP.TOTL?locations=NZ Hadley & Joe’s posit::conf talk ft. Hadley’s demo of ggbot2 → https://www.youtube.com/watch?v=tSFHQWGyRzo Wikimedia: New Zealand Total Population → https://commons.wikimedia.org/wiki/Data:New_Zealand_Total_Population.tab Posit Conf Talk: James Wade’s “Disposable Shiny Apps” → https://www.youtube.com/watch?v=smnrmTtoiOM Hadley’s unhinged {genzplyr} (R package) →https://github.com/hadley/genzplyr Simon Couch’s blog post on evaluating LLMs → https://posit.co/blog/python-llm-evaluation/ Garrick Aden-Buie mentioned working on a collection of skills → https://github.com/posit-dev/skills Max Woolf’s blog post “Does Offering ChatGPT a Tip Cause it to Generate Better Text?” → https://minimaxir.com/2024/02/chatgpt-tips-analysis/ Cat Hicks’s skill for coding agents that lets you learn from the agent → https://github.com/DrCatHicks/learning-opportunities (but also see her great thread on bluesky! https://bsky.app/profile/grimalkina.bsky.social/post/3mevvbm3a6s26 ) ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps of Questions / Topics 00:00 Introduction 01:17 Posit Assistant and using an agents.md file 02:54 Downloading TidyTuesday data using Posit Assistant 04:38 “Why use Parquet files over CSVs?” 07:11 Oops, switching Hadley’s mic, sorry! 09:27 Creating a visualization of sheep populations 10:28 “When would you switch models to Opus?” 13:28 “Do we have a Gen-Z human in the loop to verify the syntax?” (We did, folks, we did) 13:54 “How many bow ties does Hadley own?” 14:24 Asking the AI to retrieve real New Zealand population data 15:25 “Can the Posit Assistant use voice to text so people don’t have to type…?” 18:40 “What’s the purpose of the agents.md file and what goes in there?” 20:59 “How does posit AI in RStudio compare with the current positron AI assistants?” 22:39 “Will all the code generated by Posit assistant be stored in the session somehow to let somebody review what the agent generated?” 26:08 Having the AI create a Quarto report 26:54 “Does Posit Assistant also support Skills Files?” 27:49 “Were we always able to add agents.md in Positron too?” 28:17 “Did Hadley choose the name agents for agents.md or does each session have an agents file similar to having a README.md?” 29:57 Fixing the AI-chosen plot theme 31:55 “Is any of the stuff that it’s put in the report in Gen-Z speak, or is it only what it’s returning to you in the chat?” 33:24 “Can Posit Assistant create a renv as well?” 35:39 “Is Claude also the best for Python code?” 36:20 “If you add a behavior or a requirement to agents.md, does it take that new info in on the fly as you continue prompting?” 37:40 “What if you ask the agent why something happened?” 40:31 Investigating AI speculations about dairy cattle numbers 41:15 “Do you still need to know how to code in 2026?” 41:42 “Does using caps lock increase the likelihood that the LLM is going to listen to your agents.md file?” 45:47 “Could Hadley have done this faster without an AI?” 47:26 Hadley had AI do text clustering on image descriptions (previous TidyTuesday dataset) 48:41 Hadley had AI make a Shiny app to explore cluster sizes (previous TidyTuesday dataset) 52:53 “If one of the best ways to learn how to read code is to write code, what’s gonna happen if we’re writing it less?” 54:05 Wrap up and Posit AI waitlist information

Garrick Aden-Buie, Hadley Wickham, Simon Couch

Regression models still rule in P&C Insurance | Jim Weiss | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! This week’s guest was Jim Weiss, Chief Risk Officer, commercial and executive at Crum and Forster! Some topics covered in this week’s Hangout were the use of regression modeling (have you ever heard of the Tweedie distribution?) in property and casualty insurance, handling changing model results and communicating them to business stakeholders, using GenAI and “co-opetition” to identify and prevent fraudulent claims, and identifying and managing bias or confounding effects in pricing models. Resources mentioned in the video and chat: The Once and Future C&F → https://www.cfins.com/the-once-and-future-cf-landing/ Tweedie distribution → https://en.wikipedia.org/wiki/Tweedie_distribution Statistical Rethinking Lectures Playlist → https://www.youtube.com/playlist?list=PLDcUM9US4XdNOlqSyhe38US8mFgmqzI14 Considerations for Managing Potential Bias in Pricing Models → https://eforum.casact.org/article/91188-considerations-for-managing-potential-bias-in-pricing-models Pointblank for data validation (Python) → https://posit-dev.github.io/pointblank/ Pointblank (R) → https://rstudio.github.io/pointblank/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 02:16 “Who is Crum and Forster? What do you do? What types of problems does your team help them solve?” 04:14 “What kind of data types would you see in your day to day or your team would see in your day to day? And what is an example of a problem that you feel like you’ve solved lately or that you’ve been working on lately?” 10:14 “What types of regression do you use?” 13:35 “In your insurance modeling career, what is the most unusual or unexpected variable that has contributed to one of your models?” 18:48 “How do you handle it when you see that the results are significantly different across models?” 21:45 “Are you Team Bayesian or Team Frequentist when it comes to your statistics?” 27:13 “My health care organization is interested in identifying fraudulent claims. Currently, they’re looking at Excel spreadsheets, same time, different person. Do you have any advice on a better way to guide them to automation?” 30:51 “What software do you use to do your job?” 35:40 “Do you ever use instrumental variables in your models?” 47:47 “Do you have any career advice for us? Is there something you wish that you had known when you were first entering the industry?” 50:38 “How much do you and your team use AI to help you along?” 52:56 “How does your team span expertise in such varied fields?”

Python and AI

Wondering why Astral joined OpenAI? We recently hosted Charlie Marsh from Astral on The Test Set, where he discussed how AI agents were already reshaping Astral’s roadmap. Let’s hear where his head was at before the news broke. #python #pythonprogramming #ai #llm #astral #uv #openai #chatgpt

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Astral (creators of uv) joins OpenAI

Wondering why Astral joined OpenAI? We recently hosted Charlie Marsh from Astral on The Test Set, where he discussed how AI agents were already reshaping Astral’s roadmap. Let’s hear where his head was at before the news broke. #python #pythonprogramming #ai #llm #astral #uv #openai #chatgpt

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

How to use Positron’s GitHub integration | Isabella Velásquez & Libby Heeren | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren and Isabella Velasquez walk through collaborating in Positron using the GitHub integrations and extensions. They show starting a project, protecting the main branch, opening and closing issues, creating and deleting branches, stashing and popping changes, resolving merge conflicts, opening and reviewing pull requests, and more. Hosting crew from Posit: Libby Heeren, Isabella Velasquez, Isabel Zimmerman Isabella’s Bluesky: https://bsky.app/profile/ivelasq3.bsky.social Libby’s Bluesky: https://bsky.app/profile/libbyheeren.bsky.social Isabella’s LinkedIn: https://www.linkedin.com/in/ivelasq/ Libby’s LinkedIn: https://www.linkedin.com/in/libbyheeren/ Resources mentioned in the video: Positron → https://positron.posit.co/ VS Code Source Control → https://code.visualstudio.com/docs/sourcecontrol/overview Positron “create issues” triggers (add your own, too!) → positron://settings/githubIssues.createIssueTriggers GitHub → https://github.com/ Linking a GitHub issue to a pull request → https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue GitHub commands for PRs → https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue Git → https://git-scm.com/ Quarto → https://quarto.org/ GitHub Pull Requests and Issues Extension → https://marketplace.visualstudio.com/items?itemName=GitHub.vscode-pull-request-github Azure DevOps → https://azure.microsoft.com/en-us/products/devops/ Happy Git with R → https://happygitwithr.com/ Software Carpentry Git → https://swcarpentry.github.io/git-novice/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps of Questions / Topics 00:00 Introduction 02:56 Creating a Quarto book project in Positron 05:54 Initializing a Git repository and publishing to GitHub 06:49 Creating a.gitignore file 08:00 “Do you have to create a GitHub account before starting this kind of project?” 11:53 Staging changes and making the initial commit 13:34 Adding a collaborator on GitHub 14:25 Setting up branch protection rules 15:25 “Was that push to GitHub the initialization of the GitHub repo?” “What if you already had a GitHub repo with that same name, would you get a warning in positron?” 18:50 Working with pull requests and issues directly in Positron 20:18 Cloning an existing repository 24:09 Undoing your last commit 24:41 Stashing staged changes 25:12 Creating a new branch 26:03 Applying changes by popping a stash 27:03 Creating a pull request 28:37 How to put your activity bar in Positron to the top 29:40 Reviewing and merging a pull request 32:46 Deleting local and remote branches 36:08 “Are there benefits to doing things within positron vs the GitHub UI?” 39:13 “Do you know how to delete a branch if it’s an older rogue branch?” 41:40 Using keywords to automatically create GitHub issues 43:24 “Is the functionality similar if you’re working with an internal organization like Azure DevOps version of Git?” 44:12 “What is the best way to get an orientation into the world of Git and GitHub?” 46:42 Pulling and synchronizing branches 49:01 How to resolve merge conflicts

Isabel Zimmerman, Isabella Velásquez

Coding vs. thinking programmatically | Samia Baig | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! This week’s guest was Samia Baig, Senior Data Scientist/Data Engineer at Johnson & Johnson Innovative Medicine! Some topics covered in this week’s Hangout were transitioning from a background in pharmacy and public health to a data career in pharma, distinguishing the responsibilities of data scientists versus analytics engineers, strategies for making data pipelines more robust (and convincing your team that you NEED robust pipelines in the first place), and the value of joining open-source communities like Tidy Tuesday. Resources mentioned in the video and chat: Posit Data Science Lab → https://pos.it/dslab Tidy Tuesday GitHub Repository → https://github.com/rfordatascience/tidytuesday {dbplyr} → https://dbplyr.tidyverse.org/ The Missing Semester of Your CS Education → https://missing.csail.mit.edu/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 03:22 “Do you feel like analytics engineer is a good descriptor for what you do?” 04:44 “How did you get into data from being on a pharmacist’s job path?” 11:55 “What was it like in the move that you made from public health to pharma?” 16:57 “What do you say are distinguishing factors between data science and engineering?” 20:16 “What are the most popular tools that you and your team use, in your job at J&J?” 24:00 “What do you use SQL in?” 27:40 “How would you go about convincing a team of the need for a more robust pipeline?” 31:10 “Can you define robust?” 33:31 “Do you happen to have any specific resources or strategies or examples that might help students or others with that mindset of thinking programmatically?” 37:06 “Are there any non data science skills that are very helpful in your either current or former job?” 40:23 “Is there any kind of community among data scientists across the whole company?” 45:44 “What are your biggest data challenges that you have?” 46:12 “If you had a magic wand, what problem would you solve in that area?” 49:52 “What is a piece of career advice that maybe you wish you could go back in time and give yourself?”

Managing data platforms for 600 analysts | Tom Grace | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! This week’s guests were Tom Grace, Tech Lead on the Platform Team, and Darren Cope, Posit Subject Matter Expert, both at HMRC! We sadly lost connection with Darren due to wifi issues, but we hope to chat with him for a special video soon. We missed you Darren! Some topics covered in this week’s Hangout were managing Posit platforms and tools for nearly 600 analysts, strategies for evaluating and introducing new tools and packages in a large organization, utilizing dynamic compute allocation to let analysts manage their own processing budgets, and navigating change management by building empathy (empathy is always the answer, we love to see it!). Resources mentioned in the video and chat: Johnson & Johnson Positron Webinar → https://pos.it/j-and-j Announcing the Positron Notebook Editor for Jupyter Notebooks → https://posit.co/blog/announcing-the-positron-notebook-editor-for-jupyter-notebooks/ Accessibility in Data Visualization Course → https://openvisualizationacademy.org/courses/accessibility-in-data-visualization/ Posit Package Manager → https://posit.co/products/enterprise/package-manager/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 06:10 “Do you still have a lot of SAS users inside of your org?” 07:37 “Could you tell us about an example of a type of problem you tackle, the type of data that you see, and the tools you use?” 10:04 “What does that sort of composition look like for you as far as the people that you’re serving?” 11:15 “So when you say Python, is that like Streamlit apps being launched in Connect?” 11:46 “When people suggest new tools that they want to use, what is the most helpful information that they share? Can you help teach us how to get things approved by our own teams?” 18:59 “How do I find time for data science training if I’m struggling to complete routine work tasks?” 26:52 “Do stakeholders still want the reports to be in Excel?” 29:38 “Could you give us an example of what large processing means for your team on the regular?” 31:50 “Do you have any advice for handling this and providing reassurance?” 35:12 “How often do you get to upgrade your Posit environment to bring in new packages, features, etcetera?” 35:29 “When someone wants to use a package or a new tool, do you have a process that they have to go through to get packages approved and then also versions of R and Python?” 38:16 “How do you communicate technical changes with people across the organization.” 42:07 “Can you remind us the scale that we’re talking about here as far as the number of end users who might be in one of those channels asking for help?” 42:53 “What was the change management process like from your experience, and how did you overcome organizational inertia when moving to Posit?” 47:14 “As a leader, how do you raise everyone’s level so the whole team performs like your top players?”

Data analysis with Posit AI-assistants | Sara Altman & Simon Couch | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Sara Altman who walks through using Posit’s AI assistants to analyze data, including a sneak peek at Posit Assistant, and Simon Couch drops by to give us a demo of the reviewer package! Together, Sara and Simon author the Posit AI Newsletter, the best place to stay up-to-date with all the cool tools and advice on staying an informed and level-headed AI user. Hosting crew from Posit: Libby Heeren, Isabella Velasquez, Sara Altman, Simon Couch Sara’s Bluesky: https://bsky.app/profile/sara-altman.bsky.social Sara’s LinkedIn: https://www.linkedin.com/in/sarakaltman/ Sara’s GitHub: https://github.com/skaltman Posit AI Newsletter by Sara and Simon: https://posit.co/blog/?category=roundups Resources from the hosts and chat: Positron IDE → https://positron.posit.co/ Databot Extension → https://positron.posit.co/databot.html Getting started with Positron Assistant → https://positron.posit.co/assistant-getting-started.html Posit Assistant (Private Beta) → https://posit-ai-beta.share.connect.posit.cloud/ Reviewer Package (by Simon Couch) → https://github.com/simonpcouch/reviewer ellmer Package → https://elmer.tidyverse.org/ chatlas Package → https://github.com/posit-dev/chatlas Read the Posit AI Newsletter → https://posit.co/blog/?category=roundups Sign up to get the Posit AI Newsletter → http://pos.it/ai-news Simon’s blog post about local LLMs not quite being ready for primetime → https://posit.co/blog/local-models-are-not-there-yet/ Join the waitlist for Posit AI in RStudio → https://posit.co/products/ai/ Posit AI Known Issues & FAQs → https://posit-ai-beta.share.connect.posit.cloud/#frequently-asked-questions-faqs Blog post from Simon and Sara about Privacy and LLMs → https://posit.co/blog/trust-llm-tools/ DS Lab YouTube playlist → https://youtube.com/playlist?list=PL9HYL-VRX0oSeWeMEGQt0id7adYQXebhT&si=7tmU6EAJpO5S7GBh ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co The Lab: https://pos.it/dslab Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for learning with us! Timestamps 00:00 Introduction 07:23 “Would you mind real quick just briefly explaining the differences between Positron Assistant and Databot?” 15:01 “Is there any way to configure reasoning efforts when signing in with GitHub Copilot?” 15:49 “Does DataBot already support other providers beyond Cloud?” 20:36 “What is the cases with monetary penalty in the console output?” 22:14 “Do you happen to know if the column names of the dataset are very, very messy?” 23:18 “Can you add skills to DataBot?” 26:36 “This code isn’t being saved anywhere. So where does it go?” 27:38 “There a way to know what all the slash commands are?” 28:51 Requesting Databot to use the namespace operator 33:58 “Is there a way to search within that Databot pane?” 39:34 “Have you noticed any time differences with how quickly things run-in RStudio versus Positron?” 40:33 “What happens if you open that URL that it mentions at the bottom in your browser?” 40:50 Clarifying the difference between Posit Assistant and Positron Assistant 43:18 “What is the typical token burn rate?” 53:31 “Is this on CRAN and working in both Positron and RStudio?”

Sara Altman, Simon Couch

Motivating diverse data teams | Olivia Hebner | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! This week’s guest was Olivia Hebner, Data Science Manager at Summit Consulting! Some topics covered in this week’s Hangout were transitioning from an individual contributor/analyst into a management role, strategies for leading team members who possess specialized technical knowledge, navigating the modernization of legacy code systems with federal clients, and overcoming stage fright to give a successful data conference talk. Resources mentioned in the video and chat: Olivia’s posit::conf talk (Same data different tools, visualizing with R and Python) → https://www.youtube.com/watch?v=OJBLpeuHQ4s Blog: A User Guide To Working With You by Julie Zhuo → https://lg.substack.com/p/the-looking-glass-a-user-guide-to Blog: Glue work and how it affects women’s career development by Camila Granella → https://medium.com/@granellacamila/a-few-months-ago-i-discovered-the-term-glue-work-8a003dbe7173 Blog: The Value of Curiosity (and How to Cultivate a Curious Mind) by Libby Heeren → https://posit.co/blog/the-value-of-curiosity-and-how-to-cultivate-a-curious-mind/ PDF: Convincing your manager to let you go to posit::conf → https://irp.cdn-website.com/5b435773/files/uploaded/PST-Conf2026-ConvinceBoss1.pdf Call for talks for posit::conf(2026) → https://posit.co/blog/posit-conf-2026-call-for-talks/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 02:35 “I’d love to hear a little bit about your background and how you came to coding and analytics.” 04:01 “How did you decide that you were ready to make that leap from IC to people manager?” 05:31 “Do you feel like being a manager has made you a more capable data scientist in any way?” 07:06 “Because I’m not directly building things anymore (as a manager), I sometimes question whether I’m truly contributing. Do you have any advice on reframing that thought?” 08:44 “If you could give us some context on the types of problems that you solve, the types of data you work with, the types of clients you work with.” 11:16 “I would love to hear what you generally work on and what your team is made up of.” 12:22 “Do you do any reporting work or have reports that you deliver as a deliverable, like, through Quarto or any other publishing tools?” 14:41 “I’m curious about how privacy works with some of the data that you’re using. Are there rigid protocols that you follow?” 16:35 “Some people on my team are more knowledgeable than I am in certain areas, and I worry that I shouldn’t be managing them. How do you lead effectively in this situation?” 19:22 “When you’re working with your federal clients, for instance, how do you go about getting buy-in to rebuild their legacy systems or code?” 22:49 “I’m curious about what how your career transition happened, what made you make the jump to focus on data work?” 24:40 “What strategies do you use to keep feedback sessions positive and helpful?” 28:08 “What skills or personality traits do you get the vibe from them that says this person can actually turn from a research analysis mindset to a production/deployment way of thinking?” 30:24 “Do you have any tips or frameworks for getting to know your team members?” 32:06 “I would love it if you could give us a little rundown on why you wanted to give a conference talk and why you chose your topic (comparing data viz output in R vs Python to show how similar they are).” 37:07 “Is there anything you do to prepare before giving a talk since they make you nervous?” 39:15 “I manage a team where some people focus on doing their job reliably while others are really high achievers. How do you keep both groups motivated and engaged?” 42:36 “I was wondering if you could talk more about the different kinds of projects that you work on, and how often you are building technical capacity on the team that you’re working with?” 45:16 “In my team, some members reflect quietly while others jump straight into discussions. Any tips for making sure everyone’s voice is heard?” 48:33 “Is there a piece of career advice that has really helped you or that you like to give other people or maybe something you wish you had been told or learned much earlier in your career?” 51:01 “Have you tried pyspark.sql? And do you validate ML pipelines or just for end results?”

The mall package: using LLMs with data frames in R & Python | Edgar Ruiz | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Edgar Ruiz as they walk through how mall works (with ellmer) in R, and then python. The mall package lets you use LLMs to process tabular or vectors of data, letting you do things such as feeding it a column of reviews and asking mall to use an anthropic model via ellmer to add a column of summaries or sentiments. Follow along with the code here: https://github.com/LibbyHeeren/mall-package-r Hosting crew from Posit: Libby Heeren, Isabella Velasquez, Edgar Ruiz Edgar’s Bluesky: https://bsky.app/profile/theotheredgar.bsky.social Edgar’s LinkedIn: https://www.linkedin.com/in/edgararuiz/ Edgar’s GitHub: https://github.com/edgararuiz Resources from the hosts and chat: Ollama → https://ollama.com/download Posit Data Science Lab → https://posit.co/dslab mall package → https://mlverse.github.io/mall/ ellmer package → https://elmer.tidyverse.org/ Libby’s Positron theme (Catppuccin) → https://marketplace.visualstudio.com/items?itemName=Catppuccin.catppuccin-vsc GitHub repo with Libby and Edgar’s code → https://github.com/LibbyHeeren/mall-package-r LLM providers supported by ellmer → https://ellmer.tidyverse.org/index.html#providers vitals package → https://vitals.tidyverse.org/ chatlas package → https://posit-dev.github.io/chatlas/ polars package → https://pola.rs/ narwhals package → https://narwhals-dev.github.io/narwhals/ pandas package → https://pandas.pydata.org/ LM Studio → https://lmstudio.ai/ Simon Couch’s blog → https://www.simonpcouch.com/ Edgar’s dataset: TidyTuesday Animal Crossing Dataset (May 5, 2020) → https://github.com/rfordatascience/tidytuesday Libby’s dataset: Kaggle Tweets Dataset → https://www.kaggle.com/datasets/mmmarchetti/tweets-dataset Blog from Sara and Simon on evaluating LLMs → https://posit.co/blog/r-llm-evaluation-03/ Data Science Lab YouTube playlist → https://www.youtube.com/watch?v=LDHGENv1NP4&list=PL9HYL-VRX0oSeWeMEGQt0id7adYQXebhT&index=2 AWS Bedrock → https://aws.amazon.com/bedrock/ Anthropic → https://www.anthropic.com/ Google Gemini → https://gemini.google.com/ What is rubber duck debugging anyway?? → https://en.wikipedia.org/wiki/Rubber_duck_debugging ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co The Lab: https://pos.it/dslab Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for learning with us! Timestamps 00:00 Introduction to Libby, Isabella, Edgar, and the mall package + ellmer package 07:14 “What’s the difference between using mall for these NLP tasks versus traditional or classical NLP?” 09:37 “Can mall be used with a local LLM?” 17:32 “What kind of laptop specs should I realistically have to make good use of these models?” 22:12 “Are you limited to three output options?” 22:55 “Can mall return the prediction probabilities?” 24:14 “What are a rule of thumb set of specs for a machine so local LLMs are practically feasible?” 24:47 “Would that be in the additional prompt area where you’re defining things?” 25:04 “You could use the vitals package to compare models, right?” 25:24 “Can we use LM Studio instead of Ollama?” 28:35 “How do you iterate and validate the model?” 36:39 “Why use paste if it is all text?” 37:31 “Are these recent tweets (from X) or older ones from actual Twitter?” 40:23 “Is there a playlist for the Data Science Labs on YouTube?” 46:11 “Does that mean that the python version does not work with pandas?” 50:14 “Where is this data set from?”

Edgar Ruiz, Simon Couch

The Reluctant Admin: motorsports, data science, & IT | Brijesh Chejerla | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! This week’s guest was Brijesh Chejerla, Lead Data Scientist at Blue Cross Blue Shield of Florida (Florida Blue)! Some topics covered in this week’s Hangout were how to transition into different industries (such as transitioning from academia into motorsports ️), the importance of thinking like an architect and learning system design, and the impact of AI coding agents on the future of data science roles. Resources mentioned in the video and chat: Posit::conf(2026) Call for Talks → https://posit.co/blog/posit-conf-2026-call-for-talks/ Call for Talks Q&A Video → https://youtu.be/aKN8qlLjld4 Johnson & Johnson Positron Webinar → https://pos.it/j-and-j R!sk Conference 2026 → https://rconsortium.github.io/Risk_website/ Rainbow R Conference 2026 → conference.rainbowr.org Think Like a CEO Book → https://www.amazon.com/Think-Like-CEO-reacting-control/dp/B09LGWT11C Sports Analytics and Data Science Book → https://www.amazon.com/Sports-Analytics-Data-Science-Winning/dp/0133886433 Portfolio Presentation Walkthrough → https://jedmund.com/universe/portfolio-recording Register for Data Science Lab → https://pos.it/dslab ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 03:01 “What team you support at BCBS of Florida?” 03:37 “Could you talk a little bit about your stint in motorsports analytics?” 05:23 “Favorite example of data science applied in football or sports in general?” 09:41 “Would you be able to provide some resources for people who are interested in machine learning and football?” 13:04 “How did you pick an industry?” 18:57 “What is the most commonly used ML technique and where does deep learning come into play in your day to day job?” 22:45 “What tools do your teams use?” 27:31 “How did you so successfully transfer that IT admin ownership over to someone else?” 30:53 “How do you think AI is going to impact data science roles?” 38:48 “How do you quickly become the data expert?” 43:49 “I’ve been told that I need to have a portfolio to get hired. How true is that?” 46:42 “What career advice has helped you in your career?”

Strategic Budget Optimization through Marketing Mix Modeling (MMM)

Stop guessing which channels drive growth. Join Isabella Velásquez and Daniel Chen for this end-to-end workflow demo on modeling marketing spend, building ‘what-if’ scenario planners in Shiny, and deploying automated Quarto reports that give non-technical stakeholders a clear, data-backed view of ROI. We will explore: The Workflow Architecture: A look at the “plumbing” required to move from data to insights. The Interface: Wrapping an existing model in a Shiny dashboard for real-time “what-if” scenario planning. The Delivery: Automating communication via Quarto reports to keep stakeholders aligned. The Deployment: Using Posit Connect to turn your code into an accessible business asset for leaders. This workflow demo grew out of the incredible energy at our recent Data Science Hangout with Ryan Timpe at LEGO. We were so inspired by the community’s interest in MMM that we decided to build this session to address your specific questions. Thank you for the great discussion. We’re excited to show you what we’ve put together! Demo References MMM Demo GitHub Repo ( https://github.com/chendaniely/dashboard-mmm ) MMM Demo Dashboard ( https://danielchen-dashboard-mmm.share.connect.posit.cloud/ ) Ryan Timpe’s posit::conf(2023) talk, tidymodels: Adventures in Rewriting a Modeling Pipeline - posit::conf(2023) ( https://www.youtube.com/watch?v=R7XNqcCZnLg ) MMM Resources Bayesian Media Mix Modeling for Marketing Optimization, PyMC Labs ( https://www.pymc-labs.com/blog-posts/bayesian-media-mix-modeling-for-marketing-optimization ) Modernizing MMM: Best Practices for Marketers, IAB ( https://www.iab.com/wp-content/uploads/2025/12/IAB_Modernizing_MMM_Best_Practices_for_Marketers_December_2025.pdf ) The Future of Media Mix Modeling, Measured ( https://info.measured.com/hubfs/Guides/Measured_Guide%20The%20Future%20of%20Media%20Mix%20Modeling.pdf?hsCtaAttrib=184767817336 ) Media Mix Modeling: How to Measure the Effectiveness of Advertising, Hajime Takeda, PyData ( https://www.youtube.com/watch?v=u4U_PUTasPQ ) An Analyst’s Guide to MMM, Meta ( https://facebookexperimental.github.io/Robyn/docs/analysts-guide-to-MMM/ ) Full Python Tutorial: Bayesian Marketing Mix Modeling (MMM) SPECIAL GUEST: PyMC Labs, Matt Dancho ( https://www.youtube.com/watch?v=lJ_qq_IVUgg ) Reading MMM Outputs: Dashboards and Decisions for Small Teams, SmartSMSSolutions ( https://smartsmssolutions.com/resources/blog/business/reading-mmm-dashboards-article )

Isabella Velásquez

Driving Shiny adoption by removing friction | Jonathan Lin | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Jonathan Lin, Adviser of Data Science and AI at Ovintiv, to chat about scaling data science infrastructure with Kubernetes, the “show, don’t tell” method for leadership buy-in, creating internal handbooks to support users, and even a little chat around navigating a sparse job market. In this Hangout, we explore Jon’s approach to driving adoption and scaling impact without becoming a bottleneck. He discusses moving from simple virtual machines to using Kubernetes to manage memory for over 900 users and 60 publishers (wow!!). John also details how he built a comprehensive internal “handbook” to answer repeated questions, allowing him to support a growing community of R and Python users while encouraging self-service learning. Resources mentioned in the video and zoom chat: Posit Conf 2026 Call for Talks → https://posit.co/blog/posit-conf-2026-call-for-talks/ Data Science Lab → https://pos.it/dslab R Screencasts (David Robinson) → https://www.rscreencasts.com/ Python for Data Analysis (Wes McKinney) → https://wesmckinney.com/book/ Cool 3D printing ggplots and maps using Rayshader! (shared in chat) → https://www.tylermw.com/posts/data_visualization/3d-printing-rayshader.html If you didn’t join live, one great discussion you missed from the zoom chat was about Dragon Boating! Jon coaches a team, and the chat lit up with curiosity, explanations, and excitement about the sport (and its potential crossover with 3D printing paddles? We are the best community). Have you ever watched a dragon boat event?! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 10:38 “What are tips for navigating a sparse job market?” 14:33 “How do you support all of your users with different skills?” 17:49 “What types of fraud were you working with?” 21:12 “How do you get people to actually read it?” 24:55 “How did you learn R?” 29:18 “What are some techniques you use to get people to trust the tech?” 33:35 “Do you block time each week for documentation?” 36:50 “How would you recommend getting a company to buy in?” 42:15 “How do you pick the topics to learn more about?” 45:34 “Advice on how to overcome fear and feel confident enough to ask questions?” 49:09 “Do you program much in your current position?” 51:34 “Is it truly necessary to have deployment experience to get a data science job?”

The one skill AI can’t replace | Alexander Schacht | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Alexander Schacht, Senior Director at Cytel and Host of The Effective Statistician, to chat about the tensions between technical and executive work, how to increase visibility in your organization, and ethical considerations in data science (specifically regarding patient data). In this Hangout, we explore the “engine vs. wheels” analogy for data professionals. Alexander explains that while technical skills (the engine) are vital, they are useless without communication and leadership skills (the wheels) to put that power on the road. He shares advice on how to stop being the “p-value police” and instead build trust by understanding stakeholders’ fears, hopes, and business goals. Resources mentioned in the video and zoom chat: The Effective Statistician Podcast → https://theeffectivestatistician.com/podcast/ Alexander’s Book: How to be an Effective Statistician → https://theeffectivestatistician.com/book-launch/ Nonviolent Communication (Book Recommendation from Libby) → https://www.amazon.com/Nonviolent-Communication-Language-Life-Changing-Relationships/dp/189200528X If you didn’t join live, one great discussion you missed from the zoom chat was about the pressure to “look busy” versus actually delivering results, sparked by Alexander’s advice to decline more meetings. The chat debated the difficulty of doing this in a “grind culture” and how to prioritize signal over noise. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 05:28 “What are some of the tensions between technical work and executive type work?” 08:56 “Do you have any advice for increasing visibility where bias or prejudice exists?” 14:26 “Have you been able to influence the top down people?” 18:31 “How do you reconcile with some of the ethical things about work?” 23:00 “How can I constructively deal with concerns managed upwards towards me as a manager?” 30:15 “What are some strategies that you use to manage expectations?” 34:23 “Most valuable skill set to invest in this age where Gen AI can do so much?” 40:14 “Where would people acquire the niche technical skills without getting experience?” 41:57 “What is your advice to look for in a prospective employer or manager?” 45:34 “How do you juggle both [being an employee and a business owner] at the same time?” 48:56 “Have you experienced stakeholders trusting AI more than they trust experts?” 52:19 “How do you get a feeling for discussions you need to go to versus things that will work themselves out?”

Using R package structure for data science projects | Kylie Ainslie | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Kylie Ainslie who walks through how structuring data science projects as R packages provides a consistent framework that integrates documentation for you and facilitates collaboration with others by organizing things really well. Kylie says, “I stumbled on using an R package structure to organize my projects a number of years ago and it has changed how I work in such a positive way that I want to share it with others! In a world where our attention is constantly being pulled in many directions, efficiency is crucial. Structuring projects as R packages is how I work more efficiently.” Hosting crew from Posit: Libby Heeren, Isabella Velasquez Kylie’s Bluesky: @kylieainslie.bsky.social Kylie’s LinkedIn: https://www.linkedin.com/in/kylieainslie/ Kylie’s Website: https://kylieainslie.github.io/ Kylie’s GitHub: https://github.com/kylieainslie Resources from the hosts and chat: posit::conf(2026) call for talks: https://posit.co/blog/posit-conf-2026-call-for-talks/ Kylie’s posit::conf(2025) talk: https://www.youtube.com/watch?v=YzIiWg4rySA {usethis} package: https://usethis.r-lib.org/ R Packages (2e) book: https://r-pkgs.org/ Paquetes de R (R Packages in Spanish): https://davidrsch.github.io/rpkgs-es/ {box} package: https://github.com/klmr/box extdata docs in Writing R Extensions: https://cran.r-project.org/doc/manuals/R-exts.html#Data-in-packages-1 Tan Ho’s talk on NFL data: https://tanho.ca/talks/rsconf2022-github/ {rv} package: https://a2-ai.github.io/rv-docs/ Whether to Import or Depend: https://r-pkgs.org/dependencies-mindset-background.html#sec-dependencies-imports-vs-depends {pkgdown} package: https://pkgdown.r-lib.org/ Edgar Ruiz’s {pkgsite} package: https://github.com/edgararuiz/pkgsite Attendees shared examples of data packages in the chat! Here they are: https://kjhealy.github.io/nycdogs/ https://kjhealy.github.io/gssr/ https://github.com/deepshamenghani/richmondway https://github.com/kyleGrealis/nascaR.data https://github.com/ivelasq/leaidr ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co The Lab: https://pos.it/dslab Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for learning with us! Timestamps: 00:00 Introduction 06:17 Reviewing the disorganized project example 10:01 Creating the package structure using create_package 17:50 Organizing external data and scripts in the inst folder 22:55 Adding a README and License 29:06 “What are the advantages to packaging a project?” 33:35 Writing Roxygen2 documentation 36:06 “Do you type return at the end of your functions?” 41:55 Handling dependencies with use_package 43:53 “Can you just use require(dplyr) at the top?” 47:45 Setting up a pkgdown site 50:11 Creating vignettes 52:22 “What is the role of the usethis package?” 54:18 Loading the package with devtools::load_all

Edgar Ruiz

Positron workflows that make life easier | Andrew Heiss | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Andrew Heiss as he demonstrates some tips and tricks about his personal workflow and tools that he actually uses to make life easier in Positron. This is the ultimate list of data life hacks to make your workflow soooo much nicer. Check out Andrew’s blog post here to follow along with the tools he mentions: https://andhs.co/dsl Hosting crew from Posit: Libby Heeren, Isabella Velasquez Andrew’s Website: https://www.andrewheiss.com/ Andrew’s Bluesky: https://bsky.app/profile/andrew.heiss.phd Andrew’s LinkedIn: https://www.linkedin.com/in/andrewheiss/ Resources from the hosts and chat: Andrew’s blog post containing links to all of the tools he mentions: https://www.andrewheiss.com/blog/2026/01/13/dsl-positron-workflow/ Open VSX Registry: https://open-vsx.org/ DataPasta: https://milesmcbain.github.io/datapasta/ Pastum (like datapasta for Positron): https://open-vsx.org/extension/atsyplenkov/pastum Positron Project docs: https://positron.posit.co/migrate-rstudio-rproj.html Garrick’s data science extension bundle package: https://github.com/gadenbuie/positron-plus-1-e Emil’s keyboard shortcut blog post: https://emilhvitfeldt.com/post/positron-key-bindings/ Native Tabs for Mac: https://lucasprag.com/posts/underrated-vscode-feature-native-tabs/ Andrew’s posit::conf(2025) Talk: https://youtu.be/UCloM4GcfVY Arc browser that Andrew is using: https://arc.net/ Andrew’s YAML headers he sets up using espanso: https://github.com/andrewheiss/espanso/blob/52da6c43c6d1ebaf3231770b1b66971d1dfb374a/match/markdown-pandoc-quarto.yml#L118 ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co The Lab: https://pos.it/dslab Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for learning with us! Timestamps: 00:00 Introduction 01:50 Switching to Positron full-time 03:19 Extensions in Positron 04:44 How to evaluate if an extension is safe 07:05 Air extension (auto-formatting) 08:21 Better Comments extension 10:15 Moving the Activity Bar 12:20 Pastum extension (DataPasta equivalent) 14:26 Rainbow CSV extension 15:34 Spell Right extension 17:34 Managing projects in Positron vs RStudio 20:18 “Do you know if there are extensions… that will conditionally format cells?” 20:40 “Do you explicitly add a dot here file?” 21:44 Project Manager extension 25:34 “How did you discover all of these?” 26:38 “How is GitHub integrated into Positron?” 29:10 Peacock extension 31:16 The Connections pane 36:38 “When I change the Peacock color, it’s changing colors for everything.” 37:59 “Does he use any DuckDB extensions?” 39:05 Raycast 43:35 Raycast scripts 44:30 NotebookLM 45:31 “Is there a hack to manage a repo that is both a project and an R package?” 48:00 Espanso 53:15 “Is Raycast a replacement for Spotlight and Bartender?” 54:00 “Is there an easy way to see all of the shortcuts?”

Hackathons for education, not competition | Allissa Dillman | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Allissa Dillman, Founder and CEO at Biodata Sage LLC, to chat about her transition from NIH to entrepreneurship, the importance of social learning and community in coding (Libby’s favorite topic!!), organizing hackathons for education rather than just competition, and strategies for networking regardless of whether you are an introvert or an extrovert. In this Hangout, we explore the power of social learning and community. Allissa discusses why she believes we learn and grow more confidently when working with others rather than in isolation, noting how shared struggles can actually help overcome imposter syndrome. She also shares her philosophy on running hackathons that prioritize networking and experiential learning over high-pressure competition, and talks about how she navigated the leap from a stable job to starting your own business. Resources mentioned in the video and zoom chat: Allissa’s posit::conf(2025) Talk: From Solo to Social → https://youtu.be/HP3VfGkHQ1c How to network in a non-slimy way (Article by Ted Laderas shared in the chat) → https://laderast.github.io/articles/2021-09-13-how-to-network-in-a-non-slimy-way/how-to-network-in-a-non-slimy-way.html If you didn’t join live, one great discussion you missed from the zoom chat was about the dynamics of introverts versus extroverts in networking. Attendees discussed the benefits of finding a friendly neighborhood extrovert to help navigate conferences and even proposed the idea of “willing to adopt an introvert” stickers for future events. Would you be open to buddying up with an introvert at conf? Would you like an extrovert friend to adopt you? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 04:11 “Recap what you talked about at posit::conf” 07:58 “What would you say are the largest challenges of setting out on your own as an entrepreneur?” 11:47 “How did you make that transition from NIH to entrepreneurship smoothly?” 16:01 “What were some of your wins at NIH?” 19:15 “Could you expand a little bit on your work at NIH?” 22:45 “Any advice for introverts on networking?” 26:45 “Any tips on standing up those little community based groups within organizations?” 32:17 “What are some of the success stories that you’ve had with helping communities learn to code?” 35:23 “What conditions were met for you to feel comfortable bringing someone on as an employee?” 40:12 “How do you go from having connections to clients without experiencing much friction?” 45:18 “Do you market yourself on purpose?” 49:05 Rapid Fire: “Have you used tidy models in your work?” 49:18 Rapid Fire: “Was there a specific moment where you decided to turn your boss/colleagues into your clients?” 49:54 Rapid Fire: “Are there things you miss about the NIH?” 51:05 “When you organize hackathons, how do you ensure the well-being of participants and make it a fun experience?” 52:45 “Do you have a piece of career advice for us?”

Seriously, have you tried this AI stuff? I’ve never written code like this in my life.

Unearned confidence #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf #positshorts

Positron rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

What is it like submitting an abstract for posit::conf()?

Call for talks for posit::conf(2026) closes February 20th, 2026! Apply here: https://posit.co/blog/posit-conf-2026-call-for-talks/ You’ll need a talk title, a short text abstract, and a 60 second video saying who you are, why your topic matters, and what people will walk away from your talk having learned so they can apply it! Libby Heeren and Rachael Dempsey hosted a 15-minute extension to the Data Science Hangout on February 5, 2026 (pos.it/dsh) in which they covered some common questions about submitting talks to posit::conf() and some veteran conf speakers shared their experiences! Links mentioned live and in the zoom chat: posit::conf(2026) call for talks - https://posit.co/blog/posit-conf-2026-call-for-talks/ Nicholas and Rachael both shared this link from previous years about how the posit::conf() program is built - https://posit.co/blog/how-we-build-the-positconf-program/ Meghan Harris’s talk: “Please Let Me Merge Before I Start Crying”: And Other Things I’ve Said at The Git Terminal - https://youtu.be/y2qdvYKKVdc?si=OHZsNGeATSrL2xiH Meghan Harris’s talk: It’s All About Perspective: Making a Case for Generative Art - https://youtu.be/48m27OWv-uw?si=j5ygesDF6twCp1Tw Meghan Harris’s talk: Making Data Pipelines in R: A Story From A “Self-Taught” Perspective - https://youtu.be/9w4WFe48rEg?si=ZXVFzLo4LpEVg7rE ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Polars: The Blazing Fast Python Framework for Modern Clinical Trial Data Exploration

Polars: The Blazing Fast Python Framework for Modern Clinical Trial Data Exploration - Michael Chow, Jeroen Janssens Abstract: Clinical trials generate complex and standards driven datasets that can slow down traditional data processing tools. This workshop introduces Polars, a cutting-edge Python DataFrame library engineered with a high-performance backend and the Apache Arrow columnar format for blazingly fast data manipulation. Attendees will learn how Polars lays the foundation for the pharmaverse-py, streamlining the data clinical workflow from database querying and complex data wrangling to the potential task of prepping data for regulatory Tables, Figures, and Listings (TFLs). Discover the ‘delightful’ Polars API and how its speed dramatically accelerates both exploratory and regid data tasks in pharmaceutical drug development. The workshop is led by Michael Chow, a Python developer at Posit who is a key contributor to open-source data tools, notably helping to launch the data presentation library Great Tables, and focusing on bringing efficient data analysis patterns to Python. Resources mentioned in the workshop: Polars documentation: https://docs.pola.rs/ Plotnine documentation: https://plotnine.org/ pyreadstat: https://github.com/Roche/pyreadstat Examples of Great Tables and Pharma TFLs: https://github.com/machow/examples-great-tables-pharma UV Python package manager: https://docs.astral.sh/uv

Jeroen Janssens, Michael Chow

How to use {pointblank} to understand, validate, and document your data

How to use {pointblank} to understand, validate, and document your data - Rich Iannone Abstract: This workshop will focus on the data quality and data documentation workflows that the pointblank package makes possible. We will use functions that allow us to: (1) quickly understand a new dataset (2) validate tabular data using rules that are based on our understanding of the data (3) fully document a table by describing its variables and other important details. The pointblank package was created to scale from small validation problems (“Let’s make certain this table fits my expectations before moving on”) to very large (“Let’s validate these 35 database tables every day and ensure data quality is maintained”) and we’ll delve into all sorts of data quality scenarios so you’ll be comfortable using this package in your organization. Data documentation is seemingly and unfortunately less common in organizations (maybe even less than the practice of data validation). We’ll learn all about how this doesn’t have to be a tedious chore. The pointblank package allows you to create informative and beautiful data documentation that will help others understand what’s in all those tables that are so vital to an organization. Resources mentioned in the workshop: Workshop GitHub repository: https://github.com/rich-iannone/pointblank-workshop pointblank documentation: https://rstudio.github.io/pointblank/

Rich Iannone

The dessert-first approach to teaching data science | Mine Cetinkaya-Rundel | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Mine Çetinkaya-Rundel, Professor of the Practice at Duke University and Senior Developer Advocate at Posit PBC, to chat about her cake-first teaching philosophy, strategies for communicating technical results to non-technical stakeholders, and career advice on learning new tools later in life, among other things. In this Hangout, we explore Mine’s unique approach to explaining difficult topics to students. She describes her method as “let them eat cake first,” where she shows learners the final, compelling result (like a visualization) before teaching them the “ingredients” or technical steps required to get there. By establishing the motivation and the destination first, she finds students are more willing to invest the effort into learning the necessary coding and data cleaning logic. Resources mentioned in the video and zoom chat: Mine’s Coursera 4-course series: Data Science with R Specialization → https://www.coursera.org/specializations/data-science-r TidyTuesday (Weekly Data Project) → https://github.com/rfordatascience/tidytuesday, The Test Set Podcast → https://podcasts.apple.com/us/podcast/the-test-set-by-posit/id1823736938 If you didn’t join live, one great discussion you missed from the chat was about the transition from Excel to coding languages like R and Python. Attendees agreed that Excel often acts as a “gateway drug” to data science, shared war stories about managing massive VLOOKUPs (laugh/cry emojis for everyone), and debated the undeniable and lingering utility of spreadsheets for communicating with business stakeholders. Spreadsheets will never go away and many of us are totally ok with that because we still use them every day! Let us know below if you’d like to hear more about this topic ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 04:42 “Why R initially and why still R?” 10:30 “How do you balance it all?” 15:28 “How do you stay up to date on newer techniques?” 20:13 “What is your approach for explaining difficult topics to students?” 25:58 “How do you identify appropriate datasets for beginners?” 32:21 “How frustrating is it when you see statistics being used in a misleading way?” 38:26 “How to communicate with people from different fields?” 44:23 Career advice 48:28 “Have you ever convinced an organization to abandon Excel?” 49:25 “How is statistics viewed these days in the context of things like AI and ML?” 52:38 “Was there a tipping point where you felt experienced enough to give keynotes?”

Mine Çetinkaya-Rundel

People Analytics at different stages of company growth | Adrian Perez | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Adrian Perez, Head of People Analytics at GitLab, to chat about the role of people analytics in business, how he’s navigated the changing HR-data-sphere, advice for moving from an individual contributor to a manager, and the modern data stack used at GitLab. In this Hangout, we explore one of our favorite topics here: People Analytics! Adrian discusses the scale at which companies should establish a dedicated function and shares a specific case study from his time at H-E-B where survival analysis was used to improve recruiting processes. He also provides insights on how to manage relationships with stakeholders who may be skeptical of data results, emphasizing the importance of partnership over purely proving someone wrong. (Libby heavily agrees - it’s about human relationships and trust!) Resources mentioned in the video and zoom chat: GitLab Company Handbook (AMAZINGLY open resource) → https://handbook.gitlab.com/ GitLab People Analytics Team Page → https://handbook.gitlab.com/handbook/people-group/people-ops-tech-analytics/people-analytics/ Positron IDE → https://positron.posit.co/ If you didn’t join live, one great discussion you missed from the zoom chat was about advice for first-time managers, including a recommendation for the book “The Making of a Manager” by Julie Zhuo. Let us know below if you’d like to hear more about the IC-to-manager move! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 03:58 “Do you have a enormous team? Are you guys small and mighty?” 06:26 “Is there a minimum company size before people analytics becomes worthwhile?” 09:25 “Can you share some specific types of analysis or modeling tasks that are done in people analytics?” 15:30 “How do you manage HR professionals when they don’t like the results that the data showed them?” 19:28 “Does that include implementing third party applicant tracking systems, keyword analysis?” 24:26 “Any advice you’re willing to share for a first time manager?” 30:35 “What tools do you use at GitLab?” 33:59 “Have you tried Positron?” 35:00 “How do you think that this would work in the sports industry?” 39:24 “How do you manage to respect privacy of the data you work with?” 44:18 “Have you done any work on skills analysis and or leveraging graph databases?” 46:42 “Is there a qualitative element to your analysis as well?” 48:19 “Is people analytics considered a subset of human factors?” 50:35 “Is it possible to break into people analytics with data experience but no HR experience?” 51:12 “Could you leave us with a piece of career advice?”

What even is dbt? An Analytics engineer explains | Laurie Merrell & Michael Chow | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Jarvis Innovations Lead Analytics Engineer Laurie Merrell and Posit Principal Software Engineer Michael Chow as they walk us through a beginner dbt project and let us ask as many questions as we like (and we do, we ask all the questions, including, WHAT EVEN IS dbt??). This is a super friendly, MESSY, collaborative, and curious peek at dbt. It’s is a tool that’s often mysterious to data scientists and it’s a big enough framework that it can feel tough to get started with. Walking through the basics makes it way easier to get into! Hosting crew from Posit: Libby Heeren, Isabella Velasquez Laurie’s LinkedIn: https://www.linkedin.com/in/laurie-merrell/ Michael’s socials and urls: LinkedIn: https://www.linkedin.com/in/michael-a-chow/ Bluesky: https://bsky.app/profile/mchow.com GitHub: https://github.com/machow Resources from the hosts and chat: Michael Chow’s talk about dbt at the Coalesce Conference in 2022: https://www.youtube.com/watch?v=EYdb1x1cO9U Beginner dbt project Michael is using: https://github.com/dbt-labs/jaffle_shop_duckdb Laurie’s Coalesce talk with Ian and Jenna: https://www.youtube.com/watch?v=6aX7tAfMmIM Link to installation page for the DuckDB CLI: https://duckdb.org/install/?platform=macos&environment=cli “Why is dbt so important” shared by Jenna in the chat: https://highgrowthengineering.substack.com/p/why-is-dbt-so-important- dbtplyr: https://hub.getdbt.com/emilyriederer/dbtplyr/latest/ Parquet: https://parquet.apache.org/ From stored procedures to dbt: A modern migration playbook: https://www.getdbt.com/blog/stored-procedures-dbt-migration-playbook How to structure our dbt projects: https://docs.getdbt.com/best-practices/how-we-structure/1-guide-overview Jenna Jordan’s blog on dbt mesh: https://jennajordan.me/blog/data-mesh-dbt ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co The Lab: https://pos.it/dslab Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for learning with us! Timestamps 00:00 Introduction 01:09 Guest introductions: Michael Chow and Laurie Merrell 04:15 Overview of today’s session 05:51 Setting up the GitHub Codespace 07:00 The data science workflow vs. organizational needs 10:06 Why dbt is hard to learn in the abstract 13:34 “Could we back up and explain what dbt is again?” 19:12 Running ‘dbt build’ 20:00 Inspecting the database with DuckDB CLI 26:21 “Does dbt have concurrency or dependency capabilities?” 27:37 Understanding the ‘ref’ macro 29:52 “Is dbt an orchestrator?” 31:14 “Starting a project from scratch with just SQL?” 32:04 “How is this better than writing Python scripts?” 35:46 “Is data source detection dynamic with dbt?” 38:36 Generating and serving dbt docs 46:51 “Is dbt an IDE like RStudio, but for SQL?” 52:32 Branching and development environments 53:57 “Where would you begin on a brand new project?” 56:38 “How would you validate dependencies and downstream impacts?” 57:48 Defining a view versus a table

Michael Chow

AI-Powered Data Engineering Workflows: Positron for Databricks Users

Modern data engineering demands more. Move beyond the limitations of hosted notebooks and embrace the power of an IDE for modern data engineering. We’ll show you how Positron combines the best of local development with the scale of Databricks. In this session led by James Blair at Posit, we’ll cover: AI-Driven Development: Moving from concept to code faster with built-in AI. Seamless Exploration: Navigating data effortlessly with the Catalog Explorer. Production-Grade Pipelines: Building, testing, and deploying robust workflows. Helpful Links: Download Positron: https://positron.posit.co/ Databricks Asset Bundles: http://docs.databricks.com/dev-tools/bundles GitHub Repo for Demo: https://github.com/blairj09/databricks-data-engineering Lakeflow Declarative Pipelines: http://docs.databricks.com/aws/en/ldp Talk to us about integrating Posit & Databricks: https://posit.co/schedule-a-call/

Positron RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Inspecting websites to find JSON data APIs | Marcos Huerta | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Marcos Huerta, a Data Science Manager at Carmax, as he walks us through the guts of websites looking for data we can play with. He shows us how to find hidden REST/JSON APIs by using the web inspector in Safari/Firefox and then how to get what’s necessary to pull the same data programmatically in python or R. Hosting crew from Posit: Libby Heeren, Isabella Velasquez, Daniel Chen Marcos’s urls: Website: https://marcoshuerta.com GitHub: https://github.com/astrowonk/ Resources from the hosts and from participants in the Discord chat: Postman: https://www.postman.com/ Insomnia (open source alternative to Postman): https://insomnia.rest/ Baseball Savant website Marcos is using: https://baseballsavant.mlb.com/gamefeed/?gamePk=777076 Isabella Velasquez’s blog on using {polite} R package to help scrape Wikipedia: https://ivelasq.rbind.io/blog/politely-scraping/ Festivas Mac app Marcos used to add the lights to his desktop: https://festivitas.app/ Ted Laderas blog post on parsing JSON in R: https://laderast.github.io/intro_apis_json_cascadia/#/how-does-r-translate-json New rvest read_html_live() function: https://rvest.tidyverse.org/reference/read_html_live.html yyjsonr R package: https://github.com/coolbutuseless/yyjsonr tuber R package: https://github.com/gojiplus/tuber WikipediaR R package: https://www.quantargo.com/help/r/latest/packages/WikipediaR/1.1/WikipediaR-package rookiepy python package: https://pypi.org/project/rookiepy/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co The Lab: https://pos.it/dslab Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for learning with us! Timestamps 00:00 Introduction 03:05 Web scraping vs. API calls 04:12 Server-side rendering vs. client-side JSON 06:12 Warning: Rate limits and business ethics (ahem) 08:39 Demo: Baseball Savant website 08:57 Using browser Developer Tools and the Network tab 12:15 “What is curl?” 13:30 Importing curl into Postman 16:03 Generating Python code from Postman 16:50 “Are there open source alternatives to Postman?” 17:50 Using the generated code in Python/Jupyter 22:28 R packages for JSON (jsonlite, yyjsonr) 25:09 Demo: Massachusetts Lottery website 28:17 Example: scripts Marcos automated with Cron jobs 30:17 Handling logins and cookies with RookiePie 32:19 Demo: CNN Election Data 34:26 Inspecting ESPN’s website 36:58 “Can you scrape YouTube?” 38:19 Finding hidden JSON in CardsMania history 45:00 Benefits of API inspection over Beautiful Soup 46:59 New rvest function: read_html_live 50:40 Inspecting LinkedIn and finding GraphQL 53:58 Encouragement on handling API pagination

querychat in R: Query Your Data with Natural Language | Shiny + LLMs | Veerle van Leemput

Learn how to use querychat, an R package that lets users query data using natural language instead of predefined filters. Perfect for data scientists who want to build flexible, interactive Shiny dashboards without anticipating every possible user question. In this tutorial, you’ll discover: How querychat translates natural language to SQL using LLMs Setting up querychat in seconds with your data Building custom Shiny interfaces with reactive data frames Why it’s reliable, transparent, and safe for production use Enterprise deployment with Azure, AWS Bedrock, or Google Vertex AI Key Features: Drop-in Shiny component Works with any DBI-compatible database Read-only by design for data safety Full SQL transparency for reproducibility Supports Claude, GPT, Gemini via ellmer Resources: querychat Documentation: [https://posit-dev.github.io/querychat/py/index.html] Blog Post: “Where Questions Become Queries” [https://shiny.posit.co/blog/posts/querychat-python-r/ Github repo: [https://github.com/posit-dev/querychat/tree/main/pkg-r] Perfect for R users working with Shiny, data exploration, and interactive analytics. Timestamps 00:00 - Introduction: The Dashboard Problem 00:50 - Why Dashboards Can’t Answer Every Question 01:47 - What is querychat? 02:46 - Getting Started (Installation & Setup) 03:08 - API Key Setup (Claude, GPT, Gemini) 03:20 - First querychat App in 5 Lines of Code 04:04 - Live Demo: Querying Data with Natural Language 05:01 - How querychat Works Under the Hood 06:44 - Four Key Benefits (Reliability, Transparency, Reproducibility, Safety) 07:37 - Tool Calling & DuckDB Integration 08:46 - Building Custom Shiny Interfaces 09:01 - Customizing querychat Behavior (Greetings, Data Descriptions, Instructions) 11:00 - Creating Custom Dashboards with Reactive Data Frames 12:00 - Complete App Structure Walkthrough 13:10 - Safety & Security Deep Dive 14:00 - Enterprise Deployment (Azure, AWS, Google Vertex AI) 15:12 - Resources & Documentation

DBI ellmer querychat Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

querychat in Python: Query Your Data with Natural Language | Shiny + LLMs | Veerle van Leemput

Learn how to use QueryChat to add natural language querying to your Python data applications. This tutorial shows you how to let users ask questions about data without building complex filters or dashboards. In this video, data scientist Veerle Eeftink demonstrates how to integrate QueryChat with Shiny for Python to create interactive data exploration tools powered by LLMs (Claude, GPT-4, Gemini). What You’ll Learn: Set up QueryChat in minutes with any data source Understand how QueryChat translates natural language to SQL Build custom dashboards with reactive data frames Configure LLM behavior with greetings, data descriptions, and instructions Ensure safety and read-only access for production environments Key Features: Works with Claude, GPT-4, Gemini, and other LLMs Transparent SQL generation (no black box) Integrates with Shiny, Streamlit, and other frameworks Uses DuckDB or bring your own SQL database Read-only operations for data safety Enterprise-ready with private LLM support Resources: QueryChat Documentation: [https://posit-dev.github.io/querychat/py/index.html] Blog Post: “Where Questions Become Queries” [https://shiny.posit.co/blog/posts/querychat-python-r/] SheScores Demo App [https://github.com/hypebright/shescores-dashboard/tree/main/Python] #Python #DataScience #LLM #AI #Shiny #QueryChat #NaturalLanguage #SQL #DataVisualization Timestamps 00:00 - Introduction: The dashboard problem 01:28 - What is QueryChat? 02:36 - Demo: Three things you’ll learn today 03:00 - Setup: LLM API keys and installation 04:04 - Quick start: First QueryChat app in 5 lines 04:27 - Live demo: Natural language queries 06:10 - How QueryChat works under the hood 07:19 - Four benefits: Reliability, transparency, reproducibility, safety 08:17 - Tool calling explained 08:54 - Schema information and DuckDB integration 09:29 - Building custom interfaces 09:45 - Customizing behavior: Greetings and data descriptions 11:16 - Adding custom instructions to the LLM 11:35 - Building a Shiny dashboard with QueryChat 13:09 - Code walkthrough: Sidebar chat and reactive data frames 14:14 - Complete dashboard demo 14:52 - Safety and security deep dive 16:22 - Resources and links

querychat Shiny for Python Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Teaching out loud and becoming StatQuest | Josh Starmer | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Josh Starmer, Founder and Creator of the massively popular YouTube channel StatQuest with Josh Starmer, to chat about his process for simplifying complex technical topics, overcoming imposter syndrome and feeling cringe, the enduring value of foundational statistics over fleeting AI trends, and how to use AI tools responsibly without sacrificing true understanding. In this Hangout, we explore Josh’s unique approach to teaching and learning difficult concepts. He reveals that he specifically avoids watching other videos on a subject to ensure his explanations remain original. Instead, he focuses on finding the “simplest possible example” that still highlights the method’s utility—such as creating a neural network with only three data points to visualize the math. Josh emphasizes that the hardest part of his work is constantly asking, “Can I make it simpler?” and extracting the essence of an equation so that learners can “feel” the statistics rather than just memorizing them. Resources mentioned in the video and zoom chat: StatQuest with Josh Starmer (YouTube) → https://www.youtube.com/c/joshstarmer The StatQuest Illustrated Guide to Neural Networks and AI → https://www.amazon.com/dp/B0DRS71QVQ The StatQuest Illustrated Guide to Machine Learning → https://www.amazon.com/dp/B0BLM4TLPY The StatQuest Store → https://statquest.org/statquest-store/ Introduction to Statistical Learning (ISLR) → https://www.statlearning.com/ Grokking Machine Learning by Luis Serrano → https://www.manning.com/books/grokking-machine-learning Hands-On Large Language Models by Jay Alammar & Maarten Grootendorst → https://www.maartengrootendorst.com/book/ Statistical Methods for Research Workers by R. A. Fisher → https://link.springer.com/chapter/10.1007/978-1-4612-4380-9_6 If you didn’t join live, one great discussion you missed from the zoom chat was the “Smug Bayesian” vs. Frequentist debate, which sparked jokes about p-hacking and a community demand for “Smug Bayesian” t-shirts. Will Mike K. Smith answer this call?? Would you wear a Smug Bayesian shirt? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 01:47 “Who are you and how did you get to the YouTube space?” 05:38 “What was it that you really wanted to do as a career?” 07:52 “Have you got any top tips on how to make video editing easier?” 10:30 “What is your process for breaking down a difficult concept?” 15:56 “What’s the most common statistical or machine learning mistake you see in real projects?” 19:40 “How do you keep up with the hands-on stuff?” 21:43 “What is the one book that every data science professional should read?” 26:13 “What statistical concept do people think they understand but almost always misuse?” 30:42 “What would be your advice for a new grad to break out as a data science professional?” 35:55 “What are your tips for generating confidence to teach out loud?” 40:18 “Do you ever feel cringe? How do you deal with that?” 42:25 “Do you have any career advice?” 45:28 “How have your musical talents helped with your work in data?” 47:25 “How would you integrate AI as a responsible assistant and where would you forbid it?” 47:43 “What data tools and IDEs do you use?” 52:25 “What is a memorable concept that you remember having a personal moment through teaching?” 53:10 “What are you most excited for in 2026?”

Open source development practices | Isabel Zimmerman & Davis Vaughan | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Isabel Zimmerman and Davis Vaughan, Software Engineers at Posit, to chat about the life of an open source developer, strategies for navigating complex codebases, and how to leverage AI in data science workflows. Plus, NERDY BOOKS! In this Hangout, we explore the differences between maintaining established ecosystems like the Tidyverse as well as building new tools like the Positron IDE. Davis and Isabel (and sometimes Libby ) share practical advice for developers, such as the utility of AI for writing tests and “rubber ducking”, and their various approaches to writing accessible documentation that bridges the expert-novice gap. Resources mentioned in the video and zoom chat: Positron IDE → https://posit.co/positron/ Air (R formatter) → https://posit-dev.github.io/air/ Python Packages Book (free) → https://py-pkgs.org/ R Packages Book (free) → https://r-pkgs.org/ DeepWiki (AI tool mentioned for docs) → https://deepwiki.com/tidyverse/vroom If you didn’t join live, one great discussion you missed from the zoom chat was about Brandon Sanderson’s Cosmere books and the debate between starting with Mistborn vs. The Stormlight Archive. Are you a Cosmere fan?! Which book did you start with? (Libby started with Elantris years before picking up Mistborn Era 1 book 1, but she’d now recommend maybe starting with Warbreaker!) ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 04:41 “What does a day in the life of an open source dev look like?” 09:43 “What got you into building your own R packages?” 13:00 “Personal tips for working with code bases you’re not familiar with?” 16:35 “How much of what you build is in R/Python vs. lower-level languages?” 19:57 “Does Air work inside code chunks in Positron?” 20:12 “Changing the Python Quarto formatter in Positron without an extension” 22:56 “What do your side projects look like?” 26:40 “How do you approach writing documentation?” 30:55 “What interesting trends in data science are you noticing?” 33:38 “How do you leverage AI in your work?” 37:30 “What are the hexes on Davis’s back wall?” 38:50 “What career advice would you give to someone in a similar position?” 43:45 “How can I be more resilient when things go wrong?” 47:59 “Do you have keyboard preferences?” 49:25 “What is the best way to report bugs in packages?” 50:56 “Open source dev work vs. in-house dev work” 51:50 “Tips for getting started with Positron”

Davis Vaughan, Isabel Zimmerman

Integrating Shiny with Epic EHR | Matt Maloney | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Matt Maloney, Director of Applied AI and Data Science at City of Hope, to chat about applying data science to cancer care operations, integrating open source data science tools like Shiny with Electronic Health Records (like Epic), and the evolving governance of generative AI in healthcare. In this Hangout, we explore the technical and operational strategies behind integrating custom data science applications directly into clinical workflows. Matt discusses how his team moves beyond standalone tools by embedding Shiny apps and other solutions into Epic, allowing medical coders and providers to access predictions and summaries without leaving their primary software environment-of-choice. He also mentions the “build vs. buy” decision-making process as vendors release their own AI solutions, emphasizing the importance of validating external models against their specific patient population. Resources mentioned in the video and zoom chat: City of Hope → https://www.cityofhope.org Unity Health Toronto Customer Story → https://posit.co/about/customer-stories/unity-health-toronto/ pointblank (Data Validation package) → https://rstudio.github.io/pointblank/ If you didn’t join live, one great discussion you missed from the zoom chat was about where data science teams sit within community members’ organizations and whether they like it or not, specifically the pros and cons of being housed within IT versus embedded inside business units. Participants debated access to infrastructure versus proximity to business stakeholders, with several sharing their own experiences of shifting between these departments (or between companies with different structures). Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh The Lab: https://pos.it/dslab LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 03:37 “What does the data science function at City of Hope help with?” 08:52 “Tell us a little bit more about how you’re integrating Posit with Epic” 16:08 “How do you handle the needs of privacy with the push to adopt AI?” 18:40 “How do you manage to stay abreast of technical advancements?” 22:45 “At what point do you hand off your data work to the software engineering team?” 27:23 “How much has development that involves LLMs and generative AI taken hold?” 30:38 “Does your team evaluate a lot of the things that Epic might be throwing your way?” 34:41 “How does Epic pass an encounter number or a patient ID to Posit Connect?” 35:57 “How does your team handle these nuanced pieces of clinical information?” 40:29 “Do the administrators appreciate the time that it takes to do things?” 44:22 “What happens in the academic division?” 46:10 “Do you have a piece of career advice for us?”

How to deploy Shiny apps in 2026 | Alex Chisholm | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Posit product manager Alex Chisholm as he walks through the evolution of shiny app deployment over the years, how to deploy shiny apps in the modern era, and peeks into Posit’s roadmap for future development. Do you call it “deployment” or “publishing” when it comes to Shiny apps? 🤔 This is a super friendly and conversational space, and being there live in the Discord chat can’t be beat!! We hope you get to join us sometime soon. Hosting crew from Posit: Libby Heeren, Isabella Velasquez, Daniel Chen, Alex Chisholm Alex Chisholm’s LinkedIn: http://www.linkedin.com/in/chisholm1 Resources from the hosts and chat: Posit Connect Cloud for deploying Shiny apps in the modern era: https://connect.posit.cloud/ Install Positron: https://positron.posit.co/ Simon Couch’s blog post on local LLMs not being good enough yet: https://www.simonpcouch.com/blog/2025-12-04-local-agents/ Blue-Green Shiny App Deployments using Posit Connect posit::conf(2025) talk by Ryszard Szymański: https://youtu.be/QEEGLWj0nas Digital Ocean: https://www.digitalocean.com/ Ollama local LLM: https://ollama.com/ py-sidebot app template: https://shiny.posit.co/py/templates/sidebot/ querychat app template: https://shiny.posit.co/py/templates/querychat/ Dan Chen mentioned Render in the chat as an alternative to Digital Ocean: https://render.com/ Alex Chisholm’s AB testing GitHub repo example: https://github.com/alex-chisholm/shiny-r-abtesting Edward in the chat shared a GitHub repo for using GitHub actions to execute remote SSH commands: https://github.com/appleboy/ssh-action Abu in the chat shared blue-green vs. canary deployments: https://octopus.com/devops/software-deployments/blue-green-vs-canary-deployments/ Frank in the chat mentioned Simon’s blog on using local LLMs with the chores package: https://www.simonpcouch.com/blog/2025-12-10-chores-0-3-0/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co The Lab: https://pos.it/dslab Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for learning with us! Timestamps 00:00 Introduction 03:03 Meaningful applications and value creation 05:31 The evolution of Shinyapps.io and Posit Connect 08:12 DigitalOcean and Droplets 09:36 DigitalOcean vs. commercial cloud providers, 11:48 Comparisons: DigitalOcean, Azure, and AWS 14:47 Replicating local environments with Docker 16:51 The open-source Shiny Server 18:20 Use case: University of Illinois CITL 20:02 Key considerations for deployment decisions 21:53 GitHub Actions and version control 23:31 Addressing single points of failure and maintainability 24:38 Posit Connect Cloud features and portfolio 26:01 Beyond Shiny: Quarto, Streamlit, and Dash 27:07 Handling secrets and database credentials 28:56 Custom vanity links vs. UUIDs 30:04 Blue-Green deployment strategies 31:55 “Is it easy to set up a developer workflow?” 34:46 Guardrails for AI powered apps and token usage 37:32 Small language models and Ollama 38:29 Sidebot AI demo and LLM integration 39:41 Understanding manifest.json and dependencies 45:00 Automatic publish on GitHub push 46:51 The future of Shinyapps.io and migration 48:33 “Did you just build a custom agent for that specific dashboard?” 51:43 Publishing from RStudio IDE to Connect Cloud 54:16 Preview: Inspecting website APIs for data harvesting

Simon Couch

Advent of Code for R users | Emil Hvitfeldt | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Posit engineer Emil Hvitfeldt as he walks through Day 1 of Advent of Code 2026 using R. This is a super friendly, collaborative, and cheery intro to AoC! Don’t forget, you can do Advent of Code at any ole time of year Hosting crew from Posit: Libby Heeren, Isabella Velasquez, Daniel Chen, Emil Hvitfeldt Emil’s socials and urls: website: https://emilhvitfeldt.com/ GitHub: https://github.com/emilhvitfeldt Bluesky: https://bsky.app/profile/emilhvitfeldt.bsky.social LinkedIn: https://www.linkedin.com/in/emilhvitfeldt/ Resources from the hosts and chat: Advent of Code: https://adventofcode.com/ Install Positron: https://positron.posit.co/ Eric Wastl, Advent of Code: Behind the Scenes: https://www.youtube.com/watch?v=_oNOTknRTSU AoC Subreddit: https://www.reddit.com/r/adventofcode/ Kieran Healy shared a reddit post with an Advent of Code answer done in Minecraft: https://www.reddit.com/r/adventofcode/comments/1pbeyxx/2025_day_01_part_2_advent_of_code_in_minecraft/ Emil’s Solutions: https://github.com/EmilHvitfeldt/rstats-adventofcode Emil’s helper package: https://github.com/EmilHvitfeldt/aocfuns purrr::accumulate() function: https://purrr.tidyverse.org/reference/accumulate.html And, for anyone hangin’ in there at the end, Emil updated us on Discord that he figured out why his cumsum() didn’t work: he forgot to start the dial at 50! Once you fix that, it works to solve part 1:) ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co The Lab: https://pos.it/dslab Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for learning with us! Timestamps 00:00 Introduction 01:01 Tour of the Advent of Code website 02:30 Dashboard overview and puzzle schedule 03:23 How to view and access previous years’ events 03:37 Structure of puzzles: Two parts and stars 04:40 Understanding the global leaderboard 05:08 “Does that ASCII art build itself? 06:16 Setting up private leaderboards for friend 07:54 Starting Day 1: Story prompt and mechanics 09:30 Understanding unique puzzle inputs 10:51 Submission feedback and delay penalties 11:44 Safe dial logic: Left, Right, and circularity 12:50 Starting position and Part 1 success criteria 14:09 Setting up the project in Positron 16:26 Strategy for speed: Reading from the bottom up 18:49 Problem-solving strategies: Pen, paper, and visualization 19:22 Walking through the logic with a sample case 20:52 Coding Part 1: Data parsing and vectorization 23:17 Positron keyboard shortcuts for duplicating lines 24:40 Debugging the logic and handling negative numbers 26:03 Explaining the Modulo operator (%%) 28:15 Managing large inputs of over 4,000 instructions 29:21 Submitting Part 1 and transitioning to Part 2 32:03 Part 2 challenge: Counting zero “clicks” 34:02 Brainstorming Part 2 code modifications 36:19 Checking important warnings for edge cases 37:00 Coding Part 2: Nested loops and incrementing counters 38:23 Hint: Modulo vs. integer division 40:40 Success with the Part 2 test case 42:30 Alternative method: Vectorized cumulative sums 45:29 “What’s the difference between % and %%?” (percent vs modulo) 46:50 Mathematical optimization to avoid inner loops

Emil Hvitfeldt

Daniel Chen - LLMs, Chatbots, and Dashboards_ Visualize Your Data with Natural Language - PyData

LLMs have a lot of hype around them these days. Let’s demystify how they work and see how we can put them in context for data science use. As data scientists, we want to make sure our results are inspectable, reliable, reproducible, and replicable. We already have many tools to help us in this front. However, LLMs provide a new challenge; we may not always be given the same results back from a query. This means trying to work out areas where LLMs excel in, and use those behaviors in our data science artifacts. This talk will introduce you to LLms, the Chatlas package, and how they can be integrated into a Shiny to create an AI-powered dashboard. We’ll see how we can leverage the tasks LLMs are good at to better our data science products

How Open Source, Python and AI Are Shaping the Data Future with Wes McKinney

The future of analytics isn’t just about bigger models — it’s about building smarter, more interoperable data systems. Wes McKinney, Principal Architect of Posit PBC, Chief Scientist of Voltron Data and a General Partner at Composed Ventures, joins us to explore how the modern data stack is evolving and what it means for the future of analytics. Wes reflects on his journey building pandas and Apache Arrow, sharing how open-source ecosystems grow, transform and shape the way organizations work with data today. Wes also highlights the rising importance of semantic layers, agentic workflows and defensive coding practices as teams embrace AI-driven development. Key Takeaways: 00:00 Introduction. 02:32 Wes didn’t expect pandas to drive AI but he recognized Python’s unrealized potential. 05:09 A lucky convergence helped Python’s tools snowball into the AI standard. 10:40 Early big data focused on essentials, not the interoperable stacks we rely on today. 15:44 The composable data stack grew through bottom-up, grassroots open-source momentum. 21:56 Many “data science” roles ultimately became business intelligence and dashboard work. 25:24 Complex statistical work still depends on human judgment, not fully autonomous agents. 30:27 Frontier models retrieve table data reliably, while smaller models fail dramatically. 35:16 Better models and coding agents shifted Wes from an AI skeptic to an adopter. 40:07 AI-driven code demands stronger testing and review to avoid costly failures. 45:14 An AI-built finance project ballooned, revealing how agents inflate codebases. Resources Mentioned: Wes McKinney https://www.linkedin.com/in/wesmckinn/ Posit PBC | LinkedIn https://www.linkedin.com/company/posit-software/ Posit PBC | Website https://posit.co/ Voltron Data | LinkedIn https://www.linkedin.com/company/voltrondata/ Voltron Data | Website https://voltrondata.com/ Composed Ventures | LinkedIn https://www.linkedin.com/company/composedvc/ Composed Ventures | Website https://composed.vc/ pandas https://pandas.pydata.org/ Apache Arrow https://arrow.apache.org/ DuckDB https://duckdb.org/ DataFusion https://datafusion.apache.org/ Jupyter Notebook https://jupyter.org/ Parquet https://parquet.apache.org/ Iceberg https://iceberg.apache.org/ Delta Lake https://delta.io/ Thanks for listening to the “Data Masters Podcast.” If you enjoyed this episode, be sure to subscribe so you never miss our latest discussions and insights into the ever-changing world of data. #DataStrategy #DataManagement #DataMastersPodcast

Exploring Positron settings | Isabel Zimmerman & Davis Vaughan | Data Science Lab

The Data Science Lab is a live weekly call. Register at pos.it/dslab! Discord invites go out each week on lives calls. We’d love to have you! The Lab is an open, messy space for learning and asking questions. Think of it like pair coding with a friend or two. Learn something new, and share what you know to help others grow. On this call, Libby Heeren is joined by Posit engineers Isabel Zimmerman and Davis Vaughan as they share some of their favorite settings in Positron, a super customizable data science IDE. Come laugh with us as we can’t seem to figure out that VSCode calls rainbow parentheses “bracket pair colorization” Hosting crew from Posit: Libby Heeren, Isabella Velasquez, Daniel Chen, Isabel Zimmerman, Davis Vaughan Resources from the hosts and chat: Install Positron: https://positron.posit.co/ Positron docs on keyboard shortcuts: https://positron.posit.co/keyboard-shortcuts.html Nathan Jeffery’s “click to open a.RDS file” keybinding: https://nathan-jeffery.netlify.app/blog/2025-08-26-read-rds-positron/ Positron R pipe setting (paste in browser and it’ll open in Positron): positron://settings/positron.r.pipe One of Dan Chen’s faves, the native tab feature in VSCode + Positron: https://lucasprag.com/posts/underrated-vscode-feature-native-tabs/ The list of RStudio keybindings that you get when you turn on RStudio keybindings in Positron: https://positron.posit.co/migrate-rstudio-keybindings.html Indent rainbow extension: https://open-vsx.org/extension/oderwat/indent-rainbow Rainbow brackets setting (paste in browser and it’ll open in Positron): positron://settings/editor.bracketPairColorization.enabled Setting hierarchy (User vs Workspace settings) in Positron: https://code.visualstudio.com/docs/configure/settings#_settings-precedence Rainbow CSV extension (not by Posit): https://marketplace.visualstudio.com/items?itemName=mechatroner.rainbow-csv Positron +1ePositron, an extension pack for dev and data science, by Garrick Aden-Buie: https://open-vsx.org/extension/grrrck/positron-plus-1-e Publishing from VS Code or Positron: https://docs.posit.co/connect/user/publishing-positron-vscode/ Posit Connect Cloud plans: https://connect.posit.cloud/plans Enter Folder extension that Libby mentions: https://open-vsx.org/extension/xiangda/enter-folder Catppuccin themes (shared by Rory Lawless, and now some of Libby’s favorites!): https://open-vsx.org/extension/Catppuccin/catppuccin-vsc ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co The Lab: https://pos.it/dslab Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for learning with us! Timestamps 00:00 Introduction 00:42 Guest Introductions: Isabel and Davis 02:41 Positron Settings overview 04:11 How to enable “Format on Save” 04:34 “How do I open settings in JSON or UI?” 05:10 Auto Save on focus change 08:26 Enabling RStudio key bindings 09:28 “Why doesn’t the cursor move with code edits?” 12:18 User vs. Workspace settings 14:34 Creating and using Profiles 16:13 “Can I use the magrittr pipe with Control+Shift+M?” 17:23 Searching and managing keyboard shortcuts 19:42 Creating custom code snippets 21:31 The Indent Rainbow extension 24:04 Enabling rainbow parenthesis/brackets 25:08 Managing Python and R interpreters 26:32 Rearranging and hiding UI panes 28:04 Rainbow CSV and favorite extensions 29:26 Using the Enter Folder extension 31:05 Understanding the setting hierarchy 32:48 Adding symbols to Quick Open search 36:00 “Is there a way to shift focus using keyboard shortcuts?” 38:04 Modifying keybindings JSON for specific languages 39:20 “How do you find trustworthy extensions?” 43:11 “How can I publish to shinyapps.io from Positron?” 44:03 Deploying with Posit Publisher and Connect Cloud 48:32 Customizing themes with RainGlow extension 50:36 “Is there an Import Data Set wizard in Positron?” 53:01 Conclusion and community resources

Davis Vaughan, Garrick Aden-Buie, Isabel Zimmerman

Simon Couch - Practical AI for data science

Practical AI for data science (Simon Couch) Abstract: While most discourse about AI focuses on glamorous, ungrounded applications, data scientists spend most of their days tackling unglamorous problems in sensitive data. Integrated thoughtfully, LLMs are quite useful in practice for all sorts of everyday data science tasks, even when restricted to secure deployments that protect proprietary information. At Posit, our work on ellmer and related R packages has focused on enabling these practical uses. This talk will outline three practical AI use-cases—structured data extraction, tool calling, and coding—and offer guidance on getting started with LLMs when your data and code is confidential. Presented at the 2025 R/Pharma Conference Europe/US Track. Resources mentioned in the presentation: {vitals}: Large Language Model Evaluations https://vitals.tidyverse.org/ {mcptools}: Model Context Protocol for R https://posit-dev.github.io/mcptools/ {btw}: A complete toolkit for connecting R and LLMs https://posit-dev.github.io/btw/ {gander}: High-performance, low-friction Large Language Model chat for data scientists https://simonpcouch.github.io/gander/ {chores}: A collection of large language model assistants https://simonpcouch.github.io/chores/ {predictive}: A frontend for predictive modeling with tidymodels https://github.com/simonpcouch/predictive {kapa}: RAG-based search via the kapa.ai API https://github.com/simonpcouch/kapa Databot https://positron.posit.co/dat

Simon Couch

Rodrigo Silva Ferreira-When Rivers Speak Analyzing Massive Water Quality Datasets-PyData Boston 2025

Rivers have long been storytellers of human history. From the Nile to the Yangtze, they have shaped trade, migration, settlement, and the rise of civilizations. They reveal the traces of human ambition… and the costs of it. Today, from the Charles to the Golden Gate, US rivers continue to tell stories, especially through data. Over the past decades, extensive water quality monitoring efforts have generated vast public datasets: millions of measurements of pH, dissolved oxygen, temperature, and conductivity collected across the country. These records are more than environmental snapshots; they are archives of political priorities, regulatory choices, and ecological disruptions. Ultimately, they are evidence of how societies interact with their environments, often unevenly. In this talk, I’ll explore how Python and modern data workflows can help us “listen” to these stories at scale. Using the United States Geological Survey (USGS) Water Data APIs and Remote SSH in Positron, I’ll process terabytes of sensor data spanning several years and regions. I’ll demonstrate that, while Parquet and DuckDB enable scalable exploration of historical records, using Remote SSH is paramount in order to enable large-scale data analysis. By doing so, I hope to answer some analytical questions that can surface patterns linked to industrial growth, regulatory shifts, and climate change. By treating rivers as both ecological systems and social mirrors, we can begin to see how environmental data encodes histories of inequality, resilience, and transformation. Whether your interest lies in data engineering, environmental analytics, or the human dimensions of climate and infrastructure, this talk will explore topics at the intersection of environmental science, will offer both technical methods and sociological lenses to understand the stories rivers continue to tell

Bayesian MMM and silly side projects | Ryan Timpe | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Ryan Timpe, a Lead Data Scientist, to chat about Marketing Mix Modeling (MMM), silly data science side projects like brickr (see the link below to his 2020 posit::conf() talk), the benefits of working out loud, and using LLMs (Large Language Models) for data science coding (something he gave a whole talk about at posit::conf(2025)). In this Hangout, we explore a bit of what Ryan does, including Marketing Mix Modeling (MMM) and helping business partners better use their data. He shared that his work utilizes Bayesian methods to manage the complexity and high seasonality of his data, especially the Q4 holiday-buying spikes seen in most retail sales data. Using Bayesian priors helps keep the models within constraints and prevents overfitting. The foundation of the internal MMM package that Ryan developed uses tidy models, and you can check out the link to his 2023 posit::conf() talk to hear more about his model pipeline! The theme here is that Ryan gives lots of great talks - go check them out! Resources mentioned in the video and zoom chat: Ryan Timpe’s brickr GitHub Repository → https://github.com/ryantimpe/brickr Ryan Timpe’s 2020 talk on learning R with silly projects → https://youtu.be/oOG-aXP_ICI Ryan Timpe’s 2023 talk on model pipelines → https://youtu.be/R7XNqcCZnLg Ryan Timpe’s 2025 talk on learning from LLM pitfalls → https://youtu.be/vJrIahZWCw4 If you didn’t join live, one great discussion you missed from the zoom chat was about the importance of working out loud and publishing (even imperfect) data science projects. Attendees emphasized that overcoming their inner critic and sharing work helps with communication and professional development (but can be super challenging still). One participant shared that they got their current role by creating a fake MMM project with fake data, but that it showed their interviewer they could take initiative! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction of Ryan 04:41 “What kind of MMM do you guys do?” 07:01 “What was your favorite silly project that you did?” 08:06 “What is brickr?” 11:11 “Do you think brickr helped you get the job you have now?” 14:23 “How do you scope your side projects?” 17:42 “What do you recommend for setting up a Python environment?” 21:16 “When do you recommend not using LLMs, and why?” 24:19 “How do you shut up your own inner critic?” 28:00 “What kind of data do you use for your LLMs and models?” 30:32 “Do you use open source MMM package like Meridian or Robyn?” 35:13 “How do you communicate complex modeling nuances to non-technical stakeholders?” 38:06 “Does your love for LEGO help you build code better?” 41:00 “What drives priority for what your team works on next?” 43:21 “What resources do you recommend for learning marketing analytics?” 44:46 “What tools do you use for Bayesian analysis?” 45:50 “Was your career move to the LEGO Group intentional or accidental?” 47:49 “What is your end product delivery?” 49:21 “What tool do you use to implement a Bayesian MCMC model?” 49:52 “How do you make model-based reporting faster and more automated?” 51:31 “Do you have a piece of career advice that’s meaningful to you?”

The skydiver to data scientist pipeline | Kevin Dalton | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Kevin Dalton, Senior Data Scientist at Great American Insurance Group, to chat about data science in the insurance industry, MLOps and productionalization strategies, Bayesian modeling, and career development. In this Hangout, we explore MLOps and productionalization strategies, especially the distinction between “little P” and “big P” production. Kevin explained that “small p” production involves internal dashboards, interactive applications, or analytics reports used to support the research process and assess the business, generally having a lower risk profile. In contrast, “big P” production refers to ML models deployed for inference in core business systems, which demand strict Service Level Agreements (SLAs). Kevin noted that the industry is moving towards automated, cloud-centric, real-time inference engines, although production systems can still be as simple as running a notebook over new data. Resources mentioned in the video and zoom chat: The (new!) Data Science Lab on Tuesdays → https://pos.it/dslab Bayes Rules! Book → https://www.bayesrulesbook.com/ Positron link → https://positron.posit.co/ If you didn’t join live, one great discussion you missed from the zoom chat was about the humorous observations regarding Kevin’s unique professional history, with some referring to it as the “skydiving to actuary pipeline” There was some speculation about whether professional skydivers have “No fear to deploy models” and then there was a subsequent realization about all the insurance needed for a skydiving business. That tracks! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 05:06 “What does it actually mean to put models into production at an insurance company?” 07:27 “How do you actually explain that to people at the company that there’s this need for two different types of production?” 09:21 “Have you guys, in a large company, found any application for smaller language models in, like, production pipelines?” 10:17 “Are there any lessons from the skydiving experience that you apply to your data science work?” 12:45 “What recent innovative ML models in the insurance sectors have you brought live if you’re allowed to share?” 17:33 “I’m just curious to hear from you or others in chat who spend a lot of time in R if there are strategies you found effective in bridging the gap to using Python.” 20:54 “What was the main thing that got you from “who is this for?” when you first opened Positron to now using it?” 22:46 “Could you share a little bit about what your tool stack looks like at Great American Insurance Group?” 24:27 “So you actually run Posit within Snowflake. Is that right?” 26:32 “What sort of guardrails are you using that are specific to insurance to guard against drift?” 29:26 “Do you think that’ll be the future when it comes to test driven development?” 30:55 “What are some of your favorite public data sources for trying to quantify and model things like geopolitical risk?” 32:07 “If you wanna learn more about using tools you mentioned for building synthetic data, where would you recommend that people start?” 34:27 “So with the AI evolving, what kind of techniques are being recently used?” 35:28 “Broadly, how is AI affecting your day to day work in the insurance industry?” 38:21 “Is there a piece of career advice that’s been especially meaningful to you, whether it’s something that you’ve heard from a mentor or you’ve given this advice to other people?” 40:04 “How would you advise people to get domain expertise before they’ve gotten a chance to do a job in that industry?” 46:19 “Which skills would you say are evergreen irrespective of the tools that you’re using?” 47:51 “What are you learning about change management?” 49:31 “What does seeking mentorship mean for you?” 53:21 “Do you have any strategies or advice for keeping that statistical intuition sharp?” 54:16 “How do you partner with actuaries at work? Are there things that you need from the actuaries that make your job easier?”

Julia Silge: Part 1 — Positron, pineapple pizza, and the art of iteration

In part one of our conversation with Julia Silge, astronomer-turned–data science leader, we explore why data science needs a different kind of IDE. Julia takes us inside Positron, Posit’s next-generation, data-scientist-first environment, and unpacks the day-to-day realities that make data science work unlike software engineering. Along the way, we get a first-hand account of a legendary pineapple-pizza protest and how to juggle multiple projects at once. A behind-the-scenes tour of Positron and the workflows it’s built for, plus the stories, trade-offs, and team choreography required to ship an IDE on a living substrate. We talk extension ecosystems, upstream merges, data viewers, and more. Plus, Julia shares why applied systems (and messy, real-world data) are her happy place. What’s Inside: • The pineapple-pizza story that unexpectedly went viral — and what “context collapse” feels like from the inside • Why Positron is a data-science-first IDE, optimized for analysis, not general software engineering • Iteration vs. reproducibility: the central tension in data science workflows and how tooling can honor both • Hadley’s cold-turkey move from RStudio, muscle memory, and finding the new ergonomic groove • How Julia measures success by smoothing the boundaries between tools and teams • The applied, people-and-process side of data science that keeps Julia energized

Julia Silge

Hadley Wickham: Spreadsheets, bikes, and the accidental empire of R packages

Before Hadley Wickham became a pillar of modern data science, he was a spreadsheet-loving teenager making databases for his dad’s job. In this episode, he reflects on the early days of his involvement with R, the birth of tidyverse, and how real-world unpredictability — like a bear in a field — shapes data science. Data scientists around the world owe a lot to Hadley Wickham — but how did he go from wrangling databases as a teen to shaping the future of statistical computing? In this episode of The Test Set, Hadley takes us through the winding path of his early days with R, the birth of the tidyverse, and the quirks of open source development. We dig into the philosophies behind his tools, why team dynamics matter, and how large language models are sparking his curiosity all over again. What’s Inside: • Hadley’s first brush with R code … inside a Word doc • Consulting as a grad student — and learning what people really want from stats • How messy Excel sheets inspired the tidy data revolution • Writing R packages as a form of self-defense (and productivity) • The secret sauce of building the tidyverse team • On focus, burnout, and saying “no” to GitHub pull requests • Current obsession: using LLMs to make data science faster, easier, and more fun • How writing books is a form of tidying ideas, and how a Shiny textbook led to a custom bike

Hadley Wickham

Take Positron to Work with Positron Pro

While you’ve downloaded Positron on your desktop and are loving it, you may still have a few questions about using it at work: What happens when my analysis requires more memory than my laptop has? How can I bring Positron into my company’s secure environment? How can I access data in Databricks or Snowflake from Positron? Nick Rohrbaugh, Senior Product Marketing Manager at Posit shares how Positron Pro, available exclusively within Posit Workbench, transforms from a powerful desktop editor into a fully governed, enterprise-ready IDE. In this webinar, you will learn how: Data teams get immediate access to scalable, server-side compute and secure, one-click data authorization. IT leaders can centralize, secure, and manage Positron alongside RStudio, Jupyter, and VS Code, all from a single platform. Helpful links: Positron: https://posit.co/products/ide/positron/ Posit Workbench: https://posit.co/products/enterprise/workbench/

Positron rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Integrating video & data in sports analytics | Arielle Dror | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Arielle Dror, Director of Data and Analytics at Bay FC, a team in the US National Women’s Soccer League (NWSL), to chat about working out loud, integrating video with data analysis, technology they use in sports analytics, and quantifying intangible player traits. Oh, and ICE CREAM! In this Hangout, Arielle discusses how she works to integrate data into Bay FC’s decision-making processes, including recruitment, tactical analysis, and game preparation. She uses tools like Quarto and Posit Connect to automate weekly match reports. To enhance understanding among non-technical staff, Ariel’s team also uses proprietary sports software (Sportscode) to build dashboards on top of timestamped game footage with specific events tagged in it. This allows end-users to click on specific data points, such as those related to chance creation, and immediately view the corresponding video play that demonstrates the data’s meaning. This visual context is essential for translating data results to coaches. Resources mentioned in the video and zoom chat: R and AI Conference → https://rconsortium.github.io/RplusAI_website/ Bay FC Data Video on LinkedIn → https://www.linkedin.com/posts/wearebayfc_bayfc-activity-7390100562731712512-xKIY?utm_source=share&utm_medium=member_desktop&rcm=ACoAACSkp_4BRz9mhkQZvnAk0Wdehn749sDDYJY If you didn’t join live, one great discussion you missed from the zoom chat was about the “passion penalty”. Attendees discussed whether working in a field you love, such as sports, typically comes with lower pay than other industries, especially given the high supply of passionate people who want to work in the space. Do you think the passion penalty exists? 🤔 ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 03:19 “What traits in players do you wish you were able to quantify?” 06:48 “How is the data science work implemented on the field?” 10:50 “How do you translate results to coaches?” 12:44 “What tools do you wish you had access to?” 18:27 “Tell us about how you began working out loud and what that led to.” 23:26 “Why is it so hard to recruit for sports?” 24:31 “Do you feel like there’s a passion penalty for working in the sports world?” 28:31 “Are you working with PyTorch, scikit-learn, or OpenCV computer vision?” 30:28 “Do you ever correct anyone and tell them it’s football, not soccer? Is the same set of data available for everyone?” 34:08 “Were there any sporting myths you’ve been able to analyze, like home ground advantage?” 37:28 “What’s the best way to break into sports data roles?” 40:11 “Are athletes willing to share personal health data from wearables?” 42:06 “Is there commercially viable technology for 3D modeling movements?” 43:43 “How do you handle scenarios where play on the field contradicts data predictions?” 45:33 “How did you pick the analytics stack you use?” 47:41 “Do you have any advice for new people learning data science skills?” 48:44 “Is there a danger players will optimize individual statistics over team performance?” 50:22 “Is there a ‘Believe’ sign in the locker room?” 50:59 “What’s the most important thing you learned about communicating analytics to stakeholders?”

Keeping LLMs in Their Lane: Focused AI for Data Science and Research

From R+AI 2025, hosted by R Consortium Keynote LLMs are powerful, flexible, easy-to-use… and often wrong. This is a dangerous combination, especially for data analysis and scientific research, where correctness and reproducibility are core requirements. Fortunately, it turns out that by carefully applying LLMs to narrower use cases, we can turn them into surprisingly reliable assistants that accelerate and enhance, rather than undermine, scientific work. This is not just theory—I’ll showcase working examples of seamlessly integrating LLMs into analytic workflows, helping data scientists build interactive, intelligent applications without needing to be web developers. You’ll see firsthand how keeping LLMs focused lets us leverage their “intelligence” in a way that’s practical, rigorous, and reproducible. Bio Joe Cheng is the CTO and first employee at Posit, PBC (formerly known as RStudio), where he helped create the RStudio IDE, Shiny web framework, and Databot agent for exploratory data analysis. R Consortium Resources Main R Consortium Site: https://www.r-consortium.org/ R+AI website: https://rconsortium.github.io/RplusAI_website R Consortium webinars: https://r-consortium.org/webinars/webinars.html Blog: https://r-consortium.org/blog/ LinkedIn: https://www.linkedin.com/company/r-consortium/

Joe Cheng

How to use pointblank to understand, validate, and document your data

R/Medicine Webinar This workshop focused on the data quality and data documentation workflows that the pointblank package makes possible. The speaker Richard Iannone used functions that allowed us to: quickly understand a new dataset validate tabular data using rules that are based on our understanding of the data fully document a table by describing its variables and other important details The pointblank package was created to scale from small validation problems (“Let’s make certain this table fits my expectations before moving on”) to very large (“Let’s validate these 35 database tables every day and ensure data quality is maintained”) and Ionnone delved into all sorts of data quality scenarios so the viewer will be comfortable using this package in their organization. Data documentation is seemingly and unfortunately less common in organizations (maybe even less than the practice of data validation). We’ll learn all about how this doesn’t have to be a tedious chore. The pointblank package allows you to create informative and beautiful data documentation that will help others understand what’s in all those tables that are so vital to an organization. Speaker Richard Iannone, Software Engineer, Posit, PBC Rich is a software engineer at Posit that enjoys creating useful R and Python packages. He trained and worked as an atmospheric scientist and discovered working with R to be a breath of fresh air compared to the Excel-based analysis workflows common in that field. Since joining Posit he has been focused on developing packages that help organizations with data management and data visualization/publishing. When not working on R and Python packages, Rich also enjoys other things like playing and listening to music, watching movies, and getting outside

Investing in community to drive organizational change | Dooti Roy | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Dooti Roy, Head of Clinical Data Science Capability Management at Boehringer Ingelheim Pharmaceuticals, to chat about organizational change in data science, investing in internal open-source community, transitioning from SAS to R, and motivating long-term learning and adoption of new tools. In this Hangout, we explore the instrumental role of internal community in driving organizational change. Dooti shared that during the preparation for the transformation process from closed source (like SAS) to R and open source, she was advised by Rachael Dempsey (creator of the DS hangout!) to invest in community and “don’t sleep on that.” Dooti reported that following this advice proved successful, as the internal community grew from the early adopters and became a safe space where colleagues help each other understand new concepts and overcome implementation challenges. This thriving community, which includes team members from the US, Europe, China, and Japan, serves as a springboard for testing new ideas, tools, and processes, generating global feedback that helps pull the institutional change forward. Maybe our favorite part, Dootie shared that strategies like naming internal meetings “beginR” (for beginner office hours) help set the right expectation, ensuring people feel comfortable asking questions without feeling afraid of appearing naive. Resources mentioned in the video and zoom chat: Positron Data Science Lab Session Registration → pos.it/dslab R/Pharma Conference Registration (Free Event) → https://rinpharma.com/ Community Building Tips & Tricks Site → https://pos.it/community-building Boehringer Ingelheim’s Open Source Davinci Modular Framework → https://github.com/Boehringer-Ingelheim/davinci If you didn’t join live, one great discussion you missed from the zoom chat was about the struggle of finding time to learn new skills. Many attendees agreed that they are sooooo tired after work and that the learning often needs to be a work assignment to make time for it. This resonated with Dooti Roy’s observation that “I don’t have time” is one of the biggest pain points for colleagues attempting to learn new tools. Do you have the energy after work to do ambitious learning projects? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 02:52 “What did organizational change look like and how did community help?” 06:19 “Do leaders understand the commitment to open source?” 15:25 “Was this your first time promoting open source?” 18:04 “What does data science look like at Boehringer Ingelheim?” 22:55 “How do you motivate long-term learning and development?” 31:05 “What is your costume for Halloween?” 31:55 “What is your team’s workflow and technology stack?” 44:50 “How do you motivate data professionals to document their code?” 48:40 “How do you encourage people to keep up with open source developments?”

Scaling a shiny app to 100K monthly users | Tan Ho | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Tan Ho, Engineering manager at Teamworks Intelligence Soccer, to chat about scaling Shiny applications, working out loud and building a personal brand, defining “code in production,” and best practices for R package development. In this Hangout, we explore how Tan built a successful Shiny application that scaled to serve thousands of users, challenging the misconception that R does not scale. Tan shared his firsthand experience running the Dynasty Process Shiny app which peaked at 200,000 monthly users. He explained that the app was able to handle this load because user sessions were kept very short (around 30 seconds to a minute). Key advice for performance optimization includes focusing on small round-trip times for users, moving as much logic out of the app as possible (such as pre-computing or caching common requests), reducing the interface, and optimizing the actual queries (for instance, by rewriting them in data.table). Resources mentioned in the video and zoom chat: DynastyProcess GitHub → https://github.com/DynastyProcess Tan Ho’s Github → https://github.com/tanho63 Tan Ho talk: A minimum viable Shiny infrastructure for serving 95,000 monthly users → https://www.youtube.com/watch?v=zb3umo3Ka2g&t=3s Tan Ho talk: Shiny in Production 2023: Tan Ho - Effective logging for Shiny → https://www.youtube.com/watch?v=wbUXrK_YWfI Show Your Work by Austin Kleon → https://austinkleon.com/show-your-work/ If you didn’t join live, one great discussion you missed from the zoom chat was about display setups, with participants debating whether one screen is better for focus or if multiple screens (up to four!) are essential for data science work. How many monitors do you roll with? Libby can’t live without at least two external monitors, but admits that sometimes just one laptop monitor is better for focusing. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 04:15 “Tell us just, like, a snippet of your journey into R/Shiny” 07:36 “How many users did you have on your shiny app?” 09:44 “How did the techniques and skills you learned in NFLverse help translate to your professional role?” 13:03 “Do you feel like working out loud played a big part in building a personal brand?” 15:29 “What is your top advice for thinking about code in production?” 21:30 “Do you have resources for people starting their Shiny journey?” 25:01 “What’s the trigger point when you decide to bundle products into one package?” 27:25 “What were the biggest hurdles faced in scaling the Shiny app with high user load?” 31:23 “How can I do a better job of evaluating fantasy football players than ESPN projections?” 32:59 “What packages are most helpful for benchmarking and writing fast code?” 35:29 “Do you start with use case first or generic code and then refine?” 37:48 “What soft skills or personality traits help manage bugs efficiently?” 41:49 “Did you have instances when you felt you were going overboard with curiosity?” 45:25 “What are the best practices for reproducible production workflows for non-technical teams?” 49:00 “What piece of career advice would you give?”

People over computers: engineering leadership | Elliot Murphy | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Elliot Murphy, a VP of Engineering at Posit, to chat about engineering leadership philosophy, balancing maintenance versus innovation and risk, non-traditional career paths and the value of lifelong learning, and data science tool breakdowns including Posit Cloud, Posit Connect Cloud, and shinyapps.io. In this Hangout, we explore some philosophical approaches to engineering and management, where Elliot stresses that focusing on people is more important than computers, and human relationships tend to outlast relationships with a given company or employer. Elliot discusses his role as primarily listening and synthesizing the brilliance of others, creating a supportive environment, and making space for people to take on risk and propose new ideas. He also highlighted the value of clear communication when addressing high-pressure environments (like when you’re sunsetting a software product). Resources mentioned in the video and zoom chat: Cognition in the Wild (Book discussed regarding joint cognition) → https://mitpress.mit.edu/9780262581462/cognition-in-the-wild/ datapasta (R package) → https://github.com/MilesMcBain/datapasta AI Capabilities in Positron Workflow Demo → https://pos.it/workflow-demos If you didn’t join live, one great discussion you missed from the zoom chat was about the surprising value and broad resources offered by local libraries, including free access to services like LinkedIn Learning, paid periodicals like the NY Times, and streaming platforms like Kanopy. Attendees praised libraries as the “best resource we hardly hear about” and key providers of freedom of information. Do you love your local library?! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 02:04 “Can you tell us a little bit about your journey to where you are?” 04:10 “What does a VP of engineering do? And what types of things are important to you from a philosophical standpoint for engineering and management?” 06:21 “What about your job lets you focus on the people?” 06:33 “Is there also a pressure to innovate?” 09:28 “How do you argue to leadership at a strategic level that that maintenance work is important to take up people’s time?” 11:48 “How do you balance engineers and data scientists wanting to incorporate best practices with fast prototyping?” 16:46 “What distinguishes science from engineering in data and also more broadly?” 18:28 “Any resources or any books that helped you bridge the gap between a non traditional background and your current position?” 23:00 “What is the secret to creating a good environment for the team?” 25:23 “Are there basics that you feel analysts or data scientists should be aware of in terms of engineering that would help their work?” 28:47 “How do you make decisions about when to either sunset a project or change directions?” 31:20 “What can those doing maintenance work do to add value to the organization to be safe from being displaced by automation?” 35:02 “What do you think many data scientists or analysts are missing about engineering infrastructure today that could simplify their day to day workflows?” 35:52 “How can a professional transition from a business intelligence role to a data scientist position?” 43:47 “What is your mantra or best practice to crack these professional certification exams?” 45:22 “Tell us the difference between Posit Cloud, Posit Connect Cloud, Posit Connect, and shinyapps.io.” 49:03 “How can a team avoid the trap of over-engineering?”

Outgrowing your laptop with Positron - Julia Silge

Have you ever run out of memory or time when tidying data, making a visualization, or training a model? An R user may find their laptop more than sufficient to start their journey with statistical computing, but as datasets grow in size and complexity, so does the necessity for more sophisticated tooling. This talk will step through a set of approaches to scale your tasks beyond in-memory analysis on your local machine, using the Postron IDE: adopting a lazy evaluation engine like DuckDB, connecting to remote databases with fluent workflows, and even migrating from desktop analysis entirely to server or cloud compute using SSH tunnelling. The transition away from a local, in-memory programming paradigm can be challenging for R users, who may not have much exposure to tools or training for these ways of working. This talk will explore available options which make crossing this boundary more approachable, and how they can be used with an advanced development environment suited for statistical computing with R. Integrations in the Positron IDE make all these tasks easier; for example, remote development in Positron allows an R user to seamlessly write code on their local machine, and execute that code on a remote host without tedious interactions outside the IDE. Whether you train statistical models, build interactive apps, or work with large datasets, after this talk you’ll walk away with techniques for doing it better with Positron. https://positron.posit.co

Julia Silge

AI-Powered Data Science in Positron

We’re so excited to introduce Positron, a free, next-generation data science IDE that makes it easy to work in both R and Python. Positron builds on our years of experience developing RStudio and is a fork of VS Code, designed specifically for data work. This means you get a modern coding interface with features tailored for data science, like a built-in data explorer, AI assistance, interpreter management, and more. This is our 2nd event in our Positron series and focused on AI-Powered Data Science. Here at Posit, we strive to create products where AI works with you, not against you. In efforts to continue this mission, we are excited to introduce agentic AI capabilities in Positron, our new, free code editor for R and Python, that are designed from the ground up to follow these principles. Positron’s AI capabilities automate the tedious parts of the data science workflow, but always keep you, the expert, in control. 00:00 Introduction 00:32 What is Positron? (The Next-Gen Data Science IDE) 03:43 Introducing Positron Assistant 05:10 Bring your own LLM 05:47 Your environment as context 07:19 Inline code suggestions 07:58 Introducing Databot 08:22 The WEAR loop 10:15 Demo time 10:56 Opening Positron Pro in Posit Team 14:29 Opening a new folder in Positron 15:22 Cloning a repo from GitHub 17:24 Positron icons 19:15 Positron search bar and command palette 20:18 Changing interpreters and opening a Quarto document 21:30 Running code and populating the Variables Pane 22:41 Using the Data Explorer 25:28 Creating a plot and debugging with Positron Assistant 30:46 Editing code using inline code suggestions 34:42 Sharing a Quarto document 36:12 Opening Databot 37:02 Exploring data with Databot 43:37 Creating a report using Databot findings 45:12 Wrap up Additional resources: GitHub Repo for this Example: https://github.com/posit-dev/positron-ai-workshop Getting Started with Positron: Quick Tour: https://posit.co/resources/videos/get-started-with-positron-a-quick-tour-and-community-qa/ Introducing Databot (Blog Post): https://posit.co/blog/introducing-databot/ Posit AI Newsletter: https://posit.co/blog/2025-09-26-ai-newsletter/ Positron Assistant: https://positron.posit.co/assistant.html

Positron Quarto rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Accelerating Study Insights with Shiny and Posit | Regeneron x Atorus

In this session, Atorus and Regeneron, and Posit showcase their innovative partnership. Topics covered: -Building a Unified Analytical Environment: Learn how to reduce friction by integrating fragmented SAS and R workflows. -Case Study: A Config-Driven App: See a live demo of how a simple configuration file can build a fully functional application for data review and safety monitoring. -Operational Shifts: Understand how internal teams are empowered to independently manage and extend their own applications in a regulated environment. This isn’t just about technology—it’s about the strategic shift required to simplify development and accelerate insights within a regulated environment. Learn more about Posit’s work in the pharma space: https://posit.co/use-cases/pharma/?utm_source=youtube&utm_medium=social&utm_term=pharma&utm_content=atorus-regeneron

config Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Programming Education Rstats Open Source Oss Regeneron Atorus R Shiny Positron R Data Science Pharma

Lessons from a Broad & Varied Data Science Career | Arcenis Rojas | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Arcenis Rojas, Data Scientist at Indeed, to chat about econometrics, public vs private sector data science, navigating a varied career trajectory, AI integration in the hiring sphere, and making friends at conferences. In this Hangout, Arcenis talked about how his career journey has been wide as opposed to vertically narrow. He shared that this breadth of experience has given him confidence that he can quickly figure out any dataset. He feels it also taught him how to communicate effectively about data to people at different levels and across various domains. He also shared his tech stack at Indeed, including RStudio, Positron, AWS, Snowflake, Quarto for reporting, Shiny for apps, and Posit Connect for deploying them. An attendee asked about the impacts of AI on the job search space, and Arcenis shared the AI at Work Report (linked below) from the Indeed Hiring Lab. He says, based on research, generative AI is expected to assist many people but only replace small segments of the workforce in the coming 5-10 years, and that entry-level knowledge work is predicted to be the most highly impacted area. Resources mentioned in the video and zoom chat: Indeed Hiring Lab: AI at Work Report 2025 → https://www.hiringlab.org/2025/09/23/ai-at-work-report-2025-how-genai-is-rewiring-the-dna-of-jobs/ To Explain or to Predict? (Galit Shmueli, 2010) → https://arxiv.org/abs/1101.0891 Announcing the 2025 table and plotnine contests → https://posit.co/blog/announcing-the-2025-table-and-plotnine-contests/ If you didn’t join live, one great discussion you missed from the zoom chat was about the wide variety of data types data scientists work with. Attendees shared that their data included genomics, finance/trading, environmental/natural resources, e-commerce products, and medical/clinical data. What kind of data types do you work with? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 06:16 “What do you like to do for fun?” 08:51 “What are the unique aspects of financial and economic data science?” 15:07 “What are econometrics?” 16:02 “Is the difference that hard sciences stats is trying to explain what happened where econometrics might be what might happen in the future?” 19:39 “Suggestions for making data friends and going to a conference alone.” 23:26 “Do you see any misconceptions about the job market online, specifically the ATS thing?” 29:52 “How has your varied career trajectory been an advantage or a challenge in data science?” 34:08 “How is the recent hype wave of AI integration manifesting in the hiring sphere?” 40:08 “What are the tools that you use in your job for reporting?” 41:42 “How do you know when it is time to pivot and leave your role because your skills are stagnating?” 45:56 “How would you persuade leadership to use R or Python?” 49:32 “Did you find yourself always trying to use more complex models when simpler ones would serve the audience better?”

Pair Programming for Sustainable Deployment | Kris Fabick | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Kris Fabick, data scientist at Nissan North America, to chat about pair programming as a mechanism for sustainable deployment, data science in supply chain operations, the challenges of integrating pair programming into a team, and even a little about Kris’s hobby, reffing soccer! In this Hangout, we explore how pair programming can serve as a mechanism for sustainable deployment. Kris explained that using pair programming is the team’s solution to successfully deploying projects in a sustainable way, especially since they have a small data science team without extensive enterprise-level tools or IT support. Pair programming creates human redundancy in a project, meaning two (or more) people are familiar with it, allowing the team to react quickly to changes in development and deployment, and ensuring that someone can cover maintenance even if the original programmer is on vacation. When pitching the investment of time to leadership, a crucial metric was cited: pair programming requires only 15% more time to implement but results in 15% fewer defects. The value proposition is that saving defects saves far more time and downstream costs than the initial investment, offering stability that was a great comfort to leadership. Resources mentioned in the video and zoom chat: Shiny State Blog Post (by Eric Nantz) → https://shinydevseries.com/blog/2025-09-24-shinystate-cran/ Guest’s posit::conf() GitHub Repo → https://github.com/kfabick03/positconf2025 We don’t have the link just yet, but Kris’s conf talk will be on YouTube soon! You can search for the title, “Self-sufficient deployment- using pair programming to have fun and sell your solutions” and the speakers are Kris Fabick & Kristin Carr If you didn’t join live, one great discussion you missed from the zoom chat was about the difference between using AI for coding versus using human pair programming. The group discussed how “Pair programming == everyone learns MORE about the codebase” while “AI coding == everyone understands LESS about the codebase”. Chris noted that when he uses AI to help with code, he actually knows less about it and would not be able to explain the functionality to stakeholders if changes were requested. Attendees also noted that chatbots don’t show their logic or mistakes and how they back out of them, which is a valuable source of learning. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Time Stamps 00:00 Introduction 01:47 “What is the best and worst thing about being a referee?” 04:58 “Kris, could you give us a little recap of your posit::conf(2025) talk about pair programming?” 08:52 “What was the most persuasive thing or metric that you feel like you were able to use to persuade leadership to let you adopt pair programming?” 13:52 “Are there any challenges or additional benefits of pair programming to the individual, that you’ve noticed?” 17:58 “Do you have any tips for being self-conscious while coding in front of someone?” 19:53 “Could you give a high level overview of supply chain?” 21:56 “Is it better to have both devs be at the same level of experience or expertise when pair coding?” 27:32 “I was just wondering if you could tell us a little bit about the container fill ratio problem you mentioned in your talk.” 31:41 “How as a pair of programmer do you set realistic expectation from the stakeholder? How do you manage the communication gap?” 36:50 “Do you think that AI and specifically things like Copilot might be able to replace some aspects of pair programming?” 40:59 “Do you have a piece of career advice that has really stuck with you or that you like to give people that you find impactful?” 43:36 “Have you ever done any fun data stuff related to your refereeing?” 48:36 “How do you balance the time that gets added on with pair programming with tasks that need to be done quickly?” Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Keynote: I Wrote This Talk with an LLM - Hadley Wickham

In this keynote, I’ll explore the evolving relationship between data scientists, statisticians, and large language models through a unique experiment: this entire talk was created in collaboration with an LLM. From outline to slides, from code examples to key insights, I’ll share the practical realities of using AI as a thought partner in the R ecosystem. Drawing on my experience developing tidyverse packages and teaching data science, I’ll demonstrate how LLMs can augment (rather than replace) the R user’s workflow. We’ll examine specific examples where AI assistance shines—rapid prototyping, documentation generation, and creative ideation—alongside areas where human expertise remains irreplaceable. Most importantly, I’ll reflect on what this experiment reveals about the future of our community: How might AI change the way we teach R? What new skills should we prioritize? And how can we ensure that the tools we build remain accessible and empowering for all users? Join me for this meta-exploration of AI’s role in our work, with honest reflections on both the promise and limitations of these new collaborators in our statistical computing journey. This abstract was generated by Claude Sonnet 3.7 and lightly edited by me. I used the prompt: I am Hadley Wickham, chief scientist at RStudio/Posit and I’ve been invited to give a keynote on AI at the useR conference. Please write a talk abstract for a talk entitled ‘I wrote this talk with an LLM’ https://hadley.nz/

Hadley Wickham

Top AI Powered Coding Assistants

which have you tried? #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #cursor #windsurf #positshorts

Positron rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Migrating to Open Source & the Future of Biostatistics | Beth Atkinson | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Beth Atkinson, principal biostatistician at Mayo Clinic, to chat about the challenges of migrating from SAS to R, working with diverse and noisy data types (including wearable data and omics projects), foundational tooling like RMarkdown and Quarto, and maintaining statistical fundamentals amidst the hype cycle of new tools like AI. In this Hangout, we explore the challenges of working with complex, high-volume data, like the data derived from wearable devices and medical charts. A challenge with wearable device data is that it can be super noisy, with issues like computers not syncing up, people forgetting to wear the device, or someone else wearing it. Medical chart data can be inconsistent; some things are recorded, and some are not. She also talks about the R/Medicine conference, the future of modern biostatistics, and the journey of compassionately helping an organization move from proprietary tools like SAS to open source tools like R. Beth also works on omics projects, including genomics (looking at DNA), metabolomics, exposomics (chemical exposures), and multiomics, which involves looking at all of this information together in a holistic way. We hope you’ll come along with us if you’re interested in learning about the biomedical world of data! Resources mentioned in the video and zoom chat: R/Medicine Conference website → https://rconsortium.github.io/RMedicine_website/ arsenal R package (MayoVerse) → https://mayoverse.github.io/arsenal/ (The arsenal package was created to help encourage transition from SAS to R by providing equivalent functionality for summary reporting macros that people relied on.) 2025 Posit Table and Plotnine Contests → https://posit.co/blog/announcing-the-2025-table-and-plotnine-contests/ If you didn’t join live, one great discussion you missed from the zoom chat was about the general dislike of regular expressions (regex) and the tendency to rely on tools like ChatGPT to write complex regex syntax. Many in the chat agreed that regex is difficult to commit to memory but acknowledged the power of the tool. So… do you use an LLM to help with regex? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 03:25 “You do a lot of things that end in -omics. What are those things?” 05:02 “What are the types of data that you work with and some of the challenges that you face with those data?” 09:37 “What was your favorite new feature that made your work easier?” 11:42 “What is your favorite data science tool or R package that you find helpful in health research as a biostatistician?” 14:04 “I wanted to see if that’s consistent with your experience [that 80% of workflow is data prep]” 17:07 “Does it scare you to hand off data to be cleaned by someone else?” 18:11 “What have you noticed that we still need to adhere to [regarding statistics fundamentals]?” 22:48 “Do you also produce reporting products as part of your role, and is your audience primarily internal and narrow, or do you communicate with a broader external audience as well?” 26:35 “Can you talk about a little bit of your personal SAS experience as well as the bigger organizational change maybe that Mayo is is doing?” 30:05 “What are some of the roadblocks that are faced in a SAS-to-R journey and and how can we find compassion for the people that we are helping to transition?” 33:55 “What is the community aspect internally at Mayo Clinic around R?” 35:43 “How do you store and manage all of that [data]?” 40:41 “What tools and skill sets should we focus on if we want to get into biostats today? Do you think it’s important for people to still learn SAS if they’re coming in fresh? And how about the future of biostatistics as a role separate from data science?” 45:48 “Is it possible for someone with a nontraditional background to make these transitions [into computational epidemiology]?” 48:10 “What’s the source of most of these innovations?” 50:05 “Could you talk a little bit about R/Medicine conference?”

What can you do with Positron Assistant?

Positron Assistant is an AI client integrated within Positron, designed to enhance data science workflows. Positron Assistant brings LLM integration for both chat and inline code completions.This tool is built to assist with a range of tasks, including generating and refactoring code, answering questions, providing debugging support, and offering insightful suggestions for next steps in your projects. It can interact with the Variables pane, Plots pane, and the Console in your IDE. Capabilities include: • Editor inline chat (chat with Positron Assistant in your script to augment your code) • Contextual code completion (add instructional comments to your scripts to complete code) • Agent-assisted workflows (allow Positron Assistant to execute tasks autonomously) Learn more about Positron Assistant: https://positron.posit.co/assistant.html Sign up for our Workflow Demo on October 29, where we will demonstrate Positron Assistant: https://posit.co/workflow-demo/ai-powered-data-science-in-positron/

Positron RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Supporting 100 Data Scientists with a Small Team | Mike Thomson | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Mike Thomson, Data Science Manager at Flatiron Health, to chat about managing open source tools and maintaining R packages, creating reproducible reports for Word and Excel using Quarto, the “hub and spoke” support model for data scientists, and applying R and Posit tools in Real World Evidence (RWE) oncology space. In this Hangout, we explore creating reproducible outputs using Quarto for formats like Word and Excel. Flatiron Health uses Quarto because it allows the reproducible publication of analyses to multiple formats simultaneously (like HTML and a downloadable Word document) from the same source code. A specific challenge discussed was outputting formatted analytic tables to Excel, as this is not natively supported by Quarto. Erica Yim, from Mike’s team, detailed how they built an internal R function that uses the flexlsx package along with flextable to easily output pre-existing formatted tables from a Quarto document into an Excel template. Resources mentioned in the video and zoom chat: flexlsx R package GitHub repository → https://github.com/pteridin/flexlsx DBPlier PR for Snowflake Translations (contributed to by Flat Iron Health) → https://github.com/tidyverse/dbplyr/pull/860 If you didn’t join live, one great discussion you missed from the zoom chat was about the pain points of exporting data from Quarto to Word or Excel, particularly concerning table formatting and styles. Attendees in the chat strongly highlighted the difficulty of managing table formatting, including issues with table cross-references, headers, and footers. They noted that dealing with styles often requires workarounds, such as creating flextables that match desired Word styles instead of relying on default table styles. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 02:23 “Can you talk about what Flatiron does and what your teams do?” 03:29 “Could you give us a few examples of the data types or collections that you might be working with?” 05:00 “Do you have longitudinal data?” 07:46 “Are you aware of any computer vision applications in the health care industry from your perspective?” 09:38 “Do you use mixed models or Bayesian MCMC?” 10:56 “How does your team use Quarto?” 16:59 “How do you convince stakeholders of the value of going open source (and handle security concerns)?” 22:56 “Do you allow people to have a certain amount of time to contribute back to open source?” 26:03 “I just want to understand a little bit about your support model for that group.” 29:57 “Do you have any tips for asynchronous working?” 31:02 “Are you like a Jira team or an Asana team for assigning tasks or tickets?” 32:10 “How many people on your platform team support Posit teams?” 34:24 “What does your team use for unstructured document analysis?” 36:24 “How important is domain knowledge in your recruitment?” 40:02 “Where do you store all of this stuff (data storage and databases)?” 42:04 “What is the approximate timeline from the time you do analysis to final deployment of results in the real world?” 44:31 “Is there a process for people getting things approved to use in your environment?” 47:39 “How do you handle the challenge of going back from Word to Quarto source code (after changes are tracked)?” 50:22 “What does a typical Workday look like for you?” 51:47 “Is there a piece of career advice that has either really helped you, that you’ve really liked, that you try to give to other people?”

R & Python Interoperability in Data Science Teams | Dave Gruenewald | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Dave Gruenewald, Senior Director of Data Science at Centene, to chat about polyglot teams, data science best practices, right-sizing development efforts, and process automation. In this Hangout, we explore working in a polyglot team and fostering interoperability (a word that Libby loves, but struggles to pronounce out loud). Dave Gruenewald emphasizes that teams should use the tools they are comfortable with, whether that’s R or Python. Some strategies for collaboration across languages that Dave suggests include tools like Quarto to seamlessly run R and Python code in the same report. Teams utilize data science checkpoints, saving outputs as platform-agnostic file types like Parquet so that they can be accessed by any language. The use of REST APIs allows R processes to be accessed programmatically by Python (and vice versa), which can be a real game-changer. The newly released nanonext package was also highlighted as a promising development for improved interoperability. Resources mentioned in the video and zoom chat: Posit Conf 2025 Table and Plotnine Contests → https://posit.co/blog/announcing-the-2025-table-and-plotnine-contests/ nanonext 1.7.0 Tidyverse Blog Post → https://www.tidyverse.org/blog/2025/09/nanonext-1-7-0/ If you didn’t join live, one great discussion you missed from the zoom chat was about pivoting away from academia, including leaving PhD programs. Many attendees shared their personal experiences of making the difficult decision to drop out of a PhD program. The community suggested alternative terms like “pivot,” “reallocating your resources,” or being a “refugee fleeing academia” instead of “drop out.” Dave Gruenewald shared that he himself left a PhD program but has “no regrets about that.” Did you leave a PhD program? You’re not alone! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 02:21 “What types of data do your teams use?” 06:53 “Which of the three pillars you mentioned is your personal favorite to work on?” 09:26 “How do you avoid or divert scope creep?” 11:41 “How much of the project should be “planning” before any code happens?” 13:53 “Do you feel like people are just hopping in and going, hey, LLM, make me a POC?” 14:28 “Do you give them what they say they want, or do you give them what they need?” 16:40 “I’m wondering what public data do you wish existed?” 18:48 “Why not Positron yet?” 20:43 “How do you unify as a team and make it so that I can always read everybody else’s code?” 23:10 “Could you talk a little bit about how R and Python work together?” 27:28 “How to start package development with a team who are very new to package development.” 33:01 “What’s your greatest regret career wise?” 35:53 “What about your biggest wins, specifically in your early career?” 39:40 “How would you recommend building a data science culture and community from scratch?” 41:49 “Would you set a specific timeline for EDA, exploratory analysis, to scope the project better?” 45:15 “How do you define fun projects, and how much time do you allocate for exploration in those?” 48:21 “Does your team use DVC or something similar for data version control?” 50:00 “Can you talk a bit more about your pivot from academia into data science?” 51:31 “Any advice on where to look for opportunities in data science after getting a masters degree?”

Getting Started with Positron: A Quick Tour

We’re thrilled to have you on board as we continue to explore Positron, Posit’s new Integrated Development Environment. Positron is designed to streamline development workflows and reduce context switching for data scientists working with Python, R, and other languages. Over the next two months, our series will dive deeper into key areas: AI Agents: Explore Positron’s native AI capabilities, including Positron Assistant for code writing and debugging, and Databot, an experimental data exploration agent that visualizes data and recommends next steps. AI Apps: Learn to build, deploy, and scale GenAI-enabled apps quickly and securely with your preferred LLM, using pre-built examples and templates. Positron Series: Getting Started with Positron (this recording) AI-Powered Data Science in Positron: https://youtu.be/TrN-FMcOsOA?si=2nOb0q_nowMcS3xy Take Positron to Work with Positron Pro: https://youtu.be/CNwZZvtAIEM Helpful resources from this session: Positron documentation: https://positron.posit.co/ Positron announcement (Why Positron & Why Now?): https://posit.co/blog/positron-product-announcement-aug-2025/ Positron Assistant and Databot announcement: posit.co/blog/positron-ai-product-announcement-aug-2025/ uv documentation: https://docs.astral.sh/uv/guides/install-python/ Git documentation: https://git-scm.com/downloads Resources mentioned in the Q&A: Exploratory Data Analysis with R in Positron: https://youtu.be/ndq2Mm3Dju8?feature=shared Publishing from Positron: https://docs.posit.co/connect/user/publishing-positron-vscode/ For “very advanced users” regarding multi-root workspaces: https://code.visualstudio.com/docs/editing/workspaces/multi-root-workspaces Great extension pack for Positron from Garrick: https://github.com/gadenbuie/positron-plus-1-e

Deploy a Streamlit web app with me

deploy all the things! #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyternotebook #positshorts

Positron rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Theming Made Easy: Introducing brand.yml (Garrick Aden-Buie, Posit) | posit::conf(2025)

Theming Made Easy: Introducing brand.yml Speaker(s): Garrick Aden-Buie Abstract: brand.yml is an exciting new project from Posit that radically simplifies theming. Every data science tool supports some form of theme and appearance customization, but each app framework, output format, or visualization tool requires its own special syntax for theming. The goal of brand.yml is to create a portable and unified interface for brand-related theming that can be used anywhere that data science artifacts are produced. As a collaboration between the Shiny and Quarto teams, brand.yml provides a single interface to setting baseline themes in reports and apps across the R and Python ecosystems. In this talk, I’ll introduce brand.yml and showcase the many ways that brand.yml can bring consistent styles to your data science outputs. Materials - https://github.com/gadenbuie/brand-yml-posit-conf-2025 Slides - https://pkg.garrickadenbuie.com/brand-yml-posit-conf-2025 posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Garrick Aden-Buie

The Psychology of Technologists (Cat Hicks, Catharsis Consulting) | posit::conf(2025)

The Psychology of Technologists Speaker(s): Cat Hicks Abstract: Technology teams are struggling to be heard. Technologists are grappling with high turnover, high burnout, and low resilience in the face of rapidly-accelerating technological change. Even though effective technical teams are an essential part of our world continuing to work, many organizations struggle to understand their technical teams, stuck in old stereotypes that treat technical people like isolated machines. To build a thriving future, we need a new way of working together and understanding our own minds as we build technology. In my empirical research across thousands of developers, technical managers and their teams, we’ve explored what a psychologically-rich environment looks like for modern technologists and found important signals that can guide teams toward greater resilience, innovation, and thriving. In this keynote we’ll unlock access to the psychological sciences and evidence that you can use to make your every day more human. Links: Dr. Cat Hicks - https://www.drcathicks.com/ Catharsis Consulting - https://www.catharsisinsight.com/ Center for Open Science (COS) - https://www.cos.io/ Fight for the Human Newsletter - https://www.fightforthehuman.com/ posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Making the most of artificial and human intelligence for data science (Hadley Wickham, Joe Cheng)

Making the most of artificial and human intelligence for data science Speaker(s): Hadley Wickham; Joe Cheng Abstract: This presentation explores the complex and often contradictory nature of large language models (LLMs) in data science, acknowledging the simultaneous excitement and apprehension that we feel toward these technologies. We’ll provide a practical framework to help you understand the LLM ecosystem (from foundation models and hosting to SDKs and applications) that supports our current philosophy: augmenting, not replacing human intelligence. The talk demonstrates how Posit is addressing this space through two complementary approaches: building SDKs and tools that help you create your own LLM-powered solutions, and developing integrated LLM capabilities directly into data science workflows through tools like Positron assistant and databot. We’ll showcase practical, immediately useful applications while addressing current limitations, providing you with both the emotional preparation and technical foundation needed to effectively leverage LLMs in their data science practice today. posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Hadley Wickham, Joe Cheng

Election Night Reporting Using R & Quarto (Andrew Heiss & Gabe Osterhout) | posit::conf(2025)

Election Night Reporting Using R & Quarto Speaker(s): Gabe Osterhout; Andrew Heiss Abstract: Election night reporting (ENR) is often clunky, outdated, and overpriced. The Idaho Secretary of State’s office leveraged R and Quarto to create a better ENR product for the end user while driving down costs using the open-source software we all know and love. With help from Dr. Andrew Heiss, R was used in every step of the process—from {dbplyr} backend to visualizing the results using {reactable} tables and {leaflet} maps, combining the output into a visually appealing Quarto website. Quarto was the ideal solution due to its scalability, quick deployment, responsive design, and easy navigation. In addition, Dr. Heiss will discuss the advantages of using a {targets} pipeline and creating programmatic code chunks in Quarto. GitHub Repo - https://github.com/andrewheiss/election-desk posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Web applications with Shiny and React and AI (Winston Chang, Posit) | posit::conf(2025)

Web applications with Shiny and React (and AI) Speaker(s): Winston Chang If you’ve ever wanted to have complete control over the UI for a Shiny application, this talk might be for you. In this talk I’ll show how to integrate a back end written in Shiny (for R or Python) with a front end written in React.js. This lets you combine Shiny’s reactivity with the flexibility of the React web development ecosystem. In the past, writing a front end in React was a large time investment, and would be reserved only for a serious project. However, with today’s agentic AI coding tools, it can make sense to create a React front end even for smaller-scale applications. But even with AI involved, you will still need to know some React or be willing to learn about it. GitHub Repo - https://github.com/wch/shiny-react CLI tool - https://github.com/wch/create-shiny-react-app Anthropic Dashboard - https://github.com/wch/anthropic_dashboard posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Winston Chang

surveydown (John Paul Helveston, GWU) | posit::conf(2025)

surveydown: A Markdown-Based Platform for Interactive and Reproducible Surveys Using Quarto and Shiny Speaker(s): John Paul Helveston Abstract: This talk introduces the surveydown R package and survey platform, which leverages the Quarto publication system and R shiny web framework to create reproducible and interactive surveys. While most survey platforms rely on graphical interfaces or spreadsheets to define survey content, surveydown uses plain text (markdown and R code chunks), enabling version control and collaboration via tools like GitHub. It supports complex features like conditional skip logic, dynamic questions, and complex randomization as well as a diverse set of question types and formatting options. The open-source package gives researchers full control over survey implementation and data storage, with reproducible workflows that integrate with R data analysis. GitHub Repo - https://github.com/surveydown-dev/surveydown Materials - https://github.com/jhelvy/2025-posit-conf-surveydown posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Observability at scale (Barret Schloerke, Posit) | posit::conf(2025)

Observability at scale: Monitoring Shiny Applications with OpenTelemetry Speaker(s): Barret Schloerke Abstract: Understanding what happens behind the scenes in production Shiny applications has always been challenging. When users experience slow response times or unexpected behavior, developers are left guessing where bottlenecks occur. This talk introduces OpenTelemetry integration for Shiny for R, a new approach to profiling code and understanding application behavior. Through a chat-enabled weather application, we’ll explore how complex user interactions trigger cascading events across multiple processes. You’ll learn how OpenTelemetry’s “high-quality, ubiquitous, and portable telemetry” can provide complete visibility into your Shiny application’s performance with minimal setup… just a few environment variables! After walking through the chat app, you’ll have a taste of how to implement comprehensive monitoring for your Shiny applications in production, enabling you to proactively identify and resolve unexpected performance issues. Materials - https://github.com/schloerke/presentation-2025-09-17-posit-conf-otel posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Barret Schloerke

Get the Latest on Posit’s Commercial Products | posit::conf(2025)

Get the Latest on Posit’s Commercial Products Speaker(s): Kelly O’Briant; Tom Mock; Joe Roberts; Kara Woo; Alex Chisholm; Chetan Thapar; Steve Nolen Abstract: Join us for an overview of the latest developments across Posit’s commercial product ecosystem. This session will cover Posit Workbench, Package Manager, Connect, Connect Cloud, and our growing portfolio of managed services including Snowflake and beyond. Hear directly from the product managers and engineers who are building these tools, and get insights into what’s coming next. 0:00 Introduction 3:30 Audited jobs, Positron Pro sessions, and GenAI in Workbench 14:20 Auth and integrations with RStudio Pro sessions in Package Manager 21:00 An intro to Chronicle for Posit Team 25:57 Building container images in Connect 39:50 Organization plans in Connect Cloud 49:00 A Snowflake Native App offering for Connect and Workbench 1:02:00 An intro to Posit Team Dedicated posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Engineer Insightful Shiny Apps with Behavioral Science (Jeremy Winget, CrossStack) | posit::conf

Death by Dropdown? Engineer Insightful Shiny Apps with Behavioral Science Speaker(s): Jeremy Winget, PhD Abstract: Opening a Shiny app to endless dropdowns and scattered filters leads to immediate disengagement and feeling like “death by dropdown”. This talk introduces the Behavioral Insights Design (BID) Framework, a behavioral science roadmap that transforms overwhelming dashboards into guided user journeys. Using the “Interfaces Need A Smart Vision” mnemonic, we’ll explore five stages: Interpret user needs through data storytelling, Notice cognitive friction points, Anticipate user behavior, Structure the application, and Validate & empower users. Drawing from established theories and real-world production examples, attendees will see how applying behavioral science can dramatically reduce the time between “Where do I even start?” and “Aha, now I get it!”. You’ll leave with practical techniques using the new {bidux} package to help transform user confusion into confident exploration and build dashboards that guide users toward better decisions (and fewer ghosted users). BID Framework - https://github.com/jrwinget/bid-framework bidux docs - https://github.com/jrwinget/bidux posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Birthing the pregnancy package (Ella Kaye, University of Warwick) | posit::conf(2025)

Birthing the pregnancy package Speaker(s): Ella Kaye Abstract: When I was pregnant, I wrote a personal, private package for date and medication calculations, hard-coded with my data. I thought it would be easy to adapt for general use, but no! I wanted the user experience to be excellent, but how to avoid them repeatedly entering the same due date and meds? How to print useful, nicely formatted, grammatically correct information to the console? How to test a package that relies on the current date? Using the pregnancy package as a case study, I’ll show how to take advantage of base R features such as global options and %||%, how the cli and rlang packages came to the rescue for messages and how testthat’s new mocking features saved the day (literally!) to create a package that’s a pleasure to use. Slides: https://github.com/EllaKaye/pregnancy-lightning-talk posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

10 tips for data science beginners

Do you have any to add? #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyternotebook #positshorts

Positron rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Let’s try Positron Assistant for the first time together

lil ai buddy #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #positshorts

Positron rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Laboratory science to data science & the art of the growth gig | Lisa Elkin | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Lisa Elkin, Senior Principal Computational Toxicologist at Pfizer, to chat about her career transition from a lab scientist to a data scientist, building Shiny apps for scientific audiences, and the value of internal programs for up-skilling and data literacy. In this Hangout, we explore how to foster data science skills and build communities within a large organization. Lisa shares her experience with programs like an “Analytics Exchange,” where employees could dedicate 20% of their time to working on data science projects from across the company, allowing them to learn by doing. She provides tips on how to propose similar “growth gig” opportunities to leadership, highlighting the value of up-skilling the entire department to increase efficiency and innovation without necessarily hiring more specialists. Lisa also gives advice for starting a new internal community from the ground up, emphasizing the need for a few passionate individuals and the importance of repeating introductory sessions to support newcomers. Lisa runs a Data Science Hangout internally at Pfizer!! Resources mentioned in the video and zoom chat: Mastering Shiny Book → https://mastering-shiny.org/ Jenny Bryan - Code Smells and Feels Talk → https://github.com/jennybc/code-smells-and-feels Posit Academy → https://posit.co/products/enterprise/academy/ If you didn’t join live, one great discussion you missed from the zoom chat was about what a “mainframe” computer is. The conversation sparked memories of punch cards, floppy disks, and how the cloud is in some ways a return to the mainframe paradigm. Let us know below if you’d like to hear more about it! Better yet, join us live on Thursdays:) pos.it/dsh ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 04:50 “What is toxicology?” 06:19 “What are the data types that your tool users are interfacing with?” 07:19 “What is an assay?” 08:15 “How does your science background benefit or disadvantage you as a data scientist?” 14:55 “What kind of audience do you build Shiny apps for and what is your workflow?” 18:05 “What made you decide to switch to industry and what were the bottlenecks between academia and industry?” 24:39 “Do you have any tips on proposing an ‘analytics exchange’ program to leadership?” 28:38 “What tools or systems do you use for sharing apps and what tools do you use daily?” 32:33 “How much oversight do you have on your analysis and do you ever get stuck in skeptic paralysis?” 35:18 “Do you have any podcast recommendations?” 38:01 “How can organizations resolve the tension between allowing employees space to learn and being productive?” 41:12 “What advice would you give to somebody starting a brand new community within their company?” 46:05 “Any resources for learning modular Shiny apps or tool building in general?” 47:40 “What is the career progression like for a data scientist and is it possible to grow as an individual contributor?”

Jenny Bryan

SAS to R, data harmonization, & a career in pharma | Dony Unardi | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Dony Unardi, Principal Data Scientist at Genentech, to chat about the Teal framework for Shiny, data cleaning and harmonization in the pharmaceutical industry, and career transitions from SAS (and web development!) to R. In this Hangout, we explore Teal, an open-source framework built around Shiny to accelerate the creation of clinical trial analysis apps. Donnie explains that Teal provides out-of-the-box features like advanced filtering and, most importantly, code reproducibility. This allows users to generate the exact R code needed to reproduce a visualization or analysis from the app in their local environment, a critical feature for validation and submission in the pharma industry. Resources mentioned in the video and zoom chat: Teal Gallery → https://insightsengineering.github.io/teal.gallery/ Pharmaverse → https://pharmaverse.org/ SAS to R: A Data Snack from the Fred Hutch Data Science Lab → https://hutchdatascience.org/data_snacks/r_snacks/sas2r.html A SAS to R Success Story (Posit Video) → https://posit.co/resources/videos/a-sas-to-r-success-story/ R Consortium Submissions Working Group → https://rconsortium.github.io/submissions-wg/ If you didn’t join live, one great discussion you missed from the zoom chat was about making the transition from SAS to R. Many community members shared personal experiences, valuable resources, and advice, emphasizing the importance of learning R’s idioms rather than attempting a direct, one-to-one translation of SAS code. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 06:34 “What is the teal shiny app framework?” 12:10 “Does being in the Bay Area make a difference for data careers?” 16:44 “What kind of sass are you talking about? (SAS!)” 18:07 “How do you approach technical interviews?” 20:22 “Do you have any tips for a SAS programmer being told to use R?” 26:10 “What are some of the data quality challenges that you find in pharma data?” 29:43 “How is reproducibility defined in the pharma industry?” 32:37 “What does Teal offer for reproducibility that a Git repo does not?” 36:48 “What are the pharma job titles for roles focused on data cleaning and QAQC?” 39:11 “What do you mean by harmonizing data?” 42:09 “How do you prioritize projects and collaborate with your teammates?” 44:36 “Should personal data science projects be for learning or for a resume?” 47:58 “How did you get leadership support to contribute to the open-source community?”

Data viz, Shiny app design, & technical career paths | Kiegan Rice | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Keegan Rice, Senior Statistician at NORC at the University of Chicago, to chat about data visualization design, bridging the gap between technical and non-technical teams, and building large-scale, public-facing Shiny apps. In this Hangout, we explore how to effectively communicate statistical results and design intuitive data visualizations for non-technical audiences. Keegan shares her top tips, emphasizing the importance of knowing your audience and not assuming they have the same level of data literacy as a practitioner. She suggests testing your charts on friends or colleagues outside the data space to ensure they are understandable. The conversation also covers the challenges of explaining concepts like statistical significance and the value of forcing stakeholders to articulate their core business questions rather than just asking for a specific table or chart. Resources mentioned in the video and zoom chat: Kiegan’s posit::conf talk, “Wait, that’s shiny” → https://www.youtube.com/watch?v=SAbV6d7Pn0U Kiegan’s reproducible GitHub repo from the talk → https://github.com/kiegan/wait-thats-shiny Deepsha Menghani’s talk on crosstalk → https://youtu.be/AbuK2F57NEs The NCVS dashboard, an example of a tool built for a federal client → https://ncvs.bjs.ojp.gov/Home The Medicare Current Beneficiary Survey Chartbook → https://chartbook.mcbs.org/ If you didn’t join live, one great discussion you missed from the zoom chat was about favorite board games and sci-fi/fantasy books. Recommendations ranged from board games like Ark Nova, Dominion, and Terraforming Mars to book series like The Expanse, The Stormlight Archive, and Gideon the Ninth. Come hang out with us live to chat about this stuff!! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 07:40 “What are your top tips for data visualization design?” 11:15 “What are the challenges you’ve faced when communicating statistical results to non-statisticians?” 18:30 “Do you experience any imposter syndrome because you’re not really doing stats anymore?” 22:04 “How do you see data visualization evolving and what are your book recommendations?” 26:56 “How do you get federal clients to modernize beyond using tables for everything?” 29:37 “How do you decide what aspects to drop when a data visualization is too busy?” 35:57 “Is there a middle ground for Shiny between Quarto and JavaScript?” 39:51 “Which sci-fi or fantasy books are you loving?” 42:19 “What advice do you have for someone just getting started with interactive data visualization?” 47:40 “What advice do you have for attending or giving a talk at posit::conf for the first time?” 50:35 “How important is it to show the lineage of data so people can trust your visualizations?”

Data Exploration 101

Don’t skimp #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyternotebook #positshorts

Positron rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Sports analytics, baseball metrics, and Shiny apps | Brian Chase | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Brian Chase, Vice President of Baseball Systems at the Miami Marlins, to chat about the role of baseball systems and data analytics in professional sports, leveraging technology and hardware for player performance and team strategy, the evolution of sports analytics since the “Moneyball” era, and career insights for breaking into niche industries (like baseball!). In this Hangout, we explore how the Miami Marlins leverage advanced technology and data analytics to enhance player performance and team strategy. Brian details the use of innovative hardware like the Trajekt robotic pitching machine that allows batters to practice against simulated versions of opposing pitchers with exact spin rates and velocities. He also discusses the Hawkeye cameras and other systems, which capture real-time player and ball tracking data, including bio-mechanical information, using 12 cameras at LoanDepot Park. This extensive data is then platformed in tools like Snowflake and Google Cloud for analysis. The team also utilizes wearables to track bat speed and other metrics. Insights are delivered to coaches and players through pre-game and post-game reports and dashboards, often built using Posit Connect and Shiny apps, which helps translate complex data into easily digestible stories. Resources mentioned in the video and zoom chat: Miami Marlins Job Opportunities → https://www.teamworkonline.com/baseball-jobs/miamibaseball/miami-marlins Technology & AI Magazine Feature on Miami Marlins → https://technologymagazine.com/brochure/miami-marlins-using-ai-to-accelerate-competitive-sport Trajekt Sports (Trajekt pitching machine) → https://www.trajektsports.com/ MIT Sloan Sports Analytics Conference → https://www.sloansportsconference.com/ Astroball by Ben Reiter → https://www.porchlightbooks.com/products/astroball-ben-reiter-9780525576648 The MVP Machine by Ben Lindbergh and Travis Sawchik → https://www.sandmanbooks.com/book/9781541698925?srsltid=AfmBOorVPNYGMdhNFsiaUfJ2N88em1qiGfEf6Kok5Z3LmaREs-Iv25ac If you didn’t join live, one great discussion you missed from the zoom chat was about mascots! Libby shared her love for mascots, especially the San Antonio Missions “puffy taco”. Attendees chimed in, sharing their favorite team mascot pics - you really have to be there live to appreciate all the fun in the chat!! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 00:55 “Tell us a little bit about what you do and how you got here?” 09:15 “How did you get your foot in the door for an MLB job?” 14:02 “What coding languages, tools, and systems do your teams work with the most?” 19:35 “What does data analytics have to do with baseball hardware?” 23:51 “What do pre-game and post-game reports look like, and how do you use Shiny?” 26:39 “How do you measure ROI for technologies, and how do you get buy-in?” 30:41 “How has stats analytics evolved since the Moneyball era?” 34:25 “Is qualitative analysis still used alongside quantitative data from scouts?” 35:27 “Who will play you in the future of Moneyball 2?” 36:27 “Will AI benefits plateau if all teams have similar access to tools and data?” 40:22 “Are you measuring off-field factors like sleep, nutrition, and injuries in your models?” 42:57 “What’s the thought process behind developing new features and actionable metrics in sports analytics?” 47:53 “What might someone be surprised by when turning a hobby into a job?” 50:40 “Did you ever feel discouraged during your 4-year job search?” 50:50 “Was getting your MBA worth it for your career in sports?”

Positron 101: Make your DS life easier

Personally, I think the Plots pane is #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #positshorts

Positron rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Set up your first coding project in less than 10 minutes

What are you waiting for #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #positshorts

Positron rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

How to Build a Streamlit App in Positron

Want to make building and deploying your Streamlit applications a breeze? In this video, we show you how to leverage the full power of the Positron platform to create, develop, and launch your data apps. Discover Positron features that make development easier than ever. You’ll see how Positron’s integrated environment simplifies every step of the process, from project setup to dependency management and seamless deployment. This tutorial is perfect for anyone who wants to quickly create professional-looking interactive web apps without the headache of complex configurations. 0:00 Introduction 0:30 Opening a folder 1:24 Setting up a uv environment 2:26 Installing project dependencies 2:56: Walking through the app 05:24 Previewing the data in the Data Explorer 07:52: Previewing the app in the Viewer Pane 09:08 Enabling Live Reloading 11:40 Adding to app with Positron Assistant, Positron’s AI client 18:00 Deploying your Streamlit app to Posit Connect using Posit Publisher Resources: Positron documentation: https://positron.posit.co/ Download Positron: https://positron.posit.co/download.html Setting up Python in Positron: https://positron.posit.co/python-installations.html Setting up Positron Assistant: https://positron.posit.co/assistant.html GitHub repository: https://github.com/posit-marketing/streamlit-example Blog post: https://posit.co/blog/streamlit-in-positron/ UV: https://docs.astral.sh/uv/ Kyle Walker’s “Distance and proximity analysis in Python” tutorial: https://walker-data.com/posts/proximity-analysis/ Keywords: Positron, Streamlit, Streamlit deployment, Positron platform, web application, data app, data science, Python, IDE, integrated development environment, Streamlit for beginners, build a web app, interactive apps, Python programming, data analysis, how to use Positron, Streamlit on Positron

I wrote this talk with an LLM - Hadley Wickham (useR! 2025 Keynote 1)

Presented by: Hadley Wickham (PositPBC) In this keynote, I’ll explore the evolving relationship between data scientists, statisticians, and large language models through a unique experiment: this entire talk was created in collaboration with an LLM. From outline to slides, from code examples to key insights, I’ll share the practical realities of using AI as a thought partner in the R ecosystem. Drawing on my experience developing tidyverse packages and teaching data science, I’ll demonstrate how LLMs can augment (rather than replace) the R user’s workflow. We’ll examine specific examples where AI assistance shines—rapid prototyping, documentation generation, and creative ideation—alongside areas where human expertise remains irreplaceable. Most importantly, I’ll reflect on what this experiment reveals about the future of our community: How might AI change the way we teach R? What new skills should we prioritize? And how can we ensure that the tools we build remain accessible and empowering for all users? Join me for this meta-exploration of AI’s role in our work, with honest reflections on both the promise and limitations of these new collaborators in our statistical computing journey. This abstract was generated by Claude Sonnet 3.7 and lightly edited by me. I used the prompt: I am Hadley Wickham, chief scientist at RStudio/Posit and I’ve been invited to give a keynote on AI at the useR conference. Please write a talk abstract for a talk entitled ‘I wrote this talk with an LLM’

Hadley Wickham

New data science tools & old laptops on fire | Jenny Bryan | Data Science Hangout

ADD THE DATA SCIENCE HANGOUT TO YOUR CALENDAR HERE: https://pos.it/dsh - All are welcome! We’d love to see you! We were joined by Jenny Bryan, Senior Software Engineer at Posit, to chat about (setting laptops on fire,) adapting careers to embrace change and new technologies, behind-the-scenes technical advancements powering the R ecosystem with tools like Positron, demystifying project-based workflows, plus LLM integration and best practices in programming. Listen to this episode to hear us chat about topics like this: the benefits and limitations of using Large Language Models (LLMs) in programming. Jenny shared her initial skepticism towards LLMs for coding in R, but her attitude changed significantly when applying LLMs to problems involving languages she was less familiar with, like Rust or TypeScript. adapting in your career to embrace change and new technologies. Jenny, who describes herself as being on a “third career”, transitioned from management consulting to a statistics professor, and then to a senior software engineer at Posit. She talks a bit about her career journey and how she’s embracing new stuff (ahem, Typescript) so that she gets to keep doing cool stuff! Positron IDE for R package development. She specifically praises Positron’s unique test explorer and reliable console, and its integrated Data Explorer. For many, Positron offers out-of-the-box data science functionality, unlike other IDEs that require extensive customization. what new technologies like Ark, Air, and Positron mean for the longterm health of R. Jenny’s been working on lots of nerdy things behind the scenes at Posit and she talks all about how they’re great for developers, package builders, data scientists, and engineers alike. Another tidbit from this hangout: Jenny gave some advice for those looking to branch into software engineering without formal training: try reading code from admired developers, inviting code reviews, and undertaking small, recreational package development projects to gain practical experience and confidence. She also advocates for adopting a project-oriented workflow (associated with her famous “laptop on fire” remark, of course) using tools like the here package for managing project paths. Resources mentioned in the video and zoom chat: Positron IDE → https://positron.posit.co/ Happy Git with R → https://happygitwithr.com/ Jenny Bryan’s “Project-oriented workflow” blog post → https://www.tidyverse.org/blog/2017/12/workflow-vs-script/ Air R code formatter → https://posit-dev.github.io/air/ The here() package → https://here.r-lib.org/ Posit Conf → https://posit.co/conference/ Tidy Dev Day 2025 → https://www.tidyverse.org/blog/2025/07/tdd-2025/ R Packages book → https://r-pkgs.org/ If you didn’t join live, you missed a ROARINGLY active chat. Let’s just say, if you’ve ever broken down in tears over a programming project, you’re not alone! Come join us live each week if you’d like to hang out in the chat with us! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 03:39 “Is that a Wooble on your desk?” (Spoiler, it’s a gnome!!) 06:23 “As a builder of data science tools, what are the tool features data scientists want most?” 08:43 “Have you experienced needing to adapt to change recently and how have you embraced it?” 13:46 “What is ‘setting laptops on fire’ about?” 13:50 “How did you decide to change your career a few times?” 21:23 “What are your thoughts on the ease of putting models into production in Python versus R and does it make sense to shift everybody to one language or the other?” 27:30 “How do you navigate the ‘I have a hammer so everything looks like a nail’ feeling when working with emerging tools like LLMs?” 33:24 “Do you have any general advice for those data scientists who find themselves wanting to branch out more into software engineering but don’t have formal training?” 39:39 “Why should I use Positron instead of Versus Code?” 47:57 “Can you speak to the value of developing an R package and how to clear the mental hurdle of it being a huge challenge?” 52:34 “What does your career trajectory look like and what is your advice for other people who are looking to grow their career but don’t know if they want to be an IC or a manager? Does being a manager mean you don’t get to write code anymore?”

Jenny Bryan

Get starting with data - load data into your IDE

Finding the data is the hardest part, prove me wrong #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyternotebook #positshorts

Positron rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Data Science in the Energy Industry | Frank Hull | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Frank Hull, Director of Data Science and Analytics at ACES, to chat about forecasting energy demand and prices, managing over a thousand data models, full-stack data science, and advanced machine learning techniques for time series analysis. In this Hangout, we explore the necessity of managing a vast number of data models in the energy industry. Frank’s team at ACES oversees over a thousand models (nearly 2,000, actually!), a staggering number explained by the complexity and fragmentation of the wholesale energy market. The United States is divided into various Independent System Operators (ISOs), each possessing unique regulations and diverse resource mixes. Each of ACES’s 40+ portfolios can operate in different geographical areas within an ISO, presenting distinct challenges that necessitate individual modeling. These models are used to simulate a wide range of time horizons, from the next hour or day-ahead market to long-term financial planning and infrastructure decisions spanning 25 years. This intricate modeling helps in understanding hourly price shapes, demand patterns, supply mixes, and evaluating the effectiveness of new energy generators or hedging strategies, all with the goal of lowering variable costs for cooperatives and mitigating critical risks like blackouts during peak demand. Resources mentioned in the video and zoom chat: Tidymodels → https://www.tidymodels.org/ Orbital Project → https://orbital.tidymodels.org/ U.S. Energy Information Administration (EIA) Open Data → https://www.eia.gov/opendata/ Kuzco R Package → https://posit.co/blog/kuzco-computer-vision-with-llms-in-r/ If you didn’t join live, one great discussion you missed from the zoom chat was about handling imbalanced binary classification models. Participants discussed why techniques like SMOTE might not perform well in production with real-world data, shared experiences with alternative methods such as standard up/downsampling, and highlighted challenges in maintaining prediction accuracy in deployment despite strong training results. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Introduction 04:05 “What’s ISO?” 08:20 “What are your go to models for analysis in the energy field?” 10:48 “Do you tend to use traditional stochastic models for time series analysis or more of the recent ML methods?” 13:30 “What is a full stack data scientist? What’s the overlap between a full stack data scientist and something like an ML engineer or a data engineer?” 18:38 “Is there a specific data science skill set that’s needed to get into energy analysis?” 19:59 “What is the portfolio model?” 23:36 “How have you found convincing regulators and other stats oriented stakeholders to trust and believe your AI fancy machine learning models that they can’t really dive in and and prove to themselves that that’s being statistically valid? Or have you found some good ways to demonstrate that?” 26:50 “Are there any good examples of open data in energy?” 27:54 “How are you keeping on top of the documentation for all of these models? Over a thousand models is a lot. Is there any learning you could share from that experience to help other people keep on top of their documentation?” 30:33 “How would you suggest handling missing data in time series forecasting?” 33:10 “Do you see long term electricity prices decreasing in the next twenty five years due to the abundance of renewables like wind and solar in lower population areas?” 35:14 “Do you have any career advice?” 36:50 “How do you see data science evolving within the energy industry?” 38:39 “How do you keep up to date on new packages?”

The Future of Asynchronous Programming in R - Charlie Gao

Asynchronous programming can be a powerful paradigm, whereby computations are allowed to run concurrently without blocking the main session. It is an opportune time to survey the current landscape, as R infrastructure in this respect has matured significantly over recent years. Instead of running a script sequentially from top to bottom, logic that takes a long or unpredictable amount of time to complete may be offloaded to different R processes, possibly on other computers or in the cloud. In the meantime, the main session may be running constantly and non-interactively, performing operations in real time, synchronizing with these tasks only when necessary. This style of programming requires a very specific set of tooling. At the very base, there is an infrastructure layer involving key enabling packages such as later and mirai. It will be explained at a high level why these two packages together currently offer the most complete and efficient implementation of async for the R language. There are further tools which expand async functionality to cover specific needs, such as the watcher package for filesystem monitoring. There are then a range of tools built on top of these, bringing async capabilities to the end-user, such as the httr2 package for querying APIs and the ellmer package for interacting with LLMs. In addition to these existing tools, exciting developments in asynchronous programming are just around the corner. These will be previewed, together with speculation on what might be possible at some point in the future

Charlie Gao

A first look at Positron - Julia Silge

Description Positron is a next generation data science IDE built by the creators of RStudio. It has been available for beta testing for a number of months, and R users may have wondered if they should try it or if it will be a good fit for them. This new IDE is an extensible tool built to facilitate exploratory data analysis, reproducible authoring, and publishing data artifacts, and it is an IDE that supports but is not built only for R. How should an R user think about Positron, compared to the other options out there? In this talk, learn about how and why Positron is designed the way it is, what will feel familiar or new coming from other IDEs such as RStudio, and when (or if) people who use R should consider giving it a try. You’ll hear about different choices when it comes to defaults and ways of working, such as how to think about your projects or folders and how to manage multiple versions of R. You will also learn about new functionality for R users and package developers that we have never had before, like new approaches for managing R package tests and the ability to customize an IDE using extensions. If you are curious about Positron and how it fits into the R ecosystem, you’ll come away from this talk with more details about its capabilities and more clarity about whether it may be a good choice for you. Additional Material or Paper *Visit https://positron.posit.co for documentation and installers *Find us on GitHub at https://github.com/posit-dev/positron *Positron is currently available on Posit Workbench in preview

Julia Silge

Deploying Scikit-learn models for in-database scoring with Snowflake and Posit Team

Modern data science workflows often face a critical challenge: how to efficiently deploy machine learning models where the data lives. Traditional approaches require moving large datasets out of the database for scoring, creating bottlenecks, security concerns, and unnecessary data movement costs. In this demo, Nick Pelikan at Posit highlights how orbital bridges the gap between Python’s rich ML ecosystem and database-native execution by converting scikit-learn pipelines to optimized SQL. This enables data scientists to: Use familiar Python tools for model development Automatically translate complex feature engineering to SQL Deploy models without rewriting code in different languages Maintain model accuracy through faithful SQL translations While this example focuses on Python, Nick also gave this demo using Orbital for R users: https://youtu.be/pnEjYNgOG9c?feature=shared Helpful resources: Follow-along blog post: https://posit.co/blog/snowflake-orbital-scikit-learn-models/ Supported models for Orbital: https://orbital.tidymodels.org/articles/supported-models.html Orbital (Python) GitHub Repo: https://github.com/posit-dev/orbital Orbital (R) GitHub Repo: https://github.com/tidymodels/orbital Databricks and Orbital for R and Python model deployment: https://posit.co/blog/databricks-orbital-r-python-model-deployment/ Emily Riederer’s XGBoost example with Orbital: https://www.emilyriederer.com/post/orbital-xgb/ Sara Altman’s blog post on Shiny + Databases: https://posit.co/blog/shiny-with-databases/ Emil’s introduction of orbital: https://emilhvitfeldt.com/talk/2024-08-13-orbital-positconf/ Nick’s Blog on Running Tidymodel Prediction Workflows with Orbital: https://posit.co/blog/running-tidymodel-prediction-workflows-inside-databases/ If you’d like to learn more about using Posit Team, you can always schedule time to chat with Posit here: https://posit.co/schedule-a-call/

Sara Altman

Shiny tidymodels RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

What is an IDE & which one should you use as a beginner

Did you have a first? #datascience #datasciencetok #python #swe #datavisualization #dataanalytics #codinglife #vscode #ide #rstudio #positron #pycharm #jupyter #positshorts

Positron rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Uniting the pharma industry with data science | Ross Farrugia | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Ross Farrugia, Data & Insights Engineering Product Family Lead at Roche/Genentech, to chat about Pharmaverse, open source in pharma, career progression in the industry, and the impacts of AI to the pharma industry. In this Hangout, Ross tells us all about the Pharmaverse, an open-source initiative he co-founded, which aims to unite companies in the pharmaceutical industry. Instead of independently repeating similar work, companies co-develop and share the effort for building, maintaining, and testing code, especially for clinical trials and submissions to regulatory bodies. This collaborative approach, primarily built around R but open to other languages like Python, helps accelerate the delivery of drugs to patients and improves the review and approval process by health authorities. The Pharmaverse fosters a strong community, allowing for shared intellectual property (IP) and increasing trust and sustainability of packages, as demonstrated by the Admiral package with 10 co-development companies. The community, which includes nearly 2,000 people and 400 code contributors, democratically decides on package inclusion, connecting individuals across the industry to supercharge development efforts. Resources mentioned in the video and zoom chat: Pharmaverse Website → https://pharmaverse.org/ Pharmaverse Slack Community → Access via https://pharmaverse.org/FAQ Posit Podcast: The Test Set → https://posit.co/thetestset/ Monthly Workflow Demos (Orbital package) → https://posit.co/workflow-demo/seamless-r-python-model-deployment-with-snowflake-and-orbital/ Book Recommendation: The Situational Leader → https://store.situational.com/collections/books/products/situational-leader-book?utm_source=google&utm_medium=ppc&utm_campaign=CLS+Store&utm_term=the+situational+leader+book&gad_source=1&gad_campaignid=21067538390&gbraid=0AAAAACkXJNBKGqigIGr_i6NfE2yjK6IZr&gclid=CjwKCAjw1ozEBhAdEiwAn9qbzXXYLJFHz9wJSI6iVuEPZ-zO2NcrqjOTlPSOyTfRqWOvgT_zA99pDRoCsNEQAvD_BwE#gad_source_1 Libby’s Podcast Recommendation: Change Technically by Cat Hicks & Ashley Juavinett → https://www.changetechnically.fyi/ Phuse (A Global Healthcare Data Science Community) → https://phuse.global/ Ross’s Blog on Contributing to Open Source → https://pharmaverse.github.io/blog/posts/2024-03-11_tips_for__first_.../tips_for__first__time__contributors.html If you didn’t join live, one great discussion you missed from the zoom chat was about top tips for contributing to open source (though we talked about it quite a bit live!). Ross and community members emphasized starting small, learning basic Git, and contributing not just code but also by commenting on issues, raising bugs, and providing feedback. It was highlighted that helping write documentation, such as vignettes, can be a valuable contribution for newcomers, as fresh eyes can identify gaps that developers might miss. Yes, your fresh, newbie eyes are so valuable! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps 00:00 Introduction 03:50 “How is Pharmaverse different from a CRAN task view?” 06:20 “How is Pharmaverse curation handled?” 08:29 “How to convince competing companies to collaborate on open source?” 13:07 “What does career progression look like in the pharma industry?” 16:21 “Is it possible to break into pharma without prior experience?” 18:42 “Are there roles in pharma for open source package development?” 21:50 “Thoughts on the merging of R and Python communities in pharma?” 27:30 “Top tips for contributing to open source?” 31:40 “Is the pharma industry slow to adopt new software?” 35:25 “Focus on randomized control trials versus observational data?” 40:40 “Is clinical trials experience a requirement for entering pharma?” 41:27 “What career advice do you have for us?” 44:13 “Book and podcast recommendations” 46:26 “What data science professional bodies would you recommend joining?” 48:51 “How will AI influence the pharma industry and Pharmaverse?” 52:56 Wrap-up

Helping developers thrive | Cat Hicks | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Dr. Cat Hicks, Psychologist for software teams at Catharsis Consulting and upcoming positconf 2025 keynote speaker, to chat about the psychology of software teams, organizational culture and team dynamics, debunking tech industry myths, and strategies for professional visibility. Cat’s work is in applied social science, bringing evidence-based answers to real-world workplace questions, especially for software development teams. She’s not a “couch psychologist” but rather focuses on “structures of beliefs and behavioral science and team practices”. Here are some of the themes Cat touches on in the conversation: Understanding developer (and Data Scientist!) thriving Debunking industry myths, especially the “10x Developer” myth “Designing an organization that wants to understand itself” Fostering psychological safety in remote, multi-disciplinary teams Getting recognized for work by being strategically performative Cat has a unique perspective that bridges psychology and the practical realities of tech and data roles. She’s got our backs! Resources mentioned in the video and zoom chat: Cat Hicks’s newsletter, “Fight for the Human” → https://www.fightforthehuman.com/ Cat Hicks’s podcast, “Change, Technically” (specifically the episode “You Deserve Better Brain Research”) → https://www.changetechnically.fyi/2396236/episodes/17378968-you-deserve-better-brain-research Cat Hicks’s Bluesky profile → https://bsky.app/profile/grimalkina.bsky.social Developer Thriving paper (AI skill threat paper) → https://osf.io/preprints/psyarxiv/2gej5_v2 Code Review Anxiety paper: “Understanding and effectively mitigating code review anxiety” → https://link.springer.com/article/10.1007/s10664-024-10550-9 Code Review Anxiety workbook → https://developer-success-lab.gitbook.io/code-review-anxiety-workbook-1 Ted Laderas’s post on how improvisational comedy rules can help collaborations → https://laderast.github.io/posts/2022-02-27-yes-and-is-the-foundation-of-collaboration/ Posit Conference information → https://posit.co/conference/ useR 2025 conference information → https://user2025.r-project.org ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Timestamps: 00:00 Intro 01:11 Cat Hicks Introduces herself 04:04 What does Catharsis Consulting help with? 07:45 Preview of Cat Hicks’s Posit conference keynote 10:14 Tips on how to objectively assess team comfort with change 19:14 How much of developer thriving comes from your immediate team vs. the larger organization? 23:56 Strategies to foster psychological safety and empathy in remote, multi-disciplinary teams 29:04 Have you had instances where you felt the need to correct misconceptions about a paper? (the myth of “10x developers” or “10x engineers”) 34:34 Have you looked at interventions that explicitly scaffold information exchange in groups? 40:48 Ways organizations can balance top-down and bottom-up approaches to culture 47:19 What can staff with less formal power do to sow positive seeds in a low-morale culture? 52:00 How to get noticed for long-term, high-impact work without appearing unproductive or overly performative

Next-Gen Data Science: How Posit and Databricks Are Transforming Analytics at Scale

Modern data science teams face the challenge of navigating complex landscapes of languages, tools and infrastructure. Positron, Posit’s next-generation IDE, offers a powerful environment tailored for data science, seamlessly integrating with Databricks to empower teams working in Python and R. Now integrated within Posit Workbench, Positron enables data scientists to efficiently develop, iterate and analyze data with Databricks — all while maintaining their preferred workflows. In this session, we’ll explore how Python and R users can develop, deploy and scale their data science workflows by combining Posit tools with Databricks. We’ll showcase how Positron simplifies development for both Python and R and how Posit Connect enables seamless deployment of applications, reports and APIs powered by Databricks. Join us to see how Posit + Databricks create a frictionless, scalable and collaborative data science experience — so your teams can focus on insights, not infrastructure. Talk By: James Blair, Senior Product Mgr. Cloud Integrations, Posit, PBC Databricks Named a Leader in the 2025 Gartner® Magic Quadrant™ for Data Science and Machine Learning Platforms: https://www.databricks.com/blog/databricks-named-leader-2025-gartner-magic-quadrant-data-science-and-machine-learning Build and deploy quality AI agent systems: https://www.databricks.com/product/artificial-intelligence See all the product announcements from Data + AI Summit: https://www.databricks.com/events/dataaisummit-2025-announcements Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Next-gen data science: a conversation with the Ravit Show and Eric Pité

Eric Pité, SVP of Product & Strategy, sat down with the Ravit Show at Databricks Data + AI Summit to have a conversation about the evolution of open source data science, the Posit and Databricks integration, and the place of AI in modern data science. Some highlights from the conversation are below: – The evolution from RStudio to Posit It’s more than just a name change — it’s a signal of how seriously they’re embracing Python while staying true to their R roots and open-source mission. – Open source meets enterprise Eric shared how Posit is navigating the balance between community contribution and commercial sustainability — and why that mission still matters in an AI-first world. – Positron + Databricks Their strategic partnership is one to watch. Positron is making it easier to do high-quality data science (in R and Python) inside the Databricks ecosystem, with an emphasis on collaboration, reproducibility, and performance. – AI’s place in modern data science Eric and Ravit chatted about how Posit sees the role of AI in helping data teams be more productive, without losing rigor or transparency. – The power of storytelling with code One of our favorite parts is the emphasis on communication. Building models is not enough—data scientists need to share insights clearly, and Posit is building for that. Learn more about the Databricks and Posit partnership by exploring the partnership page and resources: https://posit.co/use-cases/databricks/

Positron rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Monthly Workflow Demos now on Zoom Sessions | Recordings still shared below

Please note while we previously hosted the Posit Workflow Demos here on YouTube, we have moved over to Zoom Sessions for a seamless event experience and more interactive Q&A. You can view upcoming workflow demos and register to join us here: https://events.zoom.us/ev/Ajss5j9VeRMe0zw-AtFKf7AUAsthzYhaYjYPeEIu1uYAQe1K0ud1~Agb_hSt1UGxd9fUyIDC32e6jAdEtbl7G_AaLZUYhRvyZR5cGpY5Kp_A0-w

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Building Tailored Dashboards: Drill Down Visualizations and LLM-Powered Summaries with Shiny

Led by Isabella Velásquez, Sr Product Marketing Manager at Posit PBC Scenario: Imagine the weekly scramble at DemoCo. Isabella, a lonely Marketing analyst, spends hours every Friday manually compiling marketing lead performance data from Salesforce, website logs, and event records into a sprawling Excel report. The tedious, redundant process is both a time sink and a bottleneck. Marketing Leadership, who need to identify top-performing platforms, find the mass amount of information in the report confusing. They constantly return to Isabella for clarification or, even worse, don’t reach back out and make misinformed decisions on critical budget allocation. Determined, Isabella decides it’s finally time to automate and tailor this critical dashboard, and save headaches for both Leadership and herself. In this demo, you will learn how to: Transform redundant, manual, error-prone data compilation into an automated lead performance dashboard with Shiny Integrate diverse marketing data sources for a holistic view Build tailored marketing KPI dashboards that address Leadership’s specific questions Use drill-downs, data visualizations, and LLM-powered summaries to reduce data overload and clarify complex takeaways instantly 5.Provide flexibility in dashboard functionality to meet diverse preferences and needs, from pre-selected insights derived directly from the data to implementing an LLM-powered analytics option Helpful resources: Demo Slides: https://github.com/ivelasq/2025-06-25_marketing-demo/blob/main/README.md Demo Marketing Dashboard: https://pub.current.posit.team/public/marketing-demo/ GitHub Repo (including links to packages used: pointblank, querychat, etc.): https://github.com/ivelasq/2025-06-25_marketing-demo?tab=readme-ov-file If you have specific follow-up questions about using Posit in your organization, we’d love to chat with you: https://posit.co/schedule-a-call/

Isabella Velásquez

Jeroen Janssens - Package Your Python Code as a CLI | PyData London 25

www.pydata.org Learn how to transform your Python code into a command-line tool. Jeroen Janssens, author of Data Science at the Command Line, guides you through the process of turning your scripts into reusable, executable tools, integrating them into your data workflows and harnessing the power of the Unix command line. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

Jeroen Janssens

Exploring Web APIs | PydyTuesday Uncut #1

Join Michael Chow (open source developer at Posit) and Jeroen Janssens (developer relations engineer at Posit) as they dive into this week’s #PydyTuesday dataset about Web APIs. Tools include uv, Positron, Polars, Plotnine, Great Tables, and the Unix command line. True to the “PydyTuesday Uncut” title, this video is completely unedited. Every typo, mistake, web search, and “aha!” moment is left in so you can see exactly how others approach a new dataset from scratch. Things mentioned during the session and related resources: Code produced during the session: https://github.com/jeroenjanssens/pydytuesday-uncut/blob/main/2025-06-17/01-start.py PydyTuesday https://github.com/posit-dev/pydytuesday TidyTuesday https://github.com/rfordatascience/tidytuesday Getting Data from the TidyTuesday Repo with Python https://www.youtube.com/watch?v=ol2FrSL5gVU Positron IDE https://positron.posit.co Data Science at the Command Line https://jeroenjanssens.com/dsatcl/ Python Polars: The Definitive Guide https://polarsguide.com Polars https://pola.rs Plotnine https://plotnine.org Great Tables https://posit-dev.github.io/great-tables/ The Big Year https://www.imdb.com/title/tt1053810/ 00:00 Introduction 02:46 Getting the data with uv 13:18 Positron IDE 17:42 Importing Polars 23:17 Plotting a bar chart with Plotnine 33:55 Inspecting duplicates 46:30 Handling missing values 58:56 Crafting a great table 1:38:48 Reflection

Jeroen Janssens, Michael Chow

Why regression still matters | Keith McNulty | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Keith McNulty, Analytics Leader at McKinsey & Company, to chat about his unique career path into data science, the importance of regression and network analysis, strategies for self-learning and balancing work, and navigating the evolving landscape of AI. In this Hangout, Keith talks about navigating the evolving landscape of AI, including trustworthiness, risks, and its impact on the data science profession. Keith highlights that AI is rapidly changing and discusses the balance between acquiring technical AI development skills and understanding the trustworthiness and inherent risks of large language models. He suggests prioritizing knowledge of AI’s reliability, as these challenges are likely to endure, and recommends identifying trusted sources to sift through the hype. The discussion also covers AI’s capacity to identify complex patterns in large datasets, which can be beyond human capabilities. However, it also acknowledges AI’s tendency to be convincingly incorrect. A significant point raised by Keith is his concern that data scientist could be pushed more towards software engineering, and he asked practitioners to consider if they are still engaging in the “science” aspect of data science. Resources mentioned in the video and zoom chat: Keith McNulty on LinkedIn → https://www.linkedin.com/in/keith-mcnulty/ Keith’s Regression Book (Handbook of Regression and People Analytics) → https://peopleanalytics-regression-book.org/ Keith’s ONA/Graph Book (Handbook of Graphs and Networks in People Analytics) → https://ona-book.org/ PositConf Registration (including virtual option) → https://posit.co/conference/ “Storytelling with Data” book → https://www.storytellingwithdata.com/ If you didn’t join live, one great discussion you missed from the zoom chat was about the evolving role of data scientists and the importance of “putting the science back in data science”. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! 00:00 Introduction 00:46 “Will you let us tell us a little bit about yourself, what you do, what you like to do for fun?” 03:10 “Were you that active (on social media) before you were in data science?” 05:33 “I was just wondering if you could maybe talk a little bit about your most favorite resources that you used while retraining yourself as a data scientist?” 10:12 “You wanna give a quick pitch for both of (your books) so we have some context?” 14:52 “What do you think about the possibility of extracting latent character traits about people from text?” 19:51 “How do you balance self learning with working time?” 25:16 “What kind of questions are you using networks to ask in the people analytics space?” 30:09 “What would you say is the most important AI related skill to learn for the future and to stay ahead in the workplace?” 35:59 “I’m interested in how talent is measured among white collar workers. Could you share some of the metrics you use and how you address the limitations of more straightforward measures like time in the office?” 40:26 “So I was wondering if that (early) role, that engagement role, helped you bridge this switch from academia to industry, or, how did that role help you choose the skill you wanted to build.” 43:54 “I just wanted to get your perspective on imposter syndrome, especially for people who are making some sort of transition in their career into data science.” 47:00 “Where do you source your daily math problems?” 48:19 “Do you have any thoughts on policy for AI in the current setting and and also, specifically thinking about there’s a lot of people in my company that are hesitant because of the environmental impact of AI.” 51:32 “What did we not talk about that you wish we had talked about?”

Four steps for managing data teams | Toby Hall | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Toby Hall, Executive Vice President and CIO at Delta Dental of Michigan, to chat about all kinds of things, including: data science team management, AI governance and the ethical use of data in healthcare, insurance data types and use cases, and hiring for traits vs. skills in data science. In this Hangout, among other topics, Toby mentions Delta Dental’s “guilty until proven innocent” approach to AI. This involves a two-stage approval process that critically examines use cases for ethical implications, data handling (ensuring data stays within dedicated tenants and doesn’t train public models), and tool appropriateness. The discussion also touches on the regulatory skepticism towards AI from departments of insurance and the broader environmental considerations of AI use. Toby also gives loads of career advice, but our favorite was his four-step process for managing a data team: Put the right people in the right role Give them the right vision of what you’re trying to accomplish, ensuring it’s clear and everyone is aligned on “what that is and why we’re doing it” Give them the right culture to get it done Get out of their way! Resources mentioned in the video and zoom chat: Posit Conference → https://posit.co/conference/ Delta Dental Tooth Fairy Poll → https://www.deltadental.com/us/en/tooth-fairy/the-original-poll.html TidyTuesday Data Repo (get your reps in!) → https://github.com/rfordatascience/tidytuesday Resources for doing TidyTuesday stuff in Python (PydyTuesday?) → https://github.com/posit-dev/python-tidytuesday If you didn’t join live, one great discussion you missed from the zoom chat was about the Delta Dental Tooth Fairy data, including its outrageously expensive rates and the humorous debate around the inflation of self-reported flossing data. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! 00:00 Introduction 02:28 “Tell us a little bit about your day to day, but also the data teams at Delta Dental of Michigan, what is the type of data that they work with, and what is it that they solve with it, or what decisions are made based on it?” 06:04 “What about third party data?” 07:57 “How do you all decide which language to use and for what applications?” 10:37 “How do you make sure your AI pipeline is accurate in the SQL and the regulation because you are working with health health care data?” 23:22 “What are some tips for leaning into those challenge assignments as the IC, the individual contributor?” 23:31 “How do you identify the challenge assignments? Like, how do you know this is the right thing with the stakes that are right to give to somebody?” 25:54 “Do you share your data as an API or for research?” 36:45 “Coming from a core statistical background, how much would you expect from a fresh data science graduate about statistical knowledge if you were an interviewer for a modeling role? Any specific concepts that you might ask them about in the interview?” 38:17 “How do you make sure your team stays current with new data science tools or skills or methods?” 40:18 “Did you have a ranking of necessary roles that you wanted to hire first, like data engineer versus data scientist or analyst or maybe dedicated reporting staff? And what tools or platforms did you consider essential from day one?” 43:53 “How much of your background in teaching high school students has spilled over into managing your team?” 46:21 “Do you have any advice for how someone could maybe emphasize those traits on their resume?” 49:19 “Do you have a piece of career advice that has either really, really helped you or that you try to give to everybody?”

Quarto Dashboards: from zero to publish in one hour

From R/Medicine 2025 You already analyze and summarize your data with R and Quarto. What’s next? You can share your insights or allow others to make their own conclusions in eye-catching dashboards and straight-forward to author, design, and deploy Quarto Dashboards. With Quarto Dashboards, you can create elegant and production-ready dashboards using a variety of components, including static graphics, interactive widgets, tabular data, value boxes, text annotations, and more. Additionally, with intelligent resizing of components, your Quarto Dashboards look great on devices of all sizes. And importantly, you can author Quarto Dashboards without leaving the comfort of your “home” – in plain text markdown with any text editor. In this one-hour demo we will build and publish a Quarto Dashboard – you can code-along or sit back and enjoy the show! Mine Çetinkaya-Rundel, Professor of the Practice at Duke University and Developer Educator at Posit Mine’s work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education as well as pedagogical approaches for enhancing retention of women and under-represented minorities in STEM. Mine works on the OpenIntro project, whose mission is to make educational products that are free, transparent, and lower barriers to education. As part of this project she co-authored four open-source introductory statistics textbooks – latest is the 2nd edition of Introduction to Modern Statistics. She is also a co-author on R for Data Science, the creator and maintainer of Data Science in a Box, and she teaches popular data analysis and data science with R courses on Coursera. Mine is a Fellow of the ASA and Elected Member of the ISI as well as a Waller and Hogg award winner for teaching excellence. In 2024, she was elected as Vice President of the International Association for Statistical Education (IASE). Resources R/Medicine: https://rconsortium.github.io/RMedicine_website/ R Consortium: https://www.r-consortium.org/

Mine Çetinkaya-Rundel

Posit Conf 2025 Keynote Previews | Kieran Healy & Jonathan McPherson | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! Thursdays at 12PM US Eastern We were recently joined by upcoming Posit Conf 2025 keynote speakers Kieran Healy, Professor of Sociology at Duke University, and Jonathan McPherson, Software Architect at Posit PBC, to chat about how and why open-source IDEs like RStudio and Positron get made, how to do data visualization for discovery and explanation, what their keynotes are going to be about, and what’s next for Posit’s IDE development, including AI integration. In this Hangout, Kieran talked about the trustworthy data visualization. He highlighted that while data visualization is a powerful way to condense and present information, often creating compelling and authoritative artifacts, phrases like “visual storytelling” can be problematic if they encourage presenting a predetermined narrative not fully supported by data. He emphasized that the trustworthiness of visualizations does not come solely from the techniques used or the software, but from a “web of social processes and individual commitments” that cannot be easily automated. Jonathan talked about the future of Positron and its relationship with RStudio, addressing whether Positron is intended to replace RStudio. He clarified that the long-term goal for Positron is to make it the best Integrated Development Environment (IDE) for working with data in any language. He explained that Positron is built with an extensibility layer, allowing anyone to write plugins for new languages or capabilities, making it a robust and evolving data science workbench. It does not have all of RStudio’s features and makes different design trade-offs. RStudio, having evolved over decades, is highly optimized for specific R-based workflows and remains the best at what it does for those use cases. Resources mentioned in the video and zoom chat: Posit Conference 2025 Registration → https://posit.co/conference/ Kieran Healy’s Website → https://kieranhealy.org Kieran Healy’s book “The Ordinal Society” → https://theordinalsociety.com/ Kieran Healy’s book “Data Visualization: A Practical Introduction” → https://socviz.co/ Jonathan McPherson’s LinkedIn → https://www.linkedin.com/in/jonathanmcpherson Joe Cheng’s AI Talk on Harnessing LLMs for Data Analysis → https://youtu.be/owDd1CJ17uQ?feature=shared TidyTuesday GitHub → https://github.com/rfordatascience/tidytuesday Positron IDE → https://positron.posit.co/ Will R Chase’s talk on making clear plots → https://www.youtube.com/watch?v=h5cTacaWE6I If you didn’t join live, one great discussion you missed from the zoom chat was about the ongoing debate and practical tips for moving from presenting tables of numbers to visualizations. Community members shared various strategies, including using color-mapped tables as an intermediate step, providing both tables and visuals, and ensuring accessibility and interpretability for diverse audiences. Are you team tables or team graphs? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Joe Cheng

Data Science Career Growth | Pallas Horwitz | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Pallas Horwitz, Analytics Consultant & Professional Development Coach, to chat about data career growth, effectively communicating business value in the AI era, navigating workplace dynamics, and future-proofing data science careers. In this Hangout, we explore how data science projects, even those that don’t yield expected results, contribute to valuable organizational learning. Pallas emphasized that what might seem like a “failure” is actually an opportunity to learn what doesn’t work, saving future time and resources. This broader definition of “impact” helps data professionals frame their contributions effectively on resumes and in conversations, by focusing on moving business conversations forward and enabling informed decision-making. She also highlighted the importance of translating technical insights into language that resonates with business stakeholders, often by understanding their underlying goals rather than just their explicit requests. This approach helps turn a “not yet” into a yes by empathizing with the audience and understanding what they care about. Resources mentioned in the audio recording and zoom chat.txt: Pallas Horwitz’s Coaching Calendar → https://tidycal.com/pallashorwitz/45-minute-introductory-session Pallas Horwitz LinkedIn → https://www.linkedin.com/in/pallashorwitz/ Posit Conf 2025 Virtual Experience Registration → https://posit.co/blog/posit-conf-2025-virtual-experience-registration/ How to Network in a Non-Slimy Way by Ted Laderas → https://laderast.github.io/articles/2021-09-13-how-to-network-in-a-non-slimy-way/how-to-network-in-a-non-slimy-way.html Libby Heeren’s talk, Stop Networking and Start Making Friends→ https://www.youtube.com/watch?v=V3XdLVAwmX0 If you didn’t join live, one great discussion you missed from the zoom chat was about the role of Excel in data analysis, with many participants affirming that there’s “no shame in excel!” and even calling it “the gateway drug to data management”. The chat also highlighted the importance of networking, with attendees sharing personal success stories of finding jobs through connections made in unexpected places like the gym. You really should join live! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co/ Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Welcome to PydyTuesday! | How to Level Up your Python Skills

In this video, you’ll learn where to find the PydyTuesday github landing page, where data lives on the tidytuesday repo, and how to get started to try stuff out and build new things! We hope you join us in participating in PydyTuesday! Don’t forget to use the hashtags #TidyTuesday and #PydyTuesday wherever you like to hangout online - Bluesky, Mastodon, LinkedIn, etc. - have fun out there! We can’t wait to see the predictive models, visualizations, dashboards, and data apps that you create Resources and Repos to star: TidyTuesday GitHub Repo: https://github.com/rfordatascience/ti … Posit PydyTuesday GitHub Repo: https://github.com/posit-dev/python-t … TidyTuesday hashtag search on Bluesky: https://bsky.app/search?q=tidytuesday Other videos in this PydyTuesday playlist: • PydyTuesday | Python How-to Videos #pythoncontent

RStudio Data Science Machine Learning Python Stats Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Github Data Wrangling Tidy Data Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Open Source Oss Reticulate Pydytuesday Python Projects Python Projects for Beginners Python Tutorial Positron Positron Ide

Let’s set up your Python environment with only 2 steps | PydyTuesday Python Projects

In this video, let’s get your project set up. Learn how to install Positron, create a new Python project, and set up a virtual environment. We hope you join us in participating in PydyTuesday! Don’t forget to use the hashtags #TidyTuesday and #PydyTuesday wherever you like to hangout online - Bluesky, Mastodon, LinkedIn, etc. - have fun out there! We can’t wait to see the predictive models, visualizations, dashboards, and data apps that you create Resources and Repos to star: TidyTuesday GitHub Repo: https://github.com/rfordatascience/ti … Posit PydyTuesday GitHub Repo: https://github.com/posit-dev/python-t … TidyTuesday hashtag search on Bluesky: https://bsky.app/search?q=tidytuesday Other videos in this PydyTuesday playlist: • PydyTuesday | Python How-to Videos #pythoncontent

Positron RStudio Data Science Machine Learning Python Stats Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Blogdown Gt Tidymodels Statistics Debugging Programming Education Open Source Reticulate Python Tutorial Positron Ide Python for Beginners Pydytuesday

Let’s install your first Python library in 2 minutes

Here is how to get set up to install your Python library in Positron. Learn how to use the terminal to install your first Python library, in this case pydytuesday. Also, learn the basics for managing packages using “pip.” We hope you join us in participating in PydyTuesday! Don’t forget to use the hashtags #TidyTuesday and #PydyTuesday wherever you like to hangout online - Bluesky, Mastodon, LinkedIn, etc. - have fun out there! We can’t wait to see the predictive models, visualizations, dashboards, and data apps that you create Resources and Repos to star: TidyTuesday GitHub Repo: https://github.com/rfordatascience/ti … Posit PydyTuesday GitHub Repo: https://github.com/posit-dev/python-t … TidyTuesday hashtag search on Bluesky: https://bsky.app/search?q=tidytuesday Other videos in this PydyTuesday playlist: • PydyTuesday | Python How-to Videos #pythoncontent

Positron RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Forcats Ggplot2 Purrr Github Data Wrangling Tidy Data Odbc Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Reticulate Python for Beginners Install Python Libraries Python Tutorial Positron Ide Positron Data Science

Let’s Import Free, High-Quality Datasets into your Python IDE (using Positron and PydyTuesday)

Let’s download data. Learn how to install a dataset using the pydytuesday package and do some basic visualization of the data using Positron’s data viewers. We hope you join us in participating in PydyTuesday! Don’t forget to use the hashtags #TidyTuesday and #PydyTuesday wherever you like to hangout online - Bluesky, Mastodon, LinkedIn, etc. - have fun out there! We can’t wait to see the predictive models, visualizations, dashboards, and data apps that you create Resources and Repos to star: TidyTuesday GitHub Repo: https://github.com/rfordatascience/ti … Posit PydyTuesday GitHub Repo: https://github.com/posit-dev/python-t … TidyTuesday hashtag search on Bluesky: https://bsky.app/search?q=tidytuesday Other videos in this PydyTuesday playlist: • PydyTuesday | Python How-to Videos #pythoncontent

Positron RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Package Manager CRAN Interoperability Serious Data Science Ggplot2 Github Data Wrangling Tidy Data Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Python Datasets Python for Beginners Positron Ide Pydytuesday Python Tutorials Python Tutorial

LLMs for Data Science

[2025 - Day 1 - Data Science & Algos] Hadley Wickham shares insights from practical applications of LLMs in data science, exploring three key areas where these tools prove genuinely useful beyond the hype: writing code, writing prose, and rectangling non-rectangular data. For data scientists working with text, images, videos, or audio data, this talk offers valuable perspectives on leveraging LLMs effectively for real workflows and transforming fundamentally unstructured information. ABOUT THE SPEAKER: Hadley Wickham, Chief Scientist, Posit - ️ GET YOUR TICKET TO AI COUNCIL 2026 ️ Meet the world’s top AI infrastructure minds where architects of AI share what works. Three days of high-quality technical talks and meaningful interactions. → https://aicouncil.com/sf-2026 FIND US: X: https://x.com/AICouncilConf LinkedIn: https://www.linkedin.com/company/aicouncilconf/ Website: https://aicouncil.com/

Hadley Wickham

Harnessing LLMs for Data Analysis | Led by Joe Cheng, CTO at Posit

When we think of LLMs (large language models), usually what comes to mind are general purpose chatbots like ChatGPT or code assistants like GitHub Copilot. But as useful as ChatGPT and Copilot are, LLMs have so much more to offer—if you know how to code. In this demo Joe Cheng will explain LLM APIs from zero, and have you building and deploying custom LLM-empowered data workflows and apps in no time. Posit PBC hosts these Workflow Demos the last Wednesday of every month. To join us for future events, you can register here: https://posit.co/events/ Slides: https://jcheng5.github.io/workflow-demo/ GitHub repo: https://github.com/jcheng5/workflow-demo Resources shared during the demo: Ellmer https://ellmer.tidyverse.org/ Chatlas https://posit-dev.github.io/chatlas/ Environment variable management: For R: https://docs.posit.co/ide/user/ide/guide/environments/r/managing-r.html#renviron For Python https://pypi.org/project/python-dotenv/ Shiny chatbot UI: For R, Shinychat https://posit-dev.github.io/shinychat/ For Python, ui.Chat https://shiny.posit.co/py/docs/genai-inspiration.html Deployment Cloud hosting https://connect.posit.cloud On-premises (Enterprise) https://posit.co/products/enterprise/connect/ On-premises (Open source) https://posit.co/products/open-source/shiny-server/ Querychat Demo: https://jcheng.shinyapps.io/sidebot/ Package: https://github.com/posit-dev/querychat/ If you have specific follow-up questions about our professional products, you can schedule time to chat with our team: pos.it/llm-demo

Joe Cheng

Forecasting AI Demand at Microsoft | Sajay Suresh | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Sajay Suresh, Senior Director of Data and Applied Science at Microsoft, to chat about data center supply chain planning, forecasting AI demand, and navigating data science careers. In this Hangout, we explored how the emergence of technologies like LLMs changed projections for data center demand. Sajay discussed how forecasting for something with little historical data, like AI demand, required drawing analogies from the past, such as comparing the training/inferencing model to the iPhone and its App Store. A major complexity in current supply chain planning is the lack of fungibility with modern GPUs requiring specific infrastructure like liquid cooling, meaning data centers designed for GPUs cannot easily be repurposed for traditional compute/storage workloads, increasing investment risk if demand is lower than planned. Resources mentioned in the video and zoom chat: LLM Workflow Demo with Joe Cheng → https://pages.posit.co/05-28WorkflowDemo.html Posit::conf 2025 Virtual Registration → https://posit.co/blog/posit-conf-2025-virtual-experience-registration/ Sajay Suresh on LinkedIn → https://www.linkedin.com/in/sajay-suresh-12687631/ Find mentors on ADPList → https://adplist.org/ Officeverse R packages for Office documents → https://ardata-fr.github.io/officeverse/ Microsoft team meetup video on capacity planning → https://www.youtube.com/live/07j22d4B_hA?feature=shared Seattle Data And AI Security community → https://www.linkedin.com/posts/seattle-data-and-ai-security_microsoft-fabric-tour-seattle-data-ai-security-6891902675280633856-xLw3?utm_source=share&utm_medium=member_desktop Quarto Gallery → https://quarto.org/docs/gallery/ Quarto Guide → https://quarto.org/docs/guide/ If you didn’t join live, one great discussion you missed from the zoom chat was about communities and meetups recommended for networking and learning in data science. Participants shared various groups like R-Ladies, Data Book Club, local tech meetups, and specific conference recommendations like Shiny Conf and DataConf.ai NYC. What’s your favorite data community? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Joe Cheng

Being a Data Viz Consultant | Cara Thompson | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Cara Thompson, Data Visualization Consultant at Building Stories with Data, to chat about data visualization, consulting and niche specialization, working out loud, and her uniquely winding path to data visualization as a specialty. In this Hangout, we explore all things data visualization. Cara emphasizes its role in helping data teams effectively communicate research outcomes, particularly assisting teams of specialists in communicating with a non-specialist audience. She sees the value in making viz understandable - not just making it pretty (though she’s great at that part, too!). Cara advises sometimes starting the data visualization process with pen and paper before using digital tools. A common pitfall discussed is trying to include too much information in a single visualization, rather than focusing on what the user needs to see first. Resources mentioned in the video and zoom chat: Cara Thompson’s LinkedIn → https://www.linkedin.com/in/cararthompson/ Cara Thompson’s Blog → https://www.cararthompson.com/posts Tidy Tuesday GitHub Repo → https://github.com/rfordatascience/tidytuesday Data Visualization Catalogue → https://datavizcatalogue.com/ Data to Viz → https://www.data-to-viz.com/ Cecilia Baldoni’s Shrew Scrollytelling Website → https://cecibaldoni.github.io/projects.html Kinga Stryszowska-Hill, Ph.D.’s LinkedIn (mentioned for Canva use with plots) → https://www.linkedin.com/in/kingashill/ Libby’s Favorite Data Viz Palette Tool (by Susie Lu)→ https://projects.susielu.com/viz-palette/ If you didn’t join live, one great discussion you missed from the zoom chat was about… pie charts 🥧 While Cara mentioned they do have their place despite everyone loving to pick on them, the chat saw lively debate, including comments on their utility in specific niche fields like high-throughput chemistry, humorous links to examples of “bad” 3D pie scatterplots (?!), and also some general ambivalence about their use. Maybe not everyone hates pie charts? What do you think? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

People Analytics at Pinterest | Trevor Fry | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Trevor Fry, Lead Data Analyst at Pinterest, to chat about people analytics, bridging the data science practice gap, the future of the field including AI, and measuring the value of data teams. We also celebrated a birthday In this Hangout, we explore bridging the data science practice gap—the challenge of effectively translating research findings and data insights back to business leaders and stakeholders. Trevor discusses how the nature of people data is particularly sensitive, requiring careful handling, and emphasizes the importance of adapting your communication and language to your specific audience. He also shares insights into the future of people analytics, especially the potential of AI and LLMs for analyzing unstructured data like survey comments, while noting the challenges of ensuring reliability for scientific applications. Trevor also touched upon the difficulty of measuring the return on investment (ROI) for people analytics functions and discussed common analytical methods used in the field, such as Structural Equation Modeling (SEM) and relative weight analysis (RWA). Resources mentioned in the video and zoom chat: Storytelling with Data by Cole Nussbaumer Knaflic → https://www.storytellingwithdata.com/ Society of Industrial Organizational Psychology (SIOP) → https://www.siop.org/events/the-annual-conference/ Relative Weight Analysis (RWA) R package → https://martinctc.github.io/rwa/ People Analytics: Regression Modeling by Keith McNulty → https://peopleanalytics-regression-book.org/ Structural Equation Modeling (SEM) info → https://stats.oarc.ucla.edu/r/seminars/rsem/ Ollama (local LLMs) → https://ollama.com/ Posit blog: Secure AI-Assisted data science in R with Posit and Snowflake → https://posit.co/blog/ai-assisted-ai-posit-snowflake-cortex/ TidyTuesday GitHub repo → https://github.com/rfordatascience/tidytuesday TidyTuesday in Python GitHub repo → https://github.com/posit-dev/python-tidytuesday Forecasting: Principles and Practice (Python version) → https://otexts.com/fpp3/ (R version also mentioned) Causal Inference: The Mixtape by Scott Cunningham → https://mixtape.scunning.com/ Text Analysis using Quanteda (R package) YouTube video → https://www.youtube.com/watch?v=tf5FmXiwEQE If you didn’t join live, one great discussion you missed from the zoom chat was about the definition of a “data scientist” and whether individuals feel like they are “really” data scientists, highlighting the varied backgrounds and nonlinear paths in the field. If you have any imposter syndrome around data work, you’re not alone! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Open Source in Pharma | Harvey Lieberman | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Harvey Lieberman, Associate Director of Data Science at Novartis, to chat about R/Pharma, automating processes, career advice, and data science in drug discovery vs. development. In this Hangout, Harvey talks about a lot of things, like the power of automating processes. He shares examples of how automating mundane tasks can save significant time and identify errors that humans might miss (we all know human error is a thing!). For instance, he automated the analysis of data from 48 Excel sheets that had previously taken a colleague about three months to process by hand; Harvey completed the automated analysis in one hour over lunch and found copying and pasting errors in the original manual process! Automating processes not only increases efficiency but can also help move people into more data-focused roles. Harvey suggests demonstrating that automation speeds things up and, most importantly, removes errors, which is when people start to pay attention and get interested. Resources mentioned in the video and zoom chat: R/Pharma website → https://rinpharma.com/ Cecilia Baldoni’s scrollytelling project (on shrews!) → https://cecibaldoni.github.io/projects.html Advent of Code → https://adventofcode.com/ Pharmaverse.org (pharmaceutical R packages) → https://pharmaverse.org GSK’s Journey to R → https://www.youtube.com/watch?v=xDrt6txplek Roche’s Journey to R → https://www.youtube.com/watch?v=BlJNILSoZlM R/Pharma March 2025 newsletter (LinkedIn) → https://www.linkedin.com/pulse/rpharma-march-2025-newsletter-open-source-in-pharma-wmf5c/ ggplot2 extenders club → https://ggplot2-extenders.github.io/ggplot-extension-club/ Coursera: Making Data Science Work for Clinical Reporting Course → https://www.coursera.org/learn/making-data-science-work-for-clinical-reporting hiring.cafe (for finding R jobs) → https://hiring.cafe/ Posit’s PydyTuesday GitHub → https://github.com/posit-dev/python-tidytuesday Joy’s Law (management concept) Wikipedia → https://en.wikipedia.org/wiki/Joy%27s_law_(management) If you didn’t join live, one great discussion you missed from the zoom chat was about the diverse backgrounds of attendees. Many participants shared that they came to data science “sideways,” holding degrees in fields such as sociology, psychology, mathematics, atmospheric science, education, history, chemistry, and various engineering disciplines, rather than traditional statistics or computational degrees. so many data scientists have non-traditional paths into the field! But we’re all better together. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Inside Sports Analytics | Nick Wan | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Nick Wan, Senior Director of Baseball Analytics at the Cincinnati Reds, to chat about baseball analytics, data sources and tools, applying analytics in practice, and streaming data science. In this Hangout, we explore the types of data used in baseball analytics, ranging from traditional box score data collected since the late 1890s to modern high-tech measurements. This includes physics-based data like pitch speed and spin rate, influenced by environmental factors. Computer vision and radar tracking capture detailed information. Recent advancements allow for skeletal data capture from multiple high-frame rate cameras, quantifying biomechanical stress to help prevent injury. Data is also gathered via computer vision models tagging events and manual official scorers. This diverse data is used to predict events, evaluate players, and inform strategies such as optimizing lineups based on matchups or recommending adjustments to pitch movement. We all learned A LOT about baseball in this hangout! Resources mentioned in the video and zoom chat: Nick Wan’s Twitch Stream → https://www.twitch.tv/nickwan_datasci Nick Wan’s Free Data Science Bootcamp (Day 1) → https://www.youtube.com/watch?v=pocp0KdrIdk VizBuzz YouTube Playlist → https://www.youtube.com/playlist?list=PL6PX3YIZuHhwZ-C-jZ427D-XkLGNWFPVB baseballr R package (for baseball data) → https://r.lib.hash.kr/baseballr/ Sports-reference website → https://www.sports-reference.com/ Vestaboard (Split-Flap Display) → https://shop.vestaboard.com/products/vestaboard Posit + Databricks Integrations → https://posit.co/blog/databricks-and-posit-announce-new-integrations If you didn’t join live, one great discussion you missed from the zoom chat was about the Vestaboard behind Nick (see linked resources above!). Chat members were curious about what it was and how it worked, with Nick explaining that it’s connected to his Twitch stream and can display messages from chat. How cool is that?! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

885: Python Polars: The Definitive Guide — with Jeroen Janssens and Thijs Nieuwdorp

#Python #Polars #Pandas Jeroen Janssens and Thijs Nieuwdorp are data frame library Polars’ greatest advocates in this episode with @JonKrohnLearns where they discuss their book, Python Polars: The Definitive Guide, best practice for using Polars, why Pandas users are switching to Polars for data frame operations in Python, and how the library reduces memory usage and compute time up to 10x more than Pandas. Listen to the episode to be a part of an O’Reilly giveaway! This episode is brought to you by: • Trainium2, the latest AI chip from AWS: https://aws.amazon.com/ai/machine-learning/trainium/ • Adverity, the conversational analytics platform: https://eu1.hubs.ly/H0jxK210 • Dell AI Factory with NVIDIA: https://www.dell.com/superdatascience Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: • (00:00:00) Introduction • (00:04:46) Why Jeroen and Thijs wrote Python Polars: The Definitive Guide • (00:18:18) Best practices in Polars • (00:25:08) Why Polars has so many users • (00:32:37) The benefits of the Great Tables package • (00:50:05) Jeroen and Thijs’ partnership with NVIDIA and Dell for Python Polars: The Definitive Guide Additional materials: https://www.superdatascience.com/885

Jeroen Janssens

Great Tables Python Polars: The Definitive Guide Super Data Science Podcast SuperDataScience Jon Krohn Jeroen Janssens Python Polars Data Science Expertise Development Michael Chow Data Visualization Data Presentation Styled Tables DF Style Accessor Make Data Presentable Data Science Tools Deep Learning Illustrated Publishing a Book Pandas AI Tools Polars Richie Fink Rust Programming Language Alliander Great Tables NVIDIA Dell Uv Package Manager Kurzgesagt ODSC

Easier data and asset sharing across projects and teams with {pins} and Databricks

Led by Edgar Ruiz, Software Engineer at Posit PBC April 30th at 11 am ET / 8 am PT Sharing data assets can be challenging for many teams. Some may rely on emailed files to keep analyses up to date, making it difficult to keep current or know what version of the data is used. {pins} improves sharing data and other assets across projects and teams. It enables us to publish, or ‘pin’, to a variety of places, such as Amazon S3, Posit Connect and Dropbox. Given recent customer feedback, the ability to publish, or ‘pin’ to Databricks Volumes has been added to R. The same capability is also currently in the works for the Python version of {pins}. This session on April 30th will showcase the acceleration of predictions by distributing a ‘pinned’ model using pins and Spark in Databricks. We’ll walk through integrating {pins} with Databricks in your team’s projects and cover novel uses of pins inside the Databricks ecosystem. GitHub repo: https://github.com/edgararuiz/talks/tree/main/end-to-end Here are a few additional resources that you might find interesting: Pins for R: https://pins.rstudio.com/ Pins for Python: https://rstudio.github.io/pins-python/ More information on how Posit and Databricks work together: https://posit.co/use-cases/databricks/ Customer Spotlight: Standardizing a safety model with tidymodels, Posit Team & Databricks at Suffolk Construction: https://youtu.be/yavHEWpgrCQ Q&A Recording: https://youtube.com/live/HDTDmEaK5zQ?feature=share

Edgar Ruiz

The epigenomics tech stack | Varun Dwaraka | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Varun Dwaraka, Director of Bioinformatics and Principal Investigator at TruDiagnostic, to chat about leveraging DNA methylation and machine learning for determining biological age, the journey from wet lab science to bioinformatics, the tech stack used in epigenomics research, and challenges in handling and analyzing genomic data. In this Hangout, we explore how TruDiagnostic leverages DNA methylation, a biomarker that changes with lifestyle and environment, to develop machine learning models for predicting biological age and other health metrics like smoking impact. These tests aim to provide proactive health insights, allowing individuals to track trends and modulate their biological age through lifestyle changes. But the real draw of this episode of the Hangout is Varun’s willingness to open up and talk about himself and his journey from not-so-great student who wanted to be a musician to finding himself as a PhD student and then joining a start-up (to relax a little, of course!). Resources mentioned in the video and zoom chat: Varun Dwaraka’s LinkedIn → https://www.linkedin.com/in/varunbdwaraka/ methylclock Bioconductor vignette → https://www.bioconductor.org/packages/devel/bioc/vignettes/methylclock/inst/doc/methylclock.html Optuna website → https://optuna.org/ NCBI Gene Expression Omnibus (GEO) → https://www.ncbi.nlm.nih.gov/geo/ CRAN Task View: Handling Missing Data → https://cran.r-project.org/web/views/MissingData.html Human Microbiome Project (NIH) → https://commonfund.nih.gov/human-microbiome-project-hmp minfi Bioconductor package → https://bioconductor.org/packages/release/bioc/html/minfi.html If you didn’t join live, one great discussion you missed from the zoom chat was about the complexities of data imputation in bioinformatics, including the challenges of handling missing data and the nuances of choosing appropriate methods. Data imputation is a hot topic! Let us know below if you’d like to hear more about it in the future. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Gaining trust & driving change | Victoria L. Prince | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Dr. Victoria L. Prince, Senior Manager of Statistics at Takeda Pharmaceuticals, to chat about career paths for statisticians, navigating career transitions, building foundational knowledge, and earning respect and influence in the workplace. In this Hangout, we explore Dr. Prince’s journey transitioning from academia to the pharmaceutical industry. Dr. Victoria L. Prince is a statistician and data scientist with 15 years of experience spanning academia and industry. Don’t miss this hangout if you’re interested in gaining trust and driving change (for example, adopting R or Python) in your org. Dr. Prince discussed her deliberate effort to become an unofficial go-to person for questions about R across different parts of her company. She learned through trial and error that gaining trust involves understanding your strengths and adding value to others’ lives by being generous with your knowledge. She achieved this by volunteering her expertise, not saying no to help requests unless in a complete time crunch, and openly sharing information. She believes that as you share, people share back, which enriches your own knowledge. She also mentioned making herself more visible by organizing and sending invitations to R workshops. Consequently, colleagues would recognize her name and connect it to “R” when they saw it. Resources mentioned in the video and zoom chat: Victoria Prince on LinkedIn → https://www.linkedin.com/in/viktoriialiublinska/ Takeda Pharmaceuticals Careers → https://jobs.takeda.com/ Statistics courses on edX → https://www.edx.org/learn/statistics ShinyConf website → https://www.shinyconf.com/ If you didn’t join live, one great discussion you missed from the zoom chat was about the varied titles for entry-level data roles across different organizations. Libby asked attendees to share the entry-level data person job title at their organization and attendees shared all kinds of titles, including: “specialist”, “strategy and planning analyst”, “associate”, “data analyst”, “Statistical Programmer I”, “Biostatistician I”, “Data Architect” (in a small organization), “Staff 1”, “Intern”, “Policy & Planning Analyst I”, “Graduate analyst”, and “Associate Scientist”. This just shows the importance of understanding the actual responsibilities behind a job title when exploring opportunities, as job names can be deceiving. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co/ Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Wes McKinney - Retooling for a Smaller Data Era | PyData Global 2024

www.pydata.org In this talk, I will offer my perspective on the modern data tools landscape and in particular user-facing tools for interactive data science and data exploration. The latest trends of composable data systems and embeddable query engines like DuckDB and DataFusion create both challenges and opportunities to create a more coherent and productive stack of tools for both end user data scientists and developers building data systems. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

KEYNOTE: Dr. Jeroen Janssens - Embrace the Unix Command Line and Supercharge Your PyData Workflow

www.pydata.org Discover why the Unix command line remains a powerful and relevant tool for data scientists, even in a Python-dominated landscape. This talk will demonstrate how embracing the command line and leveraging its many tools can significantly enhance your productivity, streamline data workflows, and complement your Python skills. Jeroen Janssens, PhD, is a polyglot data science consultant and certified instructor. His expertise lies in visualizing data, implementing machine learning models, and building solutions using Python, R, JavaScript, and Bash. Jeroen is passionate about open source and sharing knowledge. He is the author of Data Science at the Command Line (O’Reilly, 2021) and is currently writing Python Polars: The Definitive Guide (O’Reilly, 2025). Every now and then he blogs at https://jeroenjanssens.com. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

Jeroen Janssens

The actuary to data scientist pipeline | Bill Wilkins | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Bill Wilkins, SVP, Advanced Analytics with Practical Applications at Safety National Casualty Corporation, to chat about the evolving role of actuaries in the age of data science, the impact of technology on risk and pricing strategies in insurance, the use of third-party data and advanced analytics models, and the future challenges and opportunities for the insurance industry. Bill, who has 40 years of experience in the insurance industry and started as an actuary, shared his insights on how data science technology is affecting the actuarial profession. He noted that while the core data hasn’t changed significantly, the increased computing power allows for the utilization of more complex math, speeding up processes. However, the actuarial profession is still adapting to these technological advancements, especially considering the strict regulatory environment and the crucial issue of unintentional bias in data. Bill discussed the distinction between data scientists and actuaries, highlighting the specialized knowledge of actuaries. There was also a discussion on whether a data science background could be a pathway to becoming an actuary, with the observation that it’s more common to see actuaries moving into data science roles (as opposed to the other way around!). If you didn’t join live, one great moment you missed from the zoom chat was about what “finger sticks” referred to, after Bill mentioned it as a significant issue in hospital systems that his team was working to prevent. There was definitely a humorous reaction in the chat as we all dreaded finding out what that meant! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Should you build or buy AI? | Jay Timmerman | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Jay Timmerman, Head of Data Science & AI Platforms at Biogen, to chat about practical applications of generative AI in the pharmaceutical industry, navigating the challenges of AI like hallucinations and data privacy, the evolving role of data science teams in the age of AI, and the strategic considerations for evaluating AI vendors and building in-house solutions using platforms like Posit Connect. In this Hangout, we explore the strategic considerations for evaluating AI vendors and the concept of “AI grift”. Jay emphasized the importance of having in-house expertise to discern genuine value from vendor offerings and suggested that organizations can often build equivalent capabilities themselves by properly framing generative AI problems. This discussion highlighted a pragmatic approach to adopting AI and the need to be discerning when considering vendor solutions versus developing internal tools. Resources mentioned in the video and zoom chat: Abigail Haddad’s Blog on Unstructured Text Data with LLMs → https://presentofcoding.substack.com/ Hard Fork Podcast episode on vibe coding → https://podcasts.apple.com/us/podcast/is-google-search-cooked-were-getting-a-u-s-crypto/id1528594034?i=1000698254448 Posit Blog on Deploying a Streamlit Application with Posit Connect → https://posit.co/blog/deploying-a-streamlit-application-with-posit-connect/ Posit Blog on Deploying a Dash Application to Posit Connect → https://posit.co/blog/deploying-a-dash-application-to-posit-connect/ ragnar package for R users (mentioned in chat for RAG) → https://github.com/t-kalinowski/ragnar If you didn’t join live, one great discussion you missed from the zoom chat was about “vibe coding”, with some attendees sharing humorous takes and a mention of a Hard Fork podcast episode that featured examples of such projects. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Great Tables 3: Data Color and Polishing

This workshop is all about using Great Tables to make beautiful tables for publication and display purposes. We believe that effective tables have these things in common: structuring that aids in the reading of the table well-formatted values, fitting expectations for the field of study styling that reduces time to insight and improves aesthetics These materials are for you if: • you have some experience with data analysis in Python • you often create reporting that involves summarizations of data • you were often frustrated with making tables for display purposes outside of Python • you found beautiful-looking tables in the wild and wondered: ‘How could I do that?’ Other videos in this series: Great Tables 1: Structure, Format, and Style: https://youtu.be/QM7DbsY-nc4 Great Tables 2: Introducing Units Notation: https://youtu.be/SN0_vIL1Rhk About us: Michael Chow, Senior Software Engineer, Posit Michael is a data scientist and software engineer. He has programmed in Python for well over a decade, and he obtained a PhD in cognitive psychology from Princeton University. His interests include statistical methods, skill acquisition, and human memory. Richard Iannone, Senior Software Engineer, Posit Richard is a software engineer and table enthusiast. He’s been vigorously working on making display tables easier to create/display in Python. And generally Rich enjoys creating open source packages so that people can great things in their own work. Workshop repo: https://github.com/rich-iannone/great-tables-mini-workshop?tab=readme-ov-file Learn more at https://posit-dev.github.io/great-tables/articles/intro.html

Michael Chow

Great Tables RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Great Tables 2: Introducing Units Notation

This workshop is all about using Great Tables to make beautiful tables for publication and display purposes. We believe that effective tables have these things in common: structuring that aids in the reading of the table well-formatted values, fitting expectations for the field of study styling that reduces time to insight and improves aesthetics These materials are for you if: • you have some experience with data analysis in Python • you often create reporting that involves summarizations of data • you were often frustrated with making tables for display purposes outside of Python • you found beautiful-looking tables in the wild and wondered: ‘How could I do that?’ Other videos in this series: Great Tables 1: Structure, Format, and Style: https://youtu.be/QM7DbsY-nc4 Great Tables 3: Data Color and Polishing https://youtu.be/Huteb5OmcrA About us: Michael Chow, Senior Software Engineer, Posit Michael is a data scientist and software engineer. He has programmed in Python for well over a decade, and he obtained a PhD in cognitive psychology from Princeton University. His interests include statistical methods, skill acquisition, and human memory. Richard Iannone, Senior Software Engineer, Posit Richard is a software engineer and table enthusiast. He’s been vigorously working on making display tables easier to create/display in Python. And generally Rich enjoys creating open source packages so that people can great things in their own work. Workshop repo: https://github.com/rich-iannone/great-tables-mini-workshop?tab=readme-ov-file Learn more at https://posit-dev.github.io/great-tables/articles/intro.html

Michael Chow

Great Tables RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Great Tables 1: Structure, Format, and Style

This workshop is all about using Great Tables to make beautiful tables for publication and display purposes. We believe that effective tables have these things in common: structuring that aids in the reading of the table well-formatted values, fitting expectations for the field of study styling that reduces time to insight and improves aesthetics These materials are for you if: • you have some experience with data analysis in Python • you often create reporting that involves summarizations of data • you were often frustrated with making tables for display purposes outside of Python • you found beautiful-looking tables in the wild and wondered: ‘How could I do that?’ Other videos in this series: Great Tables 2: Introducing Units Notation: https://youtu.be/SN0_vIL1Rhk Great Tables 3: Data Color and Polishing https://youtu.be/Huteb5OmcrA About us: Michael Chow, Senior Software Engineer, Posit Michael is a data scientist and software engineer. He has programmed in Python for well over a decade, and he obtained a PhD in cognitive psychology from Princeton University. His interests include statistical methods, skill acquisition, and human memory. Richard Iannone, Senior Software Engineer, Posit Richard is a software engineer and table enthusiast. He’s been vigorously working on making display tables easier to create/display in Python. And generally Rich enjoys creating open source packages so that people can great things in their own work. Workshop repo: https://github.com/rich-iannone/great-tables-mini-workshop?tab=readme-ov-file Learn more at https://posit-dev.github.io/great-tables/articles/intro.html

Michael Chow

Great Tables RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Standardizing a safety model with tidymodels, Posit Team & Databricks at Suffolk Construction

If you’ve ever struggled with standardizing machine learning workflows, ensuring secure data access, or scaling insights across your organization, this month’s Posit Team Workflow demo is for you. Maxwell Patterson, Data Scientist at Suffolk walked us through how their team is: Standardizing model workflows using tidymodels, vetiver, Shiny, and Quarto Leveraging row-level permissions in Shiny apps to improve data governance Using Databricks and Posit to gain insights faster and more securely A few helpful links for this demo: Suffolk Customer Spotlight: https://posit.co/about/customer-stories/suffolk/ Quarto email customization: https://docs.posit.co/connect/user/quarto/#email-customization Vetiver package: https://rstudio.github.io/vetiver-r/reference/vetiver_deploy_rsconnect.html Pins package: https://pins.rstudio.com/ Tidymodels “meta-package” https://tidymodels.tidymodels.org/ More information on how Posit and Databricks work together: https://posit.co/use-cases/databricks/ Do you use both Databricks and Posit, but not together yet. You can use this link to chat more with our team as well: https://pos.it/chat-databricks Q&A Recording: https://youtube.com/live/zU-bBUJMyQ4?feature=share To add future workflow demos on your calendar: https://pos.it/team-demo ^ These demos happen the last Wednesday of every month

Johnson & Johnson’s Open Source Journey with R in Clinical Trials

Learn how Johnson & Johnson embraces R and open-source tooling in clinical trials. Abstract: Embracing open source has been a transformative journey for J&J, fueled by a commitment to collaboration, transparency, and innovation. Our R journey began with a mindset shift—transitioning from decades-old solutions to actively contributing to and harnessing the power of open-source communities. We navigated the challenges of cultural adaptation while cultivating a programmer-friendly environment that promotes sharing and continuous learning. We enhanced our technical capabilities by open-sourcing key projects focused on R and strengthened our ecosystem and industry partnerships. This webinar reflects on our experiences, lessons learned, and the strategic impact of embracing R. Speakers: Sumesh Kalappurakal, Tadeusz Lewandowski, Nicholas Masel, and Mark Bynens. Learn more about Posit’s work in Pharma: https://posit.co/use-cases/pharma/

RStudio Data Science Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Readr Tidyr Github Data Wrangling Tidy Data Odbc Gt Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Pharma Clinical Trials Clinical Trials Statistical Programming Pharmaverse Johnson & Johnson Posit Posit Data Science Pharma Data Science

Data Science at the Command Line and Polars | Jeroen Janssens | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Jeroen Janssens, Senior Developer Relations Engineer at Posit, to chat about his career journey from machine learning to developer relations, the advantages of using the command line for data science, his books “Data Science at the Command Line” and “Python Polars”, and advice for aspiring DevRel professionals. In this Hangout, we explore the benefits of working on the command line versus not. Jeroen explained that while the initial command line interface might seem stark, it offers a very different and powerful way to interact with your computer. The Unix command line is ubiquitous across various systems, from Raspberry Pis to supercomputers. Its strength lies in the ability to connect tools together through standard output and input, allowing for quick and iterative solutions by combining specialized tools. This fosters an interactive nature with a short feedback loop and provides closer interaction with the file system, making ad hoc data exploration efficient. Resources mentioned in the video and zoom chat: Jeroen’s LinkedIn → https://www.linkedin.com/in/jeroenjanssens/ Data Science at the Command Line → https://jeroenjanssens.com/dsatcl/ Python Polars: The Definitive Guide → https://polarsguide.com/ Plotnine → https://plotnine.org/ Winner of the 2024 plotnine Plotting Contest → https://posit.co/blog/winner-of-the-2024-plotnine-plotting-contest/ Talk about plotnine → https://www.youtube.com/watch?v=xdD8r84sqYY R for Data Science → https://r4ds.had.co.nz/ Jeroen’s plotnine translation of R for Data Science → https://jeroenjanssens.com/plotnine/ froggeR package → https://azimuth-project.tech/froggeR/ Reticulate → https://rstudio.github.io/reticulate/ Install Windows Subsystem for Linux (WSL) → https://learn.microsoft.com/en-us/windows/wsl/install UTM for macOS (Virtualization) → https://mac.getutm.app fish shell → https://fishshell.com/ Quartodoc → https://github.com/machow/quartodoc Focusmate (Accountability Partner Tool) → https://www.focusmate.com/ Surface Area of Luck → https://modelthinkers.com/mental-model/surface-area-of-luck CRAN R Extensions Manual → https://cran.r-project.org/doc/manuals/r-release/R-exts.html If you didn’t join live, one great thing you missed from the zoom chat was people sharing their varied experiences with the command line, with many admitting they primarily use it for basic navigation or only when necessary, and some sharing helpful tools and tips for those less familiar. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Jeroen Janssens

Data Science at the Census Bureau | Jessica Klein | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Jessica Klein, Data Scientist at the Census Bureau, to chat about open-source adoption at the Census Bureau, the growth of R and Python communities within the agency, training opportunities and internal support systems, and working with complex census datasets. In this Hangout, Jessica talked about the agency’s move towards the Cloud and its efforts to reduce reliance on proprietary software like SAS and ArcGIS. She shared insights into the internal support system, mentioning training programs, workshops, and resources for using the Census API. She also discussed the challenges and strategies involved in transitioning from SAS to R & Python, including the importance of double coding for validation with mathematical statisticians and the increasing use of version control with Git GitLab. The development of internal technical support groups for R and Python was a key element in fostering knowledge sharing and breaking down silos within the agency. Resources mentioned in the video and zoom chat: Jessica Klein’s LinkedIn → https://www.linkedin.com/in/jessica-klein-8a5a35196/ tidy census package → https://walker-data.com/tidycensus/ tigris package → https://github.com/walkerke/tigris Fast Link package for probabilistic linking → https://github.com/kosukeimai/fastLink StatQuest YouTube Channel → https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw 3Blue1Brown YouTube Channel → https://www.youtube.com/c/3blue1brown {sf} package for R spatial analysis → https://r-spatial.github.io/sf/ Resources on spatial analysis in R → https://rspatial.org/ and https://rspatialdata.github.io/index.html and https://asdar-book.org/ Apache Superset (open-source visualization tool) → https://superset.apache.org/ Coding it Forward Civic Digital Fellow program → https://blog.codingitforward.com/tagged/civic-digital-fellowship Resources shared directly by Jessica in the chat: Analyzing US Census Data: Methods, Maps, and Models in R by Kyle Walker: https://walker-data.com/census-r/index.html Webinar series on using tidycensus for analyzing and mapping Census data: https://ssdan.net/events/the-2025-ssdan-webinar-series-2023-acs-data-with-r-mapping-tools-and-the-2020-census/ A Guide to Working with US Census Data in R: https://rconsortium.github.io/censusguide/ Using the API requires a getting an API Key, which you can get for free here: https://api.census.gov/data/key_signup.html See here for the Census Data API User Guide: https://www.census.gov/content/dam/Census/data/developers/api-user-guide/api-guide.pdf Explore the ABCs of the Census API with Census Bureau staff: https://www.youtube.com/watch?v=JbThy7GUg3k See here for a tutorial on Census API Basics: Simple Steps to Better Data Access: https://www.youtube.com/watch?v=d-FJ2IyVfdk See here for a tutorial on Using the API to Get All Results for an ACS Table: https://www.census.gov/library/video/2020/using-api-all-results-for-acs-table.html See here for a tutorial on Using the API to Get Results for Multiple Estimates: https://www.census.gov/library/video/2020/using-api-results-for-multiple-estimates.html Explore ACS Data Stories–Stats in Action! to see how the public is using ACS data: https://www.census.gov/programs-surveys/acs/about/acs-data-stories.html Visit the Slack show-and-tell channel and show how you use Census data: https://app.slack.com/client/T6AL55003/CRY4R9R7S If you didn’t join live, one great discussion you missed from the zoom chat was about the enthusiastic reception and usefulness of the tidycensus package for accessing and working with census data in R, with many attendees expressing their appreciation and sharing resources related to it. Let us know below if you’d like to hear more about it! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

From Ecology PhD to Global Marketing at HP | John Stanton-Geddes | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by John Stanton Geddes, Senior Manager for the Advanced Analytics Marketing Team at HP, Inc., to chat about his career path from academia to marketing data science, the challenges and opportunities in marketing valuation using data, the evolution of marketing data science teams, and the impact of data privacy on user tracking. In this Hangout, we explore the work of John’s team at HP, which involves global marketing valuation. This includes developing media mix models to understand the return on investment across diverse marketing activities, from sponsorships like F1 to digital advertising like Google paid search and in-store promotions. John discussed the complexity of integrating data from these varied sources and the ongoing efforts to refine their models and use experimentation to drive effective marketing decisions. Resources mentioned in the video and zoom chat: FRED (Federal Reserve Economic Data) → https://fred.stlouisfed.org/ R package fredr → https://cran.r-project.org/web/packages/fredr/vignettes/fredr.html Semrush → https://www.semrush.com/ If you didn’t join live, one great discussion you missed from the zoom chat was about whether Excel is the “gateway” to data science, with many attendees sharing their agreement and experiences. Was Excel your gateway?! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Bringing data science to the construction industry | Blake Abbenante | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Blake Abbenante, Director of Analytics and Data Science at Suffolk Construction, to chat about his career journey in data science, implementing modern data practices in the construction industry, innovative applications of AI and data science in construction, and building a data-driven culture in a traditionally less tech-focused sector. In this Hangout, we explore innovative applications of AI and data science in construction. Blake shared how Suffolk Construction is leveraging cutting-edge technologies like AI to revolutionize traditional processes. One focus is their GenAI scheduling tool, which aims to augment and speed up the design and planning phases of building projects. This tool has the potential to significantly reduce the time planners spend on creating schedules, moving from weeks to potentially minutes or hours for an 80% completion rate. Blake discussed the development and implementation of safety models that forecast risk on projects, enabling proactive measures to ensure safer construction sites by predicting which projects might require additional safety personnel based on historical data. Resources mentioned in the video and zoom chat: The ellmer R package → https://ellmer.tidyverse.org/ The chatlas R package → https://github.com/posit-dev/chatlas Posit Blog Post on ellmer → https://posit.co/blog/announcing-ellmer/ If you didn’t join live, one great discussion you missed from the zoom chat was about the challenges of data collection and analysis when encountering pushback from those whose work is being analyzed, and strategies to build trust and demonstrate value. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Quarto Live: WebAssembly powered data science learning | pyOpenSci

How Quarto Live Brings Code to Life in the Browser! Quarto Live takes interactive coding & publishing to the next level with WebAssembly-powered execution—run Python & R right in the browser, no setup needed! Real-time code execution without servers ️ Dynamic exercises & instant feedback for learners Full integration with Quarto for publishing & sharing Works on any device—even mobile! Watch as George Stagg (Posit) who develops and works on Quarto Live breaks down how it works & why it matters! CHAPTERS 00:00:00 Intro 00:01:00 What is Quarto? 00:03:15 Add functionality to Quarto with extensions 00:03:30 Quarto and other interactive technologies like pyodide and web assembly 00:06:03 Web assembly powers interactive data science in the browser 00:07:53 How to use Quarto Live to create your own online interactive data science environment with Quarto 00:13:24 Create interactive data science enviornments for mobile 00:14:27 Demo interactive data science activities using quarto and quarto live 00:16:38 Wrap up and summary! pyOpenSci makes learning open source and open science concepts easier. LEARN PYTHON PACKAGING Build a pure Python package tutorial: https://www.pyopensci.org/python-package-guide/tutorials/intro.html Explore packaging tools & best practices: https://www.pyopensci.org/python-package-guide/ 🤝 SCIENTIFIC PYTHON SOFTWARE REVIEW Learn how peer review works: https://www.pyopensci.org/software-peer-review/ Check out our peer-reviewed packages: https://www.pyopensci.org/python-packages.html We partner with the Journal of OpenSource Software and other communities - learn more: https://www.pyopensci.org/software-peer-review/partners/scientific-communities.html Get Involved Submit your packagei for review: https://github.com/pyopensci/software-submission/ ‍️ pyOpenSci governance: https://www.pyopensci.org/governance/ Connect with pyOpenSci pyopensci.org 🦣 fosstodon.org/@pyOpenSci 🦋 bsky.app/profile/pyopensci.bsky.social www.linkedin.com/company/pyopensci ABOUT pyOpenSci pyOpenSci is an inclusive and vibrant community of practice that breaks down technical and social open source barriers. promotes open and reproducible research through peer-review of scientific Python packages. Through peer review, education, and community support, we help make scientific Python software more accessible, citable, and impactful. Join our community & level up your open source game and support open science! #pyopensci #opensource #openscience

George Stagg

Build a Python table in under 1 minute using Great Tables

Create wonderful-looking tables directly from Pandas or Polars DataFrames with the Great Tables package. Typically, Great Tables is used in a notebook environment or within a Quarto document and rendered in HTML or as an image file. Ready to build some awesome tables? Install from PyPI with: $ pip install great_tables. GitHub Repo: https://github.com/posit-dev/great-tables Design Philosophy of Great Tables: https://posit-dev.github.io/great-tables/blog/design-philosophy/

Great Tables Quarto RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Tidyr Purrr Github Data Wrangling Tidy Data Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Great Tables Polars Dataframe Polars Dataframes

R-Ladies Rome (English) - Interactive R, Python, and Shiny in the Browser with Quarto and Shinylive

In this session, Isabella Velásquez walks us through Quarto Live and Shinylive, powerful tools that allow us to create interactive R, Python, and Shiny applications without a server. These technologies make it easier than ever to share dynamic, engaging data science projects directly in the browser. What You’ll Learn ️ How Quarto Live brings interactive code to static documents ️ How Shinylive allows Shiny apps to run entirely in the browser ️ Practical use cases for data science, education, and collaboration This talk is ideal for data scientists, educators, and open-source enthusiasts who want to make their work more interactive and accessible. To further explore the subject, we recommend visiting the presentation of the talk: https://ivelasq.github.io/2025-02-21_r-python-shiny-in-the-browser More about the Speaker: https://www.linkedin.com/in/ivelasq/ 0:00 R-Ladies Rome Chapter Intro 5:00 Isabella’s Talk Please visit: https://rladiesrome.org

Isabella Velásquez

Pure math to data science at YouTube | Mrinal Raghupathi | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Mrinal Raghupathi, Data Scientist at YouTube, to chat about using data science to understand user behavior, causal inference versus correlation, and experimentation in the tech world, among many other things! In this Hangout, we also explored networking strategies. Mrinal shared his approach to networking, including randomly selecting people from the org chart to have coffee chats with and setting personal goals around networking each year, which let him meet people across the org without selection bias. He emphasized the importance of balancing attending talks at conferences with having hallway conversations and making connections. We here at the Hangout LOVE hallway conversations! Resources mentioned in the video and zoom chat: Trustworthy Online Controlled Experiments → https://www.amazon.com/Trustworthy-Online-Controlled-Experiments-Practical/dp/1108724264 Mrinal’s textbook, “An Introduction to the Theory of Reproducing Kernel Hilbert Spaces” → https://www.amazon.com/Introduction-Reproducing-Cambridge-Advanced-Mathematics/dp/1107104092 Ted’s Guide to Conferences for Introverts → https://laderast.github.io/posts/2018-05-17-a-introvert-s-survival-guide-to-conferences/ If you didn’t join live, one great discussion you missed from the zoom chat was about how many hours a week people get to program in their language of choice. Sixteen people chimed in to share how much they get to code, and answers varied from 0-5 hours per week to 35-40 hours per week, with most people saying they got to spend upwards of 75% of their time coding How much do you get to code every week?! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Company-branded reports, apps, and dashboards made easier with brand.yml & Posit

You will learn: How to apply consistent company branding across reports, dashboards, and apps Key Links: GitHub Repo for Example: https://github.com/skaltman/brand-yml-demo brand.yml GitHub repo: https://posit-dev.github.io/brand-yml/ Follow-along blog post: https://posit.co/blog/unified-branding-across-posit-tools-with-brand-yml/ Q&A after the Demo: https://youtube.com/live/kuEbRfmm4G4?feature=share Additional Resources Mentioned in Q&A: Quarto specific page on brand: https://quarto.org/docs/authoring/brand.html Typography: https://posit-dev.github.io/brand-yml/brand/typography.html brand.yml + pkgdown: https://github.com/rstudio/bslib/tree/main/pkgdown LLM brand.yml prompt: https://posit-dev.github.io/brand-yml/articles/llm-brand-yml-prompt/ Inspiration/gallery: https://posit-dev.github.io/brand-yml/inspiration/ Why we think this is important: Consistent company branding in your reports and apps (with your logo, colors, and fonts) can help make your work look more professional, but are often tricky to get right. Common challenges we’ve heard from the community: Excessive manual effort: Applying colors, fonts, and logos across reports, apps, and dashboards takes time and is prone to errors. Difficult to update: When brand guidelines change, it’s difficult to update all products consistently. Team consistency: Ensuring all contributors follow branding guidelines is challenging to manage. How to join future events: We host workflow demos the last Wednesday of every month. You can add them to your calendar with this link: https://www.addevent.com/event/Eg16505674 Full playlist of workflow demo recordings: https://www.youtube.com/playlist?list=PL9HYL-VRX0oRsUB5AgNMQuKuHPpNDLBVt Have suggestions? Comment below. Thank you for joining us!

TidyTuesday + Posit | PydyTuesday | Weekly Community Python Data Project

Posit software engineer Isabel Zimmerman discusses the TidyTuesday project and the Posit PydyTuesday Initiative. Learn how to participate in weekly TidyTuesday projects, watch Isabel explore Central Park squirrel data, and discover how to deploy your work to Posit Connect Cloud. Find her code here: https://github.com/isabelizimm/pydy-tuesday Check out these repositories to join the TidyTuesday and the Posit PydyTuesday Initiative: TidyTuesday repo with datasets: https://github.com/rfordatascience/tidytuesday Posit PydyTuesday repo: https://github.com/posit-dev/python-tidytuesday Learn more about Quarto and Connect Cloud: Quarto website: https://quarto.org/ Posit Connect Cloud: https://connect.posit.cloud/ Other videos in this Posit PydyTuesday playlist: https://www.youtube.com/playlist?list=PL9HYL-VRX0oSDQjicFMLIIdcLv5NuvDp9 #pythoncontent

Isabel Zimmerman

python-tidytuesday Quarto RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Tidy Tuesday Pythoncontent

Create Quarto dashboards with Python

Learn how to create Quarto dashboards with Python! Senior Product Marketing Manager Isabella Velásquez walks through building a Quarto dashboard from scratch, using water insecurity data from the TidyTuesday project. You’ll learn how to structure a Quarto document, preview dashboards, and customize layouts and themes. See Isabella’s code here: https://github.com/ivelasq/water-insecurity-dashboard And the dashboard here: https://ivelasq-water-insecurity-dashboard.share.connect.posit.cloud/ Resources: Quarto dashboards guide: https://quarto.org/docs/dashboards/ Posit Connect Cloud: connect.posit.cloud Hello, Dashboards!: https://www.youtube.com/watch?v=HW7QbqI4fH0&t=590s TidyTuesday project: https://github.com/rfordatascience/tidytuesday Posit PydyTuesday GitHub repo: https://github.com/posit-dev/python-tidytuesday Other videos in this PydyTuesday playlist: https://www.youtube.com/playlist?list=PL9HYL-VRX0oSDQjicFMLIIdcLv5NuvDp9 #pythoncontent

Isabella Velásquez

python-tidytuesday Quarto RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Open Source Oss Reticulate Tidy Tuesday Pythoncontent

How to Build a Data Science Portfolio Website with Python & Quarto!

Learn how to build and deploy a professional data portfolio website using Quarto and Python! This comprehensive tutorial covers everything from creating a basic website structure to adding navigation, custom themes, and interactive elements like dashboards and slideshows. By the end, you’ll know how to showcase your data science projects on your own custom domain using either Posit Connect Cloud or Netlify for deployment. Quarto Crash Course Video: https://youtu.be/_VKxTPWDhA4?si=1xoYNeZMy2SDvwFO Resources: Read the Quarto Docs: https://quarto.org/docs/websites/ Check out the website! https://quartoisfun.com Link to Code (Starter): https://github.com/KeithGalli/quarto-portfolio/tree/starter Link to Code (Finished): https://github.com/KeithGalli/quarto-portfolio Video by @KeithGalli Video Timeline! 0:00 - Video Overview & Topics Covered 2:22 - Getting Started Building Websites with Quarto & Python 12:46 - Adding a Sidebar with Navigation to Your Website 16:15 - Building a Data Portfolio of Our Past Work 18:44 - Choosing a Custom Website Theme 21:29 - Adding Many Pages & Sections to Our Website 28:38 - Adding Social Media Links (with Icons) to Your Site (GitHub, LinkedIn, YouTube, etc.) 29:50 - Adding More Sections to Our Site & Linking to Specific Pages 31:32 - Adding a Navbar Menu with Clickable Links to Our Website 33:30 - Adding Custom Branding and Logo Favicons & Images (_brand.yml) 40:25 - Deploying Our Website with Posit Connect Cloud 48:21 - Deploying to Your Own Custom Domain (Netlify) #pythoncontent

Shiny community, hackathons, and his AI mindset | Joe Cheng | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Joe Cheng, CTO at Posit, to chat about the Shiny contest, the use of AI in data science, and designing hackathons for learning new technologies. We were joined by several past and present Shiny contest winners who gave great advice on how to get started if you want to participate (and we really hope you do)! In this Hangout, we explore the evolution of the Shiny contest since its inception, including what made the 2024 submissions unique and the ways the contest encourages community contribution and learning. Joe also shared about his personal journey from feeling skepticism about AI to seeing and embracing its potential. We got some amazing questions from the Hangout attendees! We hope you join us live next time to ask some of your own questions Resources mentioned in the video and zoom chat: 2024 Shiny Contest Winners → https://posit.co/blog/winners-of-the-2024-shiny-contest/ Joe’s AI Hackathon Slides → https://jcheng5.github.io/llm-quickstart/quickstart.html Shiny Assistant → https://gallery.shinyapps.io/assistant/ Isabella’s blog post on prototyping with Shiny Assistant → https://posit.co/blog/ai-powered-shiny-app-prototyping/ Posit Conf Workshops → https://reg.rainfocus.com/flow/posit/positconf25/attendee-portal/page/sessioncatalog?tab.day=20250916&search.sessiontype=1675316728702001wr6r Shiny Conference 2025 → https://www.shinyconf.com/ Call for Speakers Shiny Conf 2025 → https://sessionize.com/shiny-conf-2025/ Shiny Tableau → https://rstudio.github.io/shinytableau/ Echarts4r → https://echarts4r.john-coene.com Elmer package on Github → https://github.com/tidyverse/ellmer All the Shiny app links mentioned in the video and zoom chat: Eric Nantz 2021 Shiny Contest Submission → https://forum.posit.co/t/the-hotshots-racing-dashboard-shiny-contest-submission/104925 Eric Nantz’s R/Pharma conference keynote on AI → https://youtu.be/AfMa1CVUdXU?si=ThLsKFyonntxzBUF Eric Nantz’s Haunted Places app → https://youtu.be/vX09QGMuOfo?si=K5_uPfK5bcfZZ92l Umair Durrani’s Shiny Storytelling app → https://umair.shinyapps.io/storytimegcp/ Umair’s Blue Sky profile → https://bsky.app/profile/transport-talk.bsky.social Umair’s Shiny meetings project on Github → https://github.com/shiny-meetings/shiny-meetings Abby Stamm’s Shiny Accessibility app → https://github.com/ajstamm/shiny-a11y-app If you didn’t join live, one great discussion you missed from the zoom chat was about everyone’s favorite interactive plotting tools. Someone asked whether Plotly was the best option, and lots of people said they loved ggiraph, echarts4r, ObservableJS, and others. What about you?! What’s your favorite interactive plotting library? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Joe Cheng

The changing landscape of data science | Kanchana Padmanabhan | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Kanchana Padmanabhan, Director of Data and AI at Homebase, to chat about data science team structures, the role of math in understanding LLMs, building effective hackathons, and communicating model insights to stakeholders. In this Hangout, we explore the importance of understanding the probabilistic nature of LLMs and how that understanding should influence how data scientists approach their work. We also discussed how to structure a hackathon to encourage learning, centering on the customer problem, and collaboration between technical teams and business stakeholders. Resources mentioned in the video and zoom chat: List of R Conferences for 2025 → https://rworks.dev/posts/r-conferences-2025/ Posit Conference Call for Talks → https://posit.co/blog/speak-at-posit-conf-2025/ Julia Silge’s workflow demo on model cards → https://www.linkedin.com/posts/posit-software_join-us-for-a-live-workflow-demo-on-creating-activity-7287998741557522432-jseQ?utm_source=share&utm_medium=member_desktop Shiny Assistant Gallery → https://gallery.shinyapps.io/assistant Data Science Hangout Playlist → https://www.youtube.com/playlist?list=PL9HYL-VRX0oTu3bUoyYknD-vpR7Uq6bsR Add Posit Team End-to-End Workflows to calendar → https://evt.to/aoimiohuw Making of a Manager Book → https://www.amazon.com/Making-Manager-What-Everyone-Looks/dp/0735219567 If you didn’t join live, one great discussion you missed from the zoom chat was about how to gain domain knowledge for a new industry, where attendees shared their experiences and advice. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Julia Silge

Free AI packages for R and Python #pythoncontent

Some free R and Python packages that can help when working with LLMs #datascience #artificialintelligence #machinelearning #openai #pythonprogramming #pythonprojects #dataanalysis #dataanalytics #positshorts

RStudio Data Science Machine Learning Python Stats Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Lazy Evaluation Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Artificial Intelligence Python Packages Free Python Tools

Communicating insights for impact | Noah Branham | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Noah Branham, senior manager of analytics at DraftKings, to chat about marketing measurement, data science career paths, and communicating analysis to stakeholders. In this Hangout, one topic we explore is Noah’s three-trimester framework for career development, focusing on how he progressed from developing basic data analytics skills to mastering predictive modeling and optimization problems, and finally transitioning to a management role. He emphasizes the importance of understanding business problems before applying data techniques and how the skills needed for success as an analyst differ from those needed for a manager. Resources mentioned in the video and zoom chat: Speak at Posit Conf 2025 → https://posit.co/blog/speak-at-posit-conf-2025/ Cascadia R Conf → https://cascadiarconf.com/ Unified Branding with brand.yml → https://posit.co/blog/unified-branding-across-posit-tools-with-brand-yml/ The Motivated Speaker Book → https://www.articulationinc.com/the-motivated-speaker-book/ Model cards for transparent reporting → https://vetiver.posit.co/learn-more/model-card.html Upcoming End-to-End Workflow Demo → https://pos.it/team-demo If you didn’t join live, one great discussion you missed from the zoom chat was all about running, including marathon advice, favorite races, and being a proud member of the Slow AF Run Club It didn’t have much to do with data, but it was definitely about being a community. You should really join us live to get in on the fun! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

We want GREAT tables! | Richard Iannone & Michael Chow | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Rich Iannone and Michael Chow, software engineers at Posit, to chat about their experiences building GT and Great Tables, how they drive community engagement around their packages, and their career advice for package developers. GT and Great Tables are R and Python packages for creating static tables in R and Python. They were created to fill a need for a good, maintained solution for generating tables with different output types from data frames. They’ve gained loads of popularity, and have an active community of users! Resources mentioned in the video and zoom chat: Great Tables Blog → https://posit-dev.github.io/great-tables/blog/ 2024 Table Contest Winners → https://posit.co/blog/2024-table-contest-winners/ Contributing to Public Transit Data Analysis and Tooling → https://posit-dev.github.io/great-tables/blog/open-transit-tools/ Tables as Powerful Representational Tools → https://www.researchgate.net/publication/363345970_Tables_as_Powerful_Representational_Tools The MockUp - 10+ Guidelines for Better Tables in R → https://themockup.blog/posts/2021-01-13-10-guidelines-for-better-tables-in-r/ Show Me the Numbers by Stephen Few → https://analyticspress.com/smtn.php Excel spreadsheets to gt package called {forgts} → https://github.com/luisDVA/forgts What They Forgot to Teach You About R → https://rstats.wtf/ David Robinson Talk called “The unreasonable effectiveness of public work” → https://www.youtube.com/watch?v=th79W4rv67g Publication quality tables in 2024 → https://posit.co/blog/what-we-did-with-publication-quality-tables-in-2024/ Great Tables Design Philosophy → https://posit-dev.github.io/great-tables/blog/design-philosophy/ gtsummary-to-excel → https://www.pipinghotdata.com/posts/2024-07-26-gtsummary-to-excel/ happy git with R → https://happygitwithr.com/ freeCodeCamp - how to contribute to open source → https://github.com/freeCodeCamp/how-to-contribute-to-open-source/ If you didn’t join live, one great discussion you missed from the zoom chat was about how people manage their personal and work GitHub accounts, and whether to have one account for all their work or separate accounts for each employer. Let us know YOUR thoughts on this topic below! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! #pythoncontent

Michael Chow, Rich Iannone

Transform Boring Forms into Eye-Catching UI: Shiny Party Tricks That Will Make You Look Like a Pro 🎉

Learn how to elevate your Shiny forms from basic to professional with dynamic accordions. This tutorial demonstrates how to implement intelligent form organization and visual feedback using Shiny’s accordion components. We cover both basic accordion implementation and advanced dynamic features including status indicators, automatic title updates, and section validation. Perfect for developers looking to enhance their Shiny applications with modern, user-friendly form interfaces. Key features covered: Basic form to accordion conversion Dynamic panel updates Status indicator implementation Visual feedback systems Section validation techniques Reference Documentation: For accordion implementation, see: https://shiny.posit.co/r/reference/shiny/latest/accordion For dynamic updates, see: https://shiny.posit.co/r/reference/shiny/latest/updateAccordion For input validation, see: https://shiny.posit.co/r/reference/shiny/latest/validate For modular development, see: https://shiny.posit.co/r/articles/build/modules/ Timestamps: 0:00 - Introduction 0:23 - Basic form structure overview 0:50 - Introduction to accordion components 1:11 - Basic accordion implementation 1:35 - Dynamic accordion enhancement 2:00 - Status indicators and visual feedback 2:29 - Updating accordion panels 2:55 - Documentation resources 3:18 - Conclusion

Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Stop Your Shiny Apps from Breaking on Mobile! Master Responsive Design in Minutes 📱💻

A comprehensive guide to implementing responsive design in Shiny applications using UI layout columns. This tutorial covers how to create adaptive layouts that automatically adjust based on screen size, from desktop to mobile devices. Learn how to leverage Shiny’s bootstrap-based grid system to create professional, responsive applications that look great on any device. Key concepts covered: • Default Shiny responsive behavior • UI layout column implementation • Screen size breakpoints • Bootstrap grid system • Responsive card layouts • Mobile-first design principles Reference Documentation: UI Layout Columns: https://shiny.posit.co/r/reference/shiny/latest/layout_column Layout Guide: https://shiny.posit.co/r/articles/build/layout-guide/ Bootstrap Integration: https://shiny.posit.co/r/articles/build/bootstrap/ Responsive Design: https://shiny.posit.co/r/articles/build/responsive/ Timestamps: 0:00 - Introduction 0:23 - Default Shiny layout behavior 0:38 - Understanding screen responsiveness 1:05 - Introduction to UI layout columns 1:38 - Screen size breakpoints explained 2:02 - Desktop layout demonstration 2:30 - Mobile layout adaptation 2:44 - Bootstrap grid system overview 3:21 - Documentation resources 3:45 - Conclusion

Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Shiny Forms with Accordions 🎉

Learn how to elevate your Shiny forms from basic to professional with dynamic accordions. This tutorial demonstrates how to implement intelligent form organization and visual feedback using Shiny’s accordion components. #positshorts

Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Who are the data heroes of…data heroes? 💫

#positshorts

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Wes McKinney & Hadley Wickham (on cross-language collaboration, Positron, career beginnings, & more)

We hosted a special event hosted by Posit PBC with Wes McKinney (Pandas & Apache Arrow) and Hadley Wickham (rstats & tidyverse) to ask questions, share your thoughts, and exchange insights about cross-language collaboration with fellow data community members. Here’s a preview into what came up in conversation: Cross-language collaboration between R and Python Positron, a new polyglot data science IDE Open source development, how Wes and Hadley got involved in open source and their experiences in building and maintaining open-source projects such as Pandas and the tidyverse. Documentation for R and Python, especially in the context of teams that use both languages (shoutout to Quarto!) The use of LLMs in data science The emergence of libraries like Polars and DuckDB Challenges of switching between the two languages Package development and maintenance for polyglot teams that have internal packages in both languages The future of data science The chat was on fire for this conversation and we’ve gathered most of the links shared among the community below: Documentation mentioned: Positron, next-generation data science IDE built by Posit: https://positron.posit.co/ Quarto tabset documentation: https://quarto.org/docs/output-formats/html-basics.html#tabset-groups Packages / Extensions mentioned: Pins: https://pins.rstudio.com/ Vetiver: https://vetiver.posit.co Orbital: https://orbital.tidymodels.org Elmer: https://elmer.tidyverse.org Tabby Extension: https://quarto.thecoatlessprofessor.com/tabby/ Blog posts: AI chat apps with Shiny for Python: https://shiny.posit.co/blog/posts/shiny-python-chatstream/ Using an LLM to enhance a data dashboard written in Shiny: R Sidebot & Python Sidebot Marco Gorelli Data Science Hangout (polars): https://youtu.be/lhAc51QtTHk?feature=shared Emily Riederer’s blog post on Polars: https://www.emilyriederer.com/post/py-rgo-polars/ Jeffrey Sumner’s tabset example: https://rpy.ai/posts/visualizations%20with%20r%20and%20python/r_python_visualizations Emily Riederer’s blog post on Python and R ergonomics: https://www.emilyriederer.com/post/py-rgo/11 Sam Tyner’s blog post on Lessons from “Tidy Data”: https://medium.com/@sctyner90/10-lessons-from-tidy-data-on-its-10th-anniversary-dbe2195a82b7 Other: Hadley Wickham’s cocktails website: https://cocktails.hadley.nz 5 Posit subscription management to find out about new tools, events, etc.: https://posit.co/about/subscription-management/ New to Posit? Posit builds enterprise solutions and open source tools for people who do data science with R and Python. (We are also the company formerly called RStudio) We’d love to have you join us for future community events! Every Thursday from 12-1pm ET we host a Data Science Hangout with the community and invite you to join us! You can add that event to your calendar with this link: https://www.addevent.com/event/Qv9211919

Hadley Wickham

Create slideshows with Markdown & Python Code! (Quarto Tutorial)

Learn how to transform Markdown and Python code into interactive slide presentations using Quarto and reveal.js. This tutorial covers essential features like code execution, data visualization, LaTeX equations, custom styling, and slide transitions. Starting with basic slides, we’ll explore advanced functionality including background customization, animations, chalkboard annotations, and brand theming. Perfect for data scientists, educators, and developers looking to create engaging technical presentations. Github repo: https://github.com/KeithGalli/quarto-projects Quarto slideshow demo page: https://quarto.org/docs/presentations/revealjs/ Quarto crash course video: https://youtu.be/_VKxTPWDhA4?si=VZxkW3kgtx1W_AIW Blog article on _brand.yml: https://posit.co/blog/unified-branding-across-posit-tools-with-brand-yml/ Video by @KeithGalli Video timeline! 0:00 - Video Overview 0:39 - Getting Started with the Code (link in description) 2:22 - Quarto Slideshow Basics 6:15 - Generating Powerpoint (pptx) Presentation Outputs 7:14 - RevealJS HTML Output Options & Features (Code Animation, Line Highlighting) 11:00 - Data Visualization in Quarto (Matplotlib, Plotly, Seaborn) 14:40 - Displaying Dataframes on Slides 18:15 - Presentation Features (Incrementally Revealing Items, Image Positioning, Slide Transitions, etc.) 25:02 - Keyboard Shortcuts Functionality (Zoom in, Chalkboard, Save Presentation as PDF, etc.) 29:14 - Styling Presentations with a _brand.yml File (New in Quarto 1.6) 34:39 - Jupyter Notebook to RevealJS Slideshow #Python #Quarto #DataVisualization #pythoncontent

DuckDB and the future of databases | Hannes Mühleisen | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Hannes Mühleisen, Co-founder and CEO at DuckDB Labs, to chat about the evolution of database systems, the unique architecture of DuckDB, and how to find opportunities in areas others might consider “boring.” He also showed us a picture of the real life duck, Wilbur, that inspired the name DuckDB! 🦆 In this Hangout, we explore how DuckDB is changing the traditional concept of a database by making it accessible to a wide range of users and use cases. We also discuss its origins and how it was inspired by the challenges of data analysis and the importance of focusing on real user problems when building tools. What is a database and where does it live? DuckDB is challenging database assumptions by saying… maybe your expensive laptop can hold a database instead of just pinging one? Maybe it can do more than just run web browsers! Hannes wants you to know that database doesn’t have to be a bad word, and that you can actually have a great experience with one. If the data community’s affinity and enthusiasm for DuckDB is any indication, we think he’s right. We hope you listen in to hear Hannes talk about: a simple explanation of what DuckDB is, DuckDB’s in-process architecture and why it’s so cool, different DuckDB use cases (should you be using DuckDB?), DuckDB’s compatibility with cloud data systems, and more. Resources mentioned in the video and zoom chat: DuckDB Website → https://duckdb.org/ DuckDB Labs Website → https://duckdblabs.com/ Posit Conf talk by Hannes → https://www.youtube.com/watch?v=GELhdezYmP0 Sarah Alman’s webinar on using DuckDB and Shiny → https://www.youtube.com/watch?v=6AGroJb4zPM Forbes Article on Imposter Syndrome → https://www.forbes.com/sites/marycrossan/2024/12/18/how-to-combat-imposter-syndrome-by-developing-character/ MotherDuck → https://motherduck.com/ MotherDuck Blog Post “Big Data is Dead” → https://motherduck.com/blog/big-data-is-dead/ Posit Blog post on Shiny with Databases → https://posit.co/blog/shiny-with-databases/ DSH episode with Marco Gorelli → https://www.youtube.com/watch?v=lhAc51QtTHk Emil Hvitfeldt’s talk at posit::conf about orbital → https://youtu.be/Qnm1y0KPxVM Marcos Huerta’s Blog post on DuckDB and Bert → https://marcoshuerta.com/posts/duckdb-and-bert/ If you didn’t join live, one great discussion you missed from the zoom chat was about whether industry teams who use and benefit from open source tools could devote 5-10% of their time to giving back to the open source projects with code contributions or other support. Let us know below if you’d like to hear more about this topic! ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Emil Hvitfeldt

Data science and automation in publishing | Sophia Tee | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Sophia Tee, Senior Director of Data Science at Penguin Random House, to chat about marketing automation, the use of data science in publishing, and the transition from ID to manager in the realm of data science. In this Hangout, we explore lots of topics, like how Sophia’s team has automated the campaign creation process, reducing a two-week process to 30 minutes, while also improving campaign performance. That’s pretty amazing! Needless to say, we had lots of questions. She also had some great insights to share on the data science skills to strive for in the world of publishing as she experiences it. How organized is your code? It needs to be well-organized so that it “can be shared and understood by a lot of different people.” She recognizes that not all data scientists have experience productionizing models, but that it’s important in her line of work to be a full stack data scientist. Communication is paramount. Sophia says, “at the end of the day it doesn’t matter how great your model is if you can’t explain it. It’s really hard to sell it. So, I think being able to speak in plain English about what you did, and what the outcome is, and what you expect people to do about it is really important.” She stresses the importance of understanding the underlying data and the business context, too. Remember who you are speaking to and how your results relate to their KPIs. You’ll have to listen in to hear more! Resources mentioned in the video and zoom chat: Sophia Tee on LinkedIn → https://www.linkedin.com/in/sophia-tee-7069b65/ Today’s Top Books at Penguin Random House → https://www.penguinrandomhouse.com/interactive/todays-top-books CloseRead Prize → https://posit.co/blog/closeread-prize-announcement/ Bioinformatics Training & Education Program from the NIH → https://bioinformatics.ccr.cancer.gov/btep/ Prefect AI workflow tool → https://www.prefect.io/controlflow Pixi and uv integration blog → https://prefix.dev/blog/uv_in_pixi We also talked about book recommendations! Of course we did, this was a discussion about Penguin Random House! Here are some of the data-flavored recommendations mentioned by the community live and in the chat: The Bayesian Hitman by Robert Kubinec: https://www.amazon.com/Bayesian-Hitman-Robert-M-Kubinec/dp/B0D6M4WNRZ The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives by Deirdre Nansen McCloskey & Steve Ziliak: https://press.umich.edu/Books/T/The-Cult-of-Statistical-Significance2 The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century by David Salsburg: https://us.macmillan.com/books/9781466801783/theladytastingtea/ If you didn’t join live, one great discussion you missed from the zoom chat was about different perspectives and advice on transitioning from academia to industry (or the reverse!). Let us know below if you’d like to hear more about this topic. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Pharma Open-Source Packages and the R Validation Hub | A conversation with Aaron Clark

Posit’s Director of Life Sciences, Phil Bowsher, sat down with Aaron Clark, Senior Principal Clinical Scientist at Arcus Biosciences, to discuss various topics within the open-source clinical reporting space including Aaron’s career in pharma, the R Validation Hub, the open-source package riskmetric, and more. More about Posit’s work in the pharma space: https://posit.co/use-cases/pharma/

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Readr Stringr Tidyr Github Data Wrangling Tidy Data Odbc Blogdown Gt Tidymodels Statistics Programming Education Rstats Open Source Oss R Validation Hub Clinical Reporting Drug Development Open Source Pharma Pharma SAS Pharma Data Science

Open Source in Clinical Reporting | A Conversation with Ben Arancibia at GSK

Posit’s Director of Life Sciences, Phil Bowsher, sat down with GSK’s Director of Data Science, Ben Arancibia, to discuss various topics within the open-source clinical reporting space. More about Posit’s work in the pharma space: https://posit.co/use-cases/pharma/ Watch GSK’s latest web event with Posit: https://www.youtube.com/watch?v=xDrt6txplek

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss GSK Pharma SAS Pharma Clinical Reporting Statistial Programming

Sharpening your axe and the BAU trap | Steph Locke | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Steph Locke, Digital and App Innovation Leader at Microsoft, to chat about how to persuade your manager to give you more time for professional development, why “business as usual” (BAU) work can choke development, and how killing projects is actually a good thing. In this Hangout, we explore the importance of investing time in skill development and how this can lead to long-term gains in efficiency and quality. Steph shares advice on how to talk to managers about the value of “sharpening your axe,” and why it is more efficient to train in order to do things well initially than to spend time on continuous maintenance of subpar work. Is a bunch of “business as usual” work bogging down the potential output of development teams? This is where Steph’s concept of investing in high quality work up-front comes in. She talks about the dangers of rushing to release products that aren’t built with high quality and low ongoing maintenance in mind: “…when [people] don’t necessarily have time to invest in doing things in a way that’s going to have high quality and low maintainability requirements and is easy to extend and create new things, when people aren’t doing that, anything they ship is going to then cost them more time to do something to to look after whatever they’ve shipped. That then gives them less time to ship the next thing.” If this resonates with you, give Steph a follow on LinkedIn and make sure you read her article. Resources mentioned in the video and zoom chat: Steph’s article on how BAU chokes development → https://www.linkedin.com/pulse/developer-velocity-choked-bau-stephanie-locke-fsiqe Posit’s documentation on using copilot in the RStudio IDE → https://docs.posit.co/ide/user/ide/guide/tools/copilot.html A YouTube video about using Shiny apps for data entry into a backend database → https://www.youtube.com/watch?v=zDJc8sXh2qw If you didn’t join live, one great discussion you missed from the zoom chat was around the discrepancies in pay and responsibilities between individual contributor (IC) and management tracks in tech companies, and the perception that management roles often have better compensation and advancement opportunities, even when the work may not be more valuable than IC contributions. Let us know below if you’d like to hear more about this topic! Is there a need for more non-people-leader technical leadership roles for highly skilled individual contributors? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Real-World Python Dashboard Project w/ Quarto! (Airbnb Review Analysis)

In this video we use real-world Airbnb data to put together a dashboard with Quarto & Python. We start with the basics of layout and simple components and then progress to more advanced topics such as integrating Shiny within your Quarto doc. Github Repo: https://github.com/KeithGalli/quarto-projects Check out the recent Quarto Crash Course Video! https://youtu.be/_VKxTPWDhA4?si=sf7cbvSJGj6gnlCl Check out Mine’s Quarto Dashboards with R Code Videos! https://youtu.be/HW7QbqI4fH0?si=oyJhLaJ0n3Vg4v7Y Learn more about Shiny for Dashboards! https://youtu.be/I2W7i7QyJPI?si=Osqk5odwSOZ0ub01 Bootstrap Icons: https://icons.getbootstrap.com/ Video Timeline! 0:00 - Video Overview 0:41 - About our Data & Get Started with the Code 1:38 - Basic Quarto Dashboard Layout Information 5:08 - Adding Value Boxes to our Dashboard 10:56 - Prepping our Airbnb Data for our Dashboard 17:00 - Adding Matplotlib Data Visualizations to our Dashboard (line graph, bar chart) 26:26 - Add a Map of Airbnb Listings using Latitude & Longitude (Folium Library) 33:18 - Adding Additional Dashboard Pages with Interactive Tables (itables library) 42:23 - When to use Quarto versus Shiny for Dashboards? 44:00 - Using Shiny within a Quarto Project 49:14 - Hosting your Quarto Documents with Posit Connect Cloud #python #quarto #dashboards #pythoncontent

Navigating technology for data teams | Stanislav Seltser | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Stanislav Seltser, VP of Enterprise Architecture and AI Infrastructure at State Street Global Advisors, to chat about how to strategically approach the business leadership about investing in new tools, forecasting technology obsolescence for data work, and fostering innovation and creativity in data science teams. In this Hangout, we explore the factors that can help forecast when a technology will become obsolete, including the rate of commits to open-source projects and the frequency of vendor releases. Stanislav also shares his approach to evaluating technology based on usability, cost, and maintainability, as well as how to ensure that chosen technologies provide a return on investment. Stanislav recommends treating the need for a new tool the way you would if you were pitching a start-up, with a business plan and a technical prototype, to really show your understanding of what you’re asking for. Stan also shared some solid career advice, so stick around for that. No surprise here, his advice starts with community - being social and connecting with as many people as you can - but he really emphasizes mentoring and helping others succeed. We agree, Stan! Resources mentioned in the video and zoom chat: Libby’s blog post on career advice from the Data Science Hangout → https://posit.co/blog/lessons-from-four-senior-leaders-in-the-data-space/ Databricks + Spark documentation for sparklyr → https://spark.posit.co/deployment/databricks-connect.html Rachael’s post on communicating the ROI of data science projects → https://posit.co/blog/tips-for-communicating-roi-of-data-science-projects/ Jim Hester’s talk on dependencies → https://youtu.be/mum13N7CGUI?feature=shared If you didn’t join live, one great discussion you missed from the zoom chat was a conversation around the struggles of getting out of Jupyter notebooks for developing coding projects in python and R. Do you love notebooks? Are you trying to get away from them, too? Opinions from the Hangout attendees were mixed and the discussion was illuminating! Let us know below if you’d like to hear more about this topic. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Workflow Demo Live Q&A - December 18th!

This is the Live Q&A session for our Workflow Demo on December 18th to show how to build a Shiny app that reads from and writes to a database - using DuckDB. The demo will be here: https://youtu.be/6AGroJb4zPM Sara covers how to: Set up a connection to DuckDB from a Shiny app Query the database using R or Python Use editable tables to enable users to write back to the database Securely deploy your app to Posit Connect Resources mentioned in this Q&A: Connect Authentication documentation: https://docs.posit.co/connect/user/oauth-integrations/ Git backed deployment in Posit Connect: https://posit.co/blog/git-backed-deployment-in-posit-connect/ shinylive: https://posit-dev.github.io/r-shinylive/ Mastering Shiny Best Practices: https://mastering-shiny.org/best-practices.html#best-practices Blogs: https://blog.stephenturner.us/p/duckdb-vs-dplyr-vs-base-r https://outsiderdata.netlify.app/posts/2024-04-10-the-truth-about-tidy-wrappers/benchmark_wrappers.html Using Posit tools with data in DuckDB, Databricks, and Snowflake: https://posit.co/blog/databases-with-posit/ Creating a Shiny app that interacts with a database: https://posit.co/blog/shiny-with-databases/ Appsilon has a lot of resources on the topic: https://www.appsilon.com/post/r-shiny-duckd Please note, while the monthly Posit Team Workflow Series is usually the last Wednesday of the month - this will be a week earlier. Cheers to a festive holiday season and a fantastic year ahead for you and yours! To add future Monthly Workflow Demo events to your calendar → https://www.addevent.com/event/Eg16505674 You can view all the previous workflow demos here → https://www.youtube.com/playlist?list=PL9HYL-VRX0oRsUB5AgNMQuKuHPpNDLBVt

Connecting Shiny Apps to Databases with Posit Team

Many of us learned to work with data on our laptop, but at work data is increasingly stored in databases. Join us on Wednesday, Dec 18th at 11am ET with Sara Altman, who will lead us through building a Shiny app that reads from and writes to a database - using DuckDB 🦆 Sara will cover how to: Set up a connection to DuckDB from a Shiny app Query the database using R or Python Use editable tables to enable users to write back to the database Securely deploy your app to Posit Connect Q&A Recording: https://youtube.com/live/C8j3d46AacM?feature=share Resources mentioned in this Q&A: Connect Authentication documentation: https://docs.posit.co/connect/user/oauth-integrations/ Git backed deployment in Posit Connect: https://posit.co/blog/git-backed-deployment-in-posit-connect/ shinylive: https://posit-dev.github.io/r-shinylive/ Mastering Shiny Best Practices: https://mastering-shiny.org/best-practices.html#best-practices Blogs: https://blog.stephenturner.us/p/duckdb-vs-dplyr-vs-base-r https://outsiderdata.netlify.app/posts/2024-04-10-the-truth-about-tidy-wrappers/benchmark_wrappers.html Using Posit tools with data in DuckDB, Databricks, and Snowflake: https://posit.co/blog/databases-with-posit/ Creating a Shiny app that interacts with a database: https://posit.co/blog/shiny-with-databases/ Appsilon has a lot of resources on the topic: https://www.appsilon.com/post/r-shiny-duckd Please note, while the monthly Posit Team Workflow Series is usually the last Wednesday of the month - this will be a week earlier. Cheers to a festive holiday season and a fantastic year ahead for you and yours! To add future Monthly Workflow Demo events to your calendar → https://www.addevent.com/event/Eg16505674 You can view all the previous workflow demos here → https://www.youtube.com/playlist?list=PL9HYL-VRX0oRsUB5AgNMQuKuHPpNDLBVt

Sara Altman

From paramedic to leading analytics in pharma | Dan Haight | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Dan Haight, Director of Continuous Improvement Analytics at Merck, to talk about his unconventional career path, the value of lateral career moves (check out his book recommendation on this!), and the importance of creating “great charters” for successful stakeholder communication in projects. Dan’s advice about project management in this episode is so valuable. He recognizes how tempting it is to rush into a project without knowing all the facts beforehand, but then your scope starts to balloon, you see the project getting behind, and there are all these constraints you weren’t aware of. So, he says to take time to build a charter, be upfront with stakeholders about realistic timelines, and then use the charter as a contract to negotiate scope changes and resource allocation. He also discusses the challenges of working with incomplete data and the need to triangulate information from multiple sources to ensure accuracy and build confidence in the findings. If you’re a job seeker or looking to transition, Dan came with great advice to pursue continuous learning, network, and hone the ability to effectively communicate the value of their work. You’ll have to listen to learn more! If you didn’t attend live, you missed the party in the zoom chat People were excited about posit::conf 2025 early bird tickets getting released, and there was a SUPER great discussion about how to share your work when it’s all under NDA or very private due to security reasons. There was some great suggestions, like: 🥸 Anonymize data if it’s possible, or present a similar project abstractly Use public data to demonstrate the methods you used, even if you can’t showcase the findings Emphasize lessons learned during a project (ah-hah moments) instead of the business impacts IF the overall project failed or wasn’t implemented (as is the fate of many DS projects!) Document projects as you go, in a format that you can safely take with you, so that you can remember all these things even after you leave your role Lean on your friends and colleagues to help you describe the impacts you had on a project, or the importance of what you did (we all need to be hyped up sometimes when it comes to writing resumes!!) Resources mentioned in the video and zoom chat: Up is Not the Only Way Book → https://www.amazon.com/Up-Not-Only-Way-Rethinking/dp/1523083484 Shiny Assistant helps you build shiny apps → https://shiny.posit.co/blog/posts/shiny-assistant/ Brag Documents - Julia Evans → https://jvns.ca/blog/brag-documents/ Lessons From Four Senior Leaders in the Data Space → https://posit.co/blog/lessons-from-four-senior-leaders-in-the-data-space/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us! Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Data for good, mentoring, and stellar internships | Sebastien Ouellet | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Sebastian Ouellet, Senior Staff Developer at Kinaxis, to chat about volunteering with data-for-good initiatives, his career path in data science, and developer/intern mentorship. We talked all about the value of mentorship for both the mentor and mentee, and how Kinaxis’s internship program allows interns to innovate and grow by owning brand new projects. Sebastien emphasized the positive reciprocal nature of mentorship, with advantages for both sides: mentees and interns gain invaluable knowledge, guidance, and support from experienced professionals, and regular communication enables interns to receive timely feedback, ask clarifying questions, and course-correct their approach when necessary; for mentors, the act of teaching and explaining concepts to someone new really forces them to re-examine their understanding of familiar processes, which can help identify gaps in documentation or reveal areas for improvement in existing systems. Sebastien also covered the strategic benefits of allowing interns to spearhead new and innovative (instead of existing) projects. Basically, allowing interns to own projects, coupled with a supportive mentorship structure, creates a win-win scenario with tangible benefits for both the interns and the company: Innovation: By entrusting interns with fresh projects, they foster an environment of innovation. Interns bring a new perspective, allowing them to challenge the status quo and come up with novel solutions. Knowledge Management: Interns build valuable resources, including documentation, code repositories, and presentations, as they progress through their projects. These resources create a knowledge base that can be leveraged by other team members, even after the intern leaves the team or the company. Project Pipeline: the company gets to use these intern-driven projects as a proving ground for new ideas. Successful projects can evolve into production-ready solutions (and have already)! If you didn’t attend live, you missed some GREAT conversations in the zoom chat, including one in which we all admitted to the many times we’ve reinvented the wheel before talking to people about what we were trying to do and having them tell us there was an easier way. Join our community and make some data friends so you too can prevent yourself from reinventing the wheel Resources mentioned in the video and zoom chat: Closeread Contest, Scrollytelling with Quarto → https://posit.co/blog/closeread-prize-announcement/ DataKind → https://www.datakind.org/join-us/volunteer/ DataKind’s Datakit Event → https://www.eventbrite.com/e/datakinds-datakit-event-tickets-1079849321389 rOpenSci Champions Program → https://ropensci.org/champions/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Fastest way to Convert Jupyter Notebooks into Analytics Reports! (using Quarto)

Transform messy Jupyter notebooks into polished HTML reports with Quarto. Learn how to create professional, client-ready reports that effectively communicate data science insights to stakeholders. Using a telecom customer churn analysis example, we’ll cover: Converting notebooks to formatted HTML reports Customizing layouts and interactive elements Adding business context to technical analysis Publishing and sharing reports effectively Perfect for data scientists and analysts who need to present technical work to business audiences. Link to Code: https://github.com/KeithGalli/telecom-churn-analysis Quarto Crash Course Video: https://youtu.be/_VKxTPWDhA4?si=kcbQ8M9p6HH5QE2w Share your work with Posit Connect Cloud: https://pos.it/keith_qc Video by @KeithGalli Video timeline! 0:00 - Video Overview & Accessing Code/Data 1:46 - Rendering Jupyter Notebook as HTML Output 3:30 - Making quick improvements to our HTML Report (adjusting YAML parameters, hiding code/output cells) 8:15 - Understanding the Business Context & Insights from our Analysis and adding written details to report. 14:30 - Improving page formatting (margins, body size, etc) 17:30 - Adding business recommendations for our telecom client 21:00 - Further aesthetic improvements (larger font-size, organizing info into columns, using a qmd file, etc.) 30:17 - Publishing our HTML report using Posit Connect Cloud #python #jupyter #quarto

Quarto Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Rmarkdown CRAN Interoperability Serious Data Science Dplyr Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Statistics Debugging Programming Education Rstats Open Source Oss Quarto

Janssens, Chow & Nieuwdorp - Turning DataFrames into Pretty Pictures with Plotnine | PyData NYC 2024

www.pydata.org Learn how Plotnine, a Python package inspired by R’s ggplot2, enables the creation of sophisticated and effective data visualizations with minimal effort. This tutorial will explain how Plotnine’s grammar of graphics approach provides a flexible, intuitive way to visualize data, either as ad-hoc plots or fine-tuned graphs suited for communication. Quick links Presentation: https://bit.ly/plotnine-tutorial GitHub repository: https://bit.ly/plotnine-repo Slideshow about what to expect: https://bit.ly/expect-plotnine PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

From Chemical Engineer to Dow Data Science Leader | Michael Hausinger | Data Science Hangout

To join future data science hangouts, add it to your calendar here: https://pos.it/dsh - All are welcome! We’d love to see you! We were recently joined by Michael Hausinger, Scientist in Research and Development at Dow, to chat about his non-traditional career path to becoming a data science leader. We talked about how learning R through Posit Academy helped him to transition from bench science to building tools that enhance the efficiency of other scientists at Dow. Michael shared valuable insights about the importance of finding the right tools for the job, effectively communicating the value of data science to stakeholders, and building a supportive data science community. In this Hangout, we explore the growing importance of data skills in non-traditional fields like chemical engineering. Michael’s story shows how individuals with diverse backgrounds can leverage data science tools like R and python to make a significant impact in their organizations. The conversation highlights the need for companies to invest in up-skilling their workforce in data science, and the value of creating supportive communities of practice where individuals can learn and grow together. Resources mentioned in the video and zoom chat: Dow Customer Spotlight → https://posit.co/about/customer-stories/dow/ Posit Academy → https://posit.co/products/enterprise/academy/ Upskill to R Programming with Posit & Atorus Research → https://posit.co/blog/upskill-to-r-programming-with-posit-and-atorus-research/ Creating Business Value with Community at Dow → https://youtu.be/SNneEKmSyyU?feature=shared If you didn’t join live, one great discussion you missed from the zoom chat was about transitioning into a data-focused role without a formal degree in data science. Michael shared he doesn’t have a data science degree specifically, as did many others! Let us know below if you’d like to hear more about this topic ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co Hangout: https://pos.it/dsh LinkedIn: https://www.linkedin.com/company/posit-software Bluesky: https://bsky.app/profile/posit.co Thanks for hanging out with us!

Christophe Dervieux - Exploring Quarto Dashboard for impactful and visual communication

Embark on a journey to explore how Quarto Dashboard can enhance the narrative of your analysis from your Jupyter Notebook. This talk will show how to create cool interactive charts and graphs that bring your data to life, by using Quarto - an open-source scientific and technical publishing system. Learn how to make your data communications more engaging and dynamic using Quarto Dashboard. Practical examples and simple explanations will guide you through the process, making it easy to understand and apply to your projects. https://cderv.github.io/pydata-paris-2024-quarto-dashboard/#/title-slide www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

Christophe Dervieux

Joe Cheng - Summer is Coming: AI for R, Shiny, and Pharma

Summer is Coming: AI for R, Shiny, and Pharma - Joe Cheng Abstract: R users tend to be skeptical of modern AI models, given our weird insistence on answers being accurate, or at least supported by the data. But I believe the time has come—or maybe it’s a little late—for even the most AI-cynical among us to push past their discomfort and get informed about what these tools are truly capable of. And key to that is moving beyond using AI-enabled apps, and towards building our own scripts, packages, and apps that make judicious use of AI. In this talk, I’ll tell you why I believe AI has more to offer the R community than just wrong answers from chat windows or mediocre code suggestions in our IDEs. I’ll also introduce brand-new tools we’re developing at Posit that put powerful AI tools within reach of every R user. And finally, I’ll show how adding some AI could make your next Shiny app dramatically more useful for your users. Resources mentioned in the talk: Slides: https://jcheng5.github.io/pharma-ai-2024 {elmer} Call LLM APIs from R: https://elmer.tidyverse.org/ {shinychat} Chat UI component for Shiny for R https://github.com/jcheng5/shinychat R/Pharma GenAI Day Recordings: https://www.youtube.com/playlist?list=PLMtxz1fUYA5AYryl4t2mtqBngqWDrnMXJ Presented at the 2024 R/Pharma Conference

Joe Cheng

Isabel Zimmerman - End-to-end data science with the Positron IDE | PyData NYC 2024

www.pydata.org The process of data science is inherently iterative, requiring constant inspection and visualization of data. Positron is a new, next generation integrated development environment (IDE) built to facilitate exploratory data analysis, reproducible authoring, and publishing data artifacts. This talk will discuss the motivation behind creating Positron and demonstrate the features that support these iterative data science workflows. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

Isabel Zimmerman

Generate 100s of custom reports in minutes with Python & Quarto! (Parameterized report automation)

A practical guide to generating hundreds of customized reports using Quarto and Python. Learn how to leverage Quarto’s parameter system to create PDFs and HTML reports at scale. Using a movie dataset example, we’ll cover: How to automate report generation with Python and Quarto Create dynamic templates with data visualization and formatting Adapt parameters to work with both Python and R code Build scalable reporting workflows for any dataset This tutorial demonstrates how to transform what could be hours of manual reporting work into an efficient automated process. Quarto Crash Course Video: https://youtu.be/_VKxTPWDhA4?si=Q09V5xXlyo1YVQqs Generating Analytics Reports (typst) Video: https://youtu.be/Q3phTByW138?si=FIMiAWAHP6HhUnhG Link to Code (starting): https://github.com/KeithGalli/quarto-crash-course Link to Code (finished): https://github.com/KeithGalli/quarto-crash-course/tree/parameterized-reports Information on the dataset can be found here: https://github.com/KeithGalli/quarto-crash-course/tree/master?tab=readme-ov-file#resources Video timeline! 0:00 - Video Overview 0:58 - Walking through some starter code (linked in description) 3:20 - Passing parameters with Python into Quarto Markdown Files 4:27 - Passing parameters using R 5:18 - Using a Python script to render many reports with variable parameters. 6:36 - Dynamically generating markdown sections with Python (output: asis - execution option) 11:56 - Adding images, table of contents, & pagebreaks to our reports 15:00 - Adding graphs to our reports 17:54 - Tying everything together 21:47 - Adding fenced divs & styling/formatting dynamically with code

Quarto RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Quarto Websites 4: Add lists of content with listings | Charlotte Wickham | Posit

Adding a listing page to your website is a great way to showcase your projects, talks, publications or blog posts. In this video you’ll learn how to create a listing page in Quarto and see two ways to populate it with content: Quarto documents, or a yaml file. In this video: 0:50 Use a listing to add a blog 3:36 Listing options 5:47 Why use a listing? 7:22 Use a YAML file to populate a project portfolio 9:50 Customize the display of a listing 12:10 Advanced customization of listings 13:42 Remove pages Links: Listings: https://quarto.org/docs/websites/website-listings.html Andrew Heiss’ teaching listing: https://www.andrewheiss.com/teaching/ Code: Starter source code: https://github.com/cwickham/quarto-website-video/tree/v0.3 Final source code: https://github.com/cwickham/quarto-website-video/tree/v0.4 For more in-depth coverage and slides check out: https://posit-conf-2024.github.io/quarto-websites/ Do you need a professional website to showcase your work? If you’ve used Quarto to produce a document, you’ve already got the technical skills to create a Quarto website. In this video series, you’ll learn everything else you need to build a website and customize its appearance. This video series is for you if you: Have used Quarto to generate documents (e.g. HTML, PDF, MS Word etc.) Are comfortable editing plain text documents (e.g.qmd) in your IDE (e.g. RStudio, Visual Studio Code etc.) Want to walk away with your own personal website Taught by: Charlotte Wickham ( https://www.cwick.co.nz/ ) Emil Hvitfeldt ( https://emilhvitfeldt.com/ ) Videos in this series: Build your homepage [https://youtu.be/l7r24gTEkEY] Add pages and navigation [https://youtu.be/k65E-8PXZmA] 3: Customize appearance with CSS/SCSS [https://youtu.be/pAN2Hiq0XGs] 4: Add lists of content with listings [https://youtu.be/bv_Cw-3HI1Y]

Charlotte Wickham, Emil Hvitfeldt

Quarto rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Quarto Websites 3: Customize appearance with CSS/SCSS | Emil Hvitfeldt | Posit

You now have a set of content you are happy with on your website, but how do you customize the look and feel of your site beyond options set in YAML? In this video, you’ll start by learning the basics of CSS and SCSS and how to make good design choices. Then, you’ll see how to apply these choices to your Quarto website. In this video: 0:14 What is HTML? 3:23 CSS Selectors 8:05 CSS Attributes 8:25 Layout attributes 10:24 Reducing repetition with SASS/SCSS? 15:26 Consistent design 16:36 Choosing colors 17:50 Choosing fonts 19:28 Maintaining accessibility 22:13 Apply SCSS to your website 24:16 Change the appearance of headings 25:28 Change the appearance of navigation bar 30:30 Use google fonts Links: Color contrast checker: https://colourcontrast.cc/ Google fonts: https://fonts.google.com/ Code: Starter source code: https://github.com/cwickham/quarto-website-video/tree/v0.2 Final source code: https://github.com/cwickham/quarto-website-video/tree/v0.3 For more in-depth coverage and slides check out: https://posit-conf-2024.github.io/quarto-websites/ Do you need a professional website to showcase your work? If you’ve used Quarto to produce a document, you’ve already got the technical skills to create a Quarto website. In this video series, you’ll learn everything else you need to build a website and customize its appearance. This video series is for you if you: Have used Quarto to generate documents (e.g. HTML, PDF, MS Word etc.) Are comfortable editing plain text documents (e.g.qmd) in your IDE (e.g. RStudio, Visual Studio Code etc.) Want to walk away with your own personal website Taught by: Charlotte Wickham ( https://www.cwick.co.nz/ ) Emil Hvitfeldt ( https://emilhvitfeldt.com/ ) Videos in this series: Build your homepage [https://youtu.be/l7r24gTEkEY] Add pages and navigation [https://youtu.be/k65E-8PXZmA] 3: Customize appearance with CSS/SCSS [https://youtu.be/pAN2Hiq0XGs] 4: Add lists of content with listings [https://youtu.be/bv_Cw-3HI1Y]

Charlotte Wickham, Emil Hvitfeldt

Quarto rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Quarto Websites 2: Add pages and navigation | Charlotte Wickham | Posit

Now you’ve got a homepage, you’ll likely want to add some other pages. In this video, learn how to add pages to your website, and help people find them, by adding them to your website navigation. In this video: 1:00 Add a page to your website 2:54 Your file structure determines your URL structure 5:49 Add a link to your page in navigation 7:50 Customize navigation item text and icon 9:12 Control where items appear in the navigation bar 10:16 Navigation bar options 11:11 Switch to side navigation 12:22 Other types of navigation 16:30 Wrap Up Links: List of icons you can use in navigation items: https://icons.getbootstrap.com/ Top navigation bar options: https://quarto.org/docs/websites/website-navigation.html#top-navigation Quarto website navigation: https://quarto.org/docs/websites/website-navigation.html Code: Starter source code: https://github.com/cwickham/quarto-website-video/tree/v0.1 Final source code: https://github.com/cwickham/quarto-website-video/tree/v0.2 For more in-depth coverage and slides check out: https://posit-conf-2024.github.io/quarto-websites/ Do you need a professional website to showcase your work? If you’ve used Quarto to produce a document, you’ve already got the technical skills to create a Quarto website. In this video series, you’ll learn everything else you need to build a website and customize its appearance. This video series is for you if you: Have used Quarto to generate documents (e.g. HTML, PDF, MS Word etc.) Are comfortable editing plain text documents (e.g.qmd) in your IDE (e.g. RStudio, Visual Studio Code etc.) Want to walk away with your own personal website Taught by: Charlotte Wickham ( https://www.cwick.co.nz/ ) Emil Hvitfeldt ( https://emilhvitfeldt.com/ ) Videos in this series: Build your homepage [https://youtu.be/l7r24gTEkEY] Add pages and navigation [https://youtu.be/k65E-8PXZmA] 3: Customize appearance with CSS/SCSS [https://youtu.be/pAN2Hiq0XGs] 4: Add lists of content with listings [https://youtu.be/bv_Cw-3HI1Y]

Charlotte Wickham, Emil Hvitfeldt

Quarto rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Quarto Websites 1: Build your homepage | Charlotte Wickham & Emil Hvitfeldt | Posit

In this video, you’ll get a running start by using a template we’ve designed to be functional and attractive, and that will serve as a foundation for the rest of the video series. You’ll customize the content of your homepage, and how it looks, and along the way learn about the two key files in a Quarto website index.qmd and _quarto.yml. Finally, you’ll learn one way to publish your website so other people can see it. In this video: 0:21 Use a template to get started 2:33 Preview the template homepage, index.qmd 4:12 Customize the content of your homepage 5:45 “About” pages 7:22 Customize the image on your homepage 9:24 Website configuration, _quarto.yml 10:40 Customize colors with YAML 13:45 Customize fonts with YAML 17:00 Publish your site Links: About pages: https://quarto.org/docs/websites/website-about.html Appearance options you can set in YAML: https://quarto.org/docs/output-formats/html-themes.html#basic-options Code: Starter source code: https://github.com/EmilHvitfeldt/website-template Final source code: https://github.com/cwickham/quarto-website-video/tree/v0.1 For more in-depth coverage and slides check out: https://posit-conf-2024.github.io/quarto-websites/ Do you need a professional website to showcase your work? If you’ve used Quarto to produce a document, you’ve already got the technical skills to create a Quarto website. In this video series, you’ll learn everything else you need to build a website and customize its appearance. This video series is for you if you: Have used Quarto to generate documents (e.g. HTML, PDF, MS Word etc.) Are comfortable editing plain text documents (e.g.qmd) in your IDE (e.g. RStudio, Visual Studio Code etc.) Want to walk away with your own personal website Taught by: Charlotte Wickham ( https://www.cwick.co.nz/ ) Emil Hvitfeldt ( https://emilhvitfeldt.com/ ) Videos in this series: Build your homepage [https://youtu.be/l7r24gTEkEY] Add pages and navigation [https://youtu.be/k65E-8PXZmA] 3: Customize appearance with CSS/SCSS [https://youtu.be/pAN2Hiq0XGs] 4: Add lists of content with listings [https://youtu.be/bv_Cw-3HI1Y]

Charlotte Wickham, Emil Hvitfeldt

Quarto rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

pandas vs Polars DataFrames? 👨🏻‍🍳 #python

Here’s Marco Gorelli’s take on why lazy execution matters So with pandas, everything happens eagerly. You give the library some instructions, and it evaluates your instructions the moment you give them. With Polars, there’s an option to give it some instructions, it can wait a bit, and then do them all together in the most optimal way that it can detect. If I was to tell you to cook me a recipe ‍ …and I gave you the steps one at a time …and for each step, you have to go out and buy the ingredients that you need for that step ^ This is going to be a pretty inefficient way of making a cake But if I can give you the recipe beforehand: …you can buy all the ingredients together …you can maybe do 2 steps at the same time ^ This is going to be a much nicer cooking experience #positshorts

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Quarto Crash Course | Create Professional Reports, Dashboards & Websites w/ Markdown & Python Code!

Welcome to this comprehensive Quarto crash course using Python! Whether you’re a complete beginner or an experienced user, this tutorial covers the topics you need to know about the Quarto publishing system. We’ll explore: Basic setup and installation Creating HTML reports, PDFs, and interactive dashboards from your Python script or Jupyter notebook. Building presentations with Revealjs Customizing outputs with CSS and layouts (fenced divs, classes, and more) Working with parameters for dynamic reports Publishing to Posit Connect Cloud Creating complete websites Automated report generation Perfect for data scientists, analysts, and developers looking to create beautiful, reproducible reports from their code. We’ll use Python throughout the tutorial with real-world examples using movie analytics data. Let’s dive in! Video by @KeithGalli Github repo: https://github.com/KeithGalli/quarto-crash-course Deploy with Posit Connect Cloud! https://pos.it/keith_qc #python #quarto #posit — Resources Mentioned — Slideshow example: https://quarto.org/docs/presentations/revealjs/demo/#/title-slide Slideshow example (source code): https://github.com/quarto-dev/quarto-web/blob/main/docs/presentations/revealjs/demo/index.qmd Example HTML report: https://019302a7-e9e3-3454-3575-23148999a7f7.share.connect.posit.cloud/ Quarto Gallery: https://quarto.org/docs/gallery/ Bootstrap Icons: https://icons.getbootstrap.com/ Video timeline! 0:00 - About the Crash Course 0:50 - Quarto Overview 2:12 - Installation & Setup 6:22 - Markdown Basics 8:46 - Quarto Markdown Features 19:37 - Quarto Styling & Formatting (fenced divs, CSS classes, etc.) 34:53 - Parameters & CLI Options 40:46 - HTML & Publishing 49:46 - Static Docs (PDFs, Docx) 54:53 - Dashboards 1:06:50 - Slideshows (Revealjs) 1:16:11 - Websites 1:19:52 - Automated Report Generation (Parameterized Reports)

cli Quarto revealjs RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Polars, pandas, and Narwhals, oh my! | Marco Gorelli | Data Science Hangout

Welcome back to the Data Science Hangout! This week, we’re joined by Marco Gorelli, a senior software engineer at Quansight Labs and a core developer of Pandas, Polars, and the author of Narwhals. Marco shares his insights on the evolving landscape of data frame libraries in Python, focusing on Polars, its advantages over Pandas, and the role of Narwhals in creating data frame agnostic tools. Marco discusses his journey into open source, highlighting the importance of finding your niche and contributing to areas that others might find “boring” but are essential for a project’s success. He also emphasizes the significance of fostering welcoming and inclusive open source communities. This episode explores several key topics: Polars: We learn about Polars’ key features like lazy execution, its performance benefits, and the power of its expressions API. Marco explains how lazy execution can lead to significant performance gains and answers questions about its relevance even when working with smaller datasets. Narwhals: We discover how Narwhals enables developers to write tools that work seamlessly with various data frame libraries, promoting interoperability and simplifying development workflows. Open Source Challenges: Marco addresses the challenges of maintaining work-life balance while being deeply involved in open-source projects. He offers practical advice on prioritization and managing the constant influx of tasks and requests. Resources mentioned in the chat: Pandas: https://pandas.pydata.org/ Polars: https://pola.rs/ Narwhals: https://narwhals-dev.github.io/narwhals/ Awesome Polars: https://github.com/ddotta/awesome-polars Ibis: https://ibis-project.org/ Polars Plugins Tutorial: https://marcogorelli.github.io/polars-plugins-tutorial/ Understanding Polars Expressions: https://www.youtube.com/watch?v=E7cHgN9rd9c Great Tables: https://posit-dev.github.io/great-tables/articles/intro.html Great Tables Blog - Polars Styling: https://posit-dev.github.io/great-tables/blog/polars-styling/ Great Tables Blog - BYODF: https://posit-dev.github.io/great-tables/blog/bring-your-own-df/ Ruff: https://astral.sh/ruff UV: https://docs.astral.sh/uv/ R/Pharma 2024: https://rinpharma.com/ PyladiesCon: https://conference.pyladies.com/ Beeminder: https://www.beeminder.com/ Todoist: https://todoist.com/ Nyctography: https://en.wikipedia.org/wiki/Nyctography This episode offers valuable insights for anyone working with data in Python, particularly those interested in exploring the benefits of Polars and the power of Narwhals for building data-frame-agnostic tools. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software To join future data science hangouts, add to your calendar here: https://pos.it/dsh (All are welcome! We’d love to see you!) Thanks for hanging out with us!

Running unified attribution at scale | Martin Stein @ Conversion Logix | Data Science Hangout

Join us for a conversation with Martin Stein, Chief Analytics Officer at Conversion Logix, to chat about running unified attribution at scale, the tools they leverage, languages Conversion Logix’s uses, and the workflow and operations to make it all happen. Featured Leader Bio: Martin Stein is a seasoned data scientist, entrepreneur, and Chief Analytics Officer at Conversion Logix. With over 20 years of experience in AI, machine learning, and data science, he brings a wealth of expertise to the multifamily industry, developing cutting-edge MarTech solutions that empower marketing leaders to make data-driven decisions to maximize their investments. Stein is also the co-founder of Bend.ai, an AI/machine learning company acquired by Conversion Logix. His career includes significant roles at Apple, IDG/IDC, G5, RStudio, and startups like FileThis, Defined.ai, and Union.ai. Throughout his career, he has demonstrated a proven ability to build and scale products to over $200 million in revenue. Martin shares insights on topics like: The problem with “last-touch” attribution in marketing and how his team is building solutions to provide a more comprehensive understanding of marketing channel effectiveness. Common data science struggles, including data access, data inconsistency, production challenges, and team dynamics. The value of community in a data scientist’s journey, particularly for those exploring new areas like AI. Tips for effectively communicating data science concepts and building business cases, both internally and with clients. Approaches to measuring channel success in marketing and comparing it to copy success, emphasizing the importance of considering factors beyond individual channel performance. How tools like Pins and Connect facilitate data management, workflow efficiency, and model deployment. Insights into the use of vetiver for model evaluation, highlighting its ability to assess model behavior with known and unknown data and its relevance to MLOps practices. Don’t miss this insightful episode packed with practical advice and real-world examples for data scientists at all levels! A few people in the chat said it felt like they were listening to a really great mentor speak, and we agree ️ Resources mentioned in the episode and chat: Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R by Martin Huber https://mitpress.mit.edu/9780262545914/causal-analysis/ Causal Inference: The Mixtape by Scott Cunningham https://mixtape.scunning.com/ Kevin Ushey’s R Studio Conf talk on Renv https://www.youtube.com/watch?v=4wRiPG9LM3o Rami Krispin’s VS Code and R setup https://github.com/RamiKrispin/vscode-r ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: ( https://www.linkedin.com/company/posit-software ) Twitter: ( https://x.com/posit_pbc ) To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!) Thanks for hanging out with us!

GSK’s R Journey: From Pilot Projects to Enterprise Adoption | Hosted by Posit

Discover how GSK, a leading pharmaceutical company, transforms its biostatistics toolkit by embracing R. In this web event, you’ll learn: -GSK’s Open-Source Commitment and Journey: Discover how GSK began their journey with open-source software, particularly R, and the pilots they’ve initiated to foster broader acceptance across the organization. -Challenges and Goals in R Adoption: Understand the obstacles GSK faces in shifting to end-to-end use of R due to existing processes and timelines, and learn about their internal goals to achieve parity between R and other proprietary languages in their biostatistics toolkit. -Evolution of Training Programs: See how GSK upskills teams through evolving training programs while meeting key deliverables. -Contributing Back to Open-Source: Explore the importance GSK places on giving back to the open-source community as part of their adoption journey. -Ascending the Open-Source Mountain: Join us to uncover the steps GSK has taken from pilot projects to setting large-scale enterprise adoption goals. Meet the Speakers: Andy Nicholls is the Head of Data Science Innovation and Engineering Team at GSK. Andy brings nearly 20 years of experience working with and promoting the R language. Formerly a professional R trainer and advocate at Mango Solutions, he taught over 100 R training courses and authored the book “R in 24 Hours”. Andy co-founded the R Validation Hub and co-authored an influential white paper on risk-based approaches to R package assessments. At GSK, he plays a vital role in defining and delivering the R strategy for Biostatistics. Ben Arancibia is a Director of Data Science on GSK’s Data Science Innovation and Engineering Team. Ben is a seasoned data scientist with 13 years of experience utilizing open-source languages. His career includes building analytical products for international finance and cloud computing environments for cybersecurity. At GSK, he leads teams developing tools for clinical trial analysis, focusing on innovative R training and upskilling approaches. Becca Krouse is a Data Scientist on GSK’s Data Science Innovation and Engineering Team. With over 14 years in clinical research, Becca is a trained biostatistician specializing in developing R-based tools. Her expertise bridges statistical theory and practical application, ensuring GSK’s biostatistics remains innovative. Want to learn more about Posit’s work in pharma? Access the full pharma playlist through our YouTube channel!

Data Science in CPG and the Zero-to-One journey | Joel Ash @ Kraft Heinz | Data Science Hangout

Welcome back to the Data Science Hangout! This week, we’re joined by Joel Ash, Senior Data Science Manager at Kraft Heinz, for a discussion on the unique challenges and opportunities of applying data science in the consumer packaged goods (CPG) industry. Joel provides a behind-the-scenes look at how Kraft Heinz leverages data to optimize promotional spending, which exceeds a billion dollars annually. He discusses the complexities of navigating relationships with retail partners like Target and Walmart, emphasizing the need to balance cost reduction with maintaining strong customer relationships. With some internet issues, we were only able to learn from Joel the first half of the Hangout. We switched to an open leader format with the community for the rest of the conversation. Thank you to those who jumped in as well! Discussion topics with Joel included: Decoding the CPG landscape: Joel explains the nuances of the CPG industry, highlighting the distinction between their customers (retailers) and end consumers. He provides specific examples, such as the market dynamics of cream cheese, to illustrate the challenges of predicting demand and optimizing promotional strategies. Embracing the “zero to one” journey: Joel introduces the concept of “zero to one,” referencing the challenges of building data science capabilities from the ground up within established organizations. He discusses the importance of integrating data-driven insights into legacy systems and processes, often requiring adaptability and a focus on interpretable models. The battle against cannibalization in CPG: Joel explains the concept of “cannibalization” in the CPG context, where the introduction of new products or promotions can negatively impact sales of existing products. He discusses the strategies Kraft Heinz employs to minimize cannibalization and maximize the overall return on promotional investments. Resources mentioned: Ego is the Enemy by Ryan Holiday https://egoistheenemy.com/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: ( https://www.linkedin.com/company/posit-software ) Twitter: ( https://x.com/posit_pbc ) To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!) Thanks for hanging out with us!

Quarto Dashboards 3: Theming and Styling | Mine Çetinkaya-Rundel | Posit

Theming and styling Quarto dashboards built with R and/or Python. Before watching this video, you might want to watch Parts 1 & 2. This video takes you through 0:00 - Theming (including Bootswatch themes, light/dark mode, customizing themes with SCSS) 3:55 - Styling 4:55 - Live coding demo Slides can be found at https://mine.quarto.pub/quarto-dashboards/3-theming-styling and the starter documents for the accompanying exercises at https://github.com/mine-cetinkaya-rundel/olympicdash. Materials for all parts of the videos can be accessed at https://mine.quarto.pub/quarto-dashboards. You already analyze and summarize your data in computational notebooks with R and/or Python. What’s next? You can share your insights or allow others to make their own conclusions in eye-catching dashboards and straight-forward to author, design, and deploy Quarto Dashboards, regardless of the language of your data processing, visualization, analysis, etc. With Quarto Dashboards, you can create elegant and production-ready dashboards using a variety of components, including static graphics (ggplot2, Matplotlib, Seaborn, etc.), interactive widgets (Plotly, Leaflet, Jupyter Widgets, htmlwidgets, etc.), tabular data, value boxes, text annotations, and more. Additionally, with intelligent resizing of components, your Quarto Dashboards look great on devices of all sizes. And importantly, you can author Quarto Dashboards without leaving the comfort of your “home” – in plain text markdown with any text editor (VS Code, RStudio, Neovim, etc.) or any notebook editor (JupyterLab, etc.). This workshop will walk you through building an increasingly complex dashboard using various layout options and deploy them as static web pages (with no special server required) as well as with a Shiny Server on the backend for enhanced interactivity. This course is for you if you: do data analysis in computational notebooks share your results with your audience in static or interactive dashboards want to improve the design, user interface, and experience of your dashboards

Mine Çetinkaya-Rundel

ggplot2 leaflet Quarto rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Quarto Dashboards 2: Components | Mine Çetinkaya-Rundel | Posit

Building dashboards in R and/or Python with Quarto, one component at a time. Before watching this video, you might want to watch Part 1. This video takes you through 0:00 - An overview of dashboard components 0:11 - Navigation bar and pages 4:55 - Sidebars, rows, columns, and tabsets 11:07 - Cards 22:40 - Live coding demo Slides can be found at https://mine.quarto.pub/quarto-dashboards/2-dashboard-components and the starter documents for the accompanying exercises at https://github.com/mine-cetinkaya-rundel/olympicdash. Materials for all parts of the videos can be accessed at https://mine.quarto.pub/quarto-dashboards

Mine Çetinkaya-Rundel

Quarto RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Quarto Dashboards 1: Hello, Dashboards! | Mine Çetinkaya-Rundel | Posit

You already analyze and summarize your data in computational notebooks with R and/or Python. What’s next? You can share your insights or allow others to make their own conclusions in eye-catching dashboards and straight-forward to author, design, and deploy Quarto Dashboards, regardless of the language of your data processing, visualization, analysis, etc. With Quarto Dashboards, you can create elegant and production-ready dashboards using a variety of components, including static graphics (ggplot2, Matplotlib, Seaborn, etc.), interactive widgets (Plotly, Leaflet, Jupyter Widgets, htmlwidgets, etc.), tabular data, value boxes, text annotations, and more. Additionally, with intelligent resizing of components, your Quarto Dashboards look great on devices of all sizes. And importantly, you can author Quarto Dashboards without leaving the comfort of your “home” – in plain text markdown with any text editor (VS Code, RStudio, Neovim, etc.) or any notebook editor (JupyterLab, etc.). This video takes you through 0:00 - Overview of building dashboards with Quarto 0:15 - Dashboard basics 7:40 - First dashboard in R 10:30 - First dashboard in Python 11:43 - Live coding demo Slides can be found at https://mine.quarto.pub/quarto-dashboards/1-hello-dashboards/#/title-slide and the starter documents for the accompanying exercises at https://github.com/mine-cetinkaya-rundel/olympicdash. Materials for all parts of the videos can be accessed at https://mine.quarto.pub/quarto-dashboards

Mine Çetinkaya-Rundel

ggplot2 leaflet Quarto rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Zac Davies - Elevating enterprise data through open source LLMs

In an era where data privacy and security are paramount, many organizations are keen on leveraging Large Language Models (LLMs) in conjunction with their proprietary data without exposing it to third-party services. Recognizing this need, our talk, “Elevating Enterprise Data Through Open Source LLMs,” showcases an approach that integrates the capabilities of Databricks and Posit, enabling businesses to maintain ownership and control over their data and LLMs while delivering value to their customers. The core of our discussion revolves around a system architecture that synergizes the strengths of Databricks and Posit technologies, providing a comprehensive solution for enterprise data and open-source LLMs. Databricks is responsible for data management and processing, offering a seamless environment for hosting, serving, and fine-tuning open-source LLMs. Keeping data and models in the secure perimeter of Databricks lowers the risk of data exfiltration tremendously, and also benefits from the scalable data processing and machine learning capabilities - including the recent acquisition MosaicML - that Databricks delivers. Posit steps in to streamline the process through Posit Workbench, the developer platform for data science with custom integrations for working with Databricks. This allows developers to write, test, and refine their code in a familiar and powerful setting while still being able to access the data, compute, and model serving offered by Databricks. In addition, Posit Connect offers an easy-to-use platform for deploying these applications, ensuring that the end-to-end process, from development to deployment, is efficient, secure, and aligned with enterprise standards. Attendees of this talk will gain valuable insights into constructing and deploying LLM-powered applications using their enterprise data. By the end of the session, you will have a clear understanding of how to leverage Databricks for optimal data management and LLM operations, alongside Posit’s streamlined development and deployment processes. This knowledge will empower you to deliver secure, effective, and scalable LLM-powered applications, driving innovation and value from your enterprise data while upholding the highest standards of data privacy and security. Talk by Zac Davies Slides: https://github.com/zacdav-db/presentations

Updates from Posit, with Hadley Wickham, Charlotte Wickham, George Stagg, and James Blair

6:45 Hadley introduces the conference. 10:37 Hadley on Posit, PBC. Who are we, and what we do. 20:41 Charlotte Wickham on Quarto, an open-source scientific and technical publishing system. 31:05 George Stagg on webR, R for WebAssembly. Execute R code in your web browser. 43:34 James Blair with the latest on Posit’s partnerships with Databricks and Snowflake. Please join us for our first Posit Conf 2024 keynote, where we’ll tell you about our mission, our products, and some of the exciting things we’ve been working on over the last year. Hadley Wickham, Chief Scientist, will talk briefly about Posit’s mission and products, before introducing the three speakers who will update you on some of the coolest projects we’ve worked on over the last year. James Blair, Senior Product Manager, will give you the latest on our partnerships with Databricks and Snowflake, and how we’re building seamless integrations that let you focus on data science instead of dealing with technical details. Charlotte Wickham, Developer Educator, will show you what’s new in Quarto, focusing on new ways to build beautiful PDFs with Typst. Finally, George Stagg, Senior Software Engineer, will tell you about the latest innovations in teaching using webR, a tool that lets you compile your R code into standalone HTML files. Talk by Hadley Wickham, James Blair, Charlotte Wickham, and George Stagg

Charlotte Wickham, George Stagg, Hadley Wickham

Quarto RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Mark Wang - Using GitHub Copilot in R Shiny Development

Generative AI tools, like the GitHub Copilot, are revolutionizing software development, by automating routine tasks and boosting programmers’ productivity. R Shiny development is uniquely positioned to benefit from GitHub Copilot. Copilot can accelerate the process by automating front-end tasks, especially those involving HTML and JavaScript, as well as testing and documentation, thus letting R Shiny developers focus more on the value-added aspects of data science and statistics. The basic workflow of using GitHub copilot in R Shiny development has three parts: prompt, context, and iteration. Three types of prompts: command, question, and lead-in, all have strong use cases in R development. Specific prompts create more helpful results as compared to vague ones, and Copilot can help add more specificity to your prompts when you are not familiar with the domain topic. The most important source of context for Copilot is typically your codebase, which should be well-organized and open in your IDE. Additionally, knowledge on the internet, as well as simple and concrete examples, can also be valuable contexts. However, sensitive personal information in your context can stop Copilot from generating appropriate responses. Iteration is modifying and enriching your prompt and context to improve the quality of Copilot’s responses. Function and module documentation is a valuable step of iteration. As R Shiny is often used with fast-hanging data, Copilot serves as a powerful tool in the iteration from static to dynamic app testing in the shinytest2 framework. As we iterate over our prompt and context to generate better responses from Copilot, it is worthwhile to build an internal knowledge base in the form of prompt dictionaries. A Copilot knowledge base can include information specific to your organization that can be reused across projects, such as security and compliance conventions, code and documentation standards, and employment process and environment. Talk by Mark Wang Slides: https://1drv.ms/b/c/fd70c8a06be32527/ER8WuxYEw_NKksTI6t5wCDgBrO9slTr0HaumYp1eg2xYYA?e=KwQSfb GitHub Repo: https://github.com/ZIBOWANGKANGYU/posit_2024_prez/tree/master/presentation

Computing and recommending company-wide employee training pair decisions at scale… posit conf 2024

Regis A. James developed MAGNETRON AI, an innovative, patent-pending tool that automates at-scale generation of high-quality mentor/mentee matches at Regeneron to enable rapid, yet accurate, company-wide pairings between employees seeking skill training/growth in any domain and others specifically capable of facilitating it. Built using R, Python, LLMs, shiny, MySQL, Neo4j, JavaScript, CSS, HTML, and bash, it transforms months of manual collaborative work into days. The reticulate, bs4dash, DT, plumber API, dbplyr, and neo4r packages were particularly helpful in enabling its full-stack data science. The expert recommendation engine of the AI tool has been successfully used for training a 400-member data science community of practice, and also for larger career development mentoring cohorts for thousands of employees across the company, demonstrating its practical value and potential for wider application. Talk by Regis A. James Slides: https://drive.google.com/file/d/1jq-WjuFz1Lp3m6v0SYjNoW1WcCBKPMs3/view?usp=sharing Referenced talks: https://www.youtube.com/@datadrivendecisionmaking Full Title: Computing and recommending company-wide employee training pair decisions at scale via an AI matching and administrative workflow platform developed completely in-house

Shiny 1.2 Release: Wrap your data frame with Narwhals! 🎉 | Barret Schloerke | Posit

In this video, Barret Schloerke from the Shiny team walks us through the exciting features of Shiny 1.2. One of the major updates includes the integration of Narwhals, allowing effortless computation on data frames without needing detailed knowledge of the data structure. Barret also demonstrates a simple Shiny app displaying S&P 500 data using PyArrow, Polars, and Narwhals. Learn how these updates simplify your data workflows, with minimal code needed to run complex data frame operations. Useful Links: • Shiny Component Gallery: [https://shiny.posit.co/py/components/] • Learn more about Narwhals: [https://narwhals-dev.github.io/narwhals/] Timestamps: 0:00 - Introduction and Shiny 1.2 Release Overview 0:12 - Narwhals Integration and Data Computation 0:31 - PyArrow Application Code Breakdown 1:20 - Using S&P 500 Data with Pandas, Polars, PyArrow, and Narwhals #Shiny #DataScience #narwhals #ShinyApp #Polars #Python

Barret Schloerke, Shiny Team

Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

The importance of relevant analytics | Nechama Katan @ Pfizer | Data Science Hangout

We were recently joined by Nechama Katan, Director of Innovative Data Analytics at Pfizer, for a conversation about relevant analytics, the importance of understanding business context, and strategies for bridging silos within organizations. “Technology on its own doesn’t do you any good.” Nechama highlighted the common pitfall of organizations prioritizing technology over business needs. She stressed that technology is only valuable when it directly addresses a business problem. Often, companies invest in complex data infrastructure and tools without a clear understanding of how they will be used to answer critical business questions. This can lead to frustration and a lack of adoption by business users. Nechama encouraged data professionals to prioritize understanding the business context behind the data. By asking questions, collaborating with colleagues in other departments, and actively seeking to understand the challenges and goals of the business, data scientists can ensure their work delivers tangible value. How would you recommend acquiring the business background needed to succeed? In our Hangout chat, Nechama stressed the importance of building relationships and breaking down silos within organizations. She encouraged data professionals to: ● Be curious: Ask questions relentlessly, seeking to understand the perspectives and challenges of colleagues in other departments. ● Offer help: Identify areas where your expertise can be valuable to others and proactively offer assistance. ● Be kind: Approach interactions with empathy and a genuine desire to build connections. In sharing her career advice, Nechama advocated for “managing down,” prioritizing collaboration and support for peers. Building strong relationships and fostering a culture of teamwork is essential for solving problems and achieving impactful outcomes Resources from the Chat: Mosaic Crochet Shiny App Cascades: How to Create a Movement that Drives Transformational Change by Greg Satell Launching DyslexicU, the World’s First University of Dyslexic Thinking Move Fast and Break Shit: Burning Man’s Lessons for Success in Work and Life by Heather White One Data Scientist’s Assimilation of the Product Lexicon Spurious Correlations There’s Only One R in Pfizer ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!) Thanks for hanging out with us!

Jeroen Janssens - How I hacked UMAP and won at a plotting contest | PyData Amsterdam 2024

www.pydata.org In this talk, I’ll share my journey of animating UMAP, a cutting-edge dimensionality reduction algorithm, by visualizing not just its final output but each intermediate step as well. I’ll explain why and how I modified UMAP’s source code, while also demonstrating the use of Polars for data wrangling, Plotnine for visualization, and ffmpeg for animation. The result ultimately earned me a runner-up position in the 2024 Plotnine plotting contest. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 01:06 Plotnine contest 02:18 Hoi! I’m Jeroen 03:13 Visualizing the behavior of an algorithm 07:58 Keeping track of intermediate predictions 09:23 Tool: UMAP 12:31 Tool: Polars 13:15 Python Polars: The Definitive Guide 13:40 MNIST dataset 14:46 Tool: Plotnine 17:04 Tool: FFmpeg 18:58 Final result 19:38 To conclude 20:40 Q & A

Jeroen Janssens

How Can Data Teams Get Out of Their Own Way (Alex Gold) - KNN Ep. 196

Today, I had the pleasure of interviewing Alex Gold. Alex is the Director of Solutions Engineering + Support at posit and author of DevOps for Data Science. In this episode, we talk about Alex’s experience working in data for presidential campaigns, his experience in solutions engineering, and the role that DevOps plays in the data domain. Book: https://www.routledge.com/DevOps-for-Data-Science/Gold/p/book/9781003213345 (You can use Code AFLY04 for a 20% discount) Alex’s Links: LinkedIn - https://www.linkedin.com/in/alexkgold/ Twitter - https://x.com/alexkgold Podcast Sponsors, Affiliates, and Partners: Pathrise - http://pathrise.com/KenJee | Career mentorship for job applicants (Free till you land a job) Taro - http://jointaro.com/r/kenj308 (20% discount) | Career mentorship if you already have a job 365 Data Science (57% discount) - https://365datascience.pxf.io/P0jbBY | Learn data science today Interview Query (10% discount) - https://www.interviewquery.com/?ref=kenjee | Interview prep questions Listen to Ken’s Nearest Neighbors on all the main podcast platforms! On Apple Podcasts: https://podcasts.apple.com/us/podcast/kens-nearest-neighbors/id1538368692 (Please rate if you enjoy it!) On Spotify: https://open.spotify.com/show/7fJsuxiZl4TS1hqPUmDFbl On Google: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS8xNDMwMDQxLnJzcw?sa=X&ved=0CAMQ4aUDahcKEwjQ2bGBhfbsAhUAAAAAHQAAAAAQAQ MORE DATA SCIENCE CONTENT HERE: My Twitter - https://twitter.com/KenJee_DS LinkedIn - https://www.linkedin.com/in/kenjee/ Kaggle - https://www.kaggle.com/kenjee Medium Articles - https://medium.com/@kenneth.b.jee Github - https://github.com/PlayingNumbers My Sports Blog - https://www.playingnumbers.com ️ 66DaysOfData Discord Server - https://discord.com/invite/4p37sy5muZ

Build Shiny apps with AI ✨

View the full video here: https://www.youtube.com/watch?v=fJNKdwdVQ8Q Try it out here, free: https://gallery.shinyapps.io/assistant/ Get started with Shiny for R and Python: https://shiny.posit.co #pythoncontent #positshorts

Shiny shinyapps RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Shiny Assistant: Prototype and build Shiny applications with the help of AI | Winston Chang | Posit

Have you ever had an idea for a great web application with Shiny but felt something holding you back from getting started? Maybe it’s that you don’t know where to start, or that you don’t know which packages use to build the app, or maybe it’s just that you can’t muster the energy to get started. Sometimes you just need a little help to get unstuck. We’re excited to announce a new addition to the Shiny ecosystem that can help: Shiny Assistant. Try it out here, free: https://gallery.shinyapps.io/assistant/ Get started with Shiny for R and Python: https://shiny.posit.co

Winston Chang

Shiny shinyapps RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Ralph Asher @ Data Driven Supply Chain | Data Science Hangout

In this Hangout, Ralph talks about his experiences in the supply chain industry - a hidden gem of a data job field - and his educational background in Operations Research. He talks about the types of problems supply chain analysts work to solve, how 2020 reshaped the landscape of supply chain, and how he decided to start his own business. He also gives some great advice about marketing yourself, whether you’re starting your own consulting company or looking for a new role: be specific about what you want and the problems you solve. If you’ve been curious about supply chain jobs, you’ll want to hear Ralph’s perspective! Speaker bio: Ralph Asher is the founder of Data Driven Supply Chain LLC, a Minneapolis-based consultancy working at the intersection of supply chain and decision science. Since 2021, Ralph and his team have used decision science and data science to build advanced supply chain models, helping clients make tough supply chain decisions around network design, inventory optimization, and more. Timestamps: 03:00 About Ralph Asher 05:35 What type of problems do supply chain analysts work on? 07:55 What type of data jobs are there in the Supply Chain Industry? 10:15 How did COVID-19 impact supply chain jobs? 13:10 How do you know you’re ready to start a consulting business? 15:25 With so much data, how do you make sure insights are actionable and not irrelevant? 17:30 What are some examples of data management or governance challenges in supply chain? 18:15 What career advice would you give new grads or people looking to switch careers? 21:10 Why being specific about what you want to do gets you job opportunities 23:55 Book recommendation: The Black Swan and preserving optionality, keeping doors open 25:20 Is a supply chain consultant more valuable and effective than a supply chain department? 26:50 How do you learn all the parts of running a business by yourself? The E-Myth Revisited book 29:20 Do you ever have to convince people to move past Excel? Supply chain runs on Excel. 31:05 Career advice - lean into something you’re good at. The Algebra of Wealth book 33:30 How do you manage data and metadata? How do you manage knowledge for your business? 36:25 How do you determine which clients to accept and which to turn away as a consultant? 37:45 How do you do discovery work with a consulting client to make sure they’re a fit? 39:10 Saying no to the wrong jobs so that you can say yes to the right jobs 40:00 Can client discovery be done as a paid project? Could it be its own product? 44:00 As a consultant, how do you make the unseen hours of labor visible? 46:10 What is a good entry point for a company that doesn’t have a dedicated supply chain team? 47:35 Should you go back to school for another degree to get a job that feels out of reach? 49:10 How does your career growth change between IC and management? 50:55 What are some supply chain communities to join? 52:10 Do supply chain teams work within data teams or separately? 55:50 As a consultant on your own, how do you keep your skills sharp and up to date? 57:20 How do you use Shiny in your supply chain consulting jobs? Resources mentioned in the episode: The Black Swan by Nassim Taleb: https://www.randomhousebooks.com/books/176226/ The E-Myth Revisited by Michael Gerber: https://www.michaelegerbercompanies.com/product/the-e-myth-revisited/ The Irresistible Consultant’s Guide to Winning Clients by David Fields: https://www.davidafields.com/books/#section-1 The Algebra of Wealth by Scott Galloway: https://www.penguinrandomhouse.com/books/730881/the-algebra-of-wealth-by-scott-galloway/ Notion: https://www.notion.so/ HubSpot: https://www.hubspot.com/products/crm CSCMP, Council for Supply Chain Management Professionals: https://cscmp.org/ ASCM, the Association for Supply Chain Management: https://www.ascm.org/ ISM, Institute for Supply Management: https://www.ismworld.org/ INFORMS, Institute for Operations Research and Management Sciences: https://www.informs.org/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software The Hangout is a gathering place for the whole data science community to chat about data science leadership and questions you’re all facing that happens every Thursday at 12 ET. To join future data science hangouts, add to your calendar here: https://pos.it/dsh We’d love to have you join us in the conversation live! Thanks for hanging out with us!

What AI skill should we learn? 🌱

#positshorts

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Next Generation Shiny Apps with {bslib}

In this workshop, led by Garrick Aden-Buie, a software engineer for Shiny at Posit, you will learn how to build Shiny apps using modern user interfaces and layouts with bslib, the next generation of Shiny UI. Garrick will guide you through creating stylish and convenient dashboard layouts and components, demonstrating how bslib can effectively replace shinydashboard. Additionally, you will explore innovative techniques for deploying Shiny apps using shinylive, enabling the creation of static sites that run entirely in the user’s browser without the need for a Shiny server. Main Sections 00:00 Introduction 11:00 Shiny 14:57 How bslib started 18:29 Theming from start to advanced 22:19 College Scorecard Data 39:24 bslib layouts 01:20:25 Advanced layouts 01:49:18 Filling Layouts 02:42:09 Details on demand 02:50:01 New inputs 02:53:15 Wrap up More resources R Validation Hub Site: https://www.pharmar.org/ Main Site: https://www.r-consortium.org/ News: https://www.r-consortium.org/news Blog: https://www.r-consortium.org/news/blog Join: https://www.r-consortium.org/about/join Twitter: https://twitter.com/rconsortium?lang=en LinkedIn: https://www.linkedin.com/company/r-consortium Mastodon: https://fosstodon.org/@RConsortium

Garrick Aden-Buie

Deploy an LLM-powered Shiny for Python app to Connect Cloud in minutes!

Connect Cloud lets you quickly deploy data applications and documents from public GitHub repositories for Python and R projects. This short demo showcases secret variable management on Connect Cloud to help deploy an LLM-powered Shiny for Python application. → Signup for a free Connect Cloud account → https://connect.posit.cloud/ → View the how-to guide → https://docs.posit.co/connect-cloud/how-to/python/llm-shiny-python.html

Shiny for Python Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

What is DevOps? And advice for those just starting! | Alex Gold @ Posit | Data Science Hangout

In this episode, we talked with Alex Gold about what it’s like running the solutions engineering and support team at Posit. He shared his journey into DevOps, how he became a manager, and what it was like writing a technical book in Quarto. Alex shares some amazing advice around what he as a hiring manager looks for in data job applicants, and he talks about the different struggles solo data practitioners face in organizations. He even has a little advice about how to do a handstand, and answers the questions, “Can R be put into production,” and “Can you mix R and Python in the same pipeline?” We know you’ll get a lot out of this conversation! Timestamps: 03:36 About Alex Gold 08:20 What is DevOps? What does a solutions engineer do? 13:25 How do you ask the right questions as a solutions engineer? 19:35 Do you need a PhD to be a data scientist? 24:55 Can R be put into production? Is ‘R can’t be put into production’ a myth? 28:10 Advice for people starting in devops? 31:50 What did you learn from the process of writing a book? 34:20 What tool did you use to write your book? 36:00 Any tips for doing a handstand? 37:15 Is it harder to make devops changes in a smaller or larger company? 40:55 Going from an individual contributor (IC) to managing a team 47:30 Not being able to get promoted without becoming a manager 49:45 What do you look for when hiring for a data role? 53:20 Did you use a template for publishing with CRC press? 55:25 Can you mix R and Python in the same data pipeline? Resources mentioned in this episode: Alex’s book, DevOps for Data Science: https://do4ds.com/ Alex’s blog post about dropping out of his PhD program: https://alexkgold.space/posts/2022-09-16-phd-dropout/index.html Alex’s blog post about the first year as a manager: https://alexkgold.space/mfy.html ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software The Hangout is a gathering place for the whole data science community to chat about data science leadership and questions you’re all facing that happens every Thursday at 12 ET. To join future data science hangouts, add to your calendar here: https://pos.it/dsh We’d love to have you join us in the conversation live! Thanks for hanging out with us!

Empowering individuals through AI tools, technologies, and custom apps | Chris Engelhardt @ Gen Re

“Empathy, I think, is a hallmark of a well functioning team.” Chris Engelhardt might lead data teams and AI initiatives, but he’s also got a PhD in Social Psychology and a keen interest in helping people work more happily. This episode of the Hangout went to amazing and unexpected places as we talked about AI use cases, getting buy-in from leadership, and building stakeholder trust alongside building psychological safety in your teams, code review anxiety, and team wellbeing. If you’re curious about enterprise level AI use cases, or how Posit integrates with Databricks, there’s lots to learn from Chris! We hope you’ll join us live soon! Timestamps 02:35 About Chris Engelhardt Sr. Data and AI Operations Manager at Gen Re 05:55 How do you get people engaged in a vision for AI? 08:10 What are some use cases for AI? 11:40 How do you manage international data science teams? 13:15 What one AI skill data analysts should learn? 14:50 What people skills should data analysts and data scientists learn? 16:25 How do you get buy-in from leadership and data scientists for AI initiatives? 18:25 What is AI? Where do I start? What are pitfalls of AI to avoid? 22:40 Integrating Posit with Databricks, Unity Catalog, and pins 25:55 How do you handle it when two different teams manage Posit versus Databricks? 27:30 What if AI tools provide inaccurate results? How do you regain trust with stakeholders? 29:50 How do you protect your own and your data team’s wellbeing at work? 34:20 How has AI changed hiring and job descriptions? Has AI changed data roles? 36:30 Code review anxiety and psychological safety around code critiques and pull requests 40:00 Helping your team feel safe and willing to talk about things and collaborate 41:40 How do you encourage psychological safety in a cross-cultural international team? 47:15 How do you use Posit pins with Databricks? 49:50 Career advice for working in data - you can’t control everything, but you control your effort 51:15 Don’t underestimate the value of connecting with others 53:33 Does using pins with Databricks Unity Catalog present a challenge with data governance? 56:45 How do you encourage learning and upskilling within your team? Resources mentioned in this episode: Code Review Anxiety Toolkit: https://developer-success-lab.gitbook.io/code-review-anxiety-workbook-1/part-three-mini-code-review-anxiety-toolkit/introduction Matthew Montero from Gen Re’s episode: https://posit.co/data-science-hangout/56-matthew-montero/ Schedule time to chat about Databricks and Posit here: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks Posit Team Workflow Demos: https://pos.it/team-demo The Psychological Safety Handbook Daren mentioned: https://handbook.gitlab.com/handbook/leadership/emotional-intelligence/psychological-safety/

Data-level permissions using Posit Connect (with Databricks, Snowflake, OAuth)

Should one viewer of your app be able to see more (or different) data than another? Maybe colleagues in California should only see data relevant to them? Or managers should only have access to their own employee data? The Connect team joined us for a demo on inheriting data-level permissions using Posit Connect and Databricks Unity Catalog. While this workflow uses Databricks to illustrate federated data access controls, this same methodology can also be applied to Snowflake or any external data source that supports OAuth. During this workflow demo, you will learn: How to define row-level access controls in Databricks Unity Catalog How to create a Databricks OAuth integration in Posit Connect How to write interactive applications that utilize the viewer’s Databricks credentials when reading data from Databricks Unity Catalog, providing the viewer with a personalized experience depending on their level of data access How to deploy this application to Posit Connect and share it within your organization If you’d like to talk further with our team 1:1 about doing this, you can find a time to chat here: https://posit.co/schedule-a-call/?booking_calendar__c=WorkflowDemo Ps. To enable OAuth integrations, your team will need to upgrade to Posit Connect 2024.08.0. This feature is available in Enhanced and Advanced product tiers. Helpful resources for this workflow: Full examples to get you started: https://github.com/posit-dev/posit-sdk-py/tree/main/examples/connect Admins will likely be most interested in starting here: https://docs.posit.co/connect/admin/integrations/oauth-integrations/databricks/ End users will be most interested here: https://docs.posit.co/connect/user/oauth-integrations/ Q&A Link: https://youtube.com/live/TZQY6rm6hU4?feature=share Additional resources shared: Release Blurb: https://docs.posit.co/connect/news/#posit-connect-2024.08.0 Security: https://docs.posit.co/connect/admin/integrations/oauth-integrations/security.html Publishing Quarto: https://docs.posit.co/connect/how-to/basic/publish-databricks-quarto-notebook/ sparklyr: https://github.com/sparklyr/sparklyr?tab=readme-ov-file#connecting-through-databricks-connect-v2 odbc: https://github.com/r-dbi/odbc?tab=readme-ov-file#odbc-

So You Think You Can ANALYZE? (Data Content Creator Hackathon)

Watch your favorite data science content creators compete for a piece of history. They will either prove their skills in front of the world or fail in the pursuit of eternal glory. This is so you think you can analyze. Special thanks to Posit for sponsoring this competition and make this all possible! Competitors: Team Jack @averysmith Jack Blandin @KeithGalli Team MMA @AlexTheAnalyst @nerdnourishment @Miki_ML Team Null Nick Singh Mark Freeman @SeattleDataGuy Team Shashank @ShashankData Team Posit-ively Skewed Greg Coquillo @datascienceharp Richad Nieves-Becker Ian Greengross Interviewer: Elijah Butler - https://www.tiktok.com/@imelijahbutler Posit Team Michael Chow Joe Cheng Carolos Scheidegger Julia Silge Sponsors, Affiliates, and Partners: Pathrise - http://pathrise.com/KenJee | Career mentorship for job applicants (Free till you land a job) Taro - http://jointaro.com/r/kenj308 (20% discount) | Career mentorship if you already have a job 365 Data Science (57% discount) - https://365datascience.pxf.io/P0jbBY | Learn data science today Interview Query (10% discount) - https://www.interviewquery.com/?ref=kenjee | Interview prep questions MORE DATA SCIENCE CONTENT HERE: My Twitter - https://twitter.com/KenJee_DS LinkedIn - https://www.linkedin.com/in/kenjee/ Kaggle - https://www.kaggle.com/kenjee Medium Articles - https://medium.com/@kenneth.b.jee Github - https://github.com/PlayingNumbers My Sports Blog -https://www.playingnumbers.com Check These Videos Out Next! My Leaderboard Project: https://www.youtube.com/watch?v=myhoWUrSP7o&ab_channel=KenJee 66 Days of Data: https://www.youtube.com/watch?v=qV_AlRwhI3I&ab_channel=KenJee How I Would Learn Data Science in 2021: https://www.youtube.com/watch?v=41Clrh6nv1s&ab_channel=KenJee My Playlists Data Science Beginners: https://www.youtube.com/playlist?list=PL2zq7klxX5ATMsmyRazei7ZXkP1GHt-vs Project From Scratch: https://www.youtube.com/watch?v=MpF9HENQjDo&list=PL2zq7klxX5ASFejJj80ob9ZAnBHdz5O1t&ab_channel=KenJee Kaggle Projects: https://www.youtube.com/playlist?list=PL2zq7klxX5AQXzNSLtc_LEKFPh2mAvHIO

Joe Cheng, Julia Silge, Michael Chow

What did journalism teach you about data storytelling? | Sharon Machlis | Data Science Hangout

In this episode, Sharon Machlis shares her journey from journalist to journalist-who-codes. You’ll hear her talk about the serendipitous way she found herself covering business and technical news, the lessons she learned about storytelling with data over the years, and how she knew it was time to retire. You’ll also learn how to find the data community on social media in a post-twitter world, including a primer on Mastodon and how it works without an algorithm. Timestamps: 02:45 About Sharon Machlis 04:55 How did you decide to start using R? 07:45 What did journalism teach you about data storytelling? 10:15 What’s your favorite headline you ever wrote? 10:45 How do you write high quality articles quickly? 12:30 How do you find people to follow on Mastodon? 15:35 How do you decide what new data topics to learn about or focus on? 20:55 What do you think about specialization in a world where everyone now codes? 23:25 Is domain expertise in data jobs undervalued? 27:00 What resources do you have for learning to write well? 32:00 Have you ever had a career setback that was a blessing in disguise? 34:05 How do you make data presentations more interesting? 37:00 Where is the data community now that Twitter is dead? Bluesky? Threads? 40:45 Has the evolution of graphics like the New York Times uses increased interest in data? 42:15 How did you decide you were ready to retire? 45:30 What data journalists should we be following? 47:55 What factors contribute to someone deciding to retire? 50:35 What projects can you focus on in retirement that you weren’t able to do before? 55:35 What resources do you recommend for learning Python? 57:25 Career advice from a data journalist Links mentioned in this episode: Sharon’s retirement journey: https://nextchapter.machlis.com Links Sharon shared in the chat: https://apps.machlis.com/shiny/positconf/ One way to see public posts on Mastodon with the #rstats hashtag without needing an account: https://fosstodon.org/tags/rstats Guide to R: https://www.computerworld.com/article/1533880/beginners-guide-to-r-introduction.html How to find better content on Mastodon by Sharon: https://www.infoworld.com/article/2338712/6-ways-to-find-better-content-on-mastodon.html Resources Sharon shared after the Hangout Suggested prompts to use LLMs to help you learn a skill, by Lilach Mollick and Ethan Mollick https://www.moreusefulthings.com/instructor-prompts Journalism-related resources for learning Python: First Python Notebook: A step-by-step guide to analyzing data with Python and the Jupyter notebook, by Ben Welsh, currently news applications editor at Retures https://palewi.re/docs/first-python-notebook/ Python’s Not (Just) for Unicorns, by J Soma, journalism prof at Columbia University https://littlecolumns.com/learn/python/ Journalism-related resources to learn R - both a bit old but still work: R for Journalists by Andrew Ba Tran, investigative data reporter at the Washington Post https://learn.r-journalism.com/en/ My book Practical R for Mass Communication and Journalism Several chapters free: https://www.machlis.com/R4Journalists/ My Shiny app with a searchable list of Mastodon accounts who post about R with the #RStats hashtag: https://apps.machlis.com/shiny/rstats/ Searchable table of all my Mastodon posts (it’s a public Shiny app, you don’t need a Mastodon account): https://www.machlis.com/mastodon.php Mosaic crochet Shiny app, the most popular app I ever published https://apps.machlis.com/shiny/crochetapp/ Additional Shiny apps: https://apps.machlis.com/ Thoughts on using generative AI to help with your writing: How to... use ChatGPT to boost your writing: https://www.oneusefulthing.org/p/how-to-use-chatgpt-to-boost-your or I, Cyborg: Using Co-Intellgience: https://www.oneusefulthing.org/p/i-cyborg-using-co-intelligence both by Ethan Mollick

Using controllers to write robust Shiny for Python app tests | Karan Gathani | Posit

With the Shiny for Python v1.0 release, controllers were exposed to provide a structured and consistent way for users to interact with and test UI components in Shiny applications. They offer an abstraction layer that encapsulates the complexity of interacting with specific UI elements, making it easier for developers to write robust tests and automate interactions with their Shiny apps. Reference documentation - https://shiny.posit.co/py/api/testing/ https://shiny.posit.co/py/docs/playwright-testing.html

Karan Gathani

Shiny for Python Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Creating tests for Shiny for Python apps | Karan Gathani | Posit

With the Shiny for Python v1.0 release, Shiny provides a simple way to create a test file for your Shiny app. The shiny add test command is a helpful CLI tool for Shiny app developers. It simplifies the process of creating test files for their applications. When you run this command, it prompts you to input two pieces of information: the path to your Shiny app file and the desired location for the new test file. Once you provide these details, the command automatically generates a test file at the specified location. This new file includes a pre-made test template, giving you a solid starting point for writing your app’s tests. Reference documentation - https://shiny.posit.co/py/docs/unit-testing.html https://shiny.posit.co/py/docs/playwright-testing.html

Karan Gathani

cli Shiny for Python Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

GenAI and Pharma: Learning as we go | Episode 1: Copilot

In this episode, Phil and Cole discuss using Copilot in clinical trial submissions. Due to an industry-wide shift in pharma, statistical programmers are transitioning to open-source tooling in drug development. This skills gap that occurs when moving to open source is one area that can be aided with generative AI tools. In this episode, Cole and Phil show some of the features and functionality of a popular tool in the data science space, Copilot. More about open source in Pharma: https://posit.co/solutions/pharma/

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Positron: An IDE Specialized For Data Science

Dr. Julia Silge, Engineering Manager at Posit, joins @JonKrohnLearns to introduce Positron, a fresh open-source IDE that’s perfect for exploratory data analysis and visualization. She also lays out her top picks for LLMs that boost coding efficiency and discusses when traditional NLP methods might be the smarter choice over LLMs. Plus, Julia highlights some must-know open-source libraries that make managing MLOps easier than ever. Tune in for insights that every data scientist, ML engineer, and developer will find useful. Watch the full interview “817: The Positron IDE, Tidy NLP and MLOps — with Dr. Julia Silge” here: https://www.superdatascience.com/817

Julia Silge

Parallelize R code using user-defined functions in sparklyr

If you’re an Apache Spark user, you benefit from its speed and scalability for big data processing. However, you might still want to leverage R’s extensive ecosystem of packages and intuitive syntax. One effective way to do this is by writing user-defined functions (UDFs) with sparklyr. UDFs enable you to execute R functions within Spark, harnessing Spark’s processing power and combining the strengths of both tools. In this tutorial, you’ll learn how to: Open Posit Workbench as a Databricks user Start a Databricks cluster within Posit Workbench Connect to a cluster within Posit Workbench View Databricks data in RStudio Create a prediction function Create a user-defined function with sparklyr Read our most recent blog that covers parallelizing R code using user-defined functions (UDFs) in sparklyr: https://posit.co/blog/databricks-udfs/ Learn more about our Databricks partnership: https://posit.co/solutions/databricks/ Watch other tutorials on using Databricks and RStudio: https://youtube.com/playlist?list=PL9HYL-VRX0oR-3AgWbXtlfdr29626EjRJ&feature=shared

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

6 core ideas for public speaking | Blythe Coons & Acacia Duncan @ Articulation | DS Hangout

A must-see episode of the Hangout, especially if you have ever needed to give a presentation (and who hasn’t??). Acacia and Blythe from Articulation are experts at helping presenters craft their messages, and they’ve worked with many, many Posit Conference speakers over the years. If you’ve ever heard conf speakers gush about getting to work with Articulation to prepare their talks, you’ll understand why by the end of this discussion. There’s a reason more than 200 hangout members attended this one! Timestamps: 07:15 What are threshold concepts? What does Articulation do? 09:25 Threshold concept 1 - Speaking is habitual (not natural) 10:15 Threshold concept 2 - Speaking is embodied 11:30 Threshold concept 3 - Speaking is social 12:50 Threshold concept 4 - Speaking is messy 13:45 Threshold concept 5 - Speaking contains multiple genres 15:25 Threshold concept 6 - Speaking requires feedback 17:15 How do you construct a story when presenting data? What resources are there? 19:40 How do you structure a story? 21:05 Why is the Hero’s Journey the wrong structure for storytelling with data? (Your audience is Luke Skywalker, not you!) 24:25 Where do data people go wrong? How can data communicators improve? 25:40 How much information should you include in a presentation? How much is too much? 29:00 Should you create a document to hand out alongside your slides? 32:10 How do you change a company culture of having wordy PowerPoint slides? 34:35 What happens when PowerPoint slides are too wordy? 36:10 How do you reduce stress responses when giving a presentation? 43:15 How can you slow down if you’re a fast talker or nervously speed up while presenting? 46:20 What are some tools for planning presentations? 48:20 How does presenting on Zoom differ from presenting in person? 52:50 Tips for switching back and forth between in person and Zoom 55:00 How do you coach someone on a technical presentation if you’re not an expert in it? 57:45 How can you be yourself when giving a presentation? Here are a few of the key topics covered in the discussion: The threshold Concepts of speaking. These are the core ideas you must understand in order to really succeed in public speaking. Data Storytelling. Spoiler! There is more than one type of story in your data presentation, and you’re not the hero of any of them. Structuring a story. The basics of structure, but also how to answer the right question (spoiler again, it’s not the question you’re interested in). Common struggles in data communication. Do you bury the lede? Are you a non-linear thinker? Do you get super anxious and lost? Do you talk way too fast??? Coping with Zoom. Why presenting on zoom can be a challenge and what we can we do about it. Links from this episode: Blythe on LinkedIn: https://www.linkedin.com/in/blythe-coons-05196485/ Acacia on LinkedIn: https://www.linkedin.com/in/acacia-duncan-919899b8/ Articulation’s website: https://www.articulationinc.com/ Threshold Concepts: https://www.articulationinc.com/threshold-concepts-reflections-effective-speaker/ Storytelling with Data Podcast: https://www.storytellingwithdata.com/podcast Speeko, an app for analyzing your speaking pace and tone: https://www.speeko.co/ Milanote, a tool for organizing creative projects: https://milanote.com/ Did you miss joining us in person? If so, you missed out on some awesome resources from the chat! We’ve got you covered: Isabella shared some hex wall Zoom backgrounds in the virtual swag bag for positconf2024: https://github.com/posit-marketing/posit-conf-virtual-swag-bag Several people suggested the book Storytelling with Data by Cole Nussbaumer Knaflic: https://www.storytellingwithdata.com/books Watch Laura Gast’s talk from PositConf2023 about why good design is worth the time when it comes to data communication: https://www.youtube.com/watch?v=Kp-LQe3WgDU Tom Belanger shared the Stats + Stories Podcast: https://statsandstories.net/ Jared Cornell suggested the book The Fearless Mind: https://abinoda.com/book/the-fearless-mind Michael Kaminsky shared the book Resonate by Nancy Duarte: https://www.duarte.com/resources/books/resonate/ Laura Gast shared the book “If I understood you would I have this look on my face” by Alan Alda about his days interviewing for PBS Nova: https://www.penguinrandomhouse.com/books/533869/if-i-understood-you-would-i-have-this-look-on-my-face-by-alan-alda/ The Quarto Keeper! Like a Trapper Keeper, but in Quarto, and for making notes at big meetings or conferences: https://github.com/ats/quarto-keeper (by Alan Schussman) Lydia Gibson shared that the last chapter of R4DS has some resources listed in the summary for effective communication in different types of formats (presentations, public speaking, etc.) https://r4ds.hadley.nz/quarto-formats The Center Cam for making eye contact on Zoom: https://thecentercam.com/

817: The Positron IDE, Tidy NLP and MLOps — with Dr. @JuliaSilge

#PositronIDE #Tidyverse #MLOps Dr. Julia Silge, Engineering Manager at Posit, joins @JonKrohnLearns to introduce the brand-new Positron IDE, perfect for exploratory data analysis and visualization. She also lays out her top picks for LLMs that boost coding efficiency and discusses when traditional NLP methods might be the smarter choice over LLMs. Plus, Julia highlights some must-know open-source libraries that make managing MLOps easier than ever. Tune in for insights that every data scientist, ML engineer, and developer will find useful. This episode is brought to you by Gurobi ( https://www.gurobi.com/personas/optimization-for-data-scientists/), the Decision Intelligence Leader, and by ODSC ( https://odsc.com/california), the Open Data Science Conference. Interested in sponsoring a SuperDataScience Podcast episode? Email natalie@superdatascience.com for sponsorship information. In this episode you will learn: • [00:00:00] Introduction • [00:03:23] Overview of Posit and Positron IDE • [00:08:33] How the needs of a data scientist differ from those of a software developer • [00:17:56] How to contribute to the open-source Positron • [00:34:52] MLOps and Vetiver: Tools for deploying and maintaining ML models • [00:48:34] Natural Language Processing (NLP) and the Tidyverse approach • [01:22:18] The role of AI and LLMs in data science education Additional materials: https://www.superdatascience.com/817

Julia Silge

Reproducible data science with webR and Shinylive | George Stagg | Posit

A fundamental principle of the scientific method is peer review and independent verification of results. Good science depends on transparency and reproducibility. However, in a recent study a substantial 74% of research code failed to run without errors, often caused by diverse computing environments. This talk will discuss the principles of numerical reproducibility in research and show how software can be pinned to specific versions and self-contained as a universal binary package using WebAssembly. This ensures seamless reproducibility on any machine equipped with a modern web browser and, using tools such as Shinylive, could provide a new way for researchers to share results with the community. webR demo website: https://webr.r-wasm.org/v0.3.2/ Shinylive examples: https://shinylive.io/r/ https://shinylive.io/py/ Documentation: https://docs.r-wasm.org/webr/v0.3.2/ https://github.com/posit-dev/shinylive https://github.com/quarto-ext/shinylive

George Stagg

Quarto shinylive RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Jason Foster @ Marathon Asset Management | Data Science Hangout

We were recently joined by Jason Foster, Director at Marathon Asset Management. In this episode, Jason talks about his journey into risk management and the evolving role of code-first analytics that he’s experienced in finance. He covers various aspects of risk management, describes some of his experiences in package development, and shares his approach to cross-functional communications with non-technical colleagues (you’ve got to be able to speak their language). Jason also highlights the impacts that community involvement had on his professional growth. You’ll hear him discuss his experiences with the R in Finance conference and how presenting his work and engaging with the community has led to valuable feedback and personal development. For those navigating today’s competitive job market, he offers advice on how to stand out by punctuating your strong quantitative skills with effective communication and really showing your passion for the field and its unique challenges. Timestamps: 03:50 - About Jason and his role 11:05 - What is risk management? 14:30 - How was your transition from R to Python? 19:00 - How do you help your team learn to use code-based tools? 22:10 - Was being a trainer part of your role or did your role grow in that direction organically? 25:30 - How do you share code-based solutions with non-coding colleagues? 32:15 - How do you convince stakeholders to listen to you? 35:20 - How do you get the right data for things that are hard to quantify? 42:30 - How do you work across teams that have poor working relationships? 45:50 - What is the next big thing is coding that excites you? 50:00 - How has your involvement in the community impacted your career? 57:00 - How can someone with less experience stand out in this crazy job market? Links from this episode: Jason Foster on LinkedIn: https://www.linkedin.com/in/jasonjfoster/ R in Finance Conference: https://www.rinfinance.com/ Jason’s {roll} package: https://github.com/jasonjfoster/roll (it’s also on CRAN!)

Quarto: Elevating R Markdown for Advanced Publishing | Christophe Dervieux

In the dynamic landscape of data analysis and scientific publishing, R Markdown has been pivotal for the R community, allowing users to seamlessly blend code, narrative and results in a cohesive narrative. Now, Quarto emerges as a powerful tool that builds on years of experience but also goes beyond R Markdown, providing more flexibility and power in scientific communication. This talk aims to present Quarto as the new alternative for scientific publishing. We will delve into how Quarto enhances the user experience for R enthusiasts, maintaining the syntax familiarity of R Markdown while introducing innovative and improved functionalities across multiple formats, similar to R Markdown ones. Why switch to Quarto from R Markdown? In which cases? How does Quarto integrate with existing workflows? Hopefully everyone will feel inspired to try out Quarto! https://quarto.org/docs/get-started/ Timestamps: 0:00 Introduction 0:41 Quarto is an open-source, scientific and technical publishing system 1:22 Computational documents and scientific markdown made easy for single source publishing 3:08 How to use Quarto 4:24 Quarto works with VS Code, Positron, Jupyter, & RStudio 5:22 Quarto’s multi-language workflow 7:21 Quarto syntax 8:40 Quarto formats (html, pdf, docx, typst, beamer, pptx, revealjs, etc.) 12:19 HTML Theming 14:10 Typst CSS for nice table output in PDF 16:24 Publishing (Quarto Pub, GitHub Pages, Posit Connect, Posit Cloud, Netlify, Confluence, Hugging Face, etc.) 17:36 Shortcodes 19:10 Quarto Extensions 19:49 Quarto Projects 22:53 Project configuration examples for a website and a book 23:42 Resources to get started!

Christophe Dervieux

Positron Quarto revealjs rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Simon Couch: Fair machine learning

Simon Couch Fair machine learning Cascadia R Conf 2024 Regular talk, 10:25-10:40 In recent years, high-profile analyses have called attention to many contexts where the use of machine learning deepened inequities in our communities. A machine learning model resulted in wealthy homeowners being taxed at a significantly lower rate than poorer homeowners; a model used in criminal sentencing disproportionately predicted black defendants would commit a crime in the future compared to white defendants; a recruiting and hiring model penalized feminine-coded words—like the names of historically women’s colleges—when evaluating résumés. In late 2022, a group of Posit employees across teams, roles, and technical backgrounds formed a reading group to engage with literature on machine learning fairness, a research field that aims to define what it means for a statistical model to act unfairly and take measures to address that unfairness. We then designed functionality and resources to help data scientists measure and critique the ways in which the machine learning models they’ve built might disparately impact people affected by that model. This talk will introduce the research field of machine learning fairness and demonstrate a fairness-oriented analysis of a model with tidymodels, a framework for machine learning in R. Pronouns: he/him Chicago, IL Simon Couch is a software engineer at Posit PBC (formerly RStudio) where he works on open source statistical software. With an academic background in statistics and sociology, Simon believes that principled tooling has a profound impact on our ability to think rigorously about data. He authors and maintains a number of R packages and blogs about the process at simonpcouch.com

Simon Couch

How to automatically detect data changes for your Shiny Calendar app (ft: Jira, pins, Posit Connect)

Do you manage constantly changing data and need your Shiny app to automatically update? On August 28th at 11 am ET, Isabella Velásquez demonstrated a streamlined workflow for handling frequently updated datasets in Shiny. You’ll see how to simplify your process for keeping dynamic data current and how to reflect those changes in your app or dashboard. Github repo to follow along or make it your own! https://github.com/posit-marketing/shiny-calendar Timestamps: 1:03 - Introduction of the project (end goal: calendar that integrates with Jira to track and visualize a schedule for managing deadlines of content) 2:26 - Pulling data from an API in Python or R 2:56 - Introduction to pins (and scheduling automatic refreshes of it in Posit Connect) 4:30 - Introduction to Shiny for both Python and R (its power lies in reactivity) 5:10 - Enter pin_reactive_read() function 6:12 - Introduction to Posit Team 6:37 - Opening a new session within Posit Workbench and overview of code needed to create the calendar [Github repo: https://github.com/posit-marketing/shiny-calendar] 12:07 - toastui package used for Calendar (ex: adding colors to labels) 12:47 - Writing clean data to Posit Connect board 13:16 - Rendered Quarto doc for pulling Jira data from the board 14:00 - Deploying Quarto to Posit Connect (using push button deployment) and scheduling to run 16:54 - Using the data just pinned in the Shiny app 21:17 - Overview of Shiny Content Calendar application 23:04 - Creating an issue in Jira board and adjusting schedule in Posit Connect to show new item in Shiny calendar. 24:00 - pin_reactive_read automatically detects change and shows it in the Shiny app During this workflow demo, you will learn: How {pins} stores and retrieves ever-changing data with ease How to use pin_reactive_read() in Shiny to automatically trigger updates when your data changes How Posit Connect can be set up to rerun your {pin} on a schedule, ensuring your app is updated without disruption How to deploy an always-up-to-date app for seamless sharing with stakeholders Other helpful links: pin_reactive_read: https://pins.rstudio.com/reference/pin_reactive_read.html Basic reactivity in Mastering Shiny: https://mastering-shiny.org/basic-reactivity.html#reactive-programming Understanding reactivity on the Shiny site: https://shiny.posit.co/r/articles/build/understanding-reactivity/ Github repo: https://github.com/posit-marketing/shiny-calendar Shiny Calendar: https://pub.demo.posit.team/public/shiny-calendar/ Q&A Recording If you like these workflow demos, you can join us monthly! They happen the last Wednesday of every month at 11 am ET. Add it to your calendar here: https://pos.it/team-demo

Isabella Velásquez

Great Tables: Make beautiful, publication quality tables in Python | Rich Iannone & Michael Chow

Tables are undeniably useful for data work. We have many great DataFrame libraries available in Python, and they give us flexibility in terms of manipulating data at will, but what happens when presenting tables to others? It’s nice to display tables. Tables can efficiently carry information, just like plots do, and at times it is the better way of presenting data. Indeed, it is time to bridge the divide between raw DataFrame output and wondrously structured tables suitable for publication. Now, let us turn our attention to the state of ‘display tables’ in 2024. Let us go over what comprises key components for building effective information displays in tables. It may surprise one how new a well-crafted table can be hewn. We’ll take a look at the combinations of Python packages that fit together to make this important task possible, and marvel together at the tabular results they can provide! Learn more at https://posit-dev.github.io/great-tables/ Timestamps: 0:00 Intro: Meet Rich and Michael 0:41 What we mean by “publication ready tables” 1:29 Overview of what we’ll talk about in this video 1:44 Table Goals: Ways to make a table beautiful 4:41 Tables made from reproducible code! 5:11 The history of table generation to influence our API 6:15 Our modern take on a table display framework 6:35 The problem with Excel 7:38 Introducing Great Tables! 8:00 Key Ingredients of making a Great Table 8:24 Structure: Title, column spanners and nice column labels 8:52 Format: Compact dollar values and percentages 9:23 Styling: Fill color and bold text 10:09 Imports and Polars Selectors 11:08 Coding the structure 12:27 Coding the format 13:07 Coding the styling 15:03 Putting images and plots in your table cells 15:49 Advanced Design 16:07.fmt_nanoplot(): Small plots within table cells 19:08.data_color(): Heat maps in tales 20:51 Powerful and plentiful methods to format cell values 22:48 To sum up: TABLES RULE

Michael Chow, Rich Iannone

Great Tables RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Data platform modernization in insurance | Kshitij Srivastava @ Milliman | Data Science Hangout

We were recently joined by Kshitij Srivastava, Director of Technology at Milliman to chat about data platform modernization at insurance companies. Speaker bio: Kshitij leads technology and data operations for the Life and Annuity Predictive Analytics practice in Chicago. Kshitij’s work includes data infrastructure management, data pipeline development and security operations for Milliman’s industry leading experience studies for VA, FIA, RILA and Life products. Kshitij’s work with Milliman clients also include leading data operations and actuarial modernization initiatives that have been outsourced to Milliman. Kshitij routinely consults with actuarial and data teams within the industry and advises on cloud migration, actuarial modernization, AI, and data governance topics. Kshitij also leads a multidisciplinary team focusing on data engineering, devops, information security, software development and testing. Prior to joining Milliman, Kshitij has worked as a consultant focusing on machine learning and software development for a major analytics services company and as a data scientist for a major technology company. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software The Hangout is a gathering place for the whole data science community to chat about data science leadership and questions you’re all facing that happens every Thursday at 12 ET. To join future data science hangouts, add to your calendar here: https://pos.it/dsh We’d love to have you join us in the conversation live! Thanks for hanging out with us!

Your mission matters: A sustainable future with citizen data science at Dow

How do we keep food fresh? How do we use less water in our everyday lives? How do we make trash…valuable? How do we innovate to make the world a better place? The team at Dow Chemical is pushing the boundaries of material innovation for a more sustainable planet. To reach net zero carbon emissions by 2050, requires creativity and ingenuity from all different functions across the company. With Posit Academy and their citizen data science program, they have grown from a handful of researchers to over 300 people using Posit Workbench and deploying to Posit Connect

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Data driven decision making in pharma | Ning Leng @ Roche-Genentech & Jing Huang @ Veracyte

We were recently joined by both Ning Leng, Global Head, Data Sciences Acceleration at Roche-Genentech & Jing Huang, SVP of Bioinformatics and Data Science at Veracyte to chat about embedding modernized data science solutions in pharma to enable data driven decision making. Links mentioned in the chat: R Consortium R Submissions Pilot 4 documentation portal: https://rconsortium.github.io/submissions-pilot4/ Bay Area Biotech-Pharma Statistics Workshop: https://www.bbsw.org/ San Francisco Bay Area Chapter of American Statistical Association: https://sites.google.com/view/sfasa-org/home?authuser=1/ Dahshu Data Science Symposium: https://dahshu.wildapricot.org/ DS Hangout Survey/Feedback: https://docs.google.com/forms/d/e/1FAIpQLSdqXKPCPOwC7tSLygc43gf2ezkNO4bhM4TAgjUQMeoPe_GOmg/viewform Speaker Bios: Ning Leng: Ning Leng is the ad-interim Global Head of the Data Science Acceleration Enabling Platform, under Roche Product Development Data Sciences. Ning joined Roche-Genentech in 2016 as a statistician and worked on both early and late phase oncology development, with a special interest in utilizing diverse data sources and advanced methodologies to generate insights for personalized healthcare. Since then she has been driving a number of internal and cross-industry projects on modernizing Data Science solutions in pharma. Prior to joining Roche-Genentech, Ning obtained her PhD in Statistics from University of Wisconsin-Madison and worked at the Morgridge Institute for Research. Jing Huang: Jing Huang holds a B.A. in Statistics and Probability from Peking University and a Ph.D. in Statistics and M.S. in Epidemiology from Stanford University. With over 20 years in the biomedical field, her research focuses on statistical methodologies in clinical trial design, genomic analysis, and machine learning. As SVP of Bioinformatics & Data Science at Veracyte Inc., she oversees bioinformatics pipelines, algorithm development, and statistical analyses for product development. Jing has co-authored over 30 peer-reviewed articles, with more than ten thousand citations, and is a co-inventor on over 20 patent filings. She actively promotes data science through volunteer work, including as founding president of DahShu, 2024 president of BBSW, and chapter representative of the American Statistical Association San Francisco Bay Area Chapter (SFASA). In 2023, she was elected as a Fellow of the American Statistical Association to recognize her outstanding contributions to the medical research community in the field of statistics; for numerous statistical innovations in genomic tests; and for exemplary leadership and community service to the profession. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software The Hangout is a gathering place for the whole data science community to chat about data science leadership and questions you’re all facing that happens every Thursday at 12 ET. To join future data science hangouts, add to your calendar here: https://pos.it/dsh We’d love to have you join us in the conversation live! Thanks for hanging out with us!

Using data to help artists maximize their potential | Adam Husein @ Firebird | Data Science Hangout

We were recently joined by Adam Husein, SVP, Data and Analytics at Firebird Music to chat about using data and analytics to help artists maximize their potential by empowering artists and managers to create more fans, deepen relationships, and measure impact to create material growth and engagement. Speaker bio: Adam is at the forefront of integrating data science with the dynamic needs of the music industry. At Firebird, he orchestrates a comprehensive data and technology strategy that propels the company’s mission to redefine and enhance artist careers through innovative, data-driven approaches. His role encompasses overseeing AI, Machine Learning, Data Science, and Cloud Technology, setting new standards for how recorded music and live events are planned, promoted, distributed, and priced. Throughout Adam’s extensive background with industry giants like Warner Bros, Activision, EA Games, and Jack in the Box he has repeatedly spearheaded initiatives leading to significant improvements across operational performance measures, data strategy, and governance. Links mentioned: Goldman Sachs report on music streaming: https://www.goldmansachs.com/intelligence/pages/infographics/music-streaming/ Firebird Music Career Page: https://firebirdmusic.com/join-our-team/ Taylor Swift Quarto Doc: https://www.linkedin.com/posts/posit-software_quarto-but-make-it-taylor-swift-did-activity-7131282678217105410-fxww __ To join future data science hangouts live, add to your calendar here: https://pos.it/dsh (All are welcome! We’d love to see you!) Thanks for hanging out with us! ► Subscribe to Posit’s YouTube Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc

How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python)

This is part 5 of our multi-part series on creating professional dashboards with Shiny for Python. In this video, we dive into advanced styling techniques to enhance the visual appeal and professionalism of your Shiny dashboards. We’ll cover: Adding a logo and custom title Making use of custom HTML elements Using CSS to style Shiny components Customizing Altair charts for a polished look Advanced Plotly chart modifications Applying a consistent color theme and layout By the end of this video, you’ll have a styled dashboard, ready for a professional presentation. Access the GitHub repo with all parts of this project: https://github.com/KeithGalli/shiny-python-projects Shiny for Python Homepage: https://shiny.posit.co/py/ Check out the complete documentation here: https://shiny.posit.co/py/api/express/ Video by @KeithGalli Video Timeline: 0:00 - Video Overview & Setup 1:50 - Modifying HTML and CSS in Shiny 7:44 - Adding a Logo Image 10:26 - Styling Labels and Containers (Aligning our Image w/ the Title — Custom Divs) 20:20 - Customizing Altair Charts (Gridlines, Font, Axis Labels, Etc.) 27:49 - Customizing Plotly Visualizations 37:16 - Customizing Seaborn & Folium Heatmaps 44:15 - Final Touches, Clean Up, Recap and Next Steps If you enjoyed this series, give it a thumbs up and subscribe to the channel to stay updated! All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

Shiny for Python Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Build Captivating Display Tables in Python With Great Tables | Real Python Podcast #214

Do you need help making data tables in Python look interesting and attractive? How can you create beautiful display-ready tables as easily as charts and graphs in Python? This week on the show, we speak with Richard Iannone and Michael Chow from Posit about the Great Tables Python library. Links from the show: https://realpython.com/podcasts/rpp/214/ Michael and Richard discuss the design philosophy and history behind creating display tables. We dig into the grammar of tables, the background of the project, and an ingenious way to build a collection of examples for a library. We briefly cover how Richard and Michael started contributing to open source. We also discuss practicing data skills with challenges and resources like Tidy Tuesday. This episode is sponsored by Mailtrap. Topics: 00:00:00 – Introduction 00:02:00 – Michael’s background in open source 00:04:07 – Rich’s background in open source 00:05:27 – Advice for someone starting out 00:08:55 – What do you mean by the term “display” table 00:11:32 – What components were missing from other tables? 00:13:31 – Using examples to explain features 00:16:09 – Why was there an absence of this functionality in Python? 00:19:35 – A progressive approach and the grammar of tables 00:21:26 – Sponsor: Mailtrap 00:22:01 – The design philosophy of great tables 00:25:31 – Nanoplots, spark lines, and column spanners 00:27:06 – Building a gallery of examples 00:28:56 – Heat mapping cells and automatically adjusting text color 00:32:54 – Output formats for the tables 00:34:46 – Building in accessibility 00:36:55 – Dependencies 00:37:42 – What is the common workflow? 00:41:39 – Video Course Spotlight 00:43:15 – Adding graphics 00:46:41 – Using a table contest to get examples 00:49:47 – quartodoc and documenting the project 00:55:00 – Tidy Tuesday and data science community 01:00:29 – What are you excited about in the world of Python? 01:03:46 – What do you want to learn next? 01:08:05 – How can people follow the work you do online? 01:09:57 – Thanks and goodbye Links from the show: https://realpython.com/podcasts/rpp/214/

Michael Chow

How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python)

This is part 4 of our multi-part series on creating professional dashboards with Shiny for Python. In this video, we’ll explore how to integrate popular Python visualization libraries like Matplotlib, Plotly, Seaborn, and Altair into your Shiny apps. This allows you to leverage your existing visualization skills and seamlessly include them in interactive dashboards. We’ll cover: Integrating Matplotlib and Seaborn for detailed visualizations Utilizing Altair and Plotly for dynamic charts Implementing Folium for interactive maps Customizing data tables with additional filters and selection modes By the end of this video, you’ll have a rich and diverse set of visualizations in your Shiny dashboard, setting the stage for the final styling touches in the next video. Shiny for Python Homepage: https://shiny.posit.co/py/ Access the GitHub repo with all parts of this project: https://github.com/KeithGalli/shiny-python-projects Check out the complete documentation here: https://shiny.posit.co/py/api/express/ Video by @KeithGalli Video Timeline! 0:00 - Video Overview & Recap of Previous Video Dashboards 1:38 - Getting Setup with the Code (cloning branch from GitHub) 3:15 - Adding Matplotlib-based visualizations (render.plot Shiny for Python decorator) 10:15 - Create a Seaborn Heatmap Chart (Sales Volume by Hour of the Day) 14:59 - Creating Interactive Charts with Jupyter Widgets (Plotly, Altair, Bokeh, Pydeck, & More…) | render_widget decorator 20:14 - Implementing Folium for Location-Based Heatmaps (render.ui decorator) 25:32 - Enhancing DataFrames with Filters and Selection Modes (render.data_frame, render.DataGrid, render.DataTable, etc.) 28:49 - Additional Rendering Options, Final Touches and Next Steps Stay tuned for part 5, where we’ll focus on styling and finalizing our dashboard. If you enjoyed this video, give it a thumbs up and subscribe to the channel to stay updated! All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

Shiny for Python Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Data engineering for the 99% | Will Hipson @ HIAA | Data Science Hangout

We were recently joined by Will Hipson, Data Engineer at Halifax International Airport Authority to chat about scrappy data engineering for the 99% - managing the full data lifecycle from extraction to presentation with a team of one. Speaker bio: Will Hipson is a data engineer at a medium-sized Canadian airport at the early stages of democratizing its data access. Will has a PhD in Psychology and Quantitative Analysis but transitioned out of academia after falling in love with R and data science. Now, he works across all areas of the data lifecycle, from building pipelines and maintaining databases to developing Shiny apps and Quarto docs for presenting data. He believes data engineering doesn’t need to be synonymous with massive enterprise platforms or bleeding edge LLMs. Instead, most organizations simply need help finding data and making sense of it. At HIAA, he helped architect a data platform around data discovery, data literacy, and data governance. He built creative solutions for orchestrating data pipelines that resulted in the development of an R package called maestro ( https://github.com/whipson/maestro/ ) that they recently released to the community. Links mentioned: maestro package: https://github.com/whipson/maestro/ HIAA x Posit Success Story: https://posit.co/about/customer-stories/halifax-airport/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software The Hangout is a gathering place for the whole data science community to chat about data science leadership and questions you’re all facing that happens every Thursday at 12 ET. To join future data science hangouts, add to your calendar here: https://pos.it/dsh We’d love to have you join us in the conversation live! Thanks for hanging out with us!

Matt Iorio and Rumil Legaspi @ Neuros Medical | Data Science Hangout

We were recently joined by Matt Iorio and Rumil Legaspi @ Neuros Medical to discuss how a novel clinical trial design in chronic post-amputation pain became the catalyst for the founding and growth of the company’s clinical data department. Speaker Bios: Matt Iorio: Matt is the Senior Manager of Clinical Affairs and Data Analytics at Neuros Medical, a clinical stage neuromodulation company where he specializes in overseeing end-to-end clinical data operations within the organization, including design of data pipelines, design and validation of databases and patient-reported outcome systems, as well as analysis and visualizations for regulatory submissions and commercial initiatives. He is also an adjunct Instructor in Biomedical Engineering at Case Western Reserve University, where he teaches Clinical and Regulatory Affairs and their impact on data collection and analysis. Matt holds a BSE and an MS in Biomedical Engineering from Case Western Reserve University and an MBA in Marketing from Cleveland State University. In his free time, he enjoys mentoring/teaching on all things data and Clinical Trials, playing soccer, supporting the Northeast Ohio Entrepreneurial Ecosystem and spending time with friends and family. Rumil Legaspi: Rumil is a Clinical Data Specialist at Neuros Medical, a biotech startup developing medical devices for treating chronic pain in lower limb post-amputee patients using high-frequency neuromodulation. Using his background in public health and a master’s in statistics, Rumil leverages the capabilities of R and other data science tools to analyze, automate, and optimize clinical data management and operations, ensuring patient safety and efficacy. Outside of work, he enjoys powerlifting, traveling to Japan, and exploring coffee and tea shops in Southern California. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software The Hangout is a gathering place for the whole data science community to chat about data science leadership and questions you’re all facing that happens every Thursday at 12 ET. To join future data science hangouts, add to your calendar here: https://pos.it/dsh We’d love to have you join us in the conversation live! Thanks for hanging out with us!

Predicting Lending Rates with Databricks, tidymodels, and Posit Team

Machine learning algorithms are reshaping financial decision-making, changing how the industry manages financial risk. For our workflow demo today on June 26th at 11 am ET, Garrett Grolemund at Posit will show how to use both Posit and Databricks to apply machine learning methods to the consumer credit market, where accurately predicting lending rates is critical for customer acquisition. *Please note that while the workflow focuses on a financial example, the general workflow will be useful to those using Databricks and R together across any industry. During this workflow demo, you will learn how to: Connect to historical lending rate data stored in Databricks Delta Lake Tune and cross-validate a penalized linear regression (LASSO) that predicts interest rates Select variables with the penalized linear regression model (LASSO) Build an interactive Shiny app to provide a customer-facing user interface for our model Deploy the app to production on Posit Connect, and arrange for the app to access Databricks Resources for the demo: GitHub repo for today’s materials: https://github.com/posit-dev/databricks-finance-app Accompanying Guide: https://pub.demo.posit.team/public/predicting-lending-rates/lending-rate-prediction.html Q&A Recording: https://youtube.com/live/wNI3AhHP7uM Additional follow-up links: GitHub Repo: https://github.com/posit-dev/databricks-finance-app Accompanying Guide: https://pub.demo.posit.team/content/fec42b3d-3aa9-43e1-8312-0ff553d09851/lending-rate-prediction.html While this demo uses ODBC package to connect to Databricks, you can also use sparklyr R package. Learn more about both here: https://docs.posit.co/ide/server-pro/user/rstudio-pro/guide/databricks.html Example using sparklyr instead of ODBC: https://posit.co/blog/reporting-on-nyc-taxi-data-with-rstudio-and-databricks/ Posit Workbench provides additional features for managing Databricks Credentials, learn more here: https://docs.posit.co/ide/server-pro/user/posit-workbench/guide/databricks.html#databricks-with-r For more on the Posit x Databricks partnership: https://posit.co/solutions/databricks/ Blog post on Edgar’s workshop on Databricks at conf: https://posit.co/blog/using-databricks-with-r-conf-workshop/ Solutions article on ODBC and Databricks: https://solutions.posit.co/connections/db/databases/databricks/ Want to chat more with Posit? To talk with Posit about integrating Posit & Databricks: https://posit.co/schedule-a-call/?booking_calendar__c=DatabricksJune2024Demo Had fun and want to join again? You can add the monthly recurring event to your calendar with this link: https://pos.it/team-demo

How to make your Python Dashboard look Professional! (Layouts in Shiny)

This is part 3 of our multi-part series on creating professional dashboards with Shiny for Python. In this video, we focus on enhancing the visual appeal and structure of your Shiny dashboards using various layout components. We’ll cover: Implementing layout templates for quick setup Using cards and sidebars for a clean, organized look Customizing columns and grids for better data presentation Adding tab options for multiple views within a dashboard By the end of this video, you’ll have a more polished and professional-looking dashboard, setting the stage for advanced visualizations in upcoming videos. Shiny for Python Homepage: https://shiny.posit.co/py/ Access the GitHub repo with all parts of this project: https://github.com/KeithGalli/shiny-python-projects Check out the complete documentation here: https://shiny.posit.co/py/api/express/ Video by @KeithGalli Video Timeline! 0:00 - Video Overview & Progress Thus Far 1:28 - Using Shiny Templates to Get Started Fast 3:21 - Using Layout Components to Customize our Apps (Cards, Sidebars, Tabs, etc.) 7:55 - Adding a Sidebar within a Card 12:38 - Adding a Card with Tabs to Display Various Visualizations 17:35 - Structuring Data in Columns / Grids (layout_columns() & layout_column_wrap()) 26:30 - Final Touches & Tips (Filling in Visualizations into our Tab Views) Stay tuned for part 4, where we’ll add more advanced visualizations, and part 5, where we’ll focus on customizing styles and adding final touches. If you enjoyed this video, give it a thumbs up and subscribe to the channel to stay updated! All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

Shiny for Python Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

When creating tutorials for beginners, keep this in mind

#positshorts

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Tom Mock @ Posit PBC | Data Science Hangout

We were recently joined by Tom Mock, Product Manager at Posit PBC to chat about career growth, starting out in a sales role, TidyTuesday, and being so good they can’t ignore you. Speaker Bio: Tom Mock is a Product Manager at Posit, overseeing the Posit Workbench and RStudio team. He fell in love with R and data science through his graduate research, using R and RStudio to wrangle, analyze, model, and visualize my data. He became passionate about growing the R community, and founded #TidyTuesday to help newcomers and seasoned vets improve their Tidyverse skills. Links mentioned: TidyTuesday: https://github.com/rfordatascience/tidytuesday Table Contest: https://posit.co/blog/announcing-the-2024-table-contest/ Posit Conference: https://posit.co/conference/ Monthly Workflow Demos: https://www.addevent.com/event/Eg16505674 gt package: https://gt.rstudio.com/ So Good They Can’t Ignore You book recommendation: https://www.goodreads.com/book/show/13525945-so-good-they-can-t-ignore-you Community Builder Quarto Site: https://pos.it/community-builder ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software To join future data science hangouts, add to your calendar here: https://pos.it/dsh We’d love to have you join us in the conversation live! Thanks for hanging out with us!

RStudio Team

How to make Interactive Python Dashboards! (Reactivity in Shiny)

This is a quick-start guide to Shiny for Python, part 2 of a multi-part series. Data scientists need to quickly build web applications to create and share interactive visualizations, giving others a way to interact with data and analytics. Shiny helps you do this. In this video, we’ll build off of the last tutorial where we learned the basics of building, sharing, and deploying a Shiny app in Python. This video specifically focuses on reactivity in Shiny. You can watch this video as a standalone, but it may be helpful to watch the previous video ( https://youtu.be/I2W7i7QyJPI). We’ll cover: ⬡ Creating toggle options for dynamic visualizations ⬡ Understanding Shiny’s reactivity model ⬡ Implementing various input selectors ⬡ Building reactive components and visualizations ⬡ Using reactive calculations and effects ⬡ Adding and formatting plots with Plotly ⬡ Documentation walkthrough to learn more about reactivity (reactivity.effect, reactivity.event, reactivity.isolate, reactivity.invalidate_later, etc…) Video Resources: Video #1: https://youtu.be/I2W7i7QyJPI?si=nx1dk5ovPc91pvlB Starter Code (from end of video #1): https://github.com/KeithGalli/shiny-python-projects/tree/video1 Final App: https://keithgalli.shinyapps.io/final-app/ Shiny Resources: Shiny for Python Homepage: https://shiny.posit.co/py/ Component Gallery: https://shiny.posit.co/py/components/ Express Documentation: https://shiny.posit.co/py/api/express/ Gordon Shotwell’s “How does Shiny Render Things?”: https://youtu.be/jvV4y2xogf8?si=8uGP8ZfboUj1QM4p Joe Cheng’s “Shiny Programming Practices”: https://youtu.be/B2JzHv4FOTU?si=t4Atii-RSc5ojgom Stay tuned for part 3, where we’ll explore how to make your dashboard look more professional (layouts in Shiny). Video by @KeithGalli Video Timeline! 0:00 - Intro & Overview 1:01 - Getting Started with Code 2:08 - Adding Shiny Components (Inputs, Outputs, & Display Messages) 3:21 - Creating an Additional Visualization (Sales Over Time by City) 7:55 - What are Reactive.Calcs and How Do We Use Them Properly? (DataFrame Best Practices) 10:27 - Creating an Additional Visualization (Sales Over Time by City) — Continued 14:30 - Filtering City Data with Select Inputs (UI.Input_Selectize) 21:15 - Rendering Shiny Inputs Within Text 22:15 - Quick Formatting Adjustments 22:54 - Understanding the Shiny Reactivity Model (How Does Shiny Render Things?) 24:23 - Adding a Checkbox Input to Change Out Bar Chart Marker Colors 28:00 - Deploying Our Updated App! 29:19 - Advanced Concepts in Shiny Reactivity (Reactive.Effect, Reactive.Event, Reactive.Isolate, Reactive.Invalidate_Later) & Other Resources All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

Joe Cheng

Shiny for Python Shiny shinyapps RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Being SO good they can’t ignore you

Tom recommended the book So Good They Can’t Ignore You in a recent Data Science Hangout. Libby also keeps this awesome list of Hangout book recommendations: https://datahumans.notion.site/2f4552fd18be4d439b6b6977077e6ca5?v=738d0654fd81490890b8f741a2ef0a3c #positshorts

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Hadley Wickham - R in Production

R in Production by Hadley Wickham Visit https://rstats.ai for information on upcoming conferences. Abstract: In this talk, we delve into the strategic deployment of R in production environments, guided by three core principles to elevate your work from individual exploration to scalable, collaborative data science. The essence of putting R into production lies not just in executing code but in crafting solutions that are robust, repeatable, and collaborative, guided by three key principles: Not just once: Successful data science projects are not a one-off, but will be run repeatedly for months or years. I’ll discuss some of the challenges for creating R scripts and applications that run repeatedly, handle new data seamlessly, and adapt to evolving analytical requirements without constant manual intervention. This principle ensures your analyses are enduring assets not throw away toys. Not just my computer: the transition from development on your laptop (usually windows or mac) to a production environment (usually linux) introduces a number of challenges. Here, I’ll discuss some strategies for making R code portable, how you can minimise pain when something inevitably goes wrong, and few unresolved auth challenges that we’re currently working on. Not just me: R is not just a tool for individual analysts but a platform for collaboration. I’ll cover some of the best practices for writing readable, understandable code, and how you might go about sharing that code with your colleagues. This principle underscores the importance of building R projects that are accessible, editable, and usable by others, fostering a culture of collaboration and knowledge sharing. By adhering to these principles, we pave the way for R to be a powerful tool not just for individual analyses but as a cornerstone of enterprise-level data science solutions. Join me to explore how to harness the full potential of R in production, creating workflows that are robust, portable, and collaborative. Bio: Hadley is Chief Scientist at Posit PBC, winner of the 2019 COPSS award, and a member of the R Foundation. He builds tools (both computational and cognitive) to make data science easier, faster, and more fun. His work includes packages for data science (like the tidyverse, which includes ggplot2, dplyr, and tidyr)and principled software development (e.g. roxygen2, testthat, and pkgdown). He is also a writer, educator, and speaker promoting the use of R for data science. Learn more on his website, http://hadley.nz. Mastodon: https://fosstodon.org/@hadleywickham Presented at the 2024 New York R Conference (May 17, 2024) Hosted by Lander Analytics ( https://landeranalytics.com )

Hadley Wickham

Communicating Data Science with Shiny! 🚀 Garrick Aden-Buie #datascience #datavisualization #shiny

Ever seen this diagram? It’s from R for Data Science, outlining the journey of tackling new data problems. 1️⃣ Start: Import and tidy your data. 2️⃣ Cycle: Transform, visualize, and model your data. 3️⃣ Communicate: Share your findings. But communication isn’t just the end—it’s an ongoing process! Shiny apps are your secret weapon for this. Scenario 1: Quick Shiny app for colleague feedback ️ refine your work. Scenario 2: Small app grows into a full-featured web application. From small, quick-use apps to robust final products, Shiny evolves with your project needs. Discover why Shiny is essential for every data scientist! Get Started: https://shiny.posit.co #positshorts

Garrick Aden-Buie

Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

{shinylive}: Serverless Shiny Apps | Barret Schloerke | Posit

In the rapidly evolving landscape of web technologies, the integration of R (and Python) with modern web frameworks has become increasingly important for data scientists and developers. This presentation introduces {shinylive}, a new R package that exports Shiny applications to be run within statically hosted websites. We will explore the capabilities of {shinylive} through its use of the innovative R package {webR}, which allows for the execution of R code in the browser (via WebAssembly and service workers) without the need for a centralized server. The presentation will cover the technical foundation of {shinylive}, including its architecture and the integration process with Quarto documents. We will also discuss the practical aspects and drawbacks of exporting Shiny apps with {shinylive}, highlighting the ease of exporting apps to a folder for local use or hosting them on GitHub pages. {shinylive} bridges the gap between Shiny and static websites, making it a valuable resource for interactive data analysis and presentation. Link to app: https://schloerke.com/presentation-2024-04-18-appsilon-shinylive/ Link to script: https://github.com/posit-dev/r-shinylive/blob/main/examples/deploy-app.yaml Link to use_github_action(): https://github.com/posit-dev/r-shinylive#github-pages Shinylive website: https://posit-dev.github.io/r-shinylive/ {webr} docs: https://docs.r-wasm.org/webr/latest/

Barret Schloerke

Quarto r-shinylive Shiny shinylive RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

How to inspire people to make a change ✨

Key takeaways from our conversation on the path to effective data stewardship: Change management is hard. To inspire people to change, make it as easy as possible for them to see the path to get there by creating tools to make new tasks easy to complete. A problem not only has to exist, but that problem needs to be recognized to exist. In the data governance space, legal regulations can help push us forward because they create urgency. Data stewardship and data governance enable innovation. You see more of what’s possible when you know what data exists. Core systems form the foundation that enables cutting-edge work like AI development. People have good intentions and want to get their work done and impact the organization. Don’t create processes where the only way to get your job done is to break the process. In the act of governing, remember why you started pulling data together in the first place. Community engagement is incredibly powerful and hearing different perspectives is how we grow. We all face similar problems regardless of our role or industry. An internal community for cross-organizational collaboration is vital for knowing what other people are working on and collaborating. This not only helps you with your data, but also with your career. Implementing effective data stewardship is a journey with companies at all different stages. Those further along the journey may have teams and infrastructure dedicated to this, along with executive buy-in. Others are working to make it happen at the individual level – where it often starts with writing down the definitions of your data (who created it, when it was sourced, intended use, etc.) and recognizing that someone else will use your data output, potentially not in the way you initially intended. #positshorts

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Analyze and explore data stored in Snowflake using R

James Blair, Senior Product Manager, Cloud Integrations at Posit, will demonstrate using the R language to analyze and explore data stored in Snowflake. He will also show you how easy it is to set up an R environment inside Posit Workbench that runs as a native app on Snowpark Container Services. You also find out how using the dbplyr interface can be used to push computation data into Snowflake, giving you access to greater memory and compute power than in a standard R session. It’s easy to get started. In just a few minutes, you can work in your R session securely inside Snowflake using the RStudio Pro IDE in Posit Workbench. Posit also supports VS Code and Jupyter for data scientists who prefer to work in other languages like Python, so you can continue to use the tools you know and love. Learn more about the Snowflake and Posit integration: https://posit.co/solutions/snowflake/

dbplyr rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

From Chaos to Clarity: Implementing Effective Data Stewardship

As a data scientist or data leader, data is at the center of your work. However, management of that data can feel overlooked. Many of us have asked questions like: 🤔 How do we ensure data we create is easily discoverable and reusable? 🤔 What happens when multiple people make different data transformations for similar work? The focused effort of making data accessible and secure has been called many different names: data stewardship, data enablement, data governance, etc. While it’s needed across all levels and departments within an organization, best practices for managing data remain difficult to define. We got together as a community to discuss the importance of driving data stewardship at the individual level. Jamie Warner, Managing Director, Data Science & Pricing at Plymouth Rock Assurance and Dan Boisvert, Head of Data Stewardship at Biogen kicked off the conversation by drawing from their own experiences. Resources mentioned: Follow-up Q&A Blog Post - https://posit.co/blog/the-path-to-effective-data-stewardship/ posit::conf(2024): https://posit.co/conference/ O’Reilly chapter on Data Governance over a Data Life Cycle: https://www.oreilly.com/library/view/data-governance-the/9781492063483/ch04.html Switch by Chip and Dan Heath: https://heathbrothers.com/books/switch/ To engage further with Posit on this topic: Every Thursday at 12 ET we get together for the Data Science Hangout. While there’s no set agenda for the conversation, we often talk about data science leadership, data management best practices, speaking with executives, career advice, and more. Many Posit customers also use an enterprise platform for data governance like Databricks and Snowflake to help people discover, access, and work with the right data securely and efficiently. Posit will be at the following events in June and you can use the links below to RSVP to join us at customer events there: Snowflake Summit Dev Day: https://posit.co/events/snowflake-summit/ Databricks Data + AI Summit: https://posit.co/events/databricks-data-ai-summit/

How to automate your reporting with Quarto Dashboards and Posit Connect

Get ready to up your reporting game! Isabella Velásquez dives into the practical side of lightweight dashboards made with Quarto, the next-generation R Markdown, and Posit Connect, our premier publishing platform. You’ll learn how to build and automate Quarto Dashboards with Posit Connect. We’ll showcase a Python example, but the same principles apply to R, Julia, and Observable. Helpful Links GitHub Repo: https://github.com/posit-marketing/inflation-explorer Q&A room: https://youtube.com/live/d21PQyOGlgY?feature=share For anonymous questions: https://pos.it/demo-questions Quarto dashboard documentation: https://quarto.org/docs/dashboards/ Quarto dashboard gallery: https://quarto.org/docs/dashboards/examples/ You can add the monthly recurring event to your calendar with this link: https://pos.it/team-demo Timestamps: 1:17 - Overview of the project 2:14 - The data (Consumer Price Index released monthly by Bureau of Labor Statistics) 3:00 - The benefits of using Quarto 5:41 - Introducing Quarto dashboards 7:17 - Navigation bar and pages in Quarto dashboards 7:30 - Dashboard Layout: Rows 7:47 - Dashboard Layout: Columns 8:00 - Tabsets in Quarto dashboards 8:16 - Sidebars in Quarto dashboards 8:25 - Cards in Quarto dashboards 9:50 - Theming in Quarto 10:38 - Today’s dashboard on BLS Data 11:30 - Options for deploying Quarto dashboards 11:50 - Posit Connect overview 13:02 - Demo starting in GitHub repo for project 14:15 - Creating a virtual environment for the project 15:50 - Scheduling a script to run when we know new data is coming in (using pins on Posit Connect) 19:17 - Publishing a script to Posit Connect from VS Code 20:15 - Adding environmental variables to Posit Connect 25:30 - Final dashboard reveal

Isabella Velásquez

How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny)

This is a quick-start guide to Shiny for Python. It’s part 1 of a multi-part series. Data scientists need to quickly build web applications to create and share interactive visualizations, giving others a way to interact with data and analytics. Shiny helps you do this. In this video, we’ll walk you through the basics of setting up Shiny for Python, creating your first app, and deploying it so others can use it. We’ll cover: Installing Shiny and necessary dependencies Writing and running your first Shiny app Basic UI and server structure Deploying your app online Helpful Links Shiny for Python Homepage: https://shiny.posit.co/py/ The link to the final app can be found here: https://keithgalli.shinyapps.io/final-app/ Follow along with the code examples provided in this repository: https://github.com/KeithGalli/shiny-python-projects Check out the complete documentation here: https://shiny.posit.co/py/api/express/ Stay tuned for part 2, where we’ll dive deeper into advanced features and customization options. Video Timeline! 0:00 - Intro to Shiny & Video Overview 1:43 - Getting Started with the Shinylive Playground 2:44 - Building a custom visualization with Shinylive 5:18 - Easily sharing the code/application for a Shinylive app 6:12 - Building a Shiny Express App locally (VSCode) 9:40 - How to run app if you’re not using VSCode 10:17 - Further customization of our app (adding title, using CSV data, dynamic input) 17:15 - Deploying our Shiny app to the web 21:20 - Conclusion & what’s coming next in the series Video by @KeithGalli All videos in the series: Part 1 - How to Build, Deploy, & Share a Python Application in 20 minutes! (Using Shiny): https://www.youtube.com/watch?v=I2W7i7QyJPI&t=0s Part 2 - How to make Interactive Python Dashboards! (Reactivity in Shiny): https://www.youtube.com/watch?v=SLkA-Z8HTAE&t=0s Part 3 - How to make your Python Dashboard look Professional! (Layouts in Shiny): https://www.youtube.com/watch?v=jemk7DoN4qk&t=0s Part 4 - How to combine Matplotlib, Plotly, Seaborn, & more in a single Python Dashboard! (Shiny for Python): https://youtu.be/xDgO5hB4-VU?si=kk20yhdpsBqkMYcC Part 5 - How to Perfect Your Python Dashboard with Advanced Styling! (HTML/CSS - Shiny for Python): https://youtu.be/uYZUS-eFbqw

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🐙 Lunch & Learn: Let’s Build An AI Assistant With GPT-4o (w/ Joe & Winston of @Posit)

@Posit Channel - https://www.youtube.com/@UC3xfbCMLCw1Hh4dWop3XtHg 🤖 Sign up here to get accompanying workbooks, summaries, and notifs for future lunch & learns: https://www.lonelyoctopus.com/email-signup ️ NEWSLETTER: https://tinahuang.substack.com/ It’s about learning, coding, and generally how to get your sh*t together c: Lonely Octopus: https://www.lonelyoctopus.com/ Check it out if you’re interested in learning AI & data skill, then applying them to real company projects! Affiliates# My SQL for data science interviews course (10 full interviews): https://365datascience.com/learn-sql-for-data-science-interviews/ https://365datascience.pxf.io/WD0za3 (link for 57% discount for their complete data science training) Check out StrataScratch for data science interview prep: https://stratascratch.com/?via=tina My filming setup# camera: https://amzn.to/3LHbi7N mic: https://amzn.to/3LqoFJb tripod: https://amzn.to/3DkjGHe lights: https://amzn.to/3LmOhqk Socials# instagram: https://www.instagram.com/hellotinah/ linkedin: https://www.linkedin.com/in/tinaw-h/ discord: https://discord.gg/5mMAtprshX 🤯Study with Tina# Study with Tina channel: https://www.youtube.com/channel/UCI8JpGrDmtggrryhml8kFGw How to make a studying scoreboard: https://www.youtube.com/watch?v=KAVw910mIrI Scoreboard website: scoreboardswithtina.com livestreaming google calendar: https://bit.ly/3wvPzHB Other videos you might be interested in# How I consistently study with a full time job: https://www.youtube.com/watch?v=INymz5VwLmk How I would learn to code (if I could start over): https://www.youtube.com/watch?v=MHPGeQD8TvI&t=84s ‍⬛‍⬛About me# Hi, my name is Tina and I’m an ex-Meta data scientist turned internet person! Contact# youtube: youtube comments are by far the best way to get a response from me! linkedin: https://www.linkedin.com/in/tinaw-h/ email for business inquiries only: hellotinah@gmail.com ======================== Some links are affiliate links and I may receive a small portion of sales price at no cost to you. I really appreciate your support in helping improve this channel!:)

Lessons in career growth from gardening 🌱

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Joe Cheng | Managing long-running operations in Shiny | Posit

It’s been years since Shiny evolved to allow asynchronous operations within applications, improving scalability. The introduction of the {promises} package enabled concurrent processing between multiple Shiny sessions, a significant step forward in handling background tasks. However, this did not address the need for intra-session concurrency, where users expect to interact with the application while long-running calculations are executed in the background. Recently, we added a new ExtendedTask feature to Shiny to allow for such intra-session concurrency. This new feature provides a different approach for developers to incorporate asynchronous tasks, enabling smoother user interactions during intensive computations. Alongside ExtendedTask, this talk will also discuss newer methods for launching asynchronous tasks, besides the usual {future} package. The focus will be on the practical application and integration of these features into Shiny applications. Links mentioned in the video: ⬡ Shiny in Production: Principles, practices, and tools, https://youtu.be/Wy3TY0gOmJw?feature=shared Timestamps: 0:20 Make your slow code fast 1:43 Long-running operations are a problem 3:28 Inter-session concurrency and intra-session concurrency 4:24 Introducing ExtendedTask 5:17 Demo of a slow API using ExtendedTask 6:13 Slow code example (R) 7:16 Fix slow code with ExtendedTask (R) 8:55 Slow code example (Python) 7:16 Fix slow code with ExtendedTask (Python) 10:46 Links to get started 11:06 ExtendedTask backstory intro 11:28 ExtendedTask vs. Shiny Async 15:50 How reactive programming works in Shiny 21:31 How ExtendedTask works in the reactive process 25:38 What we’re still working on 26:35 {future} alternatives 31:47 Wrapping up

Joe Cheng

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Future of Shiny to the FDA with WebR ✨

For more on How Pharma is Pioneering WebAssembly with webR & Shiny for FDA Clinical Trial Submissions: https://posit.co/blog/webr-fda-pilot/ #positshorts

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Shiny + Databricks 🫶

#positshorts

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Rafi Kurlansik @ Databricks | Data Science Hangout

We were recently joined by Rafi Kurlansik, Principal Product Specialist at Databricks to chat about helping clients scale workloads with Spark and Databricks, his passion for programmatically analyzing data with precision, and career journey. Speaker bio: Rafi is a Principal Product Specialist at Databricks where he specializes in Data Science, Machine Learning and the Developer Experience. He has also helped many customers scale their R workloads with Spark. In his spare time he enjoys gardening, cooking up a storm, and video game sessions with his three children. Resources shared: Posit x Databricks Partnership Site: https://posit.co/solutions/databricks/ Databricks x Posit integration product clips: https://www.youtube.com/playlist?list=PL9HYL-VRX0oR-3AgWbXtlfdr29626EjRJ Meet with Posit @ Databricks Data + AI Summit this June https://posit.co/events/databricks-data-ai-summit/ Edgar’s workshop on Databricks + R at posit::conf: https://reg.conf.posit.co/flow/posit/positconf24/publiccatalog/page/publiccatalog/session/1707334048159001SOSO Rafi’s Blog Post, Cost-conscious R&D in the Cloud that Data Scientists Will Love: https://medium.com/@kurlare/cost-conscious-r-d-in-the-cloud-that-data-scientists-will-love-148837b5ab51 Data Science Hangout Survey: https://forms.gle/2ebubmEhssznjhue9 ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software To join future data science hangouts, add to your calendar here: https://pos.it/dsh We’d love to have you join us in the conversation live! Thanks for hanging out with us!

AI in the 50s and 60s 👀

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Hadley Wickham on R vs Python

Learn about tidyverse, ggplot2, and the secret to a tech company’s longevity as Hadley Wickham joins @JonKrohnLearns in this episode. He talks about Posit’s rebrand, why tidyverse needs to be in every data scientist’s toolkit, and why getting your hands dirty with open-source projects can be so lucrative for your career. Watch the full interview “779: The Tidyverse of Essential R Libraries and their Python Analogues — with Dr. Hadley Wickham” here: https://www.superdatascience.com/779

Hadley Wickham

ggplot2 tidyverse Hadley Wickham on R vs Python The Tidyverse of Essential R Libraries and Their Python Analogues Tidyverse Essential R Libraries R Programming Python A R Python Data Science Posit Rstudio Ggplot2 How to Contribute to R Open Source Community Arrow Dbplyr Testthat R Duckdb Keras for R Plotnine Siuba Quarto Quarto Rstudio Shiny R Programming Shiny for Python Hadley Wickham Hadley Wickham R Data Science Podcast Jon Krohn

How to overcome fear when applying to a new job

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779: The Tidyverse of Essential R Libraries and their Python Analogues — with Dr. Hadley Wickham

#Tidyverse #RProgramming #RLibraries Tidyverse, ggplot2, and the secret to a tech company’s longevity: Hadley Wickham talks to @JonKrohnLearns about Posit’s rebrand, Tidyverse and why it needs to be in every data scientist’s toolkit, and why getting your hands dirty with open-source projects can be so lucrative for your career. This episode is brought to you by Intel and HPE Ezmeral Software ( https://bit.ly/hpeintel). Interested in sponsoring a SuperDataScience Podcast episode? Visit https://passionfroot.me/superdatascience for sponsorship information. In this episode you will learn: • [00:00:00] Introduction • [00:02:55] All about the Tidyverse • [00:15:19] Hadley’s favorite R libraries • [00:28:39] The goal of Posit • [00:34:12] On bringing multiple programming languages together • [00:50:19] The principles for a long-lasting tech company • [00:53:34] How Hadley developed ggplot2 • [01:03:52] How to contribute to the open-source community Additional materials: https://www.superdatascience.com/779

Hadley Wickham

Using your dataset in Shiny Templates | Carson Sievert | Posit

Watch the Shiny team’s Carson Sievert change the dataset in a Shiny Template. Find the right template for you at https://shiny.posit.co/py/templates/ 0:00 Intro with Carson Sievert 0:19 How to load the template code 1:14 Running your Shiny app in VS Code with a live reloading preview 1:29 How this template works 2:23 See the contents of your data in a data_frame 2:53 How this template imports data 3:33 A more optimized way to import a large amount of data 5:10 Changing the dataset 5:44 Troubleshooting the inevitable errors when changing the dataset

Carson Sievert, Shiny Team

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Most important thing a data scientist can do 💰

Data Science Hangout with Greg Shick, Director of Analytics at Charles Schwab. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh #positshorts

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Pandas - History and Future - Talk Python to Me Ep.462

This episode dives into some of the most important data science libraries from the Python space with one of its pioneers: Wes McKinney. He’s the creator or co-creator of pandas, Apache Arrow, and Ibis projects and an entrepreneur in this space. ▬▬▬▬ About the podcast ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ This video is the uncut, live recording of the Talk Python To Me podcast ( https://talkpython.fm ). We cover Python-focused topics every week and publish the edited and polished version in audio form. Subscribe in your podcast player of choice (100% free) at https://talkpython.fm/subscribe. ▬▬▬▬ Guests ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Wes’ Website: https://wesmckinney.com/ ▬▬▬▬ Links and resources from the show ▬▬▬▬▬▬▬▬▬▬▬▬ Pandas: https://pandas.pydata.org/ Apache Arrow: https://arrow.apache.org/ Ibis: https://ibis-project.org/ Python for Data Analysis - Groupby Summary: https://wesmckinney.com/book/data-aggregation.html#groupby-summary Polars: https://pola.rs/ Dask: https://www.dask.org/ Sqlglot: https://sqlglot.com/sqlglot.html Pandoc: https://pandoc.org/ Quarto: https://quarto.org/ Evidence framework: https://evidence.dev/ pyscript: https://pyscript.net/ duckdb: https://duckdb.org/ Jupyterlite: https://jupyter.org/try-jupyter/lab/ Djangonauts: https://djangonaut.space/ Listen this episode on Talk Python: https://talkpython.fm/episodes/show/462/pandas-and-beyond-with-wes-mckinney Episode transcripts: https://talkpython.fm/episodes/transcript/462/pandas-and-beyond-with-wes-mckinney ▬▬▬▬ Dive deeper ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Listen to the Talk Python To Me podcast at https://talkpython.fm Over 250 hours of Python courses at https://training.talkpython.fm/courses Follow us on on Mastodon. Michael: https://fosstodon.org/@mkennedy & Talk Python https://fosstodon.org/@talkpython

Observing the creation of your data 🍕

Data Science Hangout with Zack Fragoso, Head of MLOps at Domino’s Pizza. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh #positshorts

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MLOps for a billion pizzas a year | Zack Fragoso @ Domino’s Pizza | Data Science Hangout

We were joined by Zack Fragoso, Head of MLOPs at Domino’s Pizza to chat about his mission to Sell More Pizza and Have More Fun by making the business smarter with data. Speaker bio: Zack Fragoso is the Head of MLOPs at Domino’s Pizza, where he is on a mission to Sell More Pizza and Have More Fun by making our business smarter with data. His team develops ML/AI applications that supercharge the global pizza delivery leader’s e-commerce and in-store platforms. We’ve been able to help reward customers for ordering pizza, give them a better estimate of when their order will be ready, route orders more efficiently, and those are just a few examples! Zack is passionate about MLOPs tools that increase productivity, enhancing cross-team collaboration, and support our model life cycle management. At 11:07 we discussed two key challenges regarding MLOps: scalability and process Scalability Zack explains that while data scientists can develop accurate models on their laptops, scaling those models to handle the immense volume of Domino’s business is a significant hurdle. He emphasizes that Domino’s sells close to a billion pizzas annually, with a high concentration of orders between 5 pm and 8 pm. Events like the Super Bowl push the system even further, requiring the ability to process around 6,000 orders per minute. For Domino’s, this means that models must be not only accurate but also highly performant to handle these spikes in demand. Process The second challenge Zack identifies is related to integrating the experimental nature of data science work with a robust and reliable production process. He acknowledges that data scientists are inherently driven to experiment with data and various modeling techniques. However, translating these experimental models into a production environment requires a more structured approach that encompasses control mechanisms, versioning, and a focus on reliability. Finding the right balance between this need for flexibility in experimentation and the demands of a structured production process is an ongoing challenge for Domino’s. Resources mentioned in the chat: LinkedIn Group for the Hangout: https://www.linkedin.com/groups/12610075/ Domino’s Emergency Pizza Website: https://www.dominos.com/emergency-pizza/, offering a free emergency pizza for those who order a regular pizza. Dominik Vajda shared an article from The Guardian about a Belgian brewery using AI to improve beer taste: https://www.theguardian.com/technology/2024/mar/26/ai-beer-taste-better-belgium-scientists ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software To join future data science hangouts, add to your calendar here: https://pos.it/dsh (All are welcome! We’d love to see you!) Thanks for hanging out with us!

Starting a People Analytics team

Data Science Hangout with Elizabeth Esarove, Lead Advanced Analytics, People Analytics at AT&T to chat about using data science to provide supporting evidence for decisions regarding employee outcomes and business objectives. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh #positshorts

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Follow these tips when asking for a raise 👀

Data Science Hangout with Elizabeth Esarove, Lead Advanced Analytics, People Analytics at AT&T to chat about using data science to provide supporting evidence for decisions regarding employee outcomes and business objectives. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh #positshorts

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Introducing Shiny for Python Templates | Carson Sievert | Posit

Last month we introduced the Shiny for Python Components and Layouts galleries, which are a simple, visual overview of what Shiny can do, mixed with cheatsheet-like information. They are for new and seasoned users alike. We’re excited to announce a third section which brings those two things together into opinionated boilerplate code: Shiny Templates. This is just a start… We expect this gallery to grow as time goes on. These will allow you to hit the ground running, whether you need a quick simple app or a quick complicated one. Get started at https://shiny.posit.co/py/templates/ 0:00 Introduction to Shiny for Python Templates 0:38 How to work with a template locally in VS Code 2:24 Preview your app with the Shiny for Python VS Code Extension 2:45 A data_frame is a quick and easy way to see the contents of your data 4:39 See everything in your window with the “fillable=True” page_opts 5:16 Modifying inputs 7:40 Fix the logic in your @render.plot to show your modified inputs 9:00 Add a component to the sidebar 9:05 How to add input_dark_mode to your app 9:54 How to make your plot change to dark mode as well

Carson Sievert

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70/20/10 model for upskilling at US FDA

Data Science Hangout with Kevin Snyder & Raju Rayavarapu @ U.S. FDA to chat about democratizing data science and utilizing principles of systems biology, pharmacology, toxicology, bioinformatics, and statistics to collaboratively develop open-source software solutions that support decision making and policy development. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh #positshorts

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Evolve your perspective by leaning into these 2 things

Data Science Hangout with Brad Zielke, Sr. Director Data Sciences at Target on leaning into empathy and humility to surround yourself with diverse perspectives and evolve your perspective as a leader. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh #positshorts

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This ONE THING is what hiring managers are looking for🤯

Data Science Hangout with Jamie Warner, Managing Director at Plymouth Rock Assurance. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh #positshorts

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How to get your boss to pay attention to your work 👀

How do you capture your boss’ imagination and show the value of your data science project? Data Science Hangout with Michael Derstine, Vice President at Wells Fargo & Company. ‍️ Join us live at the Data Science Hangouts on Thursday from 12-1 ET: pos.it/dsh #positshorts

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Michael Derstine @ Wells Fargo | Data Science Hangout

We were recently joined by Michael Derstine, Vice President at Wells Fargo & Company to chat about an opinionated approach to modeling and analytics in mortgage banking. Speaker bio: Michael Derstine leads a team of analysts dedicated to the management of an asset class called Mortgage Servicing Rights (MSR). He is approaching his tenth year in mortgage finance, and has held many roles. He started as a junior analyst, compiling webs of spreadsheet reports in the early hours of the morning before the Traders started their day. Like many in this community, he was driven to make processes better (and make life easier). How do you advocate for a better way? 04:05 - Michael shared his story about waking up before 5 am as a junior financial analyst, pulling together dozens of spreadsheets with hundreds of tabs, other people’s models and recycled work Each day, he’d make sure nothing was wrong before traders started acting on these models. He was motivated to not only make his life easier, but make processes better and remove the operational risk. The spreadsheet process has since moved to data science. It’s completely automated. No junior analyst has to get up at 4:30 am. Those roles still exist but now they’re encouraging them to use languages like R and Python. Michael shared, “I’m very proud of that, but it was a lot of work technically and maybe even more work politically to get that through” If you’re changing the status quo, build a challenger processes by: Building something in parallel Running it side by side and obliterating the old competition to show how much better it is Telling the story to your bosses ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software To join future data science hangouts, add to your calendar here: https://pos.it/dsh (All are welcome! We’d love to see you!) Thanks for hanging out with us!

Deploying Databricks-backed content on Posit Connect

After you connect to Databricks using sparklyr and create your content, you’ll want to deploy and share your work. With the new pysparklyr::deploy_databricks() function, you can deploy your content to Posit Connect. Run the function and confirm to start deployment. The deploy_databricks() function will gather your document location, credentials, and URLs. It uses this information to deploy to Posit Connect. After it’s done, click the URL to see the deployed content on Posit Connect. Open the content from the publisher view. Click around and check out your work. In Posit Connect, you can edit sharing permissions, create a vanity URL, and more. Open the content URL to see how your work would look to stakeholders. Learn more: Databricks x Posit: https://posit.co/solutions/databricks/ Empowering R and Python Developers: Databricks and Posit Announce New Integrations: https://posit.co/blog/databricks-and-posit-announce-new-integrations/ Posit Connect: https://posit.co/products/enterprise/connect/ sparklyr and Databricks Connect v2: https://spark.posit.co/deployment/databricks-connect.html Deploying to Posit Connect: https://spark.posit.co/deployment/databricks-posit-connect.html Contact our sales team to schedule a demo: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Databricks Pro Driver in Posit Workbench

We’ve added a Databricks driver to our Professional Drivers. The RStudio Connections Pane allows users to connect to their Databricks clusters from the IDE. Select the Databricks driver from the list of available drivers. Select the Driver to establish the connection. The driver can be used with the new databricks() function from the odbc package to connect to Databricks clusters and SQL warehouses. Learn more: Databricks x Posit: https://posit.co/solutions/databricks/ Empowering R and Python Developers: Databricks and Posit Announce New Integrations: https://posit.co/blog/databricks-and-posit-announce-new-integrations/ RStudio IDE and Posit Workbench 2023.12.0: What’s New: https://posit.co/blog/rstudio-2023-12-0-whats-new/ Posit Professional Drivers 2024.03.0: Support for Apple Silicon: https://posit.co/blog/pro-drivers-2024-03-0/ Contact our sales team to schedule a demo: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks

odbc rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Databricks Pane in Posit Workbench

We’ve introduced a Databricks Pane in RStudio Pro for discovering and managing Databricks clusters. Users can stay in RStudio Pro without navigating to the Databricks web interface to see their clusters. Click on the ‘Connect to’ icon to open up a new Databricks Connection pop-up. This forms the initial connection to Databricks. Once you click ‘ok’, you will be connected to Databricks. Learn more: Databricks x Posit: https://posit.co/solutions/databricks/ Empowering R and Python Developers: Databricks and Posit Announce New Integrations: https://posit.co/blog/databricks-and-posit-announce-new-integrations/ RStudio IDE and Posit Workbench 2023.12.0: What’s New: https://posit.co/blog/rstudio-2023-12-0-whats-new/ Posit Professional Drivers 2024.03.0: Support for Apple Silicon: https://posit.co/blog/pro-drivers-2024-03-0/ Contact our sales team to schedule a demo: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Databricks Authentication in Posit Workbench

Posit Workbench now has delegated Databricks credentials. Users can log into a Databricks Workspace when starting an RStudio or VS Code session. Authentication relies on OAuth-backed refresh tokens rather than Personal Access Tokens (PATs). This allows for more granular control over permissions, built-in expiration, and defined scopes. Once logged in, you can interact directly with your Databricks clusters in that Workspace in your preferred environment. Learn more: Databricks x Posit: https://posit.co/solutions/databricks/ Empowering R and Python Developers: Databricks and Posit Announce New Integrations: https://posit.co/blog/databricks-and-posit-announce-new-integrations/ RStudio IDE and Posit Workbench 2023.12.0: What’s New: https://posit.co/blog/rstudio-2023-12-0-whats-new/ Posit Professional Drivers 2024.03.0: Support for Apple Silicon: https://posit.co/blog/pro-drivers-2024-03-0/ Contact our sales team to schedule a demo: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Connecting RStudio and Databricks with sparklyr

You can connect RStudio and Databricks with the sparklyr package. First, load the necessary packages. Next, set up the connection with sparklyr::spark_connect(). See your data in the Connections pane. Check which databases you’re connected to. Navigate the data structure by expanding the levels. Navigate to the table you want to explore…and analyze your data in RStudio. Learn more: Databricks x Posit: https://posit.co/solutions/databricks/ Empowering R and Python Developers: Databricks and Posit Announce New Integrations: https://posit.co/blog/databricks-and-posit-announce-new-integrations/ Posit Connect: https://posit.co/products/enterprise/connect/ sparklyr and Databricks Connect v2: https://spark.posit.co/deployment/databricks-connect.html Deploying to Posit Connect: https://spark.posit.co/deployment/databricks-posit-connect.html Contact our sales team to schedule a demo: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Connecting RStudio and Databricks with ODBC

The odbc package, in conjunction with a driver, provides DBI support and an ODBC connection. With the new odbc::databricks_connect function, you can create an ODBC connection to determine and configure the necessary settings to access your Databricks account. Your Databricks HTTP path is the only argument you need to run databricks_connect(). Provide your HTTP path and you will be able to see your Databricks data in the RStudio Connections Pane. Then, you can analyze your data in RStudio. Learn more: Databricks x Posit: https://posit.co/solutions/databricks/ Empowering R and Python Developers: Databricks and Posit Announce New Integrations: https://posit.co/blog/databricks-and-posit-announce-new-integrations/ RStudio IDE and Posit Workbench 2023.12.0: What’s New: https://posit.co/blog/rstudio-2023-12-0-whats-new/ Posit Professional Drivers 2024.03.0: Support for Apple Silicon: https://posit.co/blog/pro-drivers-2024-03-0/ Contact our sales team to schedule a demo: https://posit.co/schedule-a-call/?booking_calendar__c=Databricks

DBI odbc rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

How to build business reports with Quarto

How do you create the report look and feel that your leadership team expects? Christophe Dervieux at Posit joined us on Wednesday, March 27th to share how to style Quarto docs and send scheduled email updates to required stakeholders. Helpful resources: ️ Getting started with Quarto: https://quarto.org/docs/get-started ️ User guide: https://quarto.org/docs/guide ️ Github repo with this example: https://github.com/quarto-examples/quarto-business-report ️ Q&A Recording: https://youtube.com/live/bqk75igHo8M?feature=share ️ If you’re interested in learning more about Posit Connect, pos.it/chat-with-us Timestamps: 02:00 - What is Quarto? 02:40 - How does Quarto work? (.md,.qmd or.ipynb as source files) 03:45 - How to get started with Quarto if you’re new to it? 04:51 - Using Quarto from within RStudio 05:00 - Using Quarto within VSCode with extension & Jupyter Lab extension 05:37 - Visual Editor for Quarto 07:22 - Customer Tracker Report in RStudio IDE (using source code: https://github.com/quarto-examples/quarto-business-report ) 10:39 - Making Quarto report downloadable as Excel doc (adding download button) 11:37 - Adding a table of contents to your Quarto report 12:23 - Spread Quarto graphics across page so that they go into margin 13:10 - Customizing theme in Quarto (Bootstrap 5) https://quarto.org/docs/output-formats/html-themes.html 14:45 - Increasing font size in Quarto report 17:10 - Customizing theme rules 21:16 - Publishing Quarto report to Posit Connect 22:35 - Scheduling Quarto report to automatically run 23:35 - Preview of default / non-customized email 23:58 - Customizing your Quarto email 26:52 - Customized email preview that Posit Connect can send 27:56 - Setting access controls for Quarto report on Connect and when you want emails to send Resources shared in Q&A session: Community discussion for ongoing Quarto questions: https://forum.posit.co/tag/quarto Quarto document language: https://quarto.org/docs/authoring/language.html babelquarto (for multilingual project, book, or website): https://docs.ropensci.org/babelquarto/ Quarto Manuscripts: https://quarto.org/docs/manuscripts/ Managing Execution in Quarto: https://quarto.org/docs/projects/code-execution.html Quarto Extensions: https://quarto.org/docs/extensions/ Project Profiles in Quarto: https://quarto.org/docs/projects/profiles.html Custom branding deeper dive: https://www.youtube.com/watch?v=V82BBU9ldcM Quarto Parameters: https://quarto.org/docs/computations/parameters.html Lua Development: https://quarto.org/docs/extensions/lua.html Quarto CLI Discussions on Github: https://github.com/quarto-dev/quarto-cli/discussions Data Science Hangout every Thursday at 12 ET: https://posit.co/data-science-hangout/ Get connected with others at your org using Posit: pos.it/connect-us There is no need to register; join us here on YouTube at the time above or you can add to your calendar using the link below: pos.it/team-demo We host these Workflow Demos on the last Wednesday of every month, so you can use the link above to add the recurring event as well. If you ever have ideas for topics or questions about them, you can comment below in YouTube!

Christophe Dervieux

R-Ladies Rome(English) - Extending the data science workflow: {vetiver} and {pins}- Isabel Zimmerman

In this video, Isabel Zimmerman goes through the fundamental aspects of machine learning operations (MLOps) tasks, bridging the gap between data analysis and model deployment. While data practitioners excel in data analysis and model development, there’s often a significant gap in understanding tasks beyond the conventional data science workflow. You’ll explore crucial MLOps concepts, such as deploying models as API endpoints and monitoring model decay, while leveraging the powerful capabilities of the vetiver and pins packages. Material: presentation: https://www.isabelizimm.me/talk-extending-ds-workflow-rladies/ RStudioConf2022 talk: https://www.isabelizimm.me/talks/rstudioconf2022/ Vetiver website: https://vetiver.rstudio.com/ 0:00 Welcome & R-Ladies Rome Chapter Introduction 0:04:45 Slido Pools 0:10:15 Talk Intro 0:10:56 Isalbel’s Talk 0:47:53 Hands-on session 1:02:20 Q&A Have a look at our WebSite for more insights about our events: https://rladiesrome.quarto.pub/website/talks/

Isabel Zimmerman

R/Medicine: Quarto for Reproducible Medical Manuscripts

In this webinar, Mine Cetinkaya-Rundel, Professor of the Practice of Statistical Science at Duke University, presents a new capability in Quarto that provides a straightforward and user-friendly approach to creating reproducible manuscripts that are publication-ready for submission to science journals. This new feature, Quarto manuscripts, includes the ability to produce a bundled output containing a standardized journal format, source documents, source computations, referenced resources, and execution information into a single bundle that be ingested into journal review and production processes. In this talk, Mine demonstrates how Quarto manuscripts work and how you can incorporate them into your current manuscript development process. She also touches on pain points in your current workflow that Quarto manuscripts help alleviate. Main Sections 00:00 Introductions 03:05 Full complexity spectrum of reproducible of reproducible scientific projects 08:14 Leveraging Quarto for fully reproducible scientific manuscripts 16:38 A new project type: manuscript 17:12 Let’s write a manuscript 40:41 What’s next? 44:39 Thank you/Q&A More resources R Validation Hub Site: https://www.pharmar.org/ Main Site: https://www.r-consortium.org/ News: https://www.r-consortium.org/news Blog: https://www.r-consortium.org/news/blog Join: https://www.r-consortium.org/about/join Twitter: https://twitter.com/rconsortium?lang=en LinkedIn: https://www.linkedin.com/company/r-consortium/ Mastodon: https://fosstodon.org/@RConsortium

Adam Austin @ The Hartford | Data Science Hangout

We were recently joined by Adam Austin, Director of Data Science at The Hartford to chat more about data science as a tool, but relationships as the real driver of business value and success. Speaker bio: Adam Austin is a data science leader who, over a 10-year career, has been intensely curious about why data science projects fail despite their initial potential. Invariably he has seen that it is not sophisticated models and ideas, but relationships that enable the promise of data and analytics. In addition to advocating for good workflows and development practices, Adam has turned his attention to how data scientists can become better communicators, help business partners articulate their needs, and build bridges to success amidst complex and ambiguous business backdrops – all while having a little fun. After all, maybe the real data science is the friends we make along the way. Here are a few key takeaways from this sessions: ⭐️ Data science thrives on strong relationships. The most challenging aspects of the job often involve navigating business relationships and comprehending how data science fits into a company’s broader goals. ⭐️ When presenting ideas to executives, strive for simplicity and clarity. A straightforward visual representation of data can be more impactful than a complex, difficult-to-explain model. ⭐️ Data science initiatives often involve navigating the complexities of human motivations and organizational politics. ⭐️ Data science teams should aim to be value creators, not just ticket-takers. Transitioning from a reactive to a proactive team requires strategic planning and stakeholder engagement. ⭐️ A robust intake process is essential for vetting and prioritizing data science projects. ⭐️ Data scientists can add significant value by challenging existing metrics and proposing alternative measurement strategies. ⭐️ Growing disconnect between skills emphasized in academic data science programs and the practical needs of the industry. Universities need to equip graduates with the skills to handle real-world data challenges, including data cleaning, wrangling, and understanding business context. Resources & Links Mentioned in the chat: Veridical Data Science: https://vdsbook.com/ Yes, and" is the Foundation of Collaboration: https://laderast.github.io/posts/2022-02-27-yes-and-is-the-foundation-of-collaboration/ Data Science Mishaps Night: https://datamishapsnight.com Bike Share Dataset Example: https://solutions.posit.co/gallery/bike_predict/ Data.gov: https://data.gov Student Portfolio Examples: https://sph-midterm-projects.netlify.app/ Tweet about Data Science Hype: https://x.com/PhDemetri/status/1394847400427233282?s=20 Data Science Hangout LinkedIn Group: https://www.linkedin.com/groups/12610075/ Adam Austin on Mastodon: @ataustin@fosstodon.org Missions, Visions, Maturity Models, & Strategies: https://medium.com/policygenius-stories/missions-visions-maturity-models-strategies-ce5a4a408ab2 ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!) Thanks for hanging out with us!

How to write and deploy a Python web app with Shiny Express and Posit Team

Winston Chang at Posit joined us to share a quicker and simpler way of writing Shiny applications in Python, called Shiny Express With Shiny Express you can create interactive data dashboards and data-driven applications faster than ever. Helpful resources: ️ Introducing Shiny Express: https://shiny.posit.co/blog/posts/shiny-express/ ️ Shinylive: https://shinylive.io/py/examples/ ️ Component Gallery: https://shiny.posit.co/py/components/ ️ VS Code Shiny extension: https://marketplace.visualstudio.com/items?itemName=Posit.shiny-python ️ Q&A recording: https://youtube.com/live/zg4LP4lkihM?feature=share rsconnect-python package: https://pypi.org/project/rsconnect-python/ ️ Follow-up links: Event Survey: https://forms.gle/8AcMAnjfPFSTQZJK6 Posit Team: https://posit.co/products/enterprise/team/ Request evaluation: pos.it/chat-with-us Posit Team demo resources: pos.it/demo-resources What will you learn? If you’re new to Shiny → you’ll get started writing and deploying your first Shiny Express application in Python. If you already know Shiny → you’ll see how Shiny Express can make your development experience quicker and more efficient. Happy with the way things are? No need to change what you’re doing! We think of Shiny Express and Shiny Core as complementary, and intend to support both syntaxes indefinitely. Last month’s Workflow with Posit Team Demo featured Shiny in R and used bslib for custom theming, which you can check out here in the recording: https://youtu.be/O6WLERr5bKU?feature=shared LIVE Q&A ROOM for ~11:30 am on Feb 28th: https://youtube.com/live/zg4LP4lkihM?feature=share There is no need to register; join us here on YouTube at the time above or you can add to your calendar using the link below: pos.it/team-demo We host these Workflow Demos on the last Wednesday of every month, so you can use the link above to add the recurring event as well. #python

Winston Chang

Kevin Snyder & Raju Rayavarapu @ U.S. FDA | Data Science Hangout

We were recently joined by Kevin and Raju @ the FDA to chat about democratizing data science by teaching people the basics of data wrangling, data science and the power of open source and utilizing principles of systems biology, pharmacology, toxicology, bioinformatics, and statistics to collaboratively develop open-source software solutions that support decision making and policy development. Links mentioned: FDA Digital Transformation Symposium: https://www.fda.gov/news-events/fda-meetings-conferences-and-workshops/2023-fda-digital-transformation-symposium-12042023 precisionFDA: https://precision.fda.gov/ R4DS Online Learning Community: r4ds.io/join R Validation Hub Contact: https://www.pharmar.org/contact/ Hangout LinkedIn Group: https://www.linkedin.com/groups/12610075/ Speaker bios: Kevin Snyder - Associate Director of Nonclinical Informatics at U.S. FDA Kevin received his Bachelors in biochemistry from the University of Maryland in 2008 and his PhD in neuroscience from the University of Pennsylvania School of Medicine in 2013. He currently serves as the Associate Director of Nonclinical Informatics in the Office of New Drugs in the Center for Drug Evaluation and Research at the US FDA where he manages data science and informatics initiatives to support the pharmacology/toxicology review program. These initiatives include research efforts to develop methods to optimize the regulatory use of standardized electronic CDISC-SEND-formatted toxicology study data as well as internal informatics projects to promote the development of scientifically sound, data-driven regulatory policies. Dr. Snyder also leads an agency-wide Data Science and Software Development working group that is focused on building out the organizational infrastructure necessary to support the work of data scientists across the agency and is an active collaborator with several consortia efforts, e.g. CDISC, PHUSE, and BioCelerate, to improve the implementation and use of the SEND data standard. Raju (Rama) Rayavarapu - Data Scientist at U.S. FDA Raju comes to the FDA from the great state of Pennsylvania via South Bend, IN and Memphis, TN where he did his Ph.D. and post-doctoral work. He is the lead of ODAR’s DataForward Initiative which is focused on upskilling FDA staff in data related skills and harnessing the power of the incredible existing FDA data science community to drive data literacy and the joy that comes from working with and understanding data.He loves to spend his time talking FOSS (Python/R/whatever), Natural Language Processing, artificial Intelligence (AI), and all things data science. He also spends any night he can staring into the universe with his two dogs and his telescope. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!) Thanks for hanging out with us!

How to bring modern UI to your Shiny apps

Looking for ways to make your Shiny apps a little…shinier? Join Garrett Grolemund at Posit on Wednesday, January 31st at 11 am ET to learn how to theme and brand your own apps. The session will highlight how to: Layout an app with the bslib package (modern UI toolkit with no knowledge of CSS required) Add cards, value boxes, and logos Customize the theme of the app Tweak the theme by swapping out primary colors, secondary colors, and more. Quickly apply the theme to every plot in the app Work with bootstrap classes Helpful Resources: ️ Code at: https://github.com/garrettgman/shiny-styling-demo bslib package: https://rstudio.github.io/bslib/ bsicons package: https://github.com/rstudio/bsicons ️ Bootstrap icons: https://icons.getbootstrap.com/ ️ Bootstrap CSS classes: https://bootstrapshuffle.com/classes thematic package: https://rstudio.github.io/thematic/ gitlink package: https://github.com/colearendt/gitlink ️ Follow-up links: Posit Team: https://posit.co/products/enterprise/team/ Request evaluation: pos.it/chat-with-us Posit Team demo resources: pos.it/demo-resources LIVE Q&A ROOM for ~11:45 am on January 31st: https://youtube.com/live/1G8ZM6kbt8c?feature=share There is no need to register; join us here on YouTube at the time above or you can add to your calendar using the link below: pos.it/team-demo We host these Workflow Demos on the last Wednesday of every month, so you can use the link above to add the recurring event as well

GitHub Copilot on Posit Cloud

Speed up your coding projects in the RStudio IDE on Posit Cloud with GitHub Copilot, an AI coding assistant. Learn more in our blog post: https://posit.co/blog/github-copilot-on-posit-cloud/ Posit Cloud: https://posit.cloud/ GitHub Copilot: https://github.com/features/copilot RStudio User Guide: https://docs.posit.co/ide/user/ide/guide/tools/copilot.html

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Estimating & communicating ROI for data science projects

A recurring topic in Data Science Hangouts, meetups, and 1:1 chats is the ability to estimate, measure, and communicate the Return on Investment (ROI) for data science projects to business stakeholders. A specific question was: “Are you ever asked to estimate the ROI from an analytics project before the business is willing to invest in anything?" I asked the community if anyone had something to share on this topic, and they delivered! A special thank you to Derek Beaton, Director of Advanced Analytics at St. Michael’s Hospital and Joe Powers, Principal Data Scientist at Intuit who kicked off the group conversation last month by sharing their own experiences. Tips for communicating the ROI for data science projects: Engage stakeholders & identify gatekeepers Align evaluation metrics to stakeholder objectives Establish clear success criteria Anticipate where things can go awry Consistently communicate your central message Prioritize what’s most impactful Promote transparency while measuring project performance Ensure the benefit is unquestionably worth the time investment Additional ROI Resources Mentioned: Analytics Power Hour Podcast - Estimating the Effort for Analytics Projects: https://analyticshour.io/2023/10/31/231-estimating-the-effort-for-analytics-projects/ CDO Matters Podcast with Malcom Hawker: https://open.spotify.com/show/2OJXB8v32CrKbbsv9Uq2vn How to Measure Anything by Douglas Hubbard: https://hubbardresearch.com/7-simple-principles-for-measuring-anything/ If you want to continue conversations like this, we invite you to also join us at the Data Science Hangout every Thursday from 12-1 ET. Every week we’re joined by a different data leader from the community to share their perspectives and experience. You can add it to your calendar using this link: pos.it/dsh

Make data science slides with code using Quarto

#positshorts

Quarto RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

How to collaborate effectively with other data scientists (version control, project sharing, etc.)

Tis the season for joy and connection, and what better time to extend that collaborative spirit into your data science endeavors By popular demand, our upcoming monthly workflow with Ryan Johnson on December 27th is dedicated to enhancing teamwork. It’s recorded, so no worries if you’re out! Or perhaps you’ll add us to the mix of holiday movies and watch from the couch! This Month’s Focus: All Things Collaborative Working Version control Git-backed deployment Project sharing Date & Time: Wednesday, December 27th at 11 am ET. Packages mentioned: Shiny: https://shiny.posit.co/ bslib: https://rstudio.github.io/bslib/ ️ Follow-up links: Posit Team: https://posit.co/products/enterprise/ … Talk to us directly: pos.it/chat-with-us Posit Team demo resources: pos.it/demo-resources There is no need to register; join us here on YouTube at the time above or you can add to your calendar using the link below: pos.it/team-demo We host these Workflow Demos on the last Wednesday of every month, so you can use the link above to add the recurring event as well. We will use this thread on the Posit Community Forum for follow-up Q&A from this month’s session: https://community.rstudio.com/t/event-on-12-27-collaborative-workflows-w-posit-team-version-control-git-backed-deployment-project-sharing/179181 (shortlink: pos.it/workflow-dec-23) Happy holidays! Cheers to 2024!

You Can Lead a Horse to Water…Changing the Data Science Culture for Veterinary Scientists

Presented by Jill MacKay A retrospective look at supporting data science skills in a research-focussed veterinary school This is a talk about environment management, but not in the way you’re thinking. In many industries, domain-specific experts need enough understanding of data science to support their work and communicate with data scientists, but often have insufficient training in these skills, and limited time with which to obtain data science skills and practice them. This is particularly challenging for those who are interdisciplinary and have limited control over their workload, such as medics and field scientists. In this talk, an educational scientist describes the previous 10 years of supporting veterinary scientists to adopt open science practices surrounding data science. What worked, what failed miserably, and reflections on why it can be so hard to get a horse to drink. Materials: https://github.com/jillymackay/positconf2023_vetdata https://jillymackay.com/post/positconf2023/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Teaching data science. Session Code: TALK-1095

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Why You Should Stop Networking and Start Making Friends - posit::conf(2023)

Presented by Libby Heeren When we think about making connections, we think about networking. I’d like you to forget about networking and start thinking about making friends. I’ll share my perspective as a community builder and host of the Data Humans podcast on how I cultivated a community of practice for myself and how I became a force multiplier who increases engagement. You’ll learn how I made genuine human connections, the practical steps to making data friends, the power of vulnerability, and why we all benefit when we show up as our whole selves. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1167

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Why You Should Add Logging To Your Code (and make it more helpful) - posit::conf(2023)

Presented by Daren Eiri Learn how the log4r package can help you better understand the errors your code may produce, and how to also get promptly alerted for severe errors by leveraging cloud monitoring solutions like Azure Monitor or AWS CloudWatch When an error happens in your API, Shiny App, or quarto document, it is not always clear what line of code you need to look at, and the error messages aren’t always helpful. By walking through a simple API example, I show how you can use logging packages like log4r to provide error messages that make sense to you. I also show how you can use cloud-based data collection platforms like Azure Monitor or AWS CloudWatch to set up alerts, so you can get notified by email or text message for those severe errors that you need to be immediately aware of. Gain more visibility into the health of your code by incorporating logging and pushing your logs to the cloud. Materials: https://dareneiri.github.io/positconf2023/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1164

log4r Quarto Shiny RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

What’s New in the Torch Ecosystem - posit::conf(2023)

Presented by Daniel Falbel torch is an R port of PyTorch, a scientific computing library that enables fast and easy creation and training of deep learning models. In this talk, you will learn about the latest features and developments in torch, such as luz, a higher-level interface that simplifies your model training code, and vetiver, a new integration that allows you to deploy your torch models with just a few lines of code. You will also see how torch works well with other R packages and tools to enhance your data science workflow. Whether you are new to torch or already an experienced user, this talk will show you how torch can help you tackle your data science challenges and inspire you to build your own models. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1163

Daniel Falbel

torch RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

What’s New in Quarto?* - posit::conf(2023)

Presented by Charlotte Wickham It’s been over a year since Quarto 1.0, an open-source scientific and technical publishing system, was announced at rstudio::conf(2022). In this talk, I’ll highlight some of the improvements to Quarto since then. You’ll learn about new formats, options, tools, and ways to supercharge your content. And, if you haven’t used Quarto yet, come to see some reasons to try it out. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Quarto (1). Session Code: TALK-1072

Charlotte Wickham

Quarto rstudio Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

What I Wish I Knew Before Becoming a Data Scientist - posit::conf(2023)

Presented by Kaitlin Bustos In this talk, I’m sharing my personal journey as a data scientist and the key lessons learned along the way. I’ll emphasize the importance of finding a positive community of like minded-allies, persevering through setbacks as success is not linear, and exploring by embracing the broad nature of the data science field. By sharing my experiences and acknowledging the challenges I’ve faced attendees will gain a fresh perspective on what it takes to succeed in a data science career and inspire them to pursue their passions in the field. Overall, this talk aims to provide a glimpse into the reality of a data science career. Attendees will take away a sense of motivation and empowerment to find their own unique path to success. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1169

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

What an Early 2000s Reality Show Taught Me about File Management - posit::conf(2023)

Presented by Reiko Okamoto Clutter, whether it’s physical or digital, destroys our ability to focus; home organization ideas can be extended to create an workspace where analysts feel inspired to work with data. Ideas from home organization shows are surprisingly applicable to file management. Using a room divider to establish dedicated zones for different activities in a studio apartment is analogous to creating self-contained projects in RStudio. Likewise, swapping mismatched hangers with matching ones to tidy a closet resembles the adoption of a file naming convention to make a directory easier to navigate. In this talk, I will share good practices in file management through the lens of home organization. We all know that clutter, whether it is in our physical space or on our machine, destroys our ability to focus. These practices will help R users of all levels create a serene, relaxing environment where they feel inspired to work with data. https://reikookamoto.github.io/; https://github.com/reikookamoto/posit-conf-2023-neat Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Getting %$!@ done: productive workflows for data science. Session Code: TALK-1090

rstudio RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

We Converted our Documentation to Quarto - posit::conf(2023)

Presented by Melissa Van Bussel Elevate your Quarto projects to new heights with these practical tips and tricks! “Wiki”, “User Guide”, “Handbook” – whatever you call yours, we converted ours to Quarto! A year ago, my team’s documentation, which had been created using Microsoft Word, was large and lacked version control. Scrolling through the document was slow, and, due to confidentiality reasons, only one person could edit it at a time, which was a significant challenge for our team of multiple developers. After realizing we needed a more flexible solution, we successfully converted our documentation to Quarto. In this talk, I’ll discuss our journey converting to Quarto, the challenges we faced along the way, and tips and tricks for anyone else who might be looking to adopt Quarto too. Slides: https://melissavanbussel.quarto.pub/posit-conf-2023; Code for slides: https://github.com/melissavanbussel/posit-conf-2023; My YouTube: https://www.youtube.com/c/ggnot2; My website: https://www.melissavanbussel.com/; My Twitter: https://twitter.com/melvanbussel; My LinkedIn: https://www.linkedin.com/in/melissavanbussel/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Quarto (2). Session Code: TALK-1140

Quarto RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Visualizing Data Analysis Pipelines with Pandas Tutor and Tidy Data Tutor - posit::conf(2023)

Presented by Sean Kross The data frame is a fundamental data structure for data scientists using Python and R. Pandas and the tidyverse are designed to center building pipelines for the transformation of data frames. However, within these pipelines it is not always clear how each operation is changing the underlying data frame. To explain each step in a pipeline data science instructors resort to hand-drawing diagrams to illustrate the semantics of operations such as filtering, sorting, and grouping. In this talk, I will introduce Pandas Tutor and Tidy Data Tutor, step-by-step visual representation engines of data frame transformations. Both tools illustrate the row, column, and cell-wise relationships between an operation’s input and output data frames. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Teaching data science. Session Code: TALK-1096

tidyverse Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Validating and Testing R Dataframes with Pandera via reticulate - R-Python Interoperability

Presented by Niels Bantilan Original Full Title: Validating and Testing R Dataframes with Pandera via reticulate: A Case Study in R-Python Interoperability Data science and machine learning practitioners work with data every day to analyze and model them for insights and predictions. A major component of any project is data quality, which is a process of cleaning, and protecting against flaws in data that may invalidate the analysis or model. Pandera is an open source data testing toolkit for dataframes in the Python ecosystem: but can it validate R dataframes? This talk is composed of three parts: first I’ll describe what data testing is and motivate why you need it. Then, I’ll introduce the iterative process of creating and refining dataframe schemas in Pandera. Finally, I’ll demonstrate how to use it in R with the reticulate package using a simple modeling exercise as an example. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: R or Python? Why not both!. Session Code: TALK-1123

reticulate Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Using R, Python, and Cloud Infrastructure to Battle Aquatic Invasive Species - posit::conf(2023)

Presented by Uli Muellner and Nicholas Snellgrove Invasive species are a huge threat to lake ecosystems in Minnesota. With over 10,000 water bodies across the state, having up-to-date data and decision support is critical. Researchers at the University of Minnesota have created four complex R and Python models to support lake managers, all pulled together and presented with the most recent infestation data available. Come along with us to see how we connected these models in the AIS Explorer, a decision support application built in Shiny to help prioritize risks and placing watercraft inspectors, using tools like OCPU and cloud toolings like Lambda, EventBridge and AWS S3. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: R or Python? Why not both!. Session Code: TALK-1118

Shiny RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Using R with Databricks Connect - posit::conf(2023)

Presented by Edgar Ruiz Spark Connect, and Databricks Connect, enable the ability to interact with Spark stand-alone clusters remotely. This improves our ability to perform Data Science at-scale. We will share the work in sparklyr, and other products, that will make it easier for R users to take advantage this new framework. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Tidy up your models. Session Code: TALK-1084

Edgar Ruiz

Databricks Data Science RStudio Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Using R to develop production modeling workflows at Mayo Clinic - posit::conf(2023)

Presented by Brendan Broderick Developing workflows that help train models and also help deploy them can be a difficult task. In this talk I will share some tools and workflow tips that I use to build production model pipelines using R. I will use a project of predicting patients who need specialized respiratory care after leaving the ICU as an example. I will show how to use the targets package to create a reproducible and easy to manage modeling and prediction pipeline, how to use the renv package to ensure a consistent environment for development and deployment, and how to use plumber, vetiver, and shiny applications to make the model accessible to care providers. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Leave it to the robots: automating your work. Session Code: TALK-1149

plumber renv Shiny Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Using Data to Protect Traditional Lifeways - posit::conf(2023)

Presented by Angie Reed The spirit of Penobscot Nation’s work to protect the health of their relative, the Penobscot River, is embodied in the Penobscot water song which says ““Water, we love you, thank you so much water, we respect you.”” Because the Penobscot River is not a natural resource - she is a relative, family - this song describes the foundation of our efforts to protect her health and well-being. The identity of Penobscot people cannot be disconnected from the river, and protecting this traditional lifeway is at the heart of our work. For over a decade we have used R to manage, transform, analyze, and visualize data, and the free, open-source Posit products help us leave a legacy of good data management and the ability to share results with Penobscot Nation citizens. You will learn more about how our use of R has helped us achieve more stringent protections for the Penobscot River and how we engage young people in every step of this work. We are also part of a larger network of tribal environmental professionals, working together to learn R and share data and insights. We will give you information about how you can volunteer to help expand the network of folks providing technical assistance on any R and RStudio related topics. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: End-to-end data science with real-world impact. Session Code: TALK-1144

rstudio Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

USGS R Package Development: 10-year Reflections - posit::conf(2023)

Presented by Laura DeCicco Ten years ago, the first set of git commits was submitted to a new R software package repository “dataRetrieval” with the goal to provide an easy way for R users to retrieve U.S Geological Survey (USGS) water data. At that time, the perception within the USGS was the use of R was exclusive to an elite group of “very serious scientists.” Fast forward, we now find many newer USGS hires having a solid grasp of the language from the start along with the use of R in a wide variety of applications. In this talk, I’ll discuss my experiences maintaining the dataRetrieval package, how it’s shaped my career, impacted USGS R usage, and why data providers should consider sponsoring their own R packages wrapping their data API services. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1171

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Unlock the Power of DataViz Animation and Interactivity in Quarto - posit::conf(2023)

Presented by Deepsha Menghani Plot animated and interactive visualizations with Plotly and Crosstalk in Quarto using R. In thi sintro to Plotly & Crosstalk in R: Using code examples, learn to integrate dashboard elements into Quarto with animated plots, interactive widgets (checkboxes), and linked plots via brushing. This talk showcases how to use packages, such as Plotly and Crosstalk, to create interactive data visualizations and add dashboard-like elements to Quarto. Using a fun dataset available through the “Richmondway” package, we examine the number of times Roy Kent uses salty language throughout all seasons of ““Ted Lasso.”” We illustrate this using animated plots, interactive selection widgets such as checkboxes, and by linking two plots with brushing capabilities. Materials: Slides: https://deepshamenghani.github.io/posit_plotly_crosstalk/#/title-slide Code repo: https://github.com/deepshamenghani/posit_plotly_crosstalk Richmondway data package: https://github.com/deepshamenghani/richmondway In-Depth Guide to Creating and Publishing an R Data Package (Richmondway) Using Devtools: https://medium.com/p/245b0fd4c359 Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Quarto (2). Session Code: TALK-1143

crosstalk devtools Quarto Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Towards the Next Generation of Shiny UI

Presented by Carson Sievert Create awesome looking and feature rich Shiny dashboards using the bslib R package. Shiny recently celebrated its 10th birthday, and since its birth, has grown tremendously in many areas; however, a hello world Shiny app still looks roughly like it did 10 years ago. The bslib R package helps solve this problem making very easy to apply modern and customizable styling your Shiny apps, R Markdown / Quarto documents, and more. In addition, bslib also provides dashboard-focused UI components like expandable cards, value boxes, sidebar layouts, and more to help you create delightful Shiny dashboards. Materials: https://rstudio.github.io/bslib/ https://bslib.shinyapps.io/flights/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Shiny user interfaces. Session Code: TALK-1124

Carson Sievert

bslib Quarto rstudio Shiny shinyapps Python Shiny Databricks Data Science RStudio Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

tidymodels: Adventures in Rewriting a Modeling Pipeline - posit::conf(2023)

Presented by Ryan Timpe An overview of the benefits unlocked on our data science team by adopting tidymodels. Data science sure has changed over the past few years! Everyone’s talking about production. RStudio is now Posit. Models are now tidy. This talk is about embracing that change and updating existing models using the tidymodels framework. I recently completed this change, letting go of our in-production code and revisioning it with tidymodels. My team ended up with a faster, more scalable pipeline that enabled us to better automate our workflow and increase our scale while improving our stakeholders’ experiences. I’ll share tips and tricks for adopting the tidymodels framework in existing products, best practices for learning and upskilling teams, and advice for using tidymodel packages to build more accessible data science tools. Materials: https://www.ryantimpe.com/files/tidymodels_adventures_positconf2023.pdf Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Tidy up your models. Session Code: TALK-1082

rstudio tidymodels AIMLmodelling Automation Humans Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

The Road to Easier Shiny App Deployments - posit::conf(2023)

Presented by Liam Kalita We’re often helping developers to assess, fix and improve their Shiny apps, and often the first thing we do is see if we can deploy the app. If you can’t deploy your Shiny app, it’s a waste of time. If you can deploy it successfully, then at the very least it runs, so we’ve got something to work with. There are a bunch of reasons why apps fail to deploy. They can be easy to fix, like Hardcoded secrets, fonts, or missing libraries. Or they can be intractable and super frustrating to deal with, like manifest mismatches, resource starvation, and missing libraries. At the end of this talk, I want you to know how to identify, investigate and proactively prevent Shiny app deployment failures from happening. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: The future is Shiny. Session Code: TALK-1089

Shiny RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

The Power of Prototyping in R Shiny: Saving Millions by Building the Right Tool - posit::conf(2023)

Presented by Maria Grycuk The development of software can be costly and time-consuming. If the end users are not involved in the process from the start the tool we built may not meet their needs. In this presentation, I will discuss how prototyping in Shiny can help you build the right tool and save you from spending millions of dollars on a tool no one will use. I will explore the advantages of using Shiny for prototyping, particularly its ability to rapidly build interactive applications. I will also discuss how to design effective prototypes, including techniques for gathering user feedback and using that feedback to refine your tool. I will emphasize the importance of presenting real-life data, particularly when building data-driven tools. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Shiny user interfaces. Session Code: TALK-1125

Shiny RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

The People of Posit: Bringing Personality to R Packages - posit::conf(2023)

Presented by JP Flores and Sarah Parker The R programming language offers the versatility to perform statistical analyses, create publication-ready plots, and render high-quality reports and presentations. Despite having this environment of indispensable tools, it can be daunting for a beginner-level programmer to get started. Luckily, the Posit community is one of a kind and values inclusivity, collaboration, and empathy. By putting a face to the R packages we use on a daily basis, we hope to make every programmer feel included and capable. We want to inspire attendees to create their own projects or packages, connect with others inside and outside of their field of expertise, and challenge themselves to learn something new, knowing the community is right there to support them. Materials: http://www.sarmapar.com/people_of_posit/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1165

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

The Need for Speed - AccelerateR-ing R Adoption in GSK - posit::conf(2023)

Presented by Ben Arancibia How does a risk-averse Pharma Biostatistics organization with 900+ people switch from using proprietary software to using R and other open-source tools for delivering clinical trial submissions? First slowly, then all at once. GSK started the transition of using R for its clinical trial data analysis in 2020 and now uses R for our regulatory-reviewed outputs. The AccelerateR Team, an agile pod of R experts and data scientists, rotates through GSK Biostatistics study teams sitting side by side to answer questions and mentor during this transition. We will share our experience from AccelerateR and how other organizations can use our learnings to scale R from pilots to full enterprise adoption and contribute to open source industry R packages. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Pharma. Session Code: TALK-1068

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

The ‘I’ in Team: Peer-to-Peer Best Practices for Growing Data Science Teams - posit::conf(2023)

Presented by Liz Roten R users don’t always come in sets. Often, you may be the only user on in the cubicle-block. But, one miraculous day, your manager finally fills the void and you welcome more folks on your team. Suddenly, the little R system you created to suit your needs, like a custom package, code styling, and file organization, isn’t just for you. Want to suddenly overhaul that one package you wrote two years ago? It probably won’t work when your colleagues try to update it. Your new teammates are data.table fans, but you prefer the tidyverse. Do you need to refactor? Are style choices, like indentation important when collaborating, or are you just being persnickety? In this talk, you will learn how to bring new teammates on board and blend your respective styles without pulling your hair out. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Building effective data science teams. Session Code: TALK-1063

tidyverse Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Thanks, I Made It with Quartodoc - posit::conf(2023)

Presented by Isabel Zimmerman When Python package developers create documentation, they typically must choose between mostly auto-generated docs or writing all the docs by hand. This is problematic since effective documentation has a mix of function references, high-level context, examples, and other content. Quartodoc is a new documentation system that automatically generates Python function references within Quarto websites. This talk will discuss pkgdown’s success in the R ecosystem and how those wins can be replicated in Python with quartodoc examples. Listeners will walk away knowing more about what makes documentation delightful (or painful), when to use quartodoc, and how to use this tool to make docs for a Python package. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Data science with Python. Session Code: TALK-1139

Isabel Zimmerman

pkgdown Quarto RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Teaching Data Science in Adverse Circumstances: Posit Cloud and Quarto to the Rescue - posit::conf

Presented by Aleksander Dietrichson The focus of this presentation is on the challenges faced by teachers of data science whose students are not quantitatively inclined and may face some adversity in terms of technology resources available to them and potential language barriers. I identify three main areas of challenges and show how at Universidad Nacional de San Martín (Argentina) we addressed each of the areas through a combination of original curriculum redesign, production of course materials appropriate for the students in question; and the use of OS, and some Posit products, i.e.:posit.cloud and Quarto. I show how these technologies can be used as a pedagogical tool to overcome the challenges mentioned, even on a shoestring budget. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Teaching data science. Session Code: TALK-1094

Quarto Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Take it in Bits: Using R to Make Eviction Data Accessible to the Legal Aid Community - posit::conf

Presented by Logan Pratico One in five low-income renter households in the US experienced falling behind on rent or being threatened with eviction in 2021. Yet most are unrepresented when facing eviction in court. The complex and fast-paced legal system obscures access to timely information, leaving tenants without assistance. In this talk, I discuss the Civil Court Data Initiative’s use of R alongside AWS Cloud and SQL to analyze disaggregate eviction records. I focus on the integration of RMarkdown with Amazon Athena and EC2 to create weekly eviction reports across 20 states for legal aid groups working to assist tenants. The upshot: accessible eviction data to help legal aid providers better address local legal needs. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: End-to-end data science with real-world impact. Session Code: TALK-1146

rmarkdown RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Sustainable Growth of Global Communities: R-Ladies’ Next Ten Years - posit::conf(2023)

Presented by Riva Quiroga In this talk we share how good programming practices inspire the way we manage the R-Ladies community in order to make it sustainable. R-Ladies’ first ten years were about growing the community: from being just one chapter in 2012 to becoming a global organization in 2016, and now fostering more than 230 chapters worldwide. But how can we face the challenges of growing an organization based solely on volunteer work? In this talk, we discuss how good programming practices –such as modularity, refactoring, and testing– inspire the way we see the sustainable management of an ever-growing community. To that end, we will present our most recent efforts at creating and documenting workflows, distributing the workload, and automating tasks that allow volunteers to focus their time where it is most needed. After watching this talk, you will get some ideas on how to support volunteers in your own community or project, and on how to use GitHub Actions to automate workflows and tasks. Learn more and join at: https://rladies.org/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: It takes a village: building and sustaining communities. Session Code: TALK-1128

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Succeed in the Life Sciences with R/Python and the Cloud - posit::conf(2023)

Presented by Colby Ford This talk covers best practices and lessons learned surrounding the use of R and Python by technical teams in the cloud, focusing on Posit Workbench, Azure ML, and Databricks. In the life sciences, whether it’s pharma, biotech, research, or another type of organization, we are unique in that we blend scientific knowledge with technical skills to extract insights from large, complex datasets. In the cloud, we can architect solutions to help us scale, automate, and collaborate. Interestingly, the use of R and Python by bioinformatics, genomics, biostatistics, and data science teams can be challenging in a cloud-first world where all the data is somewhere other than your laptop (like a data lake). In this talk, I will share best practices and lessons learned surrounding the use of R and Python by technical teams in the cloud. We’ll focus on the use of Posit Workbench and RStudio on various cloud services such as Azure ML and Databricks. Tuple, The Cloud Genomics Company: https://tuple.xyz Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Pharma. Session Code: TALK-1069

rstudio RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Styling and Templating Quarto Documents - posit::conf(2023)

Presented by Emil Hvitfeldt Quarto is a powerful engine to generate documents, slides, books, websites, and more. The default aesthetics looks good, but there are times when you want and need to change how they look. This is that talk. Whether you want your slides to stand out from the crowd, or you need your documents to fit within your corporate style guide, being able to style Quarto documents is a valuable skill. Once you have persevered and created the perfect document, you don’t want the effort to go to waste. This is where templating comes in. Quarto makes it super easy to turn a styled document into a template to be used over and over again. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Compelling design for apps and reports. Session Code: TALK-1106

Emil Hvitfeldt

Quarto Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Speeding Up Plots in R/Shiny - posit::conf(2023)

Presented by Ryszard Szymański A slow plots can ruin the user experience of our dashboard. This talk covers techniques for speeding up the rendering process of our visualisations. Slow dashboards lead to a poor user experience and cause users to lose interest, or even become frustrated. A common culprit of this situation is a slowly rendering plot. During the talk, we will dive deeper into how plots are rendered in Shiny, identify common bottlenecks that can occur during the rendering process, and learn various techniques for improving the speed of plots in R/Shiny dashboards. These techniques will range from more efficient data processing to library-specific optimisations at the browser level. Materials: I’d like to include a link to my linkedin profile: https://www.linkedin.com/in/ryszard-szyma%C5%84ski-310a7017a/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1172

Shiny RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Solving a Secure Geocoding Problem (That Hardly Anybody Has) - posit::conf(2023)

Presented by Tesla DuBois Due to data security concerns, the strictest health researchers won’t send patient addresses to remote servers for geocoding. The only existing methods for offline geocoding are expensive, cumbersome, or require working with code - all limiting factors for many researchers. So, a couple of classmates and I made a standalone desktop application using shell, Docker, PostGIS, and Python to geocode addresses through a simple GUI without ever sending them off the local machine. Come for the technical ins and outs and stay for the anecdotes about how my R background played into the daunting, frustrating, but ultimately successful task of creating a data science tool using unfamiliar technologies. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Developing your skillset; building your career. Session Code: TALK-1111

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Small Package, Broad Impact: How I Discovered the Ultimate New Hire Hack - posit::conf(2023)

Presented by Trang Le Onboarding new hires can be a challenging process, but taking a problem-focused approach can make it more meaningful and rewarding. In this talk, I will share how I discovered the ultimate new hire hack by creating two small packages that gave me the confidence I needed when I started at BMS. Through building these packages, I not only learned R things like using bslib and making font files available for published dashboards, but also gained a deep understanding of my company’s internal systems and workflows, and connected with my team via lots of questions. The resulting packages are still heavily used today. Join me to discover how small packages can have a broad impact and what hiring managers can do to help. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Developing your skillset; building your career. Session Code: TALK-1112

bslib Humans Programming RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Siuba and duckdb: Analyzing Everything Everywhere All at Once - posit::conf(2023)

Presented by Michael Chow Every data analysis in Python starts with a big fork in the road: which DataFrame library should I use? The DataFrame Decision locks you into different methods, with subtly different behavior:: different table methods (e.g. polars.with_columns() vs pandas.assign()) different column methods (e.g. polars.map_dict() vs pandas.map()) In this talk, I’ll discuss how siuba (a dplyr port to python) combines with duckdb (a crazy powerful sql engine) to provide a unified, dplyr-like interface for analyzing a wide range of data sources‚ whether pandas and polars DataFrames, parquet files in a cloud bucket, or pins on Posit Connect. Finally, I’ll discuss recent experiments to more tightly integrate siuba and duckdb. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Databases for data science with duckdb and dbt. Session Code: TALK-1101

Michael Chow

dplyr RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Side Effects of a Year of Blogging - posit::conf(2023)

Presented by Millie Symns A big part of being in the R community is sharing your knowledge in different forums, no matter how big or small. So what better way to do that than a blog? And what better way than using R and Posit products to build and maintain that blog and website? This was the route I took to challenge myself in putting myself out there more in the community to find my voice, share my knowledge and learn new things. In this talk, I will reflect on lessons learned and gains I have spent the past year blogging and sharing my website for all to see. The side effects include professional and personal benefits - from a clear profile of my skills to the progression of the development of my art. You may leave inspired to try the challenge for yourself. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: It takes a village: building and sustaining communities. Session Code: TALK-1130

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

ShinyUiEditor: From Alpha to Powerful Shiny App Development Tool - posit::conf(2023)

Presented by Nick Strayer Since its alpha debut at last year’s conference, the ShinyUiEditor has experienced continuous development, evolving into a powerful tool for crafting Shiny app UIs. Some key enhancements include the integration of new bslib components and the editor’s ability to create or navigate to existing server bindings for inputs and outputs. In addition to new features, the editor is now available as a VSCode extension enabling it to integrate smoothly into more developers’ workflows. This talk will showcase how these new capabilities empower users to efficiently create visually appealing and production-ready applications with ease. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Shiny user interfaces. Session Code: TALK-1126

Nick Strayer

bslib Shiny shinyuieditor Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Shiny New Tools for Scaling your Shiny Apps - posit::conf(2023)

Presented by Joe Kirincic So you have a Shiny app your org loves, but as adoption grows, performance starts getting sluggish. Profiling reveals your cool interactive plots are the culprit. What can you do to make things snappy again? We can increase the number of app instances, sure, but suppose that isn’t an option for us. Another approach is to shift the plotting work from the server onto the client. In this talk, we’ll learn how to leverage two Javascript projects, DuckDB-WASM and Observable’s Plot.js, in our Shiny app to create fast, flexible interactive visualizations in the browser without burdening our app’s server function. The end result is an app that can scale to more users without needing to increase compute resources. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: The future is Shiny. Session Code: TALK-1088

Shiny Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Shiny for Python Machine Learning Apps with pandas, scikit-learn and TensorFlow - posit::conf(2023)

Presented by Chelsea Parlett-Pelleriti With the introduction of Shiny for Python in 2022, users can now use the power of reactivity with their favorite Python packages. Shiny can be used to build interactive reports, dashboards, and web apps, that make sharing insights and results both simple and dynamic. This includes apps to display and explore popular Machine Learning models built with staple Python packages like pandas, scikit-learn, and TensorFlow. This talk will demonstrate how to build simple Shiny for Python apps that interface with these packages, and discuss some of the benefits of using Shiny for Python to build your web apps. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: The future is Shiny. Session Code: TALK-1087

Shiny for Python Shiny tensorflow Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Shiny Developer Secrets: Insights From Over 1200 Applicants and What You MUST Know to Shine

Presented by Vedha Viyash Over 1,200 candidates applied for the R/Shiny developer role at Appsilon in the last year, and I will be sharing some insights that we have gained from going through the qualitative and quantitative feedback collected from every round of the interview process. I will be sharing some key takeaways that would help you focus on things that will make you a better Shiny developer. From reactivity to software testing, there are multiple skills that make up a good Shiny developer and you will get to know the major gaps and how to focus on them. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1173

Shiny RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Serenity Now, Productivity Later: Focus Your Project Stack with The Gonzalez Matrix - posit::conf

Presented by Patrick Tennant How should you respond when your boss has too many good ideas for data science projects? In this talk, I’ll review our use of an adapted version of the Eisenhower Matrix that lays out our projects according to the effort required and the value they will produce. Given the functionally unlimited number of data science projects a team could do, learn how we keep our team focused on valuable work while reducing the stress of a never-ending list of projects. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Building effective data science teams. Session Code: TALK-1065

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Scale Your Data Validation Workflow With {pointblank} and Posit Connect - posit::conf(2023)

Presented by Michael Garcia For the Data Services team at Medable, our number one priority is to ensure the data we collect and deliver to our clients is of the highest quality. The {pointblank} package, along with Posit Connect, modernizes how we tackle data validation within Data Services. In this talk, I will briefly summarize how we develop test code with {pointblank}, share with {pins}, execute with {rmarkdown}, and report findings with {blastula}. Finally, I will show how we aggregate data from test results across projects into a holistic view using {shiny}. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Leave it to the robots: automating your work. Session Code: TALK-1058

blastula pointblank rmarkdown Shiny Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Running R-Shiny without a Server - posit::conf(2023)

Presented by Joe Cheng A year ago, Posit announced ShinyLive, a deployment mode of Shiny that lets you run interactive applications written in Python, without actually running a Python server at runtime. Instead, ShinyLive turns Shiny for Python apps into pure client-side apps, running on a pure client-side Python installation. Now, that same capability has come to Shiny for R, thanks to the webR project. In this talk, I’ll show you how you can get started with ShinyLive for R, and why this is more interesting than just cheaper app hosting. I’ll talk about some of the different use cases we had in mind for ShinyLive, and help you decide if ShinyLive makes sense for your app. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: I can’t believe it’s not magic: new tools for data science. Session Code: TALK-1151

Joe Cheng

Shiny for Python Shiny shinylive Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Reproducible Manuscripts with Quarto - posit::conf(2023)

Presented by Mine Çetinkaya-Rundel In this talk, we present a new capability in Quarto that provides a straightforward and user-friendly approach to creating truly reproducible manuscripts that are publication-ready for submission to popular journals. This new feature, Quarto manuscripts, includes the ability to produce a bundled output containing a standardized journal format, source documents, source computations, referenced resources, and execution information into a single bundle that is ingested into journal review and production processes. We’ll demo how Quarto manuscripts work and how you can incorporate them into your current manuscript development process as well as touch on pain points in your current workflow that Quarto manuscripts help alleviate. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Quarto (1). Session Code: TALK-1070

Mine Çetinkaya-Rundel

Quarto Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Reliable Maintenance of Machine Learning Models - posit::conf(2023)

Presented by Julia Silge Maintaining machine learning models in production can be quite different from maintaining general software projects, because of the unique statistical characteristics of ML models. In this talk, learn about model drift, the different ways the word “performance” is used with models, what you can monitor about a model, how feedback loops impact models, and how you can use vetiver to set yourself up for success with model maintenance. This talk will help practitioners who are already deploying models, but this is also useful knowledge for practitioners earlier in their MLOps journey; decisions made along the way can make the difference between resilient models that are easier to maintain and disappointing or misleading models. Materials: https://github.com/juliasilge/ml-maintenance-2023 Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Tidy up your models. Session Code: TALK-1083

Julia Silge

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

R! You Going?! - posit::conf(2023)

Presented by SherAaron Hurt 3 things to remember when starting your journey to become a data scientist Everyone will have a different journey when becoming a data scientist. However, there are a few tips to consider to make the journey less daunting and more enjoyable. Listen, as I tell my story as a data scientist and offer resources and tips to build confidence for those who are new to their journey. The tools are available however, it is not always easy to find them. keywords: openscience, The Carpentries, R programming language, GPS, data science journey, data science resources Materials: https://www.linkedin.com/in/sheraaronhurt/ carpentries.org/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Teaching data science. Session Code: TALK-1097

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

R Not Only In Production - posit::conf(2023)

Presented by Kara Woo I will share what our team has learned from successfully integrating R in all areas of our company’s operations. InsightRX is a precision medicine company whose goal is to ensure that each patient receives the right drug at the optimal dose. At InsightRX, R is a first-class language that’s used for purposes ranging from customer-facing products to internal data infrastructure, new product prototypes, and regulatory reporting. Using R in this way has given us the opportunity to forge fruitful collaborations with other teams in which we can both learn and teach. Join me as I share how the skills of data science and engineering can complement each other to create better products and greater impact. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: R Not Only In Production. Session Code: KEY-1108

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Quickly get your Quarto HTML theme in order - posit::conf(2023)

Presented by Greg Swinehart A 5-minute talk to discuss how I’ve used Quarto and Bootstrap variables to quickly make Shiny’s new website look as it should. The Quarto user I have in mind works at an organization with specific brand guidelines to follow. I‚ will discuss how to set up your theme, show some key Quarto settings, and how Bootstrap‚ Sass variables are your best friend. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1170

Greg Swinehart

Quarto Shiny RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Parameterized Quarto Reports Improve Understanding of Soil Health - posit::conf(2023)

Presented by Jadey Ryan Learn how to use R and Quarto parameterized reporting in this four-step workflow to automate custom HTML and Word reports that are thoughtfully designed for audience interpretation and accessibility. Soil health data are notoriously challenging to tidy and effectively communicate to farmers. We used functional programming with the tidyverse to reproducibly streamline data cleaning and summarization. To improve project outreach, we developed a Quarto project to dynamically create interactive HTML reports and printable PDFs. Custom to every farmer, reports include project goals, measured parameter descriptions, summary statistics, maps, tables, and graphs. Our case study presents a workflow for data preparation and parameterized reporting, with best practices for effective data visualization, interpretation, and accessibility. Talk materials: https://jadeyryan.com/talks/2023-09-25_posit_parameterized-quarto/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Elevating your reports. Session Code: TALK-1160

Quarto tidyverse Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Package Management for Data Scientists - posit::conf(2023)

Presented by Tyler Finethy In my talk, “Package Management for Data Scientists,” we will discuss software dependencies for R and Python and the common issues faced during package installations. I will begin with an overview of package management, highlighting its crucial role in data science. We’ll then focus on practical strategies to prevent dependency errors and address effective troubleshooting when these problems occur. Lastly, we will look towards the future, discussing potential package management improvements, focusing on reproducibility and accessibility for those new to the field. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Managing packages. Session Code: TALK-1081

Programming Python Databricks Data Science RStudio Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Open Source Solutions to Next-Generation Submissions, After 30 Years of Industry Experience

Presented by Mike K Smith The pharmaceutical industry is undergoing rapid change, driven by a desire from both industry and regulatory agencies to move to more interactive visualizations and web applications to review data and make decisions. These changes would have been unthinkable 30 years ago when I started working at Pfizer. In this talk, I’ll consider the drivers for these changes, how open-source tools can help achieve this, and why collaboration across the industry is vital to achieving this goal. I’ll contrast this with my experience of 30 years working in the pharma industry - when the R language had only just been released, when the internet was new, and when submissions to agencies were printed out, loaded onto trucks, and shipped to their doors. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Pharma. Session Code: TALK-1067

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Open Source Property Assessment: Tidymodels to Allocate $16B in Property Taxes - posit::conf(2023)

Presented by Nicole Jardine and Dan Snow How the Cook County Assessor’s Office uses R and tidymodels for its residential property valuation models. The Cook County Assessor’s Office (CCAO) determines the current market value of properties for the purpose of property taxation. Since 2020, the CCAO has used R, tidymodels, and LightGBM to build predictive models that value Cook County’s 1.5 million residential properties, which are collectively worth over $400B. These predictive models are open-source, easily replicable, and have significantly improved valuation accuracy and equity over time. Join CCAO Chief Data Officer Nicole Jardine and Director of Data Science Dan Snow as they walk through the CCAO’s modeling process, shares lessons learned, and offer a sneak peek at changes planned for the 2024 reassessment of Chicago. Materials: https://github.com/ccao-data Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: End-to-end data science with real-world impact. Session Code: TALK-1147

tidymodels AI .Odelling Modelling Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Oops I’m a Manager - On More Effective 1-on-1 Meetings - posit::conf(2023)

Presented by Andrew Holz As a team leader (accidental or not), it’s easy to get caught up in the daily grind and overlook the importance of 1-on-1s. Bad idea. 1-on-1s are critical for building trust, providing feedback, and ensuring that everyone is on the same page. Keys to good 1-on-1s start with a small amount of prep and a running shared document of notes and takeaways. Another key is to rotate types of 1-on-1s. Possibilities include “heads down” on recent work, “heads up” looking further out, and career-focused sessions. After some tips on the right sort of questions and uncovering sneaky issues, I will also touch on effective feedback. I will share resources and hope to include Seussian visuals and a few poetic lines to help the key points stick. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Building effective data science teams. Session Code: TALK-1064

Andrew Holz

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Never again in outer par mode: making next-generation PDFs with Quarto & typst - posit::conf(2023)

Presented by Carlos Scheidegger Quarto 1.4 will introduce support for Typst. Typst is a brand-new open-source typesetting system built from scratch to support the lessons we have learned over almost half a century of high-quality computer typesetting that TeX and LaTeX have enabled. If you’ve ever had to produce a PDF with Quarto and got stuck handling an inscrutable error message from LaTeX, or wanted to create a new template but were too intimated by LaTeX’s arcane syntax, this talk is for you. I’ll show you why we need an alternative for TeX and LaTeX, and why it will make Quarto even better. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Quarto (2). Session Code: TALK-1142

Carlos Scheidegger

Quarto Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Motley Crews: Collaborating with Quarto - posit::conf(2023)

Presented by Susan McMillan, Wyl Schuth, and Michael Zenz Adoption of Quarto for document creation has transformed the collaborative workflow for our small higher-education analytics team. Historically, content experts wrote in Word documents and data analysts used R for statistics and graphics. Specialization in different software tools created challenges for producing collaborative analytic reports, but Quarto has solved this problem. We will describe how we use Quarto for writing and editing text, embedding statistical analysis and graphics, and producing reports with a standard style in multiple formats, including web pages. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Elevating your reports. Session Code: TALK-1157

Quarto Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Matching Tools to Titans: Tailoring Posit Workbench for Every Cloud - posit::conf(2023)

Presented by James Blair In an era of diverse cloud platforms, leveraging tools effectively is paramount. This talk highlights the adaptability of Posit Workbench within leading cloud platforms. Delve into strategic integrations, understand key challenges, and uncover practical solutions. By the end, attendees will be equipped with insights to harness Posit Workbench’s capabilities seamlessly across varied cloud environments. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Data science infrastructure for your org. Session Code: TALK-1115

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Making a (Python) Web App is easy! - posit::conf(2023)

Presented by Marcos Huerta Making Python Web apps using Dash, Streamlit, and Shiny for Python This talk describes how to make distribution-free prediction intervals for regression models via the tidymodels framework. By creating and deploying an interactive web application you can better share your data, code, and ideas easily with a broad audience. I plan to talk about several Python web application frameworks, and how you can use them to turn a class, function, or data set visualization into an interactive web page to share with the world. I plan to discuss building simple web applications with Plotly Dash, Streamlit, and Shiny for Python. Materials: Comprehensive talk notes here: https://marcoshuerta.com/posts/positconf2023/ https://www.tidymodels.org/learn/models/conformal-regression/ https://probably.tidymodels.org/reference/index.html#regression-predictions Corrections: In my live remarks, I said a Dash callback can have only one output: that is not correct, a Dash callback can update multiple outputs. I was trying to say that a Dash output can only be updated by one callback, but even that is no longer true as of Dash 2.9. https://dash.plotly.com/duplicate-callback-outputs"" Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: The future is Shiny. Session Code: TALK-1086

Shiny for Python Shiny tidymodels Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Magic with WebAssembly and webR - posit::conf(2023)

Presented by George Stagg Earlier this year the initial version of webR was released and users have begun building new interactive experiences with R on the web. In this talk, I’ll discuss webR’s TypeScript library and what it is able to do. The library allows users to interact with the R environment directly from JavaScript, which enables manipulation tricks that seem like magic. I’ll begin by describing how to move objects from R to JS and back again, and discuss the technology that makes this possible. I’ll continue with more advanced manipulation, such as invoking R functions from JS and talk about why you might want to do so. Finally, I’ll describe how messages are sent over webR’s communication channel and explain how this enables webR to work with Shinylive. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: I can’t believe it’s not magic: new tools for data science. Session Code: TALK-1152

George Stagg

shinylive Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Large Language Models in RStudio - posit::conf(2023)

Presented by James Wade Large language models (LLMs), such as ChatGPT, have shown the potential to transform how we code. As an R package developer, I have contributed to the creation of two packages – gptstudio and gpttools – specifically designed to incorporate LLMs into R workflows within the RStudio environment. The integration of ChatGPT allows users to efficiently add code comments, debug scripts, and address complex coding challenges directly from RStudio. With text embedding and semantic search, we can teach ChatGPT new tricks, resulting in more precise and context-aware responses. This talk will delve into hands-on examples to showcase the practical application of these models, as well as offer my perspective as a recent entrant into public package development. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: I can’t believe it’s not magic: new tools for data science. Session Code: TALK-1154

rstudio Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

It’s All About Perspective: Making a Case for Generative Art - posit::conf(2023)

Presented by Meghan Santiago Harris This talk explores how to create art in the R language while highlighting some similarities between the skills required for creating generative art and those needed to perform data science tasks in R. Because the field of data science is inherently task-oriented, it is no wonder that most people struggle to see the utility of generative art past the bounds of a casual hobby. This talk will invite the participant to learn about generative art while focusing on ““why”” people should create it and its potential place in data science. This talk is suitable for all disciplines and artistic abilities. Furthermore, this talk will aim to expand the participant’s perspective on generative art with the following concepts: What is generative art and how can it be created in R or Python Justifications for generative art within Data Science Examples of programming skills that are transferrable between generative art and pragmatic data science projects Materials: Link to the talk repo: https://github.com/Meghansaha/a_case_for_genart Link to the slides: https://meghansaha.github.io/a_case_for_genart/#/title-slide Link to the artpack package site: https://meghansaha.github.io/artpack/ Personal Site: https://thetidytrekker.com/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Developing your skillset; building your career. Session Code: TALK-1109

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

It’s Abstractions All the Way Down… - posit::conf(2023)

Presented by JD Long Abstractions rule everything around us. JD Long talks about abstractions from the board room to the silicon. Over 20 years ago Joel Spolsky famously wrote, “All non-trivial abstractions, to some degree, are leaky.” Unsurprisingly this has not changed. However, we have introduced more and more layers of abstraction into our workflows: Virtual Machines, AWS services, WASM, Docker, R, Python, data frames, and on and on. But then on top of the computational abstractions we have people abstractions: managers, colleagues, executives, stakeholders, etc. JD’s presentation will be a wild romp through the mental models of abstractions and discuss how we, as technical analytical types, can gain skill in traversing abstractions and dealing with leaks. Materials: https://github.com/CerebralMastication/Presentations/tree/master/2023_posit-conf Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: It’s abstractions all the way down …. Session Code: KEY-1161

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

It’s a Great Time to be an R Package Developer! - posit::conf(2023)

Presented by Jenny Bryan and Hadley Wickham (Due to unforeseen circumstances, Hadley Wickham presented this talk “slide karaoke” style, from materials prepared by Jenny Bryan.) In R, the fundamental unit of shareable code is the package. As of March 2023, there were over 19,000 packages available on CRAN. Hadley Wickham and I recently updated the R Packages book for a second edition, which brought home just how much the package development landscape has changed in recent years (for the better!). In this talk, I highlight recent-ish developments that I think have a great payoff for package maintainers. I’ll talk about the impact of new services like GitHub Actions, new tools like pkgdown, and emerging shared practices, such as principles that are helpful when testing a package. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Package development. Session Code: TALK-1132

Hadley Wickham, Jenny Bryan

pkgdown RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Insights in 5-D! (Using magic small-multiples layouts) - posit::conf(2023)

Presented by Matt Dzugan Using Small-Multiples (faceted graphs) is an effective way to compare patterns across many dimensions. In this talk, I’ll walk you through some ways to lay out your individual facets according to the underlying data. For example, maybe each facet represents a city or point on a 2D plane - we’ll explore ways to organize facets in a grid that mimics the data itself - unlocking your ability to explore patterns in 4+ dimensions. Other solutions to this problem rely on manually-curated lists that map common layouts to a grid, but in this talk, we’ll explore solutions that work on EVERYTHING. I’ll show you how to incorporate this technique into your viz and how I built the libraries since there are some interesting data science concepts at play. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1174

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

In-Process Analytical Data Management with DuckDB - posit::conf(2023)

Presented by Hannes Mühleisen This talks introduces DuckDB, an in-process analytical data management system that is deeply integrated into the R ecosystem. DuckDB is an in-process analytical data management system. DuckDB supports complex SQL queries, has no external dependencies, and is deeply integrated into the R ecosystem. For example, DuckDB can run SQL queries directly on R data frames without any data transfer. DuckDB uses state-of-the-art query processing techniques like vectorised execution and automatic parallelism. DuckDB is out-of-core capable, meaning that it is possible to process datasets far bigger than main memory. DuckDB is free and open source software under the MIT license. In this talk, we will describe the user values of DuckDB, and how it can be used to improve their day-to-day lives through automatic parallelisation, efficient operators, and out-of-core operations. Materials: https://duckdb.org https://duckdb.org/docs/api/r.html https://github.com/duckdb/duckdb-r Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Databases for data science with duckdb and dbt. Session Code: TALK-1099

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

HTML and CSS for R Users - posit::conf(2023)

Presented by Albert Rapp You can get the most out of popular R tools by combining them with easy-to-learn HTML & CSS commands. It’s easy to think that R users do not need HTML and CSS. After all, R is a language designed for data analysis, right? But the reality is that these web standards are everywhere, even in R. In fact, many great tools like {ggtext}, {gt}, {shiny}, and Quarto unlock their full potential when you know a little bit of HTML & CSS. In this talk, I will demonstrate specific examples where R users can benefit from HTML and CSS and show you how to get started with these two languages. Materials: Here’s the link to the video that I mention in the talk: https://youtu.be/QU8wSya-Y9E?si=zw59OSFPl1eJSY7M Part 1 of this two part series can be found at https://www.youtube.com/watch?v=jX4_Dnzhl0M Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Compelling design for apps and reports. Session Code: TALK-1105

gt Quarto Rapp Shiny Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

How You Get Value as a 1-Person Posit Connect Team - posit::conf(2023)

Presented by Sean Nguyen Sean, a sole Posit Connect developer, shares his experience in delivering business impact. He narrates his transition from crafting one-off reports to developing and deploying robust data science web applications using Python and R with Posit Connect. Despite its common association with large enterprise teams, Sean demonstrates how Posit Connect can be effectively utilized in smaller settings. He presents his work on creating and deploying end-to-end machine learning pipelines in Python, hosting them as APIs, and seamlessly integrating with Shiny apps via Posit Connect. This talk imparts practical strategies and techniques to foster user and executive adoption of Posit Connect within lean (and large) organizations. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Getting %$!@ done: productive workflows for data science. Session Code: TALK-1093

Shiny Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

How to Win Friends and Influence People (With Data) - posit::conf(2023)

Presented by Joe Powers Too many great data science products never go into production. To persuade leaders and colleagues to adopt your data science offering, you must translate your insights into terms that are relevant and accessible to them. Attempts to persuade these audiences with proofs and model performance stats will often fall flat because the audience is left feeling overwhelmed. This talk will demonstrate the data simulation, visualization, and story-telling techniques that I use to influence leadership and the community-building techniques I use to earn the trust and support of fellow analysts. These efforts were successful in persuading Intuit to adopt advanced analytic methods like sequential analysis that cut the duration of our AB tests by over 60%. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Bridging the gap between data scientists and decision makers. Session Code: TALK-1077

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

How to Keep Your Data Science Meetup Sustainable - posit::conf(2023)

Presented by Ted Laderas Many data science meetup organizers struggle with burnout. It can be daunting to plan a meetup schedule, especially with the added burden of work and life. In this talk, I want to highlight some strategies for keeping your data science meetup sustainable. Specifically, I want to highlight the role of self-care in growing and sustaining your group, as well as low-key activities like a data scavenger hunt, watching videos together, styling plots together, and sharing useful tidyverse functions. By making it easy for your members to contribute and empowering them, it takes a lot of the burden off you as an organizer. You don’t need to reinvent the wheel for meetups or have famous guests for each one. Let’s start the conversation and make your meetup last. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: It takes a village: building and sustaining communities. Session Code: TALK-1129

tidyverse RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

How to Help Developers Make Apps Users Love - posit::conf(2023)

Presented by Michał Parkoła There are many resources that can help you design better apps. But what if your org creates many apps? Scaling good design to larger groups dials the challenge up to 11. In this talk, I will share how we approach the problem at Appsilon. I will present our in-house Design Guide. I will share the successes and failures we’ve had building it and helping a wide variety of developers use it I will then share some tips about what you might want to consider if you want to help your org build better apps at scale. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Shiny user interfaces. Session Code: TALK-1127

Shiny Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

How I Learned to Stop Worrying and Love Public Packages - posit::conf(2023)

Presented by Joe Roberts The popularity of R and Python for data science is in no small part attributable to the vast collection of extension packages available for everything from common tasks like data cleaning to highly-specialized domain-specific functions. However, with that ease of sharing packages comes a larger target for bad actors trying to exploit them. We’ll explore these security risks along with approaches you can take to mitigate them using Posit Package Manager. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Managing packages. Session Code: TALK-1079

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

How Data Scientists Broke A/B Testing (And How We Can Fix It) - posit::conf(2023)

Presented by Carl Vogel As data scientists, we care about making valid statistical inferences from experiments. And we’ve adapted well-established and well-understood statistical methods to help us do so in our A/B tests. Our stakeholders, though, care about making good product decisions efficiently. I’ll describe how the way we design A/B tests can put these goals in tension and why that often causes misalignment between how A/B tests are intended to be used and how they are actually used. I’ll also talk about how I’ve used R to implement alternative experimental approaches that have helped bridge the gap between data scientists and stakeholders. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Bridging the gap between data scientists and decision makers. Session Code: TALK-1076

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Hitting the Target(s) of Data Orchestration - posit::conf(2023)

Presented by Alexandros Kouretsis We are living at a time when the size of datasets can be overwhelming. Add to this that their process involves linking together different computing systems and software, and integrating dynamically changing reference data, and for sure, you have a problem. Reproducibility, traceability, and transparency have left the building. Here is where Posit Connect along with the vast R ecosystem comes to save the day, allowing the creation of reproducible pipelines. I will share with you my first-hand experience in this presentation. In particular, how we used Targets in Posit Connect combined with AWS technologies in a bioinformatics pipeline. The result? An effective and secure workflow orchestration that is scalable and advances knowledge. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Leave it to the robots: automating your work. Session Code: TALK-1148

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Grammar of Graphics in Python with Plotnine - posit::conf(2023)

Presented by Hassan Kibirige {plotnine} brings the elegance of {ggplot2} to the Python programming language. Learn about The Grammar of Graphics and get a feel of why it is an effective way to create Statistical Graphics. ggplot2 is one of the most loved visualisation libraries. It implements a Grammar of Graphics system, which requires one to think about data in terms of columns of variables and how to transform them into geometric objects. It is elegant and powerful. This is a talk about plotnine, which brings the elegance of ggplot2 to the Python programming language. It is an invitation to learn about the Grammar of Graphics system and to appreciate it. It will include some tips on how to avoid common frustrations as you learn the system. Materials: Website: https://plotnine.org Source Code: https://github.com/has2k1/plotnine Slides for this talk: https://github.com/has2k1/my-talks Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Data science with Python. Session Code: TALK-1137

Hassan Kibirige

ggplot2 plotnine Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Github Copilot integration with RStudio, it’s finally here! - posit::conf(2023)

Presented by Tom Mock This talk closes issue #10148, “Github Copilot integration with RStudio”, the most upvoted feature request in RStudio’s history. Code generating AI tools like Github Copilot‚ promise an “AI pair programmer that offers autocomplete-style suggestions as you code”. For the first time, we’ll show a native integration of Copilot into RStudio, helping to build on that promise by providing AI-generated “ghost text” autocompletion with R and other languages. I’ll also provide a comparison of Copilot’s “ghost text” to a chat-style interface in RStudio via the {chattr} package from the Posit MLVerse team. To make the most of these new features, I’ll walk through some examples of how sharing additional context, comments, code, and other “prompt engineering” can help you go from code-generating AI tools that feels like an annoying backseat driver to an experienced copilot. We’ll close with a robust end-to-end example of how these new RStudio integrations and packages can help you be a more productive developer. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Data science infrastructure for your org. Session Code: TALK-1117

rstudio Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Getting the Most Out of Git - posit::conf(2023)

Presented by Colin Gillespie Did you believe that Git will solve all of your data science worries? Instead, you’ve been plunged HEAD~1 first into merging (or is that rebasing?) chaos. Issues are ignored, branches are everywhere, main never works, and no one really knows who owns the repository. Don’t worry! There are ways to escape this pit of despair. Over the last few years, we’ve worked with many data science teams. During this time, we’ve spotted common patterns and also common pitfalls. While one size does not fit all, there are golden rules that should be followed. At the end of this talk, you’ll understand the processes other data science teams implement to make Git work for them. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Getting %$!@ done: productive workflows for data science. Session Code: TALK-1091# Thumbnail from happygitwithr.com, still from Heaven King video

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

From Novices to Experts: Building a Community of Engaged R Users - posit::conf(2023)

Presented by Natalia Andriychuk At Pfizer, we have over 1500 users with R installed on their machines, along with an R community on MS Teams comprising over a thousand colleagues globally. How can we effectively engage with Pfizer R users and celebrate the successes of this community, while sharing best practices? Additionally, how do we avoid isolated groups duplicating efforts to solve R-related problems across different parts of the organization? To address these challenges, we established the Pfizer R Center of Excellence (CoE) in early 2022. We focus our efforts on bringing together a rapidly growing community of colleagues, providing technical expertise, and offering best-practice guidance. A well-established, maintained and engaged R community promotes an inclusive and supportive learning environment that drives innovation within organizations. Our aim is to help colleagues thrive in their R journey, regardless of their expertise level. During my talk, I will share the techniques we used to build a supportive R community, the tools employed to increase community engagement, and the successes and challenges encountered in building an engaging community of R users. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Pharma. Session Code: TALK-1066

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

From Journalist to Coder: Creating a Web Publication with Quarto - posit::conf(2023)

Presented by Brian Tarran This is the story of how a Royal Statistical Society writer discovered Quarto, learned how to code (a bit), and built realworlddatascience.net, an online publication for the data science community. In March 2022, I was tasked by the Royal Statistical Society with creating a new online publication: a data science website for data science professionals. I’ve been a print journalist for 20 years and have worked on websites in that time, but my coding ability began and ended with wrapping href tags around text and images. That is until I discovered Quarto. In this talk, I describe how I explored, learned, and fell in love with the Quarto publishing system, how I used it to build a website – Real World Data Science (realworlddatascience.net) – and how the open source community mindset helped shape my thinking about what a new publication could and should be! Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Quarto (1). Session Code: TALK-1071

Quarto RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

From Data Confusion to Data Intelligence - posit::conf(2023)

Presented by Elaine McVey and David Meza Data science teams operate in a unique environment, much different than the IT or software development life cycle. Hope from executives for the impact of data science is extremely high! Understanding of how to make data science efforts successful is very low! This creates an interesting set of organizational challenges for data and analytics teams. These are particularly clear when data science is being introduced at new companies, but plays out at organizations of all sizes. So, how do we navigate this dynamic? We will share some strategies for success. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: From Data Confusion to Data Intelligence. Session Code: KEY-1060

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

From Concept to Impact: Building and Launching Shiny Apps in the Workplace - posit::conf(2023)

Presented by Tiger Tang Learn to build and launch a Shiny app like you are working on a start-up! Unlock the potential of Shiny apps for your organization! Join Tiger as he shares insights from implementing Shiny apps at his workplace, handling over 160,000 internal requests. Discover a practical mindmap to find, build, and enhance Shiny app use cases, ensuring robustness and improved user engagement. Materials: https://tigertang.org/posit_conf_2023/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Bridging the gap between data scientists and decision makers. Session Code: TALK-1074

Shiny Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

FOCAL Point: Utilizing Python, R, and Shiny to Capture, Process, and Visualize Motion - posit::conf

Presented by Justin Markel & Alyssa Burritt One of the fastest movements in modern sports is a golf swing. Capturing this motion using a high-speed camera system creates many unique challenges in processing, analyzing, and visualizing the thousands of data points that are generated. These spatial coordinates can be quickly translated through Python scripts to well-known, industry-specific performance metrics and graphics in Shiny. Down the line, R utilities aid more complicated analyses and optimizations, driving new product innovations. This talk will cover our company’s process of implementing these tools into our workflow and highlight key program features that have helped successfully combine these applications for users with a variety of technical backgrounds. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: R or Python? Why not both!. Session Code: TALK-1120

Shiny Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Field Guide to Writing Your First R Package - posit::conf(2023)

Presented by Fonti Kar I recall writing my first package being an intimidating task. In my talk, I will share how a Biologist’s mindset can make R package writing more approachable. This talk is an encouragement and a gentle stroll through the package building process. I will show you ways to be curious when you get stuck and how to prepare for the unexpected. I hope sharing my perspective will help others see package development as wonderful as the natural world and dispel any hesitation to start! Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Package development. Session Code: TALK-1135

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Extending Quarto - posit::conf(2023)

Presented by Richard Iannone What are Quarto shortcode extensions? Think of them as powerful little programs you can run in your Quarto docs. I won’t show you how to build a shortcode extension during this talk but rather I’m going to take you on a trip across this new ecosystem of shortcode extensions that people have already written. For example, I’ll introduce you to the fancy-text extension for outputting nicely-formatted versions of fancy strings such as LaTeX and BibTeX; you’ll learn all about the fontawesome, lordicon, academicons, material-icons, and bsicons shortcode extensions that let you add all sorts of icons. This is only a sampling of the shortcode extensions I will present, there will be many other inspiring examples as well. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Quarto (2). Session Code: TALK-1141

fontawesome Quarto Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

epoxy: Super Glue for Data-driven Reports and Shiny Apps - posit::conf(2023)

Presented by Garrick Aden-Buie R Markdown, Quarto, and Shiny are powerful frameworks that allow authors to create data-driven reports and apps. But truly excellent reports require a lot of work in the final steps to get numerical and stylistic formatting just right. {epoxy} is a new package that uses {glue} to give authors templating superpowers. Epoxy works in R Markdown and Quarto, in markdown, LaTeX, and HTML outputs. It also provides easy templating for Shiny apps for dynamic data-driven reporting. Beyond epoxy’s features, this talk will also touch on tips and approaches for data-driven reporting that will be useful to a wide audience, from R Markdown experts to the Quarto and Shiny curious. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Elevating your reports. Session Code: TALK-1155

Garrick Aden-Buie

Quarto Shiny Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Dynamic Interactions: webR to Empower Educators & Researchers with Interactive Quarto Docs

Presented by James Balamuta Full talk title: Dynamic Interactions: Empowering Educators and Researchers with Interactive Quarto Documents Using webR Traditional Quarto documents often lack interactivity, limiting the ability of students and researchers to fully explore and engage with the presented topic. In this talk, we propose a novel approach that utilizes webR, a WebAssembly-powered version of R, to seamlessly embed R code directly within the browser without the need for a server. We demonstrate how this approach can transform static Quarto documents into dynamic examples by leveraging webR’s capabilities through standard Quarto code cells, enabling real-time execution of R code and dynamic display of results. Our approach empowers educators and researchers alike to harness the power of interactivity and reproducibility for enhanced learning and research experiences. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Quarto (1). Session Code: TALK-1073

Quarto Quarto Rmd Rmarkdown RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Posit Python ML

duckplyr: Tight Integration of duckdb with R and the tidyverse - posit::conf(2023)

Presented by Kirill Müller The duckplyr R package combines the convenience of dplyr with the performance of DuckDB. Better than dbplyr: Data frame in, data frame out, fully compatible with dplyr. duckdb is the new high-performance analytical database system that works great with R, Python, and other host systems. dplyr is the grammar of data manipulation in the tidyverse, tightly integrated with R, but it works best for small or medium-sized data. The former has been designed with large or big data in mind, but currently, you need to formulate your queries in SQL. The new duckplyr package offers the best of both worlds. It transforms a dplyr pipe into a query object that duckdb can execute, using an optimized query plan. It is better than dbplyr because the interface is “data frames in, data frames out”, and no intermediate SQL code is generated. The talk first presents our results, a bit of the mechanics, and an outlook for this ambitious project. Materials: https://github.com/duckdblabs/duckplyr/ Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Databases for data science with duckdb and dbt. Session Code: TALK-1100

dbplyr dplyr duckplyr tidyverse Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

dplyr 1.1.0 Features You Can’t Live Without - posit::conf(2023)

Presented by Davis Vaughan Did you enjoy my clickbait title? Did it work? Either way, welcome! The dplyr 1.1.0 release included a number of new features, such as: Per-operation grouping with.by An overhaul to joins, including new inequality and rolling joins New consecutive_id() and case_match() helpers Significant performance improvements in arrange() Join me as we take a tour of this exciting dplyr update, and learn how to use these new features in your own work! Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1162

Davis Vaughan

dplyr RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Diversify Your Career with Shiny for Python - posit::conf(2023)

Presented by Gordon Shotwell A few years ago my company made a sudden shift from R to Python which was quite bad for my career because I didn’t really know Python. The main issue was that I couldn’t find a niche that allowed me to use my existing knowledge while learning the new language. Shiny for Python is a great niche for R users because none of the Python web frameworks can do what Shiny can do. Additionally, almost all of your knowledge of the R package is applicable to the Python one. This talk will provide an overview of the Python web application landscape and articulate what Shiny adds to this landscape, and then go through the five things that R users need to know before developing their first Shiny for Python application. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Data science with Python. Session Code: TALK-1138

Shiny for Python Shiny Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Developing a Prototyping Competency in a Statistical Science Organization - posit::conf(2023)

Presented by Daniel Woodie The introduction of new tools, methods, and processes can be a struggle within a statistical science organization. Being deliberate and investing in the creation of a prototyping competency can help in accelerating progress. Prototyping allows organizations to quickly experiment with new ideas, reduce the risk of failure, identify potential issues early, and iterate until the desired outcome is achieved. I will highlight the key areas we have focused on accelerating, our framework for developing this competency, how we use Shiny, and the lessons we’ve learned along the way. Developing a prototyping competency is crucial for statistical science organizations that wish to stay competitive and innovative in today’s rapidly changing landscape. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Building effective data science teams. Session Code: TALK-1059

Shiny RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Democratizing Access to Education Data - posit::conf(2023)

Presented by Erika Tyagi Learn how the Urban Institute is making high-quality data more accessible through the Education Data Portal. Every year, government agencies release large amounts of data on schools and colleges, but this information is scattered across various websites and is often difficult to use. To make these data more accessible, the Urban Institute built the Education Data Portal, a freely available one-stop shop for harmonized data and metadata for nearly all major federal education datasets. In this talk, we’ll demonstrate how the portal works and share lessons we’ve learned about making data accessible to users with varying technical skills and preferred programming languages. The Urban Institute’s Education Data Portal: https://educationdata.urban.org Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: End-to-end data science with real-world impact. Session Code: TALK-1145

Automation Humans Python RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

dbtplyr: Bringing Column-Name Contracts from R to dbt - posit::conf(2023)

Presented by Emily Riederer starts_with(language): Translating select helpers to dbt. Translating syntax between languages transports concepts across communities. We see a case study of adapting a column-naming workflow from dplyr to dbt’s data engineering toolkit. dplyr’s select helpers exemplify how the tidyverse uses opinionated design to push users into the pit of success. The ability to efficiently operate on names incentivizes good naming patterns and creates efficiency in data wrangling and validation. However, in a polyglot world, users may find they must leave the pit when comparable syntactic sugar is not accessible in other languages like Python and SQL. In this talk, I will explain how dplyr’s select helpers inspired my approach to ‘column name contracts,’ how good naming systems can help supercharge data management with packages like {dplyr} and {pointblank}, and my experience building the {dbtplyr} to port this functionality to dbt for building complex SQL-based data pipelines. Materials: https://github.com/emilyriederer/dbtplyr https://emilyriederer.com Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Databases for data science with duckdb and dbt. Session Code: TALK-1098

dplyr pointblank tidyverse RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Data Visualization with Seaborn - posit::conf(2023)

Presented by Michael Waskom Seaborn is a Python library for statistical data visualization. After nearly a decade of development, seaborn recently introduced an entirely new API that is more explicitly based on a formal grammar of graphics. My talk will introduce this API and contrast it with the classic seaborn interface, sharing insights about the influence of the grammar of graphics on the ergonomics and maintainability of data visualization software. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Data science with Python. Session Code: TALK-1136

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Data Science in Production: The Way to a Centralized Infrastructure - posit::conf(2023)

Presented by Oliver Bracht In this talk, the success story of Covestro’s posit infrastructure is presented. The problem of the leading German material manufacturer was that no common development environment existed. With the help of eoda and Posit, a replicable, centralized development environment for R and Python was created. Although R and Python represent the core of the infrastructure, multiple languages and tools are unified. In addition to the collaboration of Covestro’s data science teams, compliance guidelines could also be better fulfilled. The staging architecture hereby provides developers with a concept for testing and going live with their products. This project presents a best practice approach to a data science infrastructure using Covestro as an example. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Data science infrastructure for your org. Session Code: TALK-1113

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

CRAN-ial Expansion: Taking Your R Package Development to New Frontiers with R-Universe - posit::conf

Presented by Mo Athanasia Mowinckel Say goodbye to installation headaches and hello to a universe of possibilities with R-Universe! Take your R package development to new frontiers by organizing and sharing packages beyond the bounds of CRAN. R-Universe’s reliable package-building process strengthens installation and usage instructions, resulting in fewer support requests and an easy installation experience for users. With webpages and an API for exploring packages, R-Universe creates a streamlined and tidy ecosystem for R-package constellations. Also, you can build a custom toolchain for your users, relieving your workload and empowering users to help themselves. Join me to learn how to explore the vastness of R-Universe and expand your package development possibilities! Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Managing packages. Session Code: TALK-1080

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Connect on Kubernetes: Content-level Containerization - posit::conf(2023)

Presented by E. David Aja, not Kelly O’Briant Running Connect with off-host content execution in Kubernetes is very cool and allows you to enable some powerful and sophisticated workflows. The question is, do you really need it? How do you evaluate and decide? Let’s have a candid conversation about whether Connect content execution on Kubernetes is right for you and your organization. Moving to Kubernetes will introduce complexity, so it’s important to have a strong motivating reason for making the switch. This talk will introduce new Connect features that are made possible by content-level containerization. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Data science infrastructure for your org. Session Code: TALK-1116

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Conformal Inference with Tidymodels - posit::conf(2023)

Presented by Max Kuhn Conformal inference theory enables any model to produce probabilistic predictions, such as prediction intervals. We’ll demonstrate how these analytical methods can be used with tidymodels. Simulations will show that the results have good coverage (i.e., a 90% interval should include the real point 90% of the time). Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Tidy up your models. Session Code: TALK-1085

Max Kuhn

tidymodels AI Modelling Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Commit to Change: How to Increase Accessibility in Your Favorite Open Source Projects - posit::conf

Presented by Rose Franzen Explore accessibility for data scientists by uncovering some common barriers in open source tools with simple fixes that anyone can implement. Dive into the often-overlooked world of accessibility for developers and data scientists! Uncover common accessibility barriers in open source tools, and learn simple steps to address them. Whether you’re a seasoned maintainer or a total novice, you’ll walk away with clear action items to implement right away. Join the movement of individuals championing the next frontier of disability inclusion in technology, shaping a more equitable future for all! Materials: https://github.com/franzenr/commit-to-change Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Package development. Session Code: TALK-1134

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Combining R and Python for the Belgian Justice Department - posit::conf(2023)

Presented by Thomas Michem We build a great case on how to combine R and Python in a production environment. So the justice department’s back office monitors the smooth processing of all traffic fines in Belgium. They gather that data from all police departments and check if any anomalies occur. The back-office monitors that using a shiny application where they can see traffic signs showing the status of the whole operation and the status is built using Python scripts that perform anomaly detection if the number of fines is in line with what they expect daily. And the results of those checks are delivered to a front-end shiny application with Python flask API. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: R or Python? Why not both!. Session Code: TALK-1122

Shiny RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Coding Tools for Industry R&D – Development Lessons from an Analytical Lab - posit::conf(2023)

Presented by Camila Saez Cabezas Are you considering or curious about developing code-based tools for scientists? Whether you are an experienced developer or a fellow Posit Academy graduate who might be stepping into this role for the first time, the aim of my story is to inspire you and help you navigate this process. While developing custom R functions, packages, and Shiny apps for diverse analytical capabilities and users in R&D, I learned why it’s important to collect certain information at the start before writing any tidying, analysis, visualization, and web application code. In this talk, I will share the essential technical questions that help me define and plan for success. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1168

Shiny RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

CI/CD Pipelines - Oh, the Places You’ll Go! - posit::conf(2023)

Presented by Trevor Nederlof Data scientists are creating incredibly useful data products at an accelerating rate. These products are consumed by others who expect them to be accurate reliable and timely, often promises unfulfilled. In this talk, we will explore how to use common CI/CD pipeline tools already within reach of attendees to automatically test and deploy their apps, APIs, and reports. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Lightning talks. Session Code: TALK-1166

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Can I Have a Word? - posit::conf(2023)

Presented by Ellis Hughes Since its release, {gt} has won over the hearts of many due to its flexible and powerful table-generating abilities. However, in cases where office products were required by downstream users, {gt}’s potential remained untapped. That all changed in 2022 when Rich Iannone and I collaborated to add Word documents as an official output type. Now, data scientists can engage stakeholders directly, wherever they are. Join me for an upcoming talk where I’ll share my excitement about the new opportunities this update presents for the R community as well as future developments we can look forward to. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Elevating your reports. Session Code: TALK-1156

Rich Iannone

gt Word Data Science Quarto Databricks RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Posit ML

Building a Flexible, Scaleable Self-Serve Reporting System with Shiny - posit::conf(2023)

Presented by Natalie O’Shea Working in the high-touch world of consulting, our team needed to develop a reporting system that was flexible enough to be tailored to the specific needs of any given partner while still reducing the highly manual nature of populating client-ready slide decks with various metrics and data visualizations. Utilizing the extensive resources developed by the R user community, I was able to create a flexible, scalable reporting system that allows users to populate templated Google slide decks with metrics and professional-grade visualizations using data pulled directly from our database at the time of query. This streamlined approach enables our consultants to spend less time copy-pasting data from one channel to another and instead focus on what they do best: surfacing business-relevant insights and recommendations for our partners. By sharing my approach to customizable self-serve reporting in Shiny, I hope attendees will walk away with new ideas about how to combine parameterized reporting and dashboard development to get the best of both worlds. Additionally, I hope to end by sharing how this project was pivotal in making the business case for procuring Posit products for my broader organization. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Bridging the gap between data scientists and decision makers. Session Code: TALK-1075

Shiny RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Black Hair and Data Science Have More in Common Than You Think - posit::conf(2023)

Presented by Kari Jordan Data science is often difficult to define because of its many intersections, including statistics, programming, analytics, and other domain knowledge. Would you believe it if I told you Black hair and data science have much in common? This talk is for those considering learning about, studying, or pursuing data science. In it, Dr. Kari L. Jordan draws parallels between approaches to caring for Black hair and approaches to learning data science. We start with the roots and end by picking the right tools and products to maintain our coiffure. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: It takes a village: building and sustaining communities. Session Code: TALK-1131

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Becoming an R Package Author (or How I Got Rich Responding to GitHub Issues) - posit::conf(2023)

Presented by Matt Herman The transition from analyzing data in R to making packages in R can feel like a big step. Writing code to clean data or make visualizations seems categorically different from building robust “software” on which other people rely. In this talk, I’ll show why that distinction is not necessarily true by discussing my personal experience from learning R in graduate school to reporting bugs on GitHub to becoming a co-author of the tidycensus package and a practicing data scientist. The positive and supportive R community on GitHub, Twitter, and elsewhere contributes to why anyone who writes R code can become a package author. I have not actually gotten rich but I did get freelance data work based on my package contributions! Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Package development. Session Code: TALK-1133

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Automating the Dutch National Flu Surveillance for Pandemic Preparedness - posit::conf(2023)

Presented by Patrick van den Berg The next pandemic may be caused by a flu strain, and with thousands of patients with the flu in Dutch hospitals annually it is important to have accurate and current data. The National Institute for Public Health and the Environment of the Netherlands (RIVM) collects and processes flu data to achieve pandemic preparedness. However, the flu reporting process used to be very laborious, stealing precious time from epidemiologists. In our journey of automating this data pipeline we learned that collaboration was the most important factor in getting to a working system. This talk will be at the cross-section of data science and epidemiology and will provide you with a valuable opportunity to learn from our experiences. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Leave it to the robots: automating your work. Session Code: TALK-1150

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

AI and Shiny for Python: Unlocking New Possibilities - posit::conf

Presented by Winston Chang In the past year, people have come to realize that AI can revolutionize the way we work. This talk focuses on using AI tools with Shiny for Python, demonstrating how AI can accelerate Shiny application development and enhance its capabilities. We’ll also explore Shiny’s unique ability to interface with AI models, offering possibilities beyond Python web frameworks like Streamlit and Dash. Learn how Shiny and AI together can empower you to do more, and do it faster. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: I can’t believe it’s not magic: new tools for data science. Session Code: TALK-1153

Winston Chang

Shiny for Python Shiny RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Adding a Touch of glitr: Developing a Package of Themes on Top of ggplot - posit::conf(2023)

Presented by Aaron Chafetz and Karishma Srikanth Please note, a power issue cut off the first five minutes of the talk. Explore how our team at the US Agency for International Development (USAID) created our own data viz branding R package on top of ggplot2 and how you can too. How do you create brand cohesion across your large team when it comes to data viz? Inspired by the BBC’s bbplot, our team at the US Agency for International Development (USAID) developed a package on top of ggplot2 to create a common look and feel for our team’s products. This effort improved not just the cohesiveness of our work, but also trustworthiness. By creating this package, we reduced the reliance on using defaults and the time spent on each project customizing numerous graphic elements. More importantly, this package provided an easier on-ramp for new teammates to adopt R. We share our journey within a federal agency developing a style guide and aim to guide and inspire other organizations who could benefit from developing their own branding package and guidance. Materials: https://speakerdeck.com/achafetz/adding-a-touch-of-glitr https://usaid-oha-si.github.io/glitr/ https://issuu.com/achafetz/docs/oha_styleguide Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Compelling design for apps and reports. Session Code: TALK-1103

ggplot2 Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

A hacker’s guide to open source LLMs - posit::conf(2023)

Presented by Jeremy Howard In this deeply informative video, Jeremy Howard, co-founder of fast.ai and creator of the ULMFiT approach on which all modern language models (LMs) are based, takes you on a comprehensive journey through the fascinating landscape of LMs. Starting with the foundational concepts, Jeremy introduces the architecture and mechanics that make these AI systems tick. He then delves into critical evaluations of GPT-4, illuminates practical uses of language models in code writing and data analysis, and offers hands-on tips for working with the OpenAI API. The video also provides expert guidance on technical topics such as fine-tuning, decoding tokens, and running private instances of GPT models. As we move further into the intricacies, Jeremy unpacks advanced strategies for model testing and optimization, utilizing tools like GPTQ and Hugging Face Transformers. He also explores the potential of specialized datasets like Orca and Platypus for fine-tuning and discusses cutting-edge trends in Retrieval Augmented Generation and information retrieval. Whether you’re new to the field or an established professional, this presentation offers a wealth of insights to help you navigate the ever-evolving world of language models. (The above summary was, of course, created by an LLM!) For the notebook used in this talk, see https://github.com/fastai/lm-hackers. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Notebooks+LLMs may just be the future of coding. Session Code: KEY-1107

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

20 questions and AI chat bots - posit::conf(2023)

Presented by Winston Chang Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: I can’t believe it’s not magic: new tools for data science. Session Code: TALK-1153

Winston Chang

RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

{slushy}: A Bridge to the Future - posit::conf(2023)

Presented by Becca Krouse Scaling the use of R can present complications for environment management, especially in regulated industries with a focus on traceability. One solution is controlled (aka “frozen”) environments, which are carefully curated and tested by tech teams. However, the speed of R development means the environments quickly become outdated and users are unable to benefit from the latest advances. Enter {slushy}: a team-friendly tool powered by {renv} and Posit Package Manager. Users can quickly mimic a controlled environment, with the easy ability to time travel between snapshot dates. Attendees will learn how {slushy} bolstered our R adoption efforts, and how this strategy enables tech teams and users to work in parallel towards a common future. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Managing packages. Session Code: TALK-1078

renv RStudio Data Science Machine Learning Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit Python ML

Shiny in Production 2023: George Stagg - R Shiny without a server: webR and Shinylive

R Shiny without a server: webR and Shinylive by George Stagg at the Shiny in Production 2023 conference, hosted by Jumping Rivers! Note from the speaker: Shinylive is now available on CRAN! https://cran.r-project.org/web/packages/shinylive/index.html Abstract# WebAssembly (Wasm) is a technology that enables software that’s normally compiled for a specific computer system to instead run anywhere, including inside web browsers. WebR is a version of the R interpreter compiled for Wasm, bringing this technology to the R world. Earlier this year, the initial version of webR was released and users have already begun building new interactive experiences with R on the web. The latest release, version 0.2.0, includes improvements to graphics, accessibility and internationalisation, developer API updates, and introduces a new webR REPL app. The release also includes expanded support for Wasm R packages, including the ability to run fully client‐side Shiny apps. In this talk, I’ll introduce webR with some simple examples and discuss some details of how the system works. I’ll talk about how JavaScript APIs can be used to integrate webR into wider web applications and describe webR’s communication channel. Finally, I’ll give a description of how Shiny apps can be run using webR without an R server, ending with a demo of an in‐development “Shinylive for R”. This event was sponsored by: Jumping Rivers National Innovation Centre for Data NU Solve Posit R Consortium Royal Statistical Society

George Stagg

How the R for Data Science (R4DS) Online Learning Community Made Me a Better Student - posit::conf

Presented by Lydia Gibson Through my participation in R4DS Online Learning Community, I have advanced my R and data science skills, making me a better student than I otherwise would have been through just my studies. As a non-traditional MS Statistics student with an undergraduate background in economics, I had absolutely no experience with the R programming language prior to pursuing my Master’s degree. In July 2021, with hopes of getting a headstart on learning R before beginning my degree program, I joined the R4DS Slack Workspace. Along with helping to improve my programming skills, R4DS has connected me with scholarships, mentorship, and other opportunities, and I think that it would be beneficial for other students to know about this great resource. Presented at Posit Conference, between Sept 19-20 2023, Learn more at posit.co/conference.# Talk Track: Developing your skillset; building your career. Session Code: TALK-1110

Databricks Data Science RStudio Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Quarto Posit ML

Eric Nantz, Alex Lauer, Rich Iannone - A Pivotal Year of Milestones in R Consortium Working Groups

A Pivotal Year of Milestones: R Submissions Working Group and R Tables for Regulatory Submissions updates - Eric Nantz, Alexandra Lauer, and Rich Iannone Resources mentioned in the presentation: R Submissions Working Group: https://rconsortium.github.io/submissions-wg/ R Tables eBook: https://rconsortium.github.io/rtrs-wg Abstract: Within the life sciences industry, Shiny has enabled tremendous innovations to produce web interfaces as frontends to sophisticated analyses, dynamic visualizations, and automation of clinical reporting across drug development. While industry sponsors have widely adopted Shiny as part of their analytics and reporting toolset, a relatively unexplored frontier has been the inclusion of a Shiny application inside a clinical submission package to regulatory agencies such as the FDA. The R Consortium R Submissions Working Group has continued the positive momentum of previous submission pilots to achieve substantial progress in this domain. In this talk, we will share the development journey of the working group’s Pilot 2 successful submission of a Shiny application to the FDA, along with the progress on the use of novel technologies such as Linux containers and web assembly to bundle a Shiny application into a self-contained package, facilitating a smoother process of both transferring and executing the application. The R Consortium’s R Tables for Regulatory Submissions (RTRS) Working Group has released the first edition of (Tables in Clinical Trials with R)[ https//rconsortium.github.io/rtrs-wg/] as a free and openly accessible ebook. The book contributes to the development of a theory of displaying tabular information by identifying a small number of table archetypes that may be used to generate the most common tables employed in clinical submissions. Chapters in the book demonstrate how these tables may be rendered in different R Packages including flextable, gt, rtables (with and without tern), tables, tfrmt and tidytlg. All tables are generated from CDISC-compliant data. Comparing the code showcases the robustness of R for aggregating and displaying tabular information and illuminates the flexibility and design tradeoff of the various R packages. The talk will discuss the motivation for the book, present the idea of table archetypes, show some representative tables, and make the case for R as a superb language for analyzing clinical trial data. The RTRS working group expects Tables in Clinical Trials with R to become a primary resource of clinical programming teams. Speaker Bios: Eric Nantz is a director within the statistical innovation center at Eli Lilly and Company, creating analytical pipelines and capabilities of advanced statistical methodologies for clinical design used in multiple phases of development. Outside of his day job, Eric is passionate about connecting with and showcasing the brilliant R community in multiple ways. You may recognize his voice from the R-Podcast that he launched in 2012. Eric is also the creator of the Shiny Developer Series where he interviews authors of Shiny-related packages and practitioners developing applications, as well as sharing his own R and Shiny adventures via livestreams on his Twitch channel. In addition, Eric is a curator for the RWeekly project and co-host of the RWeekly Highlights podcast which accompanies every issue. Alexandra Lauer is a Senior Principal Statistical Analyst at Merck KGaA, Darmstadt, Germany, with a background in mathematics. She specializes in bridging psychometrics and biostatistics, with a primary focus on Health-Related Quality of Life evaluations. Alex is an R enthusiast, co-leading the Merck-internal R User Group. Rich Iannone: My background is in programming, data analysis, and data visualization. Much of my current software engineering work on R packages is intended to make working with data easier. I truly believe that with the right approach, tools like these can be both powerful and easy to use. Presented at the 2023 R/Pharma Conference (October 26, 2023)

Rich Iannone

Quarto Dashboards | Charles Teague | Posit

Quarto has a host of exciting new features in release v1.4, with one of the most anticipated being Quarto Dashboards. In a recent internal presentation, Posit’s Charles Teague demonstrated the new capabilities and we wanted to share it with community. Please note that you can use this feature today, but to access it you need to be running on a Quarto prerelease build dated November 6, 2023, or later. We also encourage users to review our pre-release documentation which offers additional information and examples of the dashboard functionality. Quarto Pre-release builds: https://quarto.org/docs/download/prerelease.html Quarto Dashboard Documentation: https://quarto.org/docs/dashboards/

Quarto RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Joseph Powers @ Intuit & Jen Wang | Data Science Hangout

We were recently joined by Joseph Powers, Principal Data Scientist at Intuit and Jen Wang, VP Product & Growth - formerly at thredUP to chat about transitioning from academia to tech roles, building inclusive & growth-oriented teams, and how organizations can improve their experimentation and causal inference processes to generate larger and faster insights that improve their product and marketing experiences. Featured Leader Bios: Joe Powers: Joe took a very indirect path to data science. He was a Great Books major in undergrad, a custom furniture maker for 7 years, a teacher for 3 years, then completed a PhD in Educational Psychology in his 30s, which was the first time he learned any programming or statistics. Efficient training and deep (personal) learning are lifelong interests for him. He now lives in San Diego, where his learning pursuits include surfing, cooking, and raising two young learners with his wife. Jen Wang: Jen’s approach to teams and organization-building has been informed by two decades’ worth of interdisciplinary expertise and leadership across sectors and cultures. Her training includes an undergrad in molecular biology and a mixed-methods PhD focused on human behavior, organizational science, and environmental decision-making, and which also included a deep dive into R and data visualization. She most recently led Product & Growth for thredUP’s core secondhand clothing marketplace. Having faced many career transitions herself, she’s particularly passionate about helping to spread evidence-based practices and bridging the gap when it comes to early-career and DEI issues. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!) Thanks for hanging out with us!

J.J. Allaire - Keynote: Dashboards with Jupyter and Quarto | PyData NYC 2023

www.pydata.org https://drive.google.com/file/d/1O_ed6OKEXZBIzKn6yyF9W7f6NaNV-L3J/view?usp=drive_link Keynote by JJ Allaire J.J. is the Founder and CEO of Posit (which you might only know by its previous name, RStudio). J.J. is now leading the Quarto project, a Jupyter-based scientific and technical publishing system. In this talk, J.J. will introduce Quarto Dashboards, an easy way to create production quality dashboards from Jupyter Notebooks. J.J. will also more broadly discuss Posit’s recent work in the open source PyData ecosystem along with plans for significantly expanding that work in the future. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

JJ Allaire

How to build a model annotation tool with FastAPI, Quarto & Shiny for Python

Gordon Shotwell, Senior Software Engineer at Posit walks through an end-to-end machine learning workflow with Posit Team. This demo will give you a robust pattern for hosting and sharing models on Connect before you deploy them to a customer-facing system. This includes How and why to use an API layer to serve and authenticate internal models Why you should wrap your APIs in Python packages Using Shiny for Python to build a data annotation app Using Quarto to retrain models on a schedule Agenda: 11am - Quick introduction 11:02am - Demo 11:40am Q&A Session ( https://youtube.com/live/zhN8IZUBCAg?feature=share ) Timestamps: 1:27 - Quick overview of text classification model used in this example 2:15 - Overview of the people that will need to use the model (modellers, leadership, data team, annotators, other systems) 4:11 - Why APIs before UIs is a good rule 5:57 - What about Python packages? 8:23 - Advantages to using an API here 9:18 - Big picture overview of the workflow 11:17 - FastAPI on Posit Connect (Swagger interface) 15:55 - The way this model will be used (authorization by validating user) 19:00 - Building a delightful user experience by wrapping API in a package 25:07 - Quarto report for leadership team showing model statistics & deploying to Connect 26:34 - Retraining the model by scheduling Quarto doc on Connect 28:37 - Shiny for Python app for Annotators (people checking if model is producing correct results & helping improve the model) 35:28 - Overview / summary of this machine learning workflow Helpful links: Github: https://github.com/gshotwell/connect-e2e-model Anonymous questions: pos.it/demo-questions If you want to book a call with our team to chat more about Posit products: pos.it/chat-with-us Don’t want to meet yet, but curious who else on your team is using Posit? pos.it/connect-us Machine learning workflow with R: https://solutions.posit.co/gallery/bike_predict/ In this month’s Workflows with Posit Team session (Wednesday, November 29th at 11am ET) you will learn how to use Posit Connect as an end-to-end Python platform for hosting internal machine learning models. This will give you a robust pattern for hosting and sharing models on Connect before you deploy them to a customer-facing system. This will include: How and why to use an API layer to serve and authenticate internal models Why you should wrap your APIs in Python packages Using Shiny for Python to build a data annotation app Using Quarto to retrain models on a schedule During the event, we’ll be joined by Gordon Shotwell, Senior Software Engineer at Posit who will walk us through this end-to-end machine learning workflow with Posit Team. No registration is required to attend - simply add it to your calendar using this link: pos.it/team-demo Ps. We host these end-to-end workflow demos on the last Wednesday of every month. If you ever have ideas for topics or questions about them, leave a comment below:)

Gordon Shotwell & Tracy Teal - Build Simple and Scalable Apps with Shiny | PyData NYC 2023

www.pydata.org https://gshotwell.github.io/shiny-algorithm This talk explores the intuitive algorithm behind Shiny for Python and shows how it allows you to scale your apps from prototype to product. Shiny infers a reactive computation graph from your application and uses this graph to efficiently re-render components. This eliminates the need for data caching, state management, or callback functions which lets you build scalable applications quickly. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

webR 0.2: Updates to webR’s developer API | George Stagg | Posit

More from George Stagg on what’s new in the webR 0.2 release series, this time on webR’s developer API. If you’re a JavaScript or TypeScript developer, give webR a try and see what you might use it for! WebR makes it possible to run R code in the browser without the need for an R server to execute the code: the R interpreter runs directly on the user’s machine. It is possible to work entirely within webR and the R graphics devices, but you might also want to integrate webR into external JavaScript or TypeScript frameworks, including visualization packages. Learn about how to do this and more with webR’s developer API, including plotting data from webR using Observable JS, recent performance improvements, better error handling, worker event messages, and neatly terminating a webR session. 00:15 Performance improvements working with JavaScript 02:50 Example: Plotting data from webR using Observable JS 04:35 Type predicate functions and type conversion 07:30 Handling errors with webRError 09:15 Event messages from webr::canvas() 10:25 Safely handling webR termination 12:30 Links to documentation and resources Website and developer documentation: https://docs.r-wasm.org/ Examples of using webR with Observable JS: Loading webR and R data manipulation: https://observablehq.com/d/6936259b898a25ce Bitmap plotting and custom fonts: https://observablehq.com/d/32e2b7e465c0b994

George Stagg

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

webR 0.2: R Packages and Shiny for WebAssembly | George Stagg | Posit

WebR makes it possible to run R code in the browser without the need for an R server to execute the code: the R interpreter runs directly on the user’s machine. But just running R isn’t enough, you need the R packages you use every day too. webR 0.2.0 makes many new packages available (10,324 packages - about 51% of CRAN!) and it’s now possible to run Shiny apps under webR, entirely client side. George Stagg shares how to load packages with webR, know what ones are available, and get started running Shiny apps in the web browser. There’s a demo webR Shiny app too! 00:15 Loading R packages with webR 01:50 Wasm system libraries available for use with webR 05:30 Tidyverse, tidymodels, geospatial data, and database packages available 08:00 Shiny and httpuv: running Shiny apps under webR 11:05 Example Shiny app running in the web browser 12:05 Links with where to learn more Shiny webR demo app: https://shinylive.io/r/examples/ Website: https://docs.r-wasm.org/ webR REPL example: https://webr.r-wasm.org/latest/ Demo webR Shiny app in this video: https://shiny-standalone-webr-demo.netlify.app/ Source: https://github.com/georgestagg/shiny-standalone-webr-demo/ See the overview of what’s new in webR 0.2.0: https://youtu.be/Mpq9a6yMl_w

George Stagg

httpuv Shiny shinylive tidymodels tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

webR 0.2 Overview: R in the browser | George Stagg | Posit

WebR is a version of the statistical language R compiled for the browser and Node.js using WebAssembly, via Emscripten. WebR makes it possible to run R code in the browser without the need for an R server to execute the code: the R interpreter runs directly on the user’s machine. Several R packages have also been ported for use with webR, and can be loaded in the usual way using the library() function. George Stagg shares what’s new in the 0.2.0 release! 00:23 About WebR and applications of computing with WebAssembly 02:25 Overview of changes in webR 0.2.0 release 04:10 The webR REPL app 07:28 Improved speed in graphics rendering 08:43 Text rendering in plots, including international and accessible fonts 10:27 Updated support for standard bitmap devices 13:05 Lazy virtual file system allows for decreased download size 15:40 Links with where to learn more Website: https://docs.r-wasm.org/ webR REPL example: https://webr.r-wasm.org/latest/

George Stagg

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

5 Reasons R & Python developers LOVE Posit Connect

Posit Connect makes deployment the easiest step in your workflow. Connect saves data scientists time, accelerates productivity, and maximizes the impact data science teams have within their organization. With Posit Connect, you can automate common recurring data science tasks, like: Sending a daily or weekly report to your boss Updating a machine learning model Extracting new data from an API Loading new data Updating reports that your stakeholders rely on

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

How to train, evaluate, and deploy a machine learning workflow with tidymodels & Posit Team

Helpful resources: Github: https://github.com/simonpcouch/mutagen Follow-up Q&A Session: https://youtube.com/live/vwBVOBQfc_U If you want to book a call with our team to chat more about Posit products: pos.it/chat-with-us Don’t want to meet, but curious who else on your team is using Posit? pos.it/connect-us Blog post on tidymodels + Posit Connect: https://posit.co/blog/pharmaceutical-machine-learning-with-tidymodels-and-posit-connect/ Tidy Modeling with R book: https://www.tmwr.org/ Timestamps: 1:44 - Three steps for developing a machine learning model 3:35 - What is a machine learning model? 7:02 - Overview of machine learning with Posit Team 7:36: Step 1: Understand and clean data 11:05 - Step 2: Train and evaluate models (why you might be interested using tidymodels) 23:02 - Step 3: Deploying a machine learning model from Posit Workbench to Posit Connect 30:14 - Summary 31:21 - Helpful resources Machine learning models are all around us, from Netflix movie recommendations to Zillow property value estimates to email spam filters. As these models play an increasingly large role in our personal and professional lives, understanding and embracing them has never been more important; machine learning helps us make better, data-driven decisions. The tidymodels framework is a powerful set of tools for building—and getting value out of—machine learning models with R. Data scientists use tidymodels to: Gain access to a wide variety of machine learning methods Guard against common mistakes Easily deploy models through tidymodels’ integration with vetiver Join Simon Couch from the tidyverse team on Wednesday, October 25th at 11am ET as he walks through an end-to-end machine learning workflow with Posit Team. No registration is required to attend - simply add it to your calendar using this link: pos.it/team-demo

Simon Couch

Bite-sized tricks for machine learning with tidymodels | Posit

The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles. This video highlights a number of tidymodels features that could improve your modeling workflows. 0:03 Switching modeling engines is easy 0:21 Never lose your tuning results 0:36 Built-in visualizations for modeling objects 1:03 Grouped resampling 1:16 Case weights 1:32 Select variables based on role and type 2:00 Spatial resampling 2:16 Keep your tidymodels objects small Learn more at https://www.tidymodels.org/

tidymodels tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

How does Shiny render things? | Gordon Shotwell

Discussion on the Shiny Python Discord channel from Gordon Shotwell. Gordon talks about how Shiny renders things with reactive programming, how other frameworks work, and how Shiny scales for complex applications. 0:00 - How Shiny renders things - reactivity 2:31 - How do other frameworks work 3:51 - Event driven programming, what’s a better way? 6:53 - Runtime tracing 8:25 - Declarative programming 9:20 - Drawing a graph 12:05 - How reactivity scales 13:30 - Reactive calculus 18:45 - Question and answer Shiny for Python: https://shiny.posit.co/py/ Shiny for Python Discord server: https://discord.com/invite/yMGCamUMnS

Shiny for Python Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

“Shiny: Data-centric web applications in Python” - Joe Cheng (PyBay 2023)

Joe Cheng https://pybay.com/speakers/#sz-speaker-7e5324de-3afa-4614-a498-562bd5eb9986 Shiny is a web framework that is designed to let you create data dashboards, interactive visualizations, and workflow apps in pure Python or R. Shiny doesn’t require knowledge of HTML, CSS, and JavaScript, and lets you create data-centric applications in a fraction of the time and effort of traditional web stacks. Of course, Python already has several popular and high-quality options for creating data-centric web applications. So it’s fair to ask what Shiny can offer the Python community. In this talk, I will introduce Shiny for Python and answer that question. I’ll start with some basic demos that show how Shiny apps are constructed. Next, I’ll explain Transparent Reactive Programming (TRP), which is the animating concept behind Shiny, and the reason it occupies such an interesting place on the ease-vs-power tradeoff frontier. Finally, I’ll wrap up with additional demos that feature interesting functionality that is made trivial with TRP. This talk should be interesting to anyone who uses Python to analyze or visualize data, and does not require experience with Shiny or any other web frameworks. PyBay features the most influential speakers presenting the most crucial technologies to help beginners and seasoned developers alike get up-to-date quickly, in a single-track format. Whether you’re interested in web technologies, data, devops, Python internals, or performance, PyBay will help you stay on top of your game AND network with engineers at companies that are hiring! Working remotely and want to meet your teammates to boost team cohesiveness? Leverage the platform we’ve built. There are great talks, yummy food, fresh air, vitamin D… all the elements developers crave for these days. If there are talks that don’t interest your team, take the opportunity to talk to speakers, create your own team activities or book a tee-time at the adjacent miniature golf course! PyBay is the regional Python conference for the San Francisco Bay Area, bringing together Pythonistas from around the Bay Area and beyond. It is a volunteer-run organization dedicated to building a stronger Python community. PyBay offers deep-dive talks and networking opportunities that aim to enrich and empower the Python community. PyBay is part of BAPyA (Bay Area Python Association). BAPyA member organizations are the SF Python, Pyninsula, and BayPIGgies meetups. Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1 Sun Oct 8 18:30:00 2023 at Bungalo East

Joe Cheng

The River is our Relative | The Story of the Penobscot Nation

The Penobscot Nation has been around for millennia. On Indian Island, a river gives the Penobscot people life. “The tribe views the river like its own highway. We look at her as a relative, a sister, someone that gives life. Sea Lamprey, Sturgeon, Striper Bass.” Jan Paul works for the Penobscot Nation in the Department of Natural Resources and Water Quality. She’s using open-source technology, like R and the RStudio IDE, to keep the river healthy, so in turn, it can keep her people healthy. This is Jan’s inspiring story, featured for the first time at posit::conf(2023). Credits: Creative Director: Jason Restivo & Scout Studios Art Director & Illustrator: I-Nu Yeh Animator: Theera (Jay) Keeree Content Producer & Writer: Shannon McGarvey Sound Mixer: Caleb Theimer

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Posit Cloud Essentials | Ep 3: Speed up your data workflows with Project Templates

On the last Tuesday of every month, we host an event – Posit Cloud Essentials – where we explore the ins and outs of Posit Cloud, diving into its key features, valuable tips, and real-world use cases. The event is open to all and hosted on YouTube with a live Q&A during each month’s event. THIS MONTH’S EVENT: All data projects are not created equal. Sometimes you need certain packages. Sometimes you need specific data files or helper scripts. And sometimes you want to define specific compute resources to match your analysis. Creating a new project in Posit Cloud provides you with a blank canvas. Project Templates enable you to add structure to your canvas, take a snapshot of the newly defined environment, and reuse that starting point for subsequent work. This episode of Posit Cloud Essentials will showcase how you can get started more quickly on Posit Cloud with Project Templates, either by using one of our predefined workflows or building your own. WHAT IS POSIT CLOUD? Posit Cloud makes it easy to move your entire workflow into a unified online experience with project management and publishing capabilities. Use your favorite coding languages and environments and share your work seamlessly with others, all from the comfort of your web browser. ADD THE EVENT TO YOUR CALENDAR: No registration is required to join the events. Simply add the event to your calendar using the link below. Add future Posit Cloud Essential events to your calendar → http://evt.to/adahaeuow EXPLORE MORE ABOUT POSIT CLOUD: Create a free Posit Cloud account → https://posit.cloud/plans?utm_source=youtube&utm_medium=organic_social&utm_campaign=cloud_launch View the entire library of Posit Cloud Essential events → https://youtube.com/playlist?list=PL9HYL-VRX0oS4CXCA81eno41u8K3ckGVH&feature=shared

Michael Lopez @ NFL | Being forward-thinking & anticipating questions | Data Science Hangout

We were recently joined by Michael Lopez, Sr. Director of Football Data & Analytics at the National Football League to discuss using data and analytics to enhance the game of football. Timestamps: 4:29 - The process of using RFID tag data 7:31 - What kind of decision making is your work driving? 9:00 - Do you analyze player’s health stats? 11:39 - How transferrable are sports analytics skills across leagues? 14:19 - Big Data Bowl and other community initiatives 19:20 - Structured vs unstructured data? 21:51 - Transition from academia to NFL 25:23 - Communication skills that have transferred over 29:22 - What data is captured from the RFID tags? 34:32 - How could someone get into the NFL space as a data scientist focused on health? 37:18 - How are you getting people to buy-in to analytics? 40:40 - How do you prioritize what your team is working on? 43:04 - Languages and tools used in analytics at the NFL 45:06 - Translator role in football analytics 49:09 - How does the NFL go about achieving the level of analytics that you do? 50:59 - Does GAIT analysis have impact on their performance? 52:59 - How do you evangelize new data points and explore things previously un-explored? 55:48 - Fantasy football 56:34 - What are you excited about in the year ahead? Diving into a specific topic on communication – 27:38 “Sometimes we are answering the questions that we know they’re going to ask before they’ve asked them. And I think the best presentations we give are the ones where they’re asking a question and then just click Next, and it’s on the next slide. Or they ask a question and I say, yep, it’s in the Appendix. Click a couple slides forward and say, here it is. Because you have to anticipate the questions they’re going to ask. Sometimes, we’re collecting data before they’ve asked us to collect data because we hear something in the media or we hear something that they’re asking in the replay room or their coaches are asking, and we’re like, we’re going to go build this. They didn’t tell us to go collect data on quarterback slides, but we now know that quarterbacks are sliding more than ever before. We know quarterbacks are the most important position in football. So last year, Andrew Patton basically led an NFL data collection processing quarterback slides. Because we knew that when we got to the offseason, somebody was going to say, “hey, how are how are quarterbacks sliding?” Actually, we have that because we went out and collected it. So I would say that it’s a combination of great data viz and then also being purposeful and forward-thinking with the questions that people are going to be wanting to ask. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!) Come hangout with us!

Novo Nordisk’s Journey to an R based FDA Submission

Abstract: Join Novo Nordisk, leading pharmaceutical company in diabetes, obesity and other chronic diseases, as we celebrate reaching our NDA milestone, having recently submitted our first submission package featuring outputs exclusively written in R, along with select ADaM datasets also written in R. In this webinar, we will provide you with an in-depth look at our computing platform and our internal R package suite, including insights into their development. We will reflect on the process and setup we have in place and provide you with a sneak peek into what the future holds for us. You’ll get an exclusive look at our journey and learn how we overcame challenges, as well as how we continue to innovate and streamline our processes. Don’t miss this opportunity to gain insight into our approach to NDA submissions. Speakers: Ari Siggaard Knoph (MSc): Ari is a Principal Statistical Programmer and International Lead Programmer at Novo Nordisk with a seat on the pharmaverse council. He is a driving force behind the adoption of R in clinical deliverables and submissions at Novo Nordisk. Ari is also the author and maintainer of multiple R packages and leads the Novo Nordisk pharmaverse team. Steffen Falgreen Larsen (MSc, PhD): Steffen is a Statistical Specialist with over 8 years of experience in driving the shift towards R and Python at Novo Nordisk. He has a master’s degree in mathematics with a specialization in life sciences and a PhD in biostatistics and informatics. Steffen has designed and co-developed a Statistical Computing Environment for R and the internally developed package ecosystem. He is a seasoned R user and developer. Anders Ellern Bilgrau (MSc, PhD): Anders is a Statistical Specialist at Novo Nordisk and an Adjunct Associate Professor at the Department of Mathematical Sciences, Aalborg University. He holds a master’s degree in applied mathematics and a PhD in biostatistics and informatics. Anders is not only a skilled statistician but also an experienced software developer, with a special focus on R development for many years. He has played a key role in co-developing the R platform and internal packages used in Biostatistics at Novo Nordisk

Hadley Wickham @ Posit | Giving benefit to people using what you build | Data Science Hangout

We were recently joined by Hadley Wickham, Chief Scientist at Posit PBC. Listen in to hear our chat about building tools (like the tidyverse) to make data science easier, faster, and more fun. 36:57 - While I’m bought into developing open source packages to help deliver better processes, any advice to those of us doing that development in getting their company bought in? You have to give some benefit to the people using (what you’re building) You’ve got to either remove pain or add pleasure in some way because if you can’t do that and you’re not someone’s direct supervisor, it’s hard to get people to change. The way I think about the tidyverse is, how do we give people some sort of quick wins so they can be motivated to do the things that are slower where they’re gonna have to learn some new ideas or some new tools. You kind of build up some equity with that person. They build trust that you’ve helped them in the past and now they’re willing to invest a little bit more time before they see the payoff. But in the early days, it’s all about delivering payoffs as quickly as possible. And I think if you’re doing, like, you know “my company’s first R package” - the easy pain points are: make themes for your company corporate style guide, make a ggplot2 theme, make an R Markdown, a Quarto theme. Make a Shiny theme that people can just use to get, you know, something that’s reasonably close to whatever your corporate style guide dictates. That just feels like an easy win for people because it makes them look good inside the corporation and because you’ve put in all the hard work, it’s like three seconds for them to type the right function name to get the right theme. I think the other bit is making it easier to get access to data. Set up some wrappers around DBI connections to the most important data sources. Provide some conventions around authentication so that stuff just works so that they’re not struggling with “What packages do I need to install? What’s the password? Where’s the path I need?” Just give them some, like, a list of the top ten most common data sources and people will love you by and large. Follow-up question: Once you identify the things that you think would be useful for people - do you have a philosophy or a way in which you approach putting things together? When you’re in an environment of scarcity when you’ve only got so much time that you can take out of your everyday job to invest in writing a package, it’s really tough to balance. Like, how do I add new stuff versus making sure the old stuff continues to work? I think, again, some of it’s about building up trust. So, give people some wins so that when you inevitably break stuff, you’ve got some kind of cushion so people aren’t going to be really angry with you right away. They’re gonna be like, ok, well there’s a little bit of suffering now, but this person saved me so much time. But yeah, it’s really hard. And particularly as you’re starting out, like, you’re going to make mistakes. That’s inevitable. You’re going to do things that when you look back a year later, you’re like, why on earth did I do it that way? You’ll want to rip out the whole thing and ride it from scratch. And I think that if it feels horrible, you have to remember, that’s great. It means you’ve grown immensely as a programmer. Certainly if you have my kind of mindset, you have to resist the temptation to rip things out and redo them as much as possible and just focus on making the next generation better rather than breaking what stuff people already have. So I don’t have any great answers here, but I think you just have to think about those tensions of “how do I keep my forward velocity up while getting better as a programmer and evolving over time, but also thinking about how do you make the things you did a long time ago better?” ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!) Come hangout with us!

Hadley Wickham

How to keep data up-to-date with 6 pins workflows (aka avoid data-final.csv & data-final-final.csv)

Ever chase a CSV through a series of emails or had to decide between data-final.csv and data-final-final.csv? Pins (both for R & Python) is a package that a bunch of people at the Data Science Hangout wish they knew about earlier. It allows you to publish and share objects (data, models, etc.) across projects and with your colleagues. Pins package (R) - https://pins.rstudio.com/ Pins package (Python) - https://pypi.org/project/pins/ Timestamps: 1:15 - Posit Team Overview 2:18 - Introduction to pins (scenarios where you might want to consider using pins) 4:42 - Installing pins 6:24 - Workflow #1: Pinning an R Object to Posit Connect (from RStudio) 10:23 - Workflow #2: Pinning a Python Object to Posit Connect (from JupyterLab) 15:19 - Workflow #3: Reading in a Python pin in an R Session 16:07 - Workflow #4: Reading an R pin into a Python session 17:50 - Workflow #5: Pin versioning 21:50 - Workflow #6: Automating the pin writing process (through job scheduling on Connect) Helpful resources: Q&A for this session on August 30th: https://youtube.com/live/8hc9ck1ZNLE Blog post on pinning an R dataset to Posit Connect: https://posit.co/blog/pins-posit-connect/ Many people find this useful for: Scheduling reports that need to be updated with the newest data each week Reusing data across multiple projects or content (Shiny app, Jupyter Notebook, Quarto doc, etc.) We host these end-to-end workflow demos on the last Wednesday of every month. No registration is required to attend - simply add it to your calendar using this link: pos.it/team-demo If you ever have ideas for topics or questions about them, please let us know in the comments

Posit Cloud Essentials | Ep 2: Managing Data Projects with Spaces

On the last Tuesday of every month, we host an event – Posit Cloud Essentials – where we explore the ins and outs of Posit Cloud, diving into its key features, valuable tips, and real-world use cases. The event is open to all and hosted on YouTube with a live Q&A during each month’s event. This month, Alex Chisholm, Product Manager for Posit Cloud, walks through how to manage your R and Python data projects with Spaces in Posit Cloud. You can organize code with RStudio and Jupyter Notebooks, host interactive applications, and publish data-driven documents all in one dedicated environment. Then you can invite others to join your space, carefully setting permissions regarding what they can see and do. This demo will teach you everything you need to know about Posit Cloud spaces through an example of how a data consultant might organize their work. What is Posit Cloud? Posit Cloud makes it easy to move your entire workflow into a unified online experience, complete with project management and publishing capabilities. Use your favorite coding languages and environments and share your work seamlessly with others, all from the comfort of your own web browser. No registration is required to join the events. Simply add the event to your calendar using the link below. Create a free Posit Cloud account → https://posit.cloud/plans?utm_source=youtube&utm_medium=organic_social&utm_campaign=cloud_launch Add future Posit Cloud Essential events to your calendar → http://evt.to/adahaeuow View the full library of Posit Cloud Essential events → https://youtube.com/playlist?list=PL9HYL-VRX0oS4CXCA81eno41u8K3ckGVH&feature=shared

RStudio IDE | Background and Posit Workbench Jobs

This is a great feature for any long running scripts you may have, including, for example, a machine learning training step. It also allows you to run multiple jobs simultaneously. Now the main difference between background jobs and workbench jobs is that background jobs run as a child process within the same session. And workbench jobs run as a new session, either on the local server, or off host if you’re running in a multi server or clustered environment

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Teaching the tidyverse in 2023 | Mine Çetinkaya-Rundel

Recommendations for teaching the tidyverse in 2023, summarizing package updates most relevant for teaching data science with the tidyverse, particularly to new learners. 00:00 Introduction 00:46 Using addins to switch between RStudio themes (See https://github.com/mine-cetinkaya-rundel/addmins for more info) 01:40 Native pipe 03:08 Nine core packages in tidyverse 2.0.0 07:15 Conflict resolution in the tidyverse 11:30 Improved and expanded *_join() functionality 22:05 Per operation grouping 27:41 Quality of life improvements to case_when() and if_else() 31:41 New syntax for separating columns 34:51 New argument for line geoms: linewidth 36:08 Wrap up See more in the Teaching the tidyverse in 2023 blog post https://www.tidyverse.org/blog/2023/08/teach-tidyverse-23

Mine Çetinkaya-Rundel

rstudio tidyverse tidyverse.org RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Joe Cheng @ Posit | You have to be able to reason about it | Data Science Hangout

We were recently joined by Joe Cheng, CTO at Posit PBC to chat about all things Shiny - a web framework for data scientists, career journeys, being vulnerable, and so much more. At 39:53 - Joe shared what it means when he says, “you have to be able to reason about it” When you write software or do an analysis or whatever, there’s a level of “I got it to work” and then there’s the level of “it works, and I can reason about it.” Complex pieces of software are among the most complicated things that humankind has ever devised. What other human-made constructs can have hundreds of millions of pieces and yet they’re expected to all fit and work together so precisely that, if one token is off, rockets explode. I don’t know what the limit is, but even the smartest humans can only hold some small number of variables and operations in their head at any one time. When we work on software that’s non-trivial, we work on software that in its totality is more than any one human mind can hold at any one given time. The main challenge in software engineering is about, how do you take all this complexity and break it down into smaller pieces, each of which you can reason about, each of which you can hold in your head, each of which you can look at and say, “Yeah, I can fully ingest this entire function definition. I can read it, line by line, and prove to myself, this is definitely correct if the functions that it’s calling don’t have bugs and if it’s called in the right way.” So with those caveats, if the things that this is calling are correct and are called correctly, then the result will be correct because the logic here is correct. Software engineering, at all but the most beginner level, is a lot about this. How do you break up inherently complicated things that we’re trying to do into small pieces that are individually easy to reason about? That’s half the battle right there. The other half of the battle is - how do we combine them in ways that can be reliable and also easy to reason about? So it’s these two pieces– small pieces reliably composed– if you can achieve that, that’s what I’m talking about. That’s software that you can reason about. This has implications for data science as well. With data science, you’re doing some kind of analysis on some data, and it starts out as, oh, I’m just doing these simple things. I’m doing this manipulation and then I’m doing this visualization. But then as you get deeper and deeper into it, it grows and grows and grows. You’re at the point where you’re at the end and you don’t remember where these variables came from. You don’t remember what’s the difference between this data frame and this data frame. And you go back and hopefully start breaking it into functions or somehow dividing it into smaller pieces that each focus on a thing. Then you join those pieces together in your overall script. That’s this principle of small pieces individually able to be reasoned about. When you think about other rules that you might have heard about software engineering – we know when you’re writing functions, using global variables is bad. That’s another one of these things where that hurts your ability to hold the entire function in your head and to prove that it’ll work correctly. Because who knows who is setting that global variable to what value? You can’t prove to yourself this function is definitely correct. I think if there’s anything that you’re working on that needs to be correct and you do care that the answer is right, I try never to stop when I have an answer but I can’t reason about the code. I always try to go back and do the refactoring that’s necessary just so I can prove to myself that the answer is right. The other big benefit to this is that those individual pieces– if it does turn out that there’s a mistake somewhere, you can individually debug, test, unit test those individual pieces. When there are problems, you’ll much more easily be able to find them. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!) Come hangout with us! The Data Science Hangout is a gathering place for the whole data science community - including current and future data science leaders - to chat about data science leadership and questions you’re all facing that happens every Thursday at 12 ET

Joe Cheng

What does superseded mean? Package development lifecycle process and the meaning of superseded.

An important part of the process of package lifecycle and package development is not just adding new functions. It’s is equally important to remove functions. Hadley Wickham shares about the package lifecycle process and what ‘supersede’ means for functions. See the full video about the purrr 1.0 release: https://youtu.be/EGAs7zuRutY More about the package lifecycle stages: https://lifecycle.r-lib.org/articles/stages.html Maintaining the house that tidyverse built: https://youtu.be/izFssYRsLZs

Hadley Wickham

purrr tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

What does deprecated mean? Package lifecycle and the process of deprecation.

An important part of the process of package lifecycle and package development is not just adding new functions. It’s equally important to remove functions. Hadley Wickham shares about the package lifecycle process and what ‘deprecation’ means for functions. See the full video about the purrr 1.0 release: https://youtu.be/EGAs7zuRutY More about the package lifecycle stages: https://lifecycle.r-lib.org/articles/stages.html Maintaining the house that tidyverse built: https://youtu.be/izFssYRsLZs

Hadley Wickham

purrr tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Data Science Hangout featuring all of us! | Building up a data science team in a small org

Learning from our first open week, we decided we would continue to hold these open weeks once every 2 months or so and we were all the featured leaders for the week, asking questions to each other. There was a great discussion in the beginning of this week’s hangout on building up a data science team in a smaller organization. 3:53 George shared, “As the first data scientist in HR, I was tasked with starting from scratch with everything. So building out metrics, defining metrics, doing data governance, learning a new HR system, and then from there developing a team. So I think right now it’s just looking at the different tools that are out there, understanding how we might manage a data lake and trying to find the right tools. So far, my approach has been to have discussions with our HR managers - the primary stakeholders and the ones that are going to be leveraging the data - and communicating higher up or to the different managers within the organization. Also thinking about security because with HR data it’s highly secure information that we share. We don’t always want to share demographics, so also thinking about security policies. But just a lot of conversations with people about what their business needs are. 8:11 Eugene shared, “The first question is really what are going to be the highest impact projects and who would I hire as my first employee and then round out based on their weaknesses. But I would aim to try to cover these three bases: a programmer who understands production-level stuff, someone who understands the statistics side (probability distributions for example), and a contextual expert. If you’re really going to build a whole team of four or five, six people, you really need people who are, in my opinion, really good experts at each one of those three things, because it’s very rare to find somebody who’s an expert in all three. You’d be paying out a ton of money if you do find someone who’s an expert in all three.” 9:17, Libby shared that “In interviewing people or interviewing with people she finds that when they are building a team, they’re looking for somebody to hit the ground running. They always use that term: we need somebody to hit the ground running. A lot of times what I feel like they’re saying is we need somebody to do everything and we also need somebody to tell us what to do, because we don’t know what to do. That feels scary because I can’t be everything for everybody, but it also sounds like people are saying that person doesn’t really exist. My take on that is if you have somebody who does everything and knows everything, they are now your single point of failure. If they leave, all of your expertise goes in one person. Instead of needing to replace the one person who was maybe a subject matter expert, you now have nothing.” 10:52, Katherine shared, “I feel like a lot of times, looking for people who can ‘hit the ground running’ is corporate code for we are not going to onboard you or train you at all. When you are building a data science team, you need to say these are the people that I need and the skill sets that they’re going to have and then build your onboarding around what that team is going to look like. One of the things that we’ve been trying to get away from is, ‘oh, well this one person knows everything.’ For six months, all we did was write documentation about our processes and transcribe email chains from three years ago so that when we do bring on new people, we can onboard them not only on the technical aspect of the job, but also on the context of the data. This is especially important because we’re in the medical field, where you are bringing on people who may not have had any medical experience before.” At 12:06 Coulter shared, “I was in an org where data science was uncharted territory with everything done in Excel. I tried to get buy-in on this from a selfish point of view because I was dealing with all this data and I said, “This shouldn’t take this long. We’re spending a ton of time trying to do all this. Luckily, I had great leadership that said,”You know what, that’s right.” I was able to hire someone and immediately started converting everything to R to automate as many things as possible. The first time that we were able to show someone, hey, you know this thing that used to take you 16 hours, it takes 30 seconds now. And the joy in their faces– I will never forget that. I think that really showed leadership that this is something worth investing in even further. That was 2.5 years ago and now we are a team of 8.” ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!) Come hangout with us!

Posit Cloud Essentials | Ep 1: Getting Started

On the last Tuesday of every month, we host an event – Posit Cloud Essentials – where we explore the ins and outs of Posit Cloud, diving into its key features, valuable tips, and real-world use cases. The event is open to all and hosted on YouTube with a live Q&A during each month’s event. This month, Alex Chisholm, Product Manager for Posit Cloud, walks through how to get started with a free Posit Cloud account. Enabling you to conduct analysis, generate insights, and share findings from your web browser. What is Posit Cloud? Posit Cloud makes it easy to move your entire workflow into a unified online experience, complete with project management and publishing capabilities. Use your favorite coding languages and environments and share your work seamlessly with others, all from the comfort of your own web browser. No registration is required to join the events. Simply add the event to your calendar using the link below. Create a free Posit Cloud account → https://posit.cloud/ Explore the source code used in this demo → https://posit.cloud/spaces/394911/join?access_code=v-iZm0epL-n-vNHpht4JAfH47gmqWdg0cM6hyll7 Add future Posit Cloud Essential events to your calendar → http://evt.to/adahaeuow Q&A from this demo can be found here → https://app.sli.do/event/q2aLBPfVRAvUCFryNs9YuL

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

How to standardize access & ensure consistent data in data products with FastAPI & Posit Team

Episode 4: Standardizing access & ensuring consistent data in data products (using FastAPI and Posit Team) Led by: Ryan Johnson, Data Science Advisor Follow-up links: Posit Team: https://posit.co/products/enterprise/team/ Talk to us directly: https://posit.co/schedule-a-call/?booking_calendar__c=RST_YT_Demo Follow-along blog post: https://posit.co/blog/fast-api-posit-team-demo/ Source code for example: https://github.com/ryjohnson09/fastAPI_demo Posit Team demo resources: pos.it/demo-resources Q&A session link: https://youtube.com/live/nRGWO_9OovI Timestamps: 1:22 - Overview of Posit Team (Posit Workbench, Posit Connect, Posit Package Manager) 2:46 - Overview of demo workflow 3:18 - Introduction to APIs - what are they and why would you use them? 6:39 - Introduction to the data used in the example 7:31 - Building the FastAPI (data gateway in this example) 10:00 - Launching a VS Code session in Posit Workbench 13:20 - Deploying FastAPI from Workbench to Posit Connect 13:58 - Deployed FastAPI in Swagger interface on Posit Connect 15:27 - Authentication of the API 16:14 - Querying the FastAPI from Jupyter Notebook 18:02 - Querying the FastAPI from a Shiny application On the last Wednesday of every month, we host a Posit Team demo and Q&A session that is open to all. You can use this to add the event to your own calendar. Who are these monthly demos for? Everyone is welcome to join us - regardless of industry, background, or experience! We will discuss topics that will speak to: Data scientists and administrators new to Posit Team or are looking to grow their understanding of our toolchain, Teams searching for a new analytic platform built to support open-source data science, And, those that are just curious about Posit Team! What you can expect from the monthly Posit Team demo: During the session, we will walk through an end-to-end data science workflow and demo the core functionality of Posit Team while highlighting some of our latest features! While each session’s content will vary slightly, here are a few core topics we will address each month: Open Source Analytics: The future of data science is open source. We’ll discuss methods for leveraging open-source tools and packages in a secure and scalable way! Deployment: How to share the amazing data science assets your Team has built, including web applications, machine learning models, APIs, and more! Data Access: Data comes in various forms and is stored in various ways. We’ll discuss best practices for accessing, reading, and writing data! Job Scheduling: Do you have recurring data science jobs? We’ll show you how to automate these processes using Posit Connect. What is Posit Team? Posit Team is a bundle of our popular professional software (Posit Workbench, Posit Connect, and Posit Package Manager) for developing data science projects, publishing data products, and managing packages. Registration is not required. The event will be streamed through YouTube Premiere

Matt Frazier @ Pie Insurance | Removing blockers for your team | Data Science Hangout

We were recently joined by Matt Frazier, Strategic Advisor and former Chief Analytics & Underwriting at Pie Insurance At 35:50 - Matt shared that being an effective leader is about being the person who makes sure that all of the conditions are set up, so that each and every one of their employees can succeed and can fully explore the domain of their competencies. Find smart people, and then set up the conditions, and get the heck out of the way. If there’s a blocker that is in their way, find a way to remove that blocker. Matt adds, “Now, are there performance reviews and things like that? Yeah, every company has them. I am a strong proponent against them. I think that performance reviews should be done every single day between the individuals that are working together. I don’t think it should be between a boss and that boss’s employees – I think it should be the individuals and their colleagues that are working together with one another. That’s where that feedback should be coming from. …I think that if we can create an egalitarian society where everyone is responsible to everyone else and everyone is giving constant feedback to everyone else on how to improve with empathy and with honesty, that is where you can create an organization or a social structure that actually allows everyone to win, and allows everyone to succeed.” On the topic of removing blockers, what should you do if you’ve identified certain roadblocks you need to get past? 38:12 - Whether it’s an I or a we…sometimes there’s a blocker for a whole team and sometimes there’s a blocker for an individual. I don’t know if any of you have taken any courses in nonviolent communication. I don’t believe all of it, but I think it is a toolset where you can sort of mix and match. Listen for needs and express needs. Start with, “I’m trying to do this for the company. I believe that this is going to be very valuable, do you agree?” You’ve got to get buy-in that what you’re trying to do is valuable for the company. In order to do that, I have certain needs that are not being met. This is what those needs are– it could be an individual, could be a group of individuals, a dynamic. It could be technology that isn’t there or budget. And then you really express that as a need. Not as so-and-so is doing something to me, and I’m mad. That’s a sort of selfish communication. What you want to do is you want to try to abstract that as much as you can. Now, you can’t always because sometimes there’s just a person who isn’t on board and so, you say, “Look, I really need this person to be on board, but I don’t think they are. And I want to ask you what we can do together in order to try and identify that other person’s needs and discover why it is that they appear to be a blocker. It may be in my own head, and I’m just in an echo chamber. But let’s have that conversation.” And if you don’t feel confident having that conversation yourself, go to someone who you think can have that conversation. It doesn’t have to be a manager, It could be a colleague who knows that person really well, and can set up that meeting so that you can have that meeting and identify what those needs are. There’s another book that I would really strongly recommend called Never Split The Difference by Chris Voss: https://www.blackswanltd.com/never-split-the-difference That book basically teaches you to talk to an individual, have a dialogue with them until you get to why they’re saying no to whatever thing you want to get through. Yeses that aren’t really yeses, don’t matter. Make sure you understand what the other person or organization’s needs are and then make sure that you’re constructing the right conversation, and having those conversations through an expression of needs on both sides. Usually, that is the Black Swan that will bust right through that blocker. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!) Come hangout with us!

J j Allaire Jupyter Notebooks + Quarto for customizable and reproducible documents, websites and

To share our results and communicate effectively in data science, we need to weave together narrative text and code to produce elegantly formatted, interactive output. Not only does it need to look great, but it needs to be reproducible, accessible, easily editable, diffable, version controlled and output in a variety of formats, such as PDF, HTML and MS Word for convenience and often compliance. Jupyter has already made so much of this possible. By combining Jupyter with the open-source publishing platform, Quarto, built on Pandoc, you can easily create the output format and the styling that you need for any situation. With Quarto, you can author documents as plain text markdown or Jupyter notebooks with scientific markdown, including equations (LaTeX support!), citations, cross references, figure panels, callouts, advanced layouts, and more. You can also engage readers by adding interactive data exploration to your documents using Jupyter Widgets, htmlwidgets for R, Observable JS, and Shiny. In this talk, we’ll discuss authoring these dynamic, computational documents with Quarto and Python that bring code, output, and prose together, leveraging integrations with both Jupyter and the Quarto VS Code extension. Whether you’re new to Jupyter or have thousands of notebooks already, we’ll walk you through using a single source document to target multiple formats - transforming a simple document into a presentation, a scientific manuscript, a website, a blog, and a book in a variety of formats including HTML, PDF and MS Word. We’ll also show how you can change themes and styling, and publish these artifacts directly from the command line to the web, so they’re immediately available online

Brian Fannin @ CAS | Copy & paste is operationally dangerous | Data Science Hangout

We were recently joined by Brian Fannin, Research Actuary at Casualty Actuarial Society. During the hangout (38:46) with Brian, we spoke a bit about the areas where actuaries are turning to code-first data science. A big selling point for actuaries is when they find out they can replace a very typical actuarial and analytic workflow of creating a plot in Excel and copying and pasting it into Word. That’s operationally inefficient. It’s operationally dangerous. The notion that my entire analytics report is in one file: all of the code, all of the narrative, all of the tables, it’s like telling me that I can breathe underwater and fly. I think that as actuaries become aware of that capability, they will get happy about it as will the people who need to communicate with actuaries. Imagine you’re in a meeting and you have everyone looking at the same file and the plot didn’t update. “What do you mean we’re losing $1 billion a day?” “Oh, sorry, that’s yesterday’s graph. Today it is…” Resources shared during the hangout: R for Actuaries and Data Scientists: https://info.actexmadriver.com/r-for-actuaries Deep Learning with R (Second Edition): https://www.manning.com/books/deep-learning-with-r-second-edition The Theory That Would Not Die: https://yalebooks.yale.edu/book/9780300188226/the-theory-that-would-not-die/ CASDatasets: http://cas.uqam.ca/ R4DS Community: https://r4ds.io/join ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!) Come hangout with us!

What Exactly Is Digital Transformation? | Posit + PING

Doug Linsmeyer, VP of Digital Technology at PING, clarifies how he thinks about digital transformation, adopting new technologies, and how Posit Connect helps data scientists at PING continue to innovate. Read and watch the full story here: https://posit.co/about/customer-stories/ping/ Learn more about Posit Connect: https://posit.co/products/enterprise/connect/

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Katherine Gerton @ Centene | Making sure you know other teams exist! | Data Science Hangout

We were recently joined by Katherine Gerton, Manager, Data Analytics & Reporting at Centene to chat about the power of embedded teams for bridging the distance between cool data science and meaningful business insights and bringing those teams together to create a community of practice that supports each other and the bottom line. During the conversation Katherine spoke about building tools that make your own life better, and then also spreading them to other people as a really valuable way to do work. Part of being successful in this is also making sure you know other teams exist! At 32:51, Katherine shares: There’s a lot of that community building and reaching out to people that I’ve incorporated into some of my leadership work – a lot of it is making sure you know that other teams exist. In an organization the size of Centene, this can be really difficult. For example: “Oh, I didn’t know that this team was doing this kind of thing with membership. I have a need to do something with membership. Let me make sure that I know who to reach out to.” That’s something that we’ve been really working hard on. In our internal R user group meetings – we’re highlighting different teams and what they are working on. “This is how we’re using the Centene tools.” It’s not always R but it’s just seeing how others are using our data to keep building those relationships. I think going back to something I said earlier, I did it for me. And then I’m more than happy that it’s also helping other people in Centene. How do you actually build those relationships? When you’re really close to a data project and there’s one piece that you don’t know - it’s might be shooting the email to the person that you saw last accessed that database because you can check those things. You can say, “Hey, I saw you were using this data. What’s going on with it?” Those kinds of small emails are important. It’s also getting over that hump of self-doubt, and thinking, “Oh, am I emailing the right person? Are they going to hate me for derailing their day for emailing the wrong person?” Just send an email because the worst thing that happens is that they don’t respond to you. And then you send an email to another person and build that out. As I’ve moved into a leadership role, it’s getting plugged in with other team leaders as well, where we now know the name of each other’s team and can say, “Do you have somebody on your team who knows about this or who can help with this?” That’s also something that the Women in STEM Group has helped with because women in STEM are across the entire organization. They are not just in corporate IT as your cloud engineers. Almost anyone with a data analyst title or anything like that could be in the STEM group. I’ve been exposed to so many more people and so many more incredible women at Centene through that other organization. It doesn’t just have to be the data science community where you are meeting all these other people across different parts of the company. __ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!) Come hangout with us!

Why RStudio is now Posit (J.J. Allaire | Posit CEO) - KNN Ep. 158

Today, I had the pleasure of interviewing J.J. Allaire. J.J. is the founder of RStudio and the creator of the RStudio IDE. He is an author of several packages in the R Markdown publishing ecosystem including rmarkdown, flexdashboard, learnr, and distill, and also worked extensively on the R interfaces to Python, Spark, and TensorFlow. J.J. is now leading the Quarto project, which is a new Jupyter-based scientific and technical publishing system. In this episode, we learn about why RStudio has now repositioned itself as Posit, how it maximizes its open-source nature as a B Corp, and how J.J. as an open-source advocate views the private nature of many LLMs. I really enjoyed this conversation, and I hope you will as well! Posit - https://posit.co/ Podcast Sponsors, Affiliates, and Partners: Pathrise - http://pathrise.com/KenJee | Career mentorship for job applicants (Free till you land a job) Taro - http://jointaro.com/r/kenj308 (20% discount) | Career mentorship if you already have a job 365 Data Science (57% discount) - https://365datascience.pxf.io/P0jbBY | Learn data science today Interview Query (10% discount) - https://www.interviewquery.com/?ref=kenjee | Interview prep questions Listen to Ken’s Nearest Neighbors on all the main podcast platforms! On Apple Podcasts: https://podcasts.apple.com/us/podcast/kens-nearest-neighbors/id1538368692 (Please rate if you enjoy it!) On Spotify: https://open.spotify.com/show/7fJsuxiZl4TS1hqPUmDFbl On Google: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5idXp6c3Byb3V0LmNvbS8xNDMwMDQxLnJzcw?sa=X&ved=0CAMQ4aUDahcKEwjQ2bGBhfbsAhUAAAAAHQAAAAAQAQ MORE DATA SCIENCE CONTENT HERE: My Twitter - https://twitter.com/KenJee_DS LinkedIn - https://www.linkedin.com/in/kenjee/ Kaggle - https://www.kaggle.com/kenjee Medium Articles - https://medium.com/@kenneth.b.jee Github - https://github.com/PlayingNumbers My Sports Blog - https://www.playingnumbers.com ️ 66DaysOfData Discord Server - https://discord.com/invite/4p37sy5muZ

flexdashboard learnr Quarto rmarkdown rstudio tensorflow Ken Jee Ken Jee Podcast Kjp Podcast Data Science Data Science Podcast Knn Ken's Nearest Neighbors Knn Podcast Ken's Nearest Neighbors Podcast Jj Allaire Jj Allaire Interview RStudio Posit Coding Languages Programming Language Wars Founder R Markdown Quarto Jupyter Open Source Llms Open Ai Sports Data Bill James Open Source Software

[84] Reproducible Publications with Python and Quarto (Thomas Mock)

Join our Meetup group: https://www.meetup.com/data-umbrella Tom Mock: Reproducible Publications with Python and Quarto Resources# slides: https://thomasmock.quarto.pub/python-umbrella/#/ Full transcript# https://blog.dataumbrella.org/quarto-blog About the Event# Quarto is an open-source scientific and technical publishing system that builds on standard markdown with features essential for scientific communication. The system has support for reproducible embedded computations, equations, citations, crossrefs, figure panels, callouts, advanced layouts, and more. In this talk we’ll explore the use of Quarto with Python, describing both integration with IPython/Jupyter and the Quarto VS Code extension. Users can author Jupyter notebooks or documents as plain text markdowns with code in Python, R, Julia or Observable. Quarto includes the ability to publish high-quality articles, reports, presentations, websites, blogs, and books in HTML, PDF, MS Word, ePub, Reveal.js and more. Timestamps# 00:00 Data Umbrella introduction 03:41 Introduce the speaker, Thomas Mock 04:14 Thomas begins 05:14 RStudio is now Posit 05:55 What is Quarto? 07:13 Origins of Quarto 08:31 Goal: Computation Document 09:09 Goal: Scientific Markdown 10:03 Goal: Single Source Publishing 10:33 Simple example of what Quarto looks like (YAML, Markup, Markdown, code chunks) 12:29 Simple example: multi-format (output formats: html, pdf, docx, epub, pptx, revealjs) 13:16 List of what is possible with Quarto 14:02 So, what is Quarto: quarto is a language-agnostic command line interface (CLI) 15:27 Basic Quarto workflow 16:43 Difference between “render” and “preview” 17:16 IPython 18:43 Stored/frozen computation and reproducibility 20:36 A *.qmd is a plain text file 21:28 Quarto doesn’t have to be plain text 22:12 Rendering pipeline 22:57 What to do with my existing.ipynb? 24:23 Comfort of your own workspace: JupyterLab, Visual Studio Code, 25:00 Auto-completion in RStudio + VSCode 26:01 Quarto Extensions and Visual / Live Editor 27:19 Quarto, unified document layout 29:54 Quarto, unified syntax across Markdown and code 31:11 Built-in vs Custom 33:01 Extending Quarto with Extensions 33:51 Interactivity, Jupyter Widgets (with plots, matplotlib, etc) 34:15 Interactivity, Observable 35:01 Interactivity, on the fly Observable “widgets” 36:24 Parameters - one source, many outputs 37:36 Rendering with parameters 38:27 Quarto Publish 38:57 Quarto, crafted with love and care (the team) 39:30 Quarto Resources (installation) 39:44 Quarto resources: video tutorials 40:13 Q: Can Quarto documents be shared like Overleaf docs and can users import article templates for specific journals into Quarto? 41:39 new! Manuscript option to bundle an entire project together (bundle can be shipped to a journal) 42:48 Q: Is Quarto git friendly? 43:28 Q: Has Quarto already been used in published scientific work? 44:14 publishing books with Quarto 44:22 Q: Any general suggestions for outputting to docx (Word)? 45:20 Q: Any tips on how Quarto can help conda users? 46:14 Q: Can you use GitHub Actions with Quarto? 47:18 Q: Can you have individual environments for each blog post? 49:50 Download CLI (command line interface) for Quarto 51:10 Example Gallery 51:44 nbdev project 53:14 Quarto blog, Shinylive extension 55:12 Q: How can I use Quarto to write scientific papers? About the Speaker: Tom Mock# Twitter: https://twitter.com/thomas_mock GitHub: https://github.com/jthomasmock #python #quarto #rstats

Python support in Posit Connect

Posit Connect: Deploy everything you create in R & Python, including interactive applications (Shiny, Streamlit, Dash), documents, notebooks, and dashboards. Learn more at https://posit.co/products/enterprise/connect/

Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Live Q&A Session - Ep 3. Scheduling a Quarto Doc (with custom branding) on Posit Connect

Live Q&A Session for the End-to-End Workflow Demo on June 28th *Please note this was the live Q&A portion of the event. You can view the demo recording here: https://youtu.be/V82BBU9ldcM Ep 3. Scheduling a Quarto Doc (with custom branding) on Posit Connect | End-to-end workflows Follow-up links: Demo recording: https://youtu.be/V82BBU9ldcM Posit Team: https://posit.co/products/enterprise/team/ Talk to us directly: https://posit.co/schedule-a-call/?booking_calendar__c=RST_YT_Demo Follow-along blog post: To be added Source code for example: To be added Posit Team demo resources: pos.it/demo-resources

Quarto RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

How to schedule a Quarto document on Posit Connect

Episode 3: Scheduling a Quarto Doc (with custom branding) on Posit Connect Led by: Ryan Johnson, Data Science Advisor Live Q&A recording: https://youtu.be/JUgChPCa3vs Follow-up links: Posit Team: https://posit.co/products/enterprise/team/ Talk to us directly: https://posit.co/schedule-a-call/?booking_calendar__c=RST_YT_Demo Follow-along blog post: https://posit.co/blog/scheduling-a-quarto-doc-on-posit-connect/ Source code for example: https://github.com/ryjohnson09/quarto-job-scheduling Posit Team demo resources: pos.it/demo-resources Timestamps: 1:45 - What is Posit Team? 3:31 - The data we are analyzing: R package download data from within Posit Package Manager (via experimental API) 4:44 - What is Quarto? (we are creating two Quarto docs today) 8:06 - Create a new session in Posit Workbench 8:56 - Create a new project within the RStudio IDE 10:13 - Create the first Quarto document (ETL: extra, transform, load workflow) 13:55 - Publish the first Quarto doc to Posit Connect 16:50 - Take the package download results and pin it to Posit Connect (overview of pins) 18:30 - Schedule the first Quarto doc to run every day at 7am on Posit Connect 20:49 - Create the second Quarto document (report for stakeholders) 23:41 - View the first “boring report” 24:54 - Using a custom Posit format for the report 27:39 - First look of the themed report without modifications 29:00 - Adding Posit themed colors to the gt table 31:35 - Apply code chunk options to hide code from output 32:44 - Publish the second Quarto document to Posit Connect 34:28 - View finished custom branded Quarto document 34:44 - Define specific users who have access to the Quarto doc on Posit Connect 35:10 - Schedule the second Quarto doc to read in the pinned data from the first Quarto doc 36:30 - Example emailed report from the scheduled Quarto report On the last Wednesday of every month, we host a Posit Team demo and Q&A session that is open to all. You can use this to add the event to your own calendar. Who are these monthly demos for? Everyone is welcome to join us - regardless of industry, background, or experience! We will discuss topics that will speak to: Data scientists and administrators new to Posit Team or are looking to grow their understanding of our toolchain, Teams searching for a new analytic platform built to support open-source data science, And, those that are just curious about Posit Team! What you can expect from the monthly Posit Team demo: During the session, we will walk through an end-to-end data science workflow and demo the core functionality of Posit Team while highlighting some of our latest features! While each session’s content will vary slightly, here are a few core topics we will address each month: Open Source Analytics: The future of data science is open source. We’ll discuss methods for leveraging open-source tools and packages in a secure and scalable way! Deployment: How to share the amazing data science assets your Team has built, including web applications, machine learning models, APIs, and more! Data Access: Data comes in various forms and is stored in various ways. We’ll discuss best practices for accessing, reading, and writing data! Job Scheduling: Do you have recurring data science jobs? We’ll show you how to automate these processes using Posit Connect. What is Posit Team? Posit Team is a bundle of our popular professional software (Posit Workbench, Posit Connect, and Posit Package Manager) for developing data science projects, publishing data products, and managing packages. Registration is not required. The event will be streamed through YouTube Premiere

Tracy Teal - It’s not just code: managing an open source project | PyData Seattle 2023

www.pydata.org An open source project starts with ideas and code, but it continues with people. We know that most open source projects rely on just one or two people for most of the work. And code is just a small part of these roles, which also include project management, conflict resolution, decision making in uncertain situations, building an inclusive community, and lots and lots of communication. Whether you’re just starting a project, interested in getting involved in open source, or already have a community of thousands, there are some tips, tricks and templates that you can use to make maintaining an open source project more manageable. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

Open Source in Drug Development | Thomas Nietmann and Posit

Thomas Neitmann, Associate Director at Denali Therapeutics, sat down with Posit to talk about open source in clinical trials, his work at Roche, the creation of admiral, career beginnings, and his future predictions for data science in the pharma space. Posit’s work in Pharma: https://posit.co/solutions/pharma/

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Roche Roche Clinical Trials Thomas Neitmann

Joe Cheng - Shiny: Data-centric web applications in Python | PyData Seattle 2023

www.pydata.org Shiny is a web framework that is designed to let you create data dashboards, interactive visualizations, and workflow apps in pure Python or R. Shiny doesn’t require knowledge of HTML, CSS, and JavaScript, and lets you create data-centric applications in a fraction of the time and effort of traditional web stacks. Of course, Python already has several popular and high-quality options for creating data-centric web applications. So it’s fair to ask what Shiny can offer the Python community. In this talk, I will introduce Shiny for Python and answer that question. I’ll start with some basic demos that show how Shiny apps are constructed. Next, I’ll explain Transparent Reactive Programming (TRP), which is the animating concept behind Shiny, and the reason it occupies such an interesting place on the ease-vs-power tradeoff frontier. Finally, I’ll wrap up with additional demos that feature interesting functionality that is made trivial with TRP. This talk should be interesting to anyone who uses Python to analyze or visualize data, and does not require experience with Shiny or any other web frameworks. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

Joe Cheng

J.J. Allaire - Publishing Jupyter Notebooks with Quarto | PyData Seattle 2023

www.pydata.org Quarto is a multi-language, open-source toolkit for creating data-driven websites, reports, presentations, and scientific articles. Quarto is built on Jupyter, and in this talk we’ll demonstrate using Quarto to publish Jupyter notebooks as production quality websites, books, blogs, presentations, PDFs, Office documents, and more. We’ll also cover how to publish notebooks within existing content management systems like Hugo, Docusaurus, and Confluence. Finally, we’ll explore how Quarto works under the hood along with how the system can be extended to accommodate unique requirements and workflows. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

Brad Weiner @ CU Boulder | Using data to influence institutional decisions | Data Science Hangout

We were recently joined by Brad Weiner, Chief Data Officer at University of Colorado Boulder to talk about the intersection of data science and higher ed. At 8:53 Brad shared an example where their data team influenced an institutional-level decision. Brad shares, “Doing the data work is the easier part, getting people to actually use the data and inform policies is much trickier. I always like to point to the fact that it really does take a village. We need all kinds of people to be invested and interested in a project.” One example we’ve been pointing to lately: We really want students to be successful. Not just because that’s our core mission but because we’re measured on that. All universities are measured on that in a lot of ways. Students want to go to a place where they’re going to be successful. Building a college degree really is a supply-chain problem. You need to have all of these constituent parts and they need to land at the right time. There’s tons of dependencies and those dependencies need to be built out years in advance. So if we don’t have enough chemistry labs, that student doesn’t get to take chemistry. If not enough students take chemistry, they don’t have the required courses and they can’t graduate. We’re constantly looking for these things. One of the things that we have found from the people in the bookstore is that students found the prices of their textbooks to be something of a barrier. We’re not only trying to graduate students, but we’re trying to graduate everybody as equitably as possible. If the experience is one way for some students and one way for another group of students, that is not considered success. What we were finding, and what we had heard, what we had learned, was that students were potentially choosing which courses to take based on the cost of the textbooks. Students of today are used to subscription services. Nobody ever thinks, how many songs did I listen to on Spotify? Am I getting my money’s worth there? They’re used to the Netflix models where you just pay one price and you get everything. It doesn’t matter how much you use it or how little you use it. We’re one of the first dozen or 15 schools that have gone in on this bookstore-equity access model, which is basically, students pay one price. This gives them the financial stability, predictability, and their ability to budget on books, but also the ability to be really flexible in what classes they take. By paying that one price, they get what we call day-one digital access. There’s evidence that having digital, immediate access to your books is actually a student success lever. So for a variety of reasons, we wanted to implement this program. One of the things that we were asked to do is figure out what price should the bundle be? Pricing is a really complicated problem - if the price is too high, nobody participates and the whole thing collapses on itself. And if it’s too low, you don’t cover your costs. We had a whole team of people who were thinking through that problem. We are happy to say that we used our super-sweet Posit Workbench set-up to simulate a whole bunch of different options. The Data Science team did that and we were able to guide them on what that price was. And it got deployed out. So next year, from what I understand, all undergraduates at CU Boulder will pay $279 a semester, and get all of their books on day one. If they drop a class, then those books go away and the new ones come in. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

Natalia Andriychuk @ Pfizer | Hosting an internal data science hangout | Data Science Hangout

We were joined by Natalia Andriychuk, Statistical Data Scientist at Pfizer to discuss running an internal Data Science Hangout, building a supportive R community, discovering and learning new R packages/tools, and contributing to open-source initiatives. Resources shared: Rachael’s guide for hosting your own data science hangout: https://docs.google.com/document/d/1uKgLRacVyUJA1dH4f675dKP1gnbaw--QztxwIIiLAAA/edit?usp=sharing RTP useR Group: https://www.meetup.com/rtp-r-user-group/ Data Science Hangout LinkedIn Group: https://www.linkedin.com/groups/12610075/ Shiny app submitted to FDA Pilot: https://www.r-consortium.org/blog/2022/12/07/update-successful-r-based-package-submission-with-shiny-component-to-fda 56:41 - What’s something that you’ve learned from that process of starting your own internal data science hangout? Summarized from below: Start with a list of 10 or so people you’d like to invite Ask colleagues you have an established relationship with to be your first featured leaders Create a calendar of events that you can invite people to Use a form to collect the speaker’s bio, photo, etc. Schedule a 30 minute call with each featured leader the week before Try to plan out 3 sessions in advance Start inviting people:) Natalia’s response: It was a little bit overwhelming to think about how to start it but one thing you would need to start is a list of 10-15 people who you’d like to invite. First I would create the calendar of events. At Pfizer, we do our data science hangouts every other week. I specifically put the dates and send people an invitation asking them to be a part of hangout. I actually started the hangouts with Mike Smith and Douglas Robinson, who are part of our core team and are moving this initiative forward. For your first few hangouts, it can be easier to have colleagues join us as your co-host that you already have an established rapport with. I set up a Microsoft Form that I would send out: asking about: occupation, bio, certain questions they want me to ask, etc. I also ask them to provide their headshot. I think it makes the hangout invitation a little more colorful. For the featured leaders, I schedule a 30 min call with each guest the week prior to go over any questions that person might have if they never attended before. It helps me to know more about what they’re doing, especially if this is the first time I’m having a 1:1 with this colleague. I’m pretty new to the company as well, so we would chat a little bit about their interests - and then we have the hangout session. I’ll try to have at least 3 people booked in advance. So find your featured leader, and then start inviting people. Having an internal hangout has been very valuable because we are able to talk more about certain topics within our organization. For example, if I come to this hangout and we discuss certain topics, sometimes I might have to say, “Well, this is a great question, but I can’t talk about it because the information is proprietary.” With your internal hangout, you do not have to worry about that. This is how certain problems get solved faster. It also brings people together that wouldn’t work together necessarily. I think this is a very valuable part of it. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

JD Williams @ Zoetis | 6-steps for POC to production & long-term value | Data Science Hangout

We were recently joined by J.D. Williams, Head of Business Insights and Analytics at Zoetis to discuss delivering significant business outcomes through data science solutions. Timestamps: 2:53 - Intro to Zoetis team 9:39 - Building a data science team from scratch 14:47 - Being flexible on location when hiring 17:02 - Promoting data science in the company 21:26 - Tips for building relationships with execs 23:13 - Example app showing their community growth 25:18 - Making an impact on manufacturing (where they use Posit tools the most today) 33:40 - Building up a data team at a startup 36:39 - Moving from proof of concept to production-ready apps (6-step process) 41:34 - Being open with the tools the team can use 47:22 - Designing requirements for data science and data analysts 52:50 - Keeping up with technology trends 6-step process for getting from Proof of Concept to Production & Long-Term Value. Identification/prioritization. For our manufacturing and supply chain leaders, we had a small steering committee of leaders and analytics leaders that came together to identify and prioritize the highest value use cases. Feasibility We took a little bit of time to gather the data and look at the data available (do we have enough data and the right kind of data to do a proof of concept? If so, we moved to next step. Proof of concept Here’s where we would build– we’re talking the ideal would be between two and four weeks – a quick proof of concept, using the data that we have sort of in a sandbox or offline. We’re not building production ready code, we’re not building something that’s ready to go live. We’re just trying to prove the value. Two things– can we do it and does it have value? If the answer to both of those is yes, there’s real business value and we’re successfully able to do it. Then we go to the fourth stage, which is the pilot. Pilot This is where we would make it more production ready. Here’s where we were going to partner with our data engineering team to take the analytic solution and put it into production on a small scale. And think of it as maybe one manufacturing site, where you’re not scaling it across many different places, but being more deliberate in how you’re doing it. This is kind of a risk based approach and we were given permission to fail. A lot of the proof of concepts succeeded, there were some that did not. Or some succeeded, but maybe didn’t have as much value as we anticipated. Focus on those highest value proof of concepts going on to the next stage of piloting. Scale up stage Those pilots that are successful, we want to scale that to other sites, other products, other stages of the process and leverage what we learn there and scale it across other areas. Sustaining How do we sustain the value long term? Those six stages are how we thought about it upfront. Within the first year or so of us getting started, the total number of use cases in our use case tracker was about 100 in various stages of those mentioned. Some were just ideas, some were being prioritized somewhere in the feasibility step, some of the proof of concept, et cetera. From there now, we’ve taken the most valuable of those and we’re actually focusing more on those and how we can highlight them and scale them. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

RStudio + Amazon SageMaker | Build Beyond Your Laptop

Did you know that you can use RStudio, the best IDE for R and Python users, with Amazon Sagemaker? RStudio on Amazon SageMaker makes it easy for R users to quickly and easily get started coding in RStudio on AWS from their browser, no server setup required, by using a new integration with Posit Workbench. In this webinar, Posit team members will show you how to get started with RStudio on Amazon SageMaker to analyze your organization’s data in S3 and train ML models. As a fully managed offering on Amazon SageMaker, this release makes it easy for DevOps teams and IT Admins to administer, secure, and scale their organization’s centralized data science infrastructure with familiar AWS tools and frameworks. Learn more at: https://posit.co/products/cloud/sagemaker/ Talk to us about using RStudio and SageMaker: https://posit.co/schedule-a-call/?booking_calendar__c=Sagemaker

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Posit Presents: A Game of Numbers with PING

The innovators at PING use Posit solutions such as Posit Connect to impact how golf data is analyzed and shared with golfers worldwide. High-level data, once only available to professional players, is now in the hands of everyday golfers thanks to public-facing apps like Ballnamic, which allows golfers to choose the golf ball that best suits their game. Through the use of Shiny apps, Posit Workbench APIs, and Posit Connect, the team at PING can now innovate faster, accelerate product development, and advance the democratization of golf data. Learn more at https://posit.co/about/customer-stories/ping/

Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss PING PING Golf

How to deploy a Shiny application using clinical trial data to Posit Connect

Episode 2: Publishing a Shiny application in R to Posit Connect - Using Clinical Trial Data Led by: Ryan Johnson, Data Science Advisor Follow-up links: Posit Team: https://posit.co/products/enterprise/team/ Talk to us directly: https://posit.co/schedule-a-call/?booking_calendar__c=RST_YT_Demo Follow-along blog post: https://posit.co/blog/publishing-a-shiny-app-in-r-with-clinical-trial-data-to-posit-connect/ Source code for example: https://github.com/ryjohnson09/adam_analysis Posit Team demo resources: pos.it/demo-resources Timestamps: 1:35 - High-level overview of Posit Team 3:30 - Overview of clinical trial data used 5:31 - Opening up RStudio session on Posit Workbench 7:51 - Creating a new directory in RStudio 9:16 - Upload the ADaM dataset to Posit Workbench 10:17 - Using packages from a validated repository on Posit Package Manager 12:37 - Install packages for your Shiny application 13:49 - Pasting the code for the Shiny application ( https://github.com/ryjohnson09/adam_analysis ) 16:16 - Publishing your Shiny application to Posit Connect 18:36 - Changing access controls to published Shiny application 20:25 - Using renv to record your R environment On the last Wednesday of every month, we host a Posit Team demo and Q&A session that is open to all. You can use this to add the event to your own calendar. Who are these monthly demos for? Everyone is welcome to join us - regardless of industry, background, or experience! We will discuss topics that will speak to: Data scientists and administrators new to Posit Team or are looking to grow their understanding of our toolchain, Teams searching for a new analytic platform built to support open-source data science, And, those that are just curious about Posit Team! What you can expect from the monthly Posit Team demo: During the session, we will walk through an end-to-end data science workflow and demo the core functionality of Posit Team while highlighting some of our latest features! While each session’s content will vary slightly, here are a few core topics we will address each month: Open Source Analytics: The future of data science is open source. We’ll discuss methods for leveraging open-source tools and packages in a secure and scalable way! Deployment: How to share the amazing data science assets your Team has built, including web applications, machine learning models, APIs, and more! Data Access: Data comes in various forms and is stored in various ways. We’ll discuss best practices for accessing, reading, and writing data! Job Scheduling: Do you have recurring data science jobs? We’ll show you how to automate these processes using Posit Connect. What is Posit Team? Posit Team is a bundle of our popular professional software (Posit Workbench, Posit Connect, and Posit Package Manager) for developing data science projects, publishing data products, and managing packages. Registration is not required. The event will be streamed through YouTube Premiere

Alix Schmidt & Amanda Ahrens @ Dow | Creating business value with community | Data Science Hangout

We were recently joined by Amanda Ahrens & Alix Schmidt from Dow Chemical to discuss designing enterprise data architecture to enable sustainability, how data science is really all about the people, and about leading a data science community of over 500 researchers at Dow. At 10:02 in the recording, Alix and Amanda shared that one of their executive leaders at Dow asked, “How can we use this community to start creating value for the company?” They suggested, why don’t you go figure out how to do a hackathon? That word can mean a lot of things to a lot of people but after really talking about it, we settled on this sort of Shark Tank- like the TV show Shark Tank - style thing where groups come up with a project idea and pitch it to this executive panel. How did you go from that one executive sponsor to a panel of eight executives in the first challenge? What really works is having the executive that’s already onboard sell it to the rest. It doesn’t work so much if you’re out there by yourself. If you can go along with that executive that’s already on board and explain the vision of why you want to do a Data Science Challenge or a Data Science Conference and what’s in it for them - it’s a lot easier to get them to say yes. They felt like it was an opportunity the other executive was giving them to really help set the data science direction for the company. In all three Data Science Challenges, winning projects were implemented in some way. Even if they weren’t funded directly initially, there was such high interest by the executives in moving forward with the idea. The number one project from last year actually just won an Edison Award, which is a big research and development in STEM award. That’s external recognition too! With that project, pieces of it were happening already, but a lot of value is created at the interface of different functions and different organizations in a company as big as Dow, which is like 37,000 employees. One of the things we encourage folks to do is network and get out there and find people in parts of the company that you wouldn’t normally talk to. That project brought three very small projects from our toxicology department, our molecular discovery research area, and our supply chain area together to make a really powerful tool for making safer and more sustainable products. That project is such an awesome example of exactly the vision of the event and to get that external recognition was really cool. A huge part of being able to bring people together across the company is making sure that you set a theme that resonates with the executives, but also, the mid-level managers because they have to be supportive of their employees actually participating in the Data Science Challenge. Our Shark Tank style challenge, wasn’t just a day or two days where people were working on a problem with the same data set. It was actually over a longer period of time. Beginning to end, the challenge was more around two months. There was a lot of time spent there, so people had to feel like it was worthwhile for their team. The first theme was more general - improving EBITDA. Then the second theme was around employee experience and customer experience. Then the last one that we did in 2022 was all around sustainability and so that really resonated with everybody. Everybody at Dow is trying to prove that they’re contributing to that, and that united a lot of people to come up with some great ideas cross-functionally. Did you come up with those themes with the executives– or the original executive sponsor? The latest theme was brought up to me by one of the technical principals at the company who thought it would be a really uniting theme. We pitched it to the executives that we knew were already on board and going to help us. They said, absolutely - let’s go forward with it. Dow seeks to be the most sustainable material science company in the world, and so that’s a very front and center theme for the company, not just the Data Science Challenge. So it’s pretty easy to get executives to rally around that. They were already interested in it, it was already on their scorecard, so might as well add this to help them achieve their goals. Finding themes that are enterprise-level for the whole company is really important, because we want to make sure the theme doesn’t exclude any one that has a certain type of job that just doesn’t touch that area. Executive sponsors tend to have goals at this company-wide level. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

Keynote, Winston Chang: Lessons and opportunities with Shiny for Python

About the keynote: For the past 10 years, the R community has been able to use Shiny to bring interactive data analyses to the web. Last summer, we announced that Shiny would also be available to for Python. In this talk, I’ll discuss what we’ve learned along the way, as well as the opportunities that Shiny for Python opens up. We are at the beginning of an exciting new era for Shiny, and I hope that community of old and new Shiny users make the most of it! Speaker’s bio: Winston Chang is a software engineer at Posit, PBC, who has served in various roles on the Shiny team, including team lead. He has contributed to many widely-used packages in the R ecosystem, including Shiny, devtools, and ggplot2, and is the creator of several packages, including shinydashboard, R6, and profvis. Recently he has been busy working on Shiny and Shinylive for Python. Winston has a Ph.D. in psychology from Northwestern University and is the author of the R Graphics Cookbook, published by O’Reilly Media

Shiny Team, Winston Chang

Python made easier with Posit

Tom Mock, Workbench Product Manager at Posit, discusses our commitment to Python from open-source projects to enterprise support. Specifically, Tom discusses Posit Team, the all-in-one solution for your data science team to build, share and maintain your R & Python data products. Posit Team comprises our three professional products, Posit Workbench, Connect, and Package Manager. Posit Workbench give your data scientists access to their favorite development tools in a centralized environment. Enabling them to work in the language they prefer, all while sharing IT resources and collaborating to find actionable insights. Posit Connect is where your team can securely host all of their data products and share them with the push of a button. Schedule reports to update automatically and give users access to only the insights they need. Posit Package Manager allows your team to deliver reproducible data science without the burden of manual package management. Organize and centralize R and Python packages across your team, department, or organization. Posit Team: https://posit.co/products/enterprise/team/ Posit Workbench: https://posit.co/products/enterprise/workbench/ Posit Connect: https://posit.co/products/enterprise/connect/ Posit Package Manager: https://posit.co/products/enterprise/package-manager/

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Data Science Hangout featuring all of us!

We did things a bit different this week as our featured leader had to reschedule - and we were all the featured leaders for the week, asking questions to each other. Libby & Mike put Rachael, our Pro Community Lead in the hot seat to start and then we all answered the questions the came in Feeling all the community feels on this week’s call especially with everybody jumping in! A few topics that came up in discussion: 6:02 - How your skills & knowledge that help you in your career can come from places outside work 7:43 - Being a part of the initiation process to use data science tools in your organization / value of open source 11:29 - What are people thinking about using ChatGPT in their companies? 17:02 - What have you learned from a marketing background that can help us sell our data tools? 25:55 - Is there a way for people to connect after the Hangout? ( https://www.linkedin.com/groups/12610075/ ) 26:45 - Favorite data science podcasts or YouTube Channels? (thanks Faythe for consolidating! https://www.linkedin.com/feed/update/urn:li:activity:7057411912606617602?utm_source=share&utm_medium=member_desktop ) 32:35 - Relationship dynamics in organizations, organizational network analysis 35:47 - Data storytelling courses, how do you present data to be able to persuade people? 41:25 - What makes it hard to get in the door with decision makers? 47:44 - Tips on public speaking 55:42 - How to give an unbiased approach to what you’re presenting? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!) Data Science Hangout site: posit.co/data-science-hangout

Quarto for Academics | Mine Çetinkaya-Rundel

This video highlights some of Quarto’s features that are especially useful for academics, as educators and as researchers. 00:00 Introduction 01:06 Linking to documentation from code with code-link 02:27 Informative YAML errors and YAML completion 03:41 Creating Quarto slides with revealjs 04:20 PDF export of HTML slides 05:03 Annotating slides with chalkboard 06:40 Advancing slides for your audience with multiplex 07:38 Revealing code in slides with echo 08:16 Highlighting code with code-line-numbers 09:37 Customizing output location with output-location 10:42 Showing code chunk fences with echo: fenced 12:03 Code annotation 14:11 Authoring manuscripts with Quarto templates 16:49 Inserting citations from Zotero or from a DOI with the RStudio Visual Editor 19:59 Wrap up and learning resources

Mine Çetinkaya-Rundel

Quarto revealjs rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Carson Sievert: Towards the next generation of Shiny UI

About the talk: Shiny, the R package for creating interactive web graphics, recently celebrated its 10th birthday. Since then, Shiny has grown tremendously in many areas (e.g., performance, functionality, extensions, etc); however, a “hello world” Shiny app still looks like it did 10 years ago. This is mostly because Shiny goes to great lengths to ensure backwards compatibility; and as a result, default Shiny UI will likely continue to be based on Bootstrap 3 (a CSS styling framework released in 2010). However, thanks to the new bslib R package, it is now easy to opt-into a modern Bootstrap 5 foundation that “just works” with Shiny, R Markdown, flexdashboard, pkgdown, bookdown, and more. In addition to upgrading Shiny’s Bootstrap dependency, bslib also makes it much easier to do custom theming, leverage modern layout techniques, and create custom components (all from R without any CSS/HTML/JS required). At this point, bslib is still maturing, and does not yet provide what we’d consider a “complete UI toolkit”, but it should eventually replace and/or improve upon all of Shiny UI. In this talk, I’ll highlight bslib features that we’re most excited about (e.g., expandable cards, accordions, (sidebar) layouts, input controls, etc.), discuss some best design practices for improving user experience with these tools, and present some real world examples of these tools in action. Speakers’ bio: Carson is a software engineer on the Shiny team at Posit. He joined Posit in 2018, and in recent years, has focused primarily on Shiny for Python and improving Shiny UI. The Shiny UI work has manifested in the creation and development of many R packages such as bslib, thematic, htmltools, htmlwidgets, sass, shiny, rmarkdown, flexdashboard, and more. Carson also has a PhD in statistics, is a recipient of the ASA’s Chambers Statistical Software Award, has maintained the R package plotly since 2015, authored the book “Interactive data visualization with R, plotly, and shiny”, and ran a successful freelance consulting service for numerous years

Carson Sievert, Shiny Team

Posit Connect | Provide Authenticated Access to Your Data Products

Posit Connect is all about getting your data products into the hands of stakeholders and collaborators. Here, we demonstrate how to provide authenticated access to a Dash application. For more information about Posit Connect, go to: https://posit.co/products/enterprise/connect/

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Josh King @ The Knot Worldwide | Creating your data and analytics roadmap | Data Science Hangout

We were recently joined by Josh King, Director of Marketing Data Science & Analytics at The Knot Worldwide. At 36:28, we discussed: What goes into creating your data and analytics roadmap? I’m actually in the midst of this right now– as in the hour before this meeting. One of the things I like to come back to is from the CTO at my last company. He had this really good framework for thinking through this, and it’s to break it out into 4 things: Your mission Your vision Your maturity model Your strategy If you want to look it up, his name is Dave Kaplan and he has a great article on Medium: https://medium.com/policygenius-stories/missions-visions-maturity-models-strategies-ce5a4a408ab2 This gets into a good framework for building out and identifying the different aspects of a strategy that you should consider and having a clear alignment for what that will look like. Generally, a mission is like a single sentence for why your team or your department or your company exists. Your vision is what it means to succeed within that. So, if I’m thinking through marketing data science & analytics - I’m thinking what does it mean to have a best in class marketing data science organization across relevant brands? Your maturity model is, where are you along that map from very basic early foundational building into “I am best in class”? Then your strategy is how you get from where you are now to where you want to be in your vision. So, this really aligns a good framework for what it is that you’re building out. And to the other part of your question, does it cover all data work? I have data scientists and analytics that report into me, but there’s also some of a dotted line from all of our data engineers that focus on marketing problems so that can include BI, data operations, data warehouse, tracking architecture. As we’re thinking through a bit more holistically in terms of all of our data functions that are supporting marketing, I identify, what do we need to drive decision making for our marketing organization and not only how does data science and analytics fit into that, but what is the infrastructure we need from a data engineering perspective that would enable us to be able to do this work? Yes, it’s not necessarily my vision or my strategy that will get us there but working really closely with our Head of Data Platform so that my vision and his vision are really aligned together and we’re thinking through a bit more holistically as we’re building that out. How long is it? My current focus is on building out a 1 year, 3 year, and a 5 year roadmap to really have some intermediate stages along the way that we’re getting towards. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

Get started with Quarto | Mine Çetinkaya-Rundel

This video walks you through creating documents, presentations, and websites and publishing with Quarto. The video features authoring Quarto documents with executable R code chunks using the RStudio Visual Editor ( https://quarto.org/docs/visual-editor/). 00:00 Introduction 00:34 Authoring a document with Quarto 01:13 Using the RStudio visual editor 04:13 Code chunks and chunk options 06:31 Inserting cross references to figures and tables ( https://quarto.org/docs/authoring/cross-references.html ) 08:56 Adding a citation from a DOI ( https://quarto.org/docs/visual-editor/technical.html#citations ) 10:10 Seamlessly switching between output formats 10:58 Creating Quarto presentations ( https://quarto.org/docs/presentations/ ) 14:36 Customizing the output location of code in presentations ( https://quarto.org/docs/presentations/revealjs/#output-location ) 16:09 Creating a website from scratch ( https://quarto.org/docs/websites/ ) 19:19 Creating multi-format documents ( https://quarto.org/docs/output-formats/html-multi-format.html ) 20:22 Publishing the website to QuartoPub ( https://quarto.org/docs/publishing/quarto-pub.html )

Mine Çetinkaya-Rundel

Quarto revealjs rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Why Shiny for Python? - Posit PBC

Learn how Shiny for Python’s design philosophy sets it apart from Streamlit, Dash, and traditional web development frameworks. With Shiny for Python out of alpha as of April, many have wondered how it stacks up against other popular alternatives. In this video, Gordon Shotwell – developer advocate on the Shiny team at Posit – explores the design philosophy behind Shiny for Python and how it compares to other frameworks for developing data science web applications. If you are a data scientist working mostly in Python, we hope this motivates you to take a serious look at Shiny for Python. Learn more on Posit blog, https://posit.co/blog/why-shiny-for-python/ 00:00 What is Shiny for Python? 03:05 What is Reactivity? 04:19 How does Shiny compare to Streamlit? 05:14 What are the main differences between Streamlit and Shiny for Python? 07:05 Why should I prefer Shiny over Streamlit? 09:09 How does Shiny compare to Dash? 10:22 What is the difference between a stateless and a stateful application? 12:19 Why consider Shiny for Python over Dash? 13:37 Shiny for Python’s Design Philosophy

Shiny Team

Shiny for Python Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop:Enhancing Communication & Collaboration with Quarto and Jupyter Notebooks

Register now: http://pos.it/conf Instructor: Hamel Husain Workshop Duration: 1-Day Workshop This workshop is for you if you: • have some experience with Python and Jupyter and want to learn how Quarto can support and enhance your workflows • want to learn about turning your notebooks to websites and publications • want to learn how to write python packages with Jupyter notebooks and Quarto with the help of nbdev The workshop will assume some prior experience with Python and Jupyter Notebooks. Sharing knowledge through writing is a critical aspect of scientific activity, including data science. It allows researchers to communicate their findings and insights to a wider audience, build upon existing work, and collaborate with others in their field. However, until recently, there have been limited options for publishing long-form writing and expository analyses authored in Jupyter Notebooks, a popular medium for data scientists. Enter Quarto - an innovative, open-source scientific and technical publishing system compatible with Jupyter Notebooks and other popular mediums. Quarto provides data scientists with a seamless way to publish their work in a high-quality format that is easily accessible and shareable. With Quarto, researchers can turn their Jupyter Notebooks into professional-looking publications in a variety of formats, including web pages, books, and slides. In this workshop, we will demonstrate how Quarto enables data scientists to turn their work products into professional, high-quality publications, websites, blog posts, and other shareable artifacts. As a bonus, we will also discuss how you can create and document Python packages using Jupyter notebooks and Quarto with the help of nbdev. The learning outcomes for the workshop include: • examine case studies where sharing scientific knowledge has greatly improved the efficacy of data science teams • author documents in plain text markdown or Jupyter notebooks with equations, citations, crossrefs, figure panels, callouts, and advanced layouts • learn how to author content in IPython/Jupyter and the Quarto VS Code extension • leverage Quarto for creating different types of publications, including personal blogs, knowledge management for teams, notes, books, websites, and presentation slides • extend Quarto with notebook filters and extensions • host websites and publications on platforms like GitHub Pages, QuartoPub, and Netlify • test notebooks and documentation with Quarto’s execution options • create and document Python packages with nbdev and Quarto

Quarto RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: What They Forgot to Teach You About R

Register now: http://pos.it/conf Instructors: Shannon Pileggi and David Aja Workshop Duration: 1-Day Workshop This course is for you if you answer yes to these questions: • Have you been using R for a while and feel there might be better ways to organize your R life, but don’t know what they are? • Do you want to put programming on pause and learn about actionable programming-adjacent workflows for streamlining analysis in R? • Are you willing to feel a bit of (git) pain to leverage the benefits of version control for collaboration and time travel? This 1-day What They Forgot (WTF) To Teach You About R workshop is for experienced R and RStudio users who want to (re)design their R lifestyle via project-oriented workflows and version control for data science (Git/GitHub). At the conclusion of the workshop, you will have strategies for organizing data science projects and workflows, employing robust file paths, constructing human and machine-readable file names, and facilitating collaboration with yourself or others via version control

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Web Design for Shiny Developers

Register now: http://pos.it/conf Instructors: Maya Gans and David Granjon This course is for you if: • you are an R developer with basic Shiny knowledge • you want to quickly test new business ideas • you want to increase the reach of your apps and websites Website design and development is one of the most critical factors contributing to whether the user has a good or poor experience while browsing your site, directly influencing the overall impression of your brand. Besides, bad design decisions can significantly impact app performances. By exposing you to common governing rules of design, this course will walk you through the entire design process, from ideation to execution. These rules will help you to become a better collaborator to design teams, and enable you to create beautiful front-end experiences for Shiny

Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Tidy time series and forecasting in R

Register now: http://pos.it/conf Instructor: Rob J Hyndman Workshop Duration: 2-Day Workshop This course is for you if you: • already use the tidyverse packages in R such as dplyr, tidyr, tibble and ggplot2 • need to analyze large collections of related time series • would like to learn how to use some tidy tools for time series analysis including visualization, decomposition and forecasting It is common for organizations to collect huge amounts of data over time, and existing time series analysis tools are not always suitable to handle the scale, frequency and structure of the data collected. In this workshop, we will look at some packages and methods that have been developed to handle the analysis of large collections of time series. On day 1, we will look at the tsibble data structure for flexibly managing collections of related time series. We will look at how to do data wrangling, data visualizations and exploratory data analysis. We will explore feature-based methods to explore time series data in high dimensions. A similar feature-based approach can be used to identify anomalous time series within a collection of time series, or to cluster or classify time series. Primary packages for day 1 will be tsibble, lubridate and feasts (along with the tidyverse of course). Day 2 will be about forecasting. We will look at some classical time series models and how they are automated in the fable package, and we will explore the creation of ensemble forecasts and hybrid forecasts. Best practices for evaluating forecast accuracy will also be covered. Finally, we will look at forecast reconciliation, allowing millions of time series to be forecast in a relatively short time while accounting for constraints on how the series are related

dplyr ggplot2 lubridate tibble tidyr tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Teaching Data Science Masterclass

Register now: http://pos.it/conf Instructor: Dr. Mine Çetinkaya-Rundel Workshop Duration: 1-Day Workshop This course is for you if you: • you want to learn / discuss curriculum, pedagogy, and computing infrastructure design for teaching data science with R and RStudio using the tidyverse and Quarto • you are interested in setting up your class in Posit Cloud • you want to integrate version control with git into your teaching and learn about tools and best practices for running your course on GitHub This masterclass is aimed primarily at participants teaching data science in an academic setting in semester-long courses, however much of the information and tooling we introduce is applicable for shorter teaching experiences like workshops and bootcamps as well. Basic knowledge of R is assumed and familiarity with the tidyverse and Git is preferred. There has been significant innovation in introductory statistics and data science courses to equip students with the statistical, computing, and communication skills needed for modern data analysis. Success in data science and statistics is dependent on the development of both analytical and computational skills, and the demand for educators who are proficient at teaching both these skills is growing. The goal of this masterclass is to equip educators with concrete information on content, workflows, and infrastructure for painlessly introducing modern computation with R and RStudio within a data science curriculum. In a nutshell, the day you’ll spend in this workshop will save you endless hours of solo work designing and setting up your course. Topics will cover teaching the tidyverse in 2023, highlighting updates to R for Data Science (2nd ed) and Data Science in a Box as well as present tooling options and workflows for reproducible authoring, computing infrastructure, version control, and collaboration. The workshop will be comprised of four modules: • Teaching data science with the tidyverse and Quarto • Teaching data science with Git and GitHub • Organizing, publishing, and sharing of course materials • Computing infrastructure for teaching data science Throughout each module we’ll shift between the student perspective and the instructor perspective. The activities and demos will be hands-on; attendees will also have the opportunity to exchange ideas and ask questions throughout the session. In addition to gaining technical knowledge, participants will engage in discussion around the decisions that go into developing a data science curriculum and choosing workflows and infrastructure that best support the curriculum and allow for scalability. We will also discuss best practices for configuring and deploying classroom infrastructures to support these tools

Mine Çetinkaya-Rundel

Quarto rstudio tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Steal like an Rtist: Creative Coding in R

Register now: http://pos.it/conf Instructors: Ijeamaka Anyene Fumagalli & Sharla Gelfand Workshop Duration: 1-Day Workshop This workshop is for you if you: • are comfortable with R and RStudio, experience with tidyverse and ggplot2 • are interested in applying data visualization skills more creatively, but may not know where to start or how to develop style/inspiration • are an artist interested in exploring code as another medium for creating their work R is a tool for data analysis but also can be used for self-expression. This workshop will be an introduction to creative coding in R in order to make visual art. We will take an inspiration-first approach, using compelling pieces to discuss and learn the techniques that shape the work. This workshop takes guidance from its namesake, the book “Steal Like An Artist” by Austin Kleon - once we have identified and learned to recreate existing works, we will cover how to take this inspiration and transform, remix, or reinterpret it in the pursuit of developing our own work and artistic styles. This workshop is hands-on and will cover color theory and manipulation, a reintroduction of the data frame as the foundation for creating art (instead of just for analyzing data!), using ggplot2 as an artistic canvas, creating basic and specialized shapes, tiling and pattern making, developing your own functions and using iteration. We will also discuss how to use controlled randomness to convert a standalone piece into a generative art system that can produce many distinct outputs. Creative coding may seem a world apart from data analysis, but we see a large overlap and intersection of the skills used in both, not to mention the creative muscles that are already used in data visualization

ggplot2 rstudio tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Shiny in Production: Tools and Techniques

Register now: http://pos.it/conf Instructors: Eric Nantz and Mike Thomas Workshop Duration: 1-Day Workshop This course is for you if you: • had a Shiny application work just fine on your machine, but encounters critical issues after deployment • are eager to prospectively apply techniques before deployment to plan for the unexpected • want to know the benefits and trade-offs between various ways of hosting Shiny applications Shiny brings tremendous possibilities to share innovative data science workflows with others inside an intuitive web interface. Many in the Shiny community have shared effective development techniques for building a robust application. Even with the best intentions during application development, a myriad of issues can arise once it leaves the confines of your machine. In this one-day workshop, you will implement core techniques to account for common scenarios that arise once your application is used in production, such as accounting for thousands of simultaneous users, how effective profiling can address performance bottlenecks, and ensuring your application is doing as little as possible to ensure a smooth and responsive experience. This course assumes intermediate knowledge of building Shiny applications in R and prior experience deploying an application to a platform such as the shinyapps.io service or products like Posit Connect

Shiny shinyapps RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Shiny Dashboards

Register now: http://pos.it/conf Instructor: Colin Rundel Workshop Duration: 1 Day Workshop This course is for you if you: • have some experience with Shiny and want to improve your skills, • are interested in building dashboards for reporting, and • want to learn about styling and theming your dashboard. In this workshop we will explore all of the interesting and variety of ways you can use Shiny: from adding dynamic elements to your existing RMarkdown / Quarto documents to building and deploying dashboards for reporting, and customizing the appearance and themeing of the app (and your outplots like plots and tables). This workshop assumes that you have a basic familiarity with Shiny (e.g. the ability to write simple apps and basics of reactivity)

Quarto rmarkdown Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Machine Learning and Deep Learning with Python

Register now: http://pos.it/conf Instructor: Sebastian Raschka Workshop Duration: 1-Day Workshop In this workshop, you will learn the machine and deep learning fundamentals using a modern open-source stack. We’ll start with a brief introduction to Python’s scientific computing libraries, including NumPy, Pandas, and Matplotlib, which provide the foundation for data analysis and visualization. From there, we will dive into the scikit-learn API, a user-friendly, open-source library for machine learning in Python. You will learn how to use it to create machine learning classifiers and apply tree-based models like random forests, gradient boosting, and XGBoost. In the second part of this workshop, we will also cover deep learning concepts and introduce PyTorch, the most widely used deep learning research library. You will also learn about training multi-layer neural networks efficiently using multi-GPU and mixed-precision techniques. Finally, we will explore how to use a pretrained large language transformer with scikit-learn and fine-tune it on a custom downstream task using PyTorch. By the end of this workshop, you will have a good understanding of the fundamental principles of machine learning and be able to construct advanced classification pipelines for tabular data using scikit-learn. Additionally, you will gain experience in image classification and natural language processing techniques using PyTorch and be able to implement them in your own predictive modeling projects effectively

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: It’s Not Just Code: Managing an Open Source Project

Register now: http://pos.it/conf Instructor: Tracy Teal Workshop Duration: 1-Day Workshop This workshop is for you if you: • are involved in maintaining an open source project and struggling to feel like it’s sustainable, or are looking for practice or guidance • are starting out or interested in being involved in maintaining an open source project, and want to learn how to set up the project for the most effective engagement from contributors and users • are interested in learning more about the people side of open source project maintenance and connecting with other maintainers

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Introduction to tidymodels

Register now: http://pos.it/conf Instructors: Hannah Frick, Simon Couch, Emil Hvitfeldt Workshop Duration: 1-Day Workshop This workshop is for you if you: • have intermediate R knowledge, experience with tidyverse packages, and either of the R pipes • can read data into R, transform and reshape data, and make a wide variety of graphs • have had some exposure to basic statistical concepts such as linear models, random forests, etc. Intermediate or expert familiarity with modeling or machine learning is not required. This workshop will teach you core tidymodels packages and their uses: data splitting/resampling with rsample, model fitting with parsnip, measuring model performance with yardstick, and basic pre-processing with recipes. Time permitting, you’ll be introduced to model optimization using the tune package. You’ll learn tidymodels syntax as well as the process of predictive modeling for tabular data

Emil Hvitfeldt, Hannah Frick, Simon Couch

parsnip rsample tidymodels tidyverse yardstick RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Introduction to Quarto with R + RStudio

Register now: http://pos.it/conf Instructor: Andrew Bray Workshop Duration: 1-Day Workshop This course is for you if you: • have a basic knowledge of how to use the RStudio IDE • have some familiarity with markdown, or • are excited to author flexible single documents like technical reports and slide presentations Seasoned users of R Markdown will get more out of the Advanced Quarto with R and RStudio: Projects, Websites, Books, and More workshop, which is focused on projects, a distinct strength of Quarto in authoring work that spans multiple documents. This workshop will prepare you to author a rich array of documents in Quarto, the next generation of R Markdown. Quarto is an open-source scientific and technical publishing system that offers multilingual programming language support to create dynamic and static documents, books, presentations, blogs, and other online resources. The focus for this workshop will be on single documents. You will learn to create static documents, to add interactivity to them with Shiny and htmlwidgets, or steer them in the direction of sophisticated scientific documents. In the afternoon you’ll take the same authoring approaches to create slide presentations in various formats such as reveal.js, beamer, and pptx

Quarto rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Introduction to Data Science with R and Tidyverse

Register now: http://pos.it/conf Instructors: Posit Academy Instructors Workshop Duration: 2-Day Workshop This course is ideal for: • those new to R or the Tidyverse • anyone who has dabbled in R, but now wants a rigorous foundation in up-to-date data science best practices • SAS and Excel users looking to switch their workflows to R This is not a standard workshop, but a six-week online apprenticeship that culminates in two in-person days at posit::conf(2023). Begins August 7th, 2023. No knowledge of R required. Visit posit.co/academy to learn more about this uniquely effective learning format. Here, you will learn the foundations of R and the Tidyverse under the guidance of a Posit Academy mentor and in the company of a close group of fellow learners. You will be expected to complete a weekly curriculum of interactive tutorials, and to attend a weekly presentation meeting with your mentor and fellow students. Topics will include the basics of R, importing data, visualizing data with ggplot2, wrangling data with dplyr and tidyr, working with strings, factors, and date-times, modelling data with base R, and reporting reproducibly with quarto

dplyr ggplot2 Quarto tidyr tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Introduction to Data Science with Python

Register now: http://pos.it/conf Instructors: Posit Academy Instructors Workshop Duration: 1 Day Workshop This course is ideal for: • those new to Python • anyone who has dabbled in Python, but is not sure how to use Python to do data science • R users who want to work more closely with Python users on their team This is not a standard workshop, but a six-week online apprenticeship that culminates in one in-person day at posit::conf(2023). Begins August 7th, 2023. No knowledge of Python required. Visit posit.co/academy to learn more about this uniquely effective learning format. Here, you will learn the foundations of Python and data analysis under the guidance of a Posit Academy mentor and in the company of a close group of fellow learners. You will be expected to complete a weekly curriculum of interactive tutorials, and to attend a weekly presentation meeting with your mentor and fellow students. Topics will include importing packages and datasets, visualizing data with plotnine, wrangling data with pandas, writing and applying functions, and reporting reproducibly with quarto

plotnine Quarto RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Getting Started with Shiny for R

Register now: http://pos.it/conf Instructor: Colin Rundel Workshop Duration: 1-Day Workshop This course is for you if you: • are comfortable with the basics of R, such as writing functions, indexing vectors and lists, debugging simple errors, and working with data structures like data frames • are interested in creating interactive web applications • have no or minimal experience with Shiny for R Shiny is an R package that makes it easy to build interactive web apps straight from R. This workshop will start at the beginning: designing and creating user interfaces, learning and mastering the reactive model that connects your R code to the interface, and deploying apps publicly and privately. We will wrap up with some intermediate-level tools: debugging and modularizing your apps and implementing dynamic user interfaces. In the end, you’ll be a confident Shiny user, able to design interactive apps to achieve your purpose and produce a polished and professional implementation. If you have a bit of experience, you’ll see things in a new way. If you don’t, we’ll get you started on the right footing

Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Fundamentals of Package Development

Register now: http://pos.it/conf Instructor: Andy Teucher Workshop Duration: 1-Day Workshop This workshop is for you if: • You have written several R scripts and find yourself wondering how to reuse or share the code you’ve written • You know how to write functions in R • You are looking for a way to take the next step in your R programming journey We will be demonstrating some workflows using Git and GitHub. Knowledge of these tools is not required, and you will absolutely be able to complete the workshop without them, but some of the lessons will be more rewarding to you if you are prepared to try them out. If you are looking to get started with Git and GitHub, we recommend you register for the “What they forgot to teach you about R” workshop on Day 1, and join us for this workshop on Day 2. We are often faced with the need to share our code with others, or find ourselves writing similar code over and over again across different projects. In R, the fundamental unit of reusable code is a package, containing helpful functions, documentation, and sometimes sample data. This workshop will teach you the fundamentals of package development in R, using tools and principles developed and used extensively by the tidyverse team - specifically the ‘devtools’ family of packages including usethis, testthat, and roxygen2. These packages and workflows help you focus on the contents of your package rather than the minutiae of package structure. You will learn the structure of a package, how to organize your code, and workflows to help you develop your package iteratively. You will learn how to write good documentation so that users can learn how to use your package, and how to use automated testing to ensure it is functioning the way you expect it to, now and into the future. You will also learn how to check your package for common problems, and how to distribute your package for others to use. This will be an interactive 1-day workshop, and we will be using the RStudio IDE to work through the materials, as it has been designed to work well with the development practices we will be featuring

devtools roxygen2 rstudio testthat tidyverse usethis RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: From R User to R Programmer

Register now: http://pos.it/conf Instructors: Emma Rand and Ian Lyttle Workshop Duration: 1-Day Workshop This course is for you if you: • have experience equivalent to an introductory data science course using tidyverse • feel comfortable with the Whole game chapter of R for Data Science This is a one-day, hands-on workshop for those who have embraced the tidyverse and want to improve their R programming skills and, especially, reduce the amount of duplication in their code. The two main ways to reduce duplication are creating functions and using iteration. We will use a tidyverse approach to cover function design and iteration with {purrr}. • Master the art of writing functions that do one thing well, adhere to existing conventions and can be fluently combined together to solve more complex problems. • Learn how to perform the same action on many objects using code which is succinct and easy to read

purrr tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Engaging and Beautiful Data Visualizations with ggplot2

Register now: http://pos.it/conf Instructor: Cédric Scherer Workshop Duration: 1-Day Workshop This course will be appropriate for you if you: • already know how to create basic graphics with the ggplot2 package • aim to improve the design of your ggplot outputs • want to learn how to create more complex charts which feature multiple layers, annotations, text styling, custom themes, and more Creating effective and easily accessible data visualizations of high quality in an efficient and preferably reproducible way is an essential skill for everyone working in a data-related field. Luckily, by leveraging the functionality of ggplot2, the most famous package for data visualization with R, and related extension packages one can create highly customized data visualization without the need for post-processing. This workshop provides everything one needs to know to create and customize numerous chart types with ggplot2. Participants will learn the most important steps and helpful tips to create visually appealing and informative graphics with a code-only approach. The power of ggplot2 and related extension packages will be illustrated with advanced real–life examples that help to understand useful coding tricks and the process of creating engaging and effective visualizations. The workshop will particularly focus on more advanced tasks with ggplot2 such as styling labels and titles, customizing themes and visual aesthetics, and using less-common chart types

ggplot2 RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: DevOps for Data Scientists

Register now: http://pos.it/conf Instructor: Rika Gorn Workshop Duration: 2-Day Workshop This workshop is for you if you: • are a data scientist or analyst who wants to put their work into production, • want to learn a more about Docker containers and virtual machines, or • want to better understand importance of networking, security, storage, and different server architectures to deploying data apps. In this workshop, we will discuss ways to better containerize, deploy, and scale your data products. We’ll use both R and shell scripting to virtualize Posit products in the cloud. You’ll learn about servers, networking, security, and authorization to be dangerous – or at least to understand your DevSecOps teams. By the end of the workshop you will have the tools to start deploying your own data science assets into production

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Designing Data Visualizations to Successfully Tell a Story

Register now: http://pos.it/conf Instructor: Cédric Scherer Workshop Duration: 1-Day Workshop This course is for you if you: • want to understand and learn the art of communicating data with impactful visualizations • aim to improve your data visualization design to create effective and informative graphics • are willing to spend a bit more time to choose the right chart, proper color palettes, and suitable fonts along with additional elements to guide the viewer Communicating data through meaningful and easily accessible visualization is a critical competence for most data-related roles including data scientists, analysts, scientific researchers, and managers. A well-designed graphic is able to inform, spark engagement, explain patterns, and drive decisions and actions. At the same time, poor choices in the design process can complicate interpretation or even, intentionally or unintentionally, mislead the audience. The aim of this course is to demystify the creative processes of data visualization design to turn data into a meaningful story. Participants will learn helpful tips and tricks to create appealing and informative data visualizations that are not “just showing the numbers” but successfully tell a story. We will cover principles of good data visualization design, explore different options to display data, and discuss ways to guide and engage viewers with the aim to create impactful graphics. The course also features sessions on picking suitable yet beautiful colors, what to consider when choosing and pairing typefaces, and the layout of graphics, also in the context of dashboard building

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Deploy and Maintain Models with vetiver

Register now: http://pos.it/conf Instructor: Julia Silge Workshop Duration: 1-Day Workshop This workshop is for you if you: • have intermediate R or Python knowledge (this will be a “choose your own adventure” workshop where you can work through the exercises in either R or Python) • can read data from CSV and other flat files, transform and reshape data, and make a wide variety of graphs • can fit a model to data with your modeling framework of choice We expect participants to have exposure to basic modeling and machine learning practice, but NOT expert familiarity with advanced ML or MLOps topics. Many data scientists understand what goes into training a machine learning or statistical model, but creating a strategy to deploy and maintain that model can be daunting. In this workshop, learn what MLOps (machine learning operations) is, what principles can be used to create a practical MLOps strategy, and what kinds of tasks and components are involved. We’ll use vetiver, a framework for MLOps tasks in Python and R, to version, deploy, and monitor the models you have trained and want to deploy and maintain in production reliably and efficiently

Julia Silge

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Data Science Workflows with Posit Tools — R Focus

Register now: http://pos.it/conf Instructor: Ryan Johnson and Katie Masiello Workshop Duration: 1-Day Workshop This course is for you if you: • Build finished data products starting from raw data and are looking to improve your workflow • Are looking to expand your knowledge of Posit open source and professional tools • Want to improve interoperability between data products in your work or on your team • Have experience developing in R. An analogous course with a Python focus is also offered In this R-focused workshop, we will discuss ways to improve your data science workflows! During the course, we will review packages for data validation, alerting, modeling, and more. We’ll use Posit’s open source and professional tools to string all the pieces together for an efficient workflow. We’ll discuss environments, managing deployed content, working with databases, and interoperability across data products

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Data Science Workflows with Posit Tools — Python Focus

Register now: http://pos.it/conf Instructors:Gagan Singh and Sam Edwardes Workshop Duration: 1-Day Workshop Workshop Duration: 1-Day Workshop This course is for you if you: • Build finished data products starting from raw data and are looking to improve your workflow • Are looking to expand your knowledge of Posit open source and professional tools • Want to improve interoperability between data products in your work or on your team • Have experience developing in Python. An analogous course with an R focus is also offered In this Python-focused workshop, we will discuss ways to improve your data science workflows! During the course, we will review packages for data validation, alerting, modeling, and more. We’ll use Posit’s open source and professional tools to string all the pieces together for an efficient workflow. We’ll discuss environments, managing deployed content, working with databases, and interoperability across data products

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Causal Inference with R

Register now: http://pos.it/conf Instructors: Malcolm Barrett and Travis Gerke This course is for you if you: • know how to fit a linear regression model in R • have a basic understanding of data manipulation and visualization using tidyverse tools • are interested in understanding the fundamentals behind how to move from estimating correlations to causal relationships In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. In both data science and academic research, prediction modeling is often not enough; to answer many questions, we need to approach them causally. In this workshop, we’ll teach the essential elements of answering causal questions in R through causal diagrams, and causal modeling techniques such as propensity scores and inverse probability weighting. We’ll also show that by distinguishing predictive models from causal models, we can better take advantage of both tools. You’ll be able to use the tools you already know–the tidyverse, regression models, and more–to answer the questions that are important to your work

tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Big Data with Arrow

Register now: http://pos.it/conf Instructors: Nic Crane and Stephanie Hazlitt Workshop Duration: 1-Day Workshop This course is for you if you: • want to learn how to work with tabular data that is too large to fit in memory using existing R and tidyverse syntax implemented in Arrow • want to learn about Parquet and other file formats that are powerful alternatives to CSV files • want to learn how to engineer your tabular data storage for more performant access and analysis with Apache Arrow Data analysis pipelines with larger-than-memory data are becoming more and more commonplace. In this workshop you will learn how to use Apache Arrow, a multi-language toolbox for working with larger-than-memory tabular data, to create seamless “big” data analysis pipelines with R. The workshop will focus on using the the arrow R package—a mature R interface to Apache Arrow— to process larger-than-memory files and multi-file data sets with arrow using familiar dplyr syntax. You’ll learn to create and use interoperable data file formats like Parquet for efficient data storage and access, with data stored both on disk and in the cloud, and also how to exercise fine control over data types to avoid common large data pipeline problems. This workshop will provide a foundation for using Arrow, giving you access to a powerful suite of tools for performant analysis of larger-than-memory data in R

dplyr tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Advanced tidymodels

Register now: http://pos.it/conf Instructor: Max Kuhn, Software Engineer, Posit Workshop Duration: 1-Day Workshop This workshop is for you if you: • have used tidymodels packages like recipes, rsample, and parsnip • are comfortable with tidyverse syntax (e.g. piping, mutates, pivoting) • have some experience with resampling and modeling (e.g., linear regression, random forests, etc.), but we don’t expect you to be an expert in these In this workshop, you will learn more about model optimization using the tune and finetune packages, including racing and iterative methods. You’ll be able to do more sophisticated feature engineering with recipes. Time permitting, model ensembles via stacking will be introduced. This course is focused on the analysis of tabular data and does not include deep learning methods. Participants who have completed the “Introduction to tidymodels” workshop will be well-prepared for this course. Participants who are new to tidymodels will benefit from taking the Introduction to tidymodels workshop before joining this one

Max Kuhn

finetune parsnip rsample tidymodels tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf(2023) Workshop: Advanced Quarto with R + RStudio

Register now: http://pos.it/conf Instructor: Andrew Bray Workshop Duration: 1-Day Workshop This course is for you if you: • have a basic knowledge of how to use the RStudio IDE • have experience working with single R Markdown and/or Quarto files • are excited to author multi-document projects like books, websites, and blogs Participants who are new to computational documents will benefit from taking Intro to Quarto with R and RStudio: Documents and Presentations before joining this workshop. This workshop will prepare you to author a rich array of documents in Quarto, the next generation of R Markdown. Quarto is an open-source scientific and technical publishing system that offers multilingual programming language support to create dynamic and static documents, books, presentations, blogs, and other online resources. The focus for this workshop will be on projects that weave together multiple documents and allow you to write books and build websites. You will also learn various ways to deploy and publish your Quarto projects on the web

Quarto rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

posit::conf Workshop:Leveraging & Contributing to the Pharmaverse for Clinical Trial Reporting in R

Register now: http://pos.it/conf Instructors: Pawel Rucki, Christina Fillmore, Thomas Neitmann Workshop Duration: 1-Day Workshop You should take this workshop if you are interested in the future of clinical trial reporting, and would like to join this shift to between company collaboration. In this workshop we will introduce the pharmaverse, a collection of open source R packages that provide the next generation backbone for clinical trial reporting. As of March 2023, 123 people from more than 10 companies have contributed to the pharmaverse packages - and this workshop is about how you can both leverage their work to date and get involved to help evolve this important collection of efforts. We will create ADaM datasets, prepare tables and figures as well as interactive shiny apps. This course will also give a grounding in how to navigate pharmaverse R packages, and their repositories, and understand how you can contribute

Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

[79] Create a Python Web App Using Shiny (Gordon Shotwell)

Join our Meetup group for more events! https://www.meetup.com/data-umbrella Resources# website for presentation: https://shiny.rstudio.com/py/ https://shiny.rstudio.com/py/docs/reactive-mutable.html https://www.shinyapps.io/ https://huggingface.co/new-space https://shiny.rstudio.com/py/docs/shinylive.html https://shiny.rstudio.com/py/api/reactive.poll.html#shiny.reactive.poll About the Event# Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. If you want to develop a python web application you usually need to choose between simple, limited frameworks like Streamlit and more extensible frameworks like Dash. This can cause a lot of problems if you get started with a simple framework but then discover that you need to refactor your application to accommodate the next user request. Shiny for Python differs from other frameworks because it has tremendous range. You can build a small application in a few minutes with the confidence that the framework can handle much more complex problems. In this workshop we will go through the core limitations of Streamlit, and build a Shiny app which avoids those limitations. Timestamps# 00:00 Welcome 00:23 Reshama introduces Data Umbrella 03:45 Reshama introduces Gordon Shotwell 04:21 Gordon Shotwell begins 04:29 The motivation to develop Shiny for Python 06:05 The main strength of both the R and Python library 06:56 What Gordon Shotwell will build during his presentation 07:25 Shiny documentation website 08:01 QuickStart for R users showing differences between the R and Python libraries 08:44 All the function reference in Shiny 09:08 Demo starts 09:50 Virtual environment 10:36 How to start shiny app in the terminal 11:15 Install shiny extension in VS Code which makes it easier to preview the web app 11:36 How the output function works on the preview app to execute 12:22 Penguin dataset description for the demo 12:45 Modules/submodules shiny app is built on 13:04 How to add a sidebar layout (sidebar, panel sidebar and panel main) 13:43 How to read in the data and the output functions 14:31 How to define some server logic 14:59 The conventional shiny rule 16:30 Use of slide input 17:50 Where the reactive magic comes in 19:30 Important note on what can really slow down your shiny app 20:14 Importance of Python data copy method when using external dataset 21:01 Important note to avoid dependency inside the render function 21:30 Q&A 29:35 Adding a plot to the output: The UI sides 30:12 Adding a plot to the output: The render sides 32:16 The core principle of reactivity in which you do not want to repeat yourself 33:26 Reactivate calculation concept which allows you to store intermediate values in one place 37:24 Q&A 38:53 Reactive calculations and rendering functions 39:30 Side-effects or user effect. Another class of interactions 41:18 How to tell reactive effect what it should respond to or what events to watch before executing 41:53 How to update the data filter in the side-effect function 42:22 The second important pattern for shiny 43:00 One of the important things to pay attention to once you start learning/using shiny 44:45 Series of Q&A until the end of the video. Some response includes live demo 01:01:03 Gordon Shotwell ends his presentation 01:01:17 Reshama closes the session About the Speaker# Gordon Shotwell is a Software Engineer at Posit. He’s been using Shiny to solve business problems for the past ten years. LinkedIn: https://www.linkedin.com/in/gshotwell/ Key Links# Transcript: https://github.com/data-umbrella/event-transcripts/blob/main/2023/78-gordon-shiny.md Meetup Event: https://www.meetup.com/data-umbrella/events/292848290/ Video: https://youtu.be/pXidQWYY14w #python #deployment

Deploying a Python application with Posit Connect

Posit Connect is our flagship publishing platform for data products built in R and Python. Learn more: https://posit.co/products/enterprise/connect/ Book a demo of Connect: https://posit.co/schedule-a-call/?booking_calendar__c=RSC_YT_Demo With Connect, you can deploy, manage, and share your R and Python content, including Shiny applications, Dash, Streamlit, and Voilà applications, R Markdown reports, Jupyter Notebooks, Quarto documents, dashboards, APIs (Plumber, Flask), and more. Give stakeholders authenticated access to the content they need, and schedule reports to update automatically

plumber Quarto Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Launch different development environments and manage cluster options with Posit Workbench

Posit Workbench: https://posit.co/products/enterprise/workbench/ Data scientists should be able to use the language and development environment they prefer. Jupyter Notebook, JupyterLab, VS Code, and RStudio are all available development environments within Posit Workbench. Workbench is also exceptional for managing compute resources. Use Kubernetes and Slurm and adjust the CPU and memory to match the job you’re trying to run

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Deploying a Shiny for Python application with Posit Connect

Last week’s blog post announced that Shiny for Python is now generally available and Python programmers can feel confident deploying Shiny for Python apps in production. This video will serve as a guide to deploying your applications so that users can interact with your apps and leverage their robust functionality using Posit Team. Follow along blog post: https://posit.co/blog/deploying-a-shiny-for-python-application-with-posit-connect/ 0:25 - Step 1: Load packages (in VS Code on Posit Workbench) Step 2: Create the app (example app here: https://shinylive.io/py/examples/#app-with-plot ) 1:02 - Step 3: Run the app locally in VS Code 1:36 - Step 4: Deploy to Posit Connect Learn more We’d love to have you join us at one of our monthly Posit Team Demos where we walk through a different end-to-end data science workflow each month. They are on the last Wednesday of every month at 11 am ET on YouTube. You can add the event to your calendar here: pos.it/team-demo What is Posit Team? Posit Team is the bundle of our most popular products including Workbench (for development work), Connect (for hosting and sharing your data products), and Package Manager (for managing your R and Python packages). Together, this bundle delivers an end-to-end toolchain for data science teams committed to R and Python, improving every step of a data scientist’s workflow, from developing insights to deploying data products, to managing environments. If you have any questions and would like to talk with our team, you can schedule a time to chat: https://posit.co/schedule-a-call/

What is Posit Team? [in less than 2 minutes]

Posit Team is the bundle of our most popular products including: Workbench, Connect, and Package Manager. Together, this bundle delivers an end-to-end toolchain for data science teams committed to R and Python, improving every step of a data scientist’s workflow, from developing insights, to deploying data products, to managing environments. Learn more at https://posit.co/products/enterprise/team/

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Running multiple R & Python sessions at once on Posit Workbench

Posit Workbench is the premier development experience for professional data scientists who use R and Python. One of the many great features Workbench offers is the ability to run multiple sessions at once, across different languages and development environments. In this video, we give you a glimpse of that feature. To watch the full demo of our professional software, go here: https://youtu.be/1KEX3gZTQnE Learn more about Posit Workbench: https://posit.co/products/enterprise/workbench/

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Emily Riederer @ Capital One | Explicit design at the start of a project | Data Science Hangout

We were recently joined by Emily Riederer, Senior Manager - Customer Management Data Science & Analytics at Capital One. We discussed how a strong foundation in high quality data infrastructure and reproducible tools sets the stage for innovation in modeling, causal inference, and analytics, and so much more. Diving into a question asked at (42:38): What is your thought process for solving a problem that you don’t know how to solve immediately? One thing that I think is a really undervalued part of that process is thinking about how you will know a good solution when you find one? Also, how would you know if there was a good solution staring you in the face and you already had it? I think the more unstructured and complicated a problem can be, it can almost be a little deceptive of what’s good– which can have one or two bad outcomes. You find a good solution, but you don’t realize it’s good so you keep going You spend a lot of time chasing after an outcome, and only then do you realize, I solved the problem I was trying to solve but it wasn’t the problem I wanted to solve. Something I’ve really been experimenting with in my own work is having a lot more of an explicit design stage at the beginning of a project and thinking, how can you do a pilot? If I’m trying to predict some target, can I take those two values of that target and plug them into a downstream problem I actually thought that I was going to solve, and make sure that’s actually what I want to solve? Almost like frontloading model evaluation with even a fake solution is the first step versus last step. Then, I’ll check on one other point. I think the other aspect of that – going back to that level of abstraction – is figuring out how to take the context out of my problem to make it something more Googleable. So I mean thinking, not being like, “oh, this experiment, the random seeds were wrong, so I don’t have a control population - what do I do?” Backing that into more of a general question - “how do you sample a synthetic control through observational data?” which is something you can Google and then find a ton of resources about. I think pushing myself on what I want, and then finding the right framing at which to ask for help. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

How to deploy a Dash application from VS Code to Posit Connect

Episode 1: Publishing a Dash application to Posit Connect Led by: Ryan Johnson, Data Science Advisor Follow-up links: Posit Team: https://posit.co/products/enterprise/team/ Talk to us directly: https://posit.co/schedule-a-call/?booking_calendar__c=RST_YT_Demo Follow-along blog post: https://posit.co/blog/deploying-a-dash-application-to-posit-connect/ Source code for example: https://github.com/sol-eng/python-examples Posit Team demo resources: pos.it/demo-resources Timestamps: 00:53 - What is Posit Team? (Posit Workbench, Posit Connect, Posit Package Manager) 3:25 - JumpStart Examples in Posit Connect 4:00 - Opening up a VS Code Session in Posit Workbench 8:03 - Downloading the Dash JumpStart example in Posit Connect 9:40 - Open up the JumpStart example in VS Code in Posit Workbench 11:18 - Install the required Python packages using pip 12:55 - Python packages on Posit Package Manager 15:12 - Defining the packages you want to install (latest, specific date, etc.) 19:12 - Creating an API key in Posit Connect 20:29 - Deployment process from VS Code to Posit Connect 21:36 - Dash application hosted on Posit Connect 22:23 - Access controls in Posit Connect (sharing with the people who need to see your app) 24:10 - Adjusting the runtime of an application in Posit Connect (controlling compute resources) 26:35 - Tags in Posit Connect 26:48 - Vars (if your content requires privileged access) 27:15 - Recap of what was covered in the demo On the last Wednesday of every month, we host a Posit Team demo and Q&A session that is open to all. You can use this to add the event to your own calendar. Who are these monthly demos for? Everyone is welcome to join us - regardless of industry, background, or experience! We will discuss topics that will speak to: Data scientists and administrators new to Posit Team or are looking to grow their understanding of our toolchain, Teams searching for a new analytic platform built to support open-source data science, And, those that are just curious about Posit Team! What you can expect from the monthly Posit Team demo: During the session, we will walk through an end-to-end data science workflow and demo the core functionality of Posit Team while highlighting some of our latest features! While each session’s content will vary slightly, here are a few core topics we will address each month: Open Source Analytics: The future of data science is open source. We’ll discuss methods for leveraging open-source tools and packages in a secure and scalable way! Deployment: How to share the amazing data science assets your Team has built, including web applications, machine learning models, APIs, and more! Data Access: Data comes in various forms and is stored in various ways. We’ll discuss best practices for accessing, reading, and writing data! Job Scheduling: Do you have recurring data science jobs? We’ll show you how to automate these processes using Posit Connect. What is Posit Team? Posit Team is a bundle of our popular professional software (Posit Workbench, Posit Connect, and Posit Package Manager) for developing data science projects, publishing data products, and managing packages. Registration is not required. The event will be streamed through YouTube Premiere

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Dash

The Evolution of Shiny with Posit’s CTO, Joe Cheng

Posit’s Joe Cheng talks about the evolution of Shiny and the future of Shiny for Python. Joe is one of the original architects of Shiny and leads Posit’s Open Source software development. Learn more about Shiny for Python at shiny.posit.co/py

Joe Cheng

Shiny for Python Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Natalie O’Shea @ BetterUp | Focus on relationships first | Data Science Hangout

We were recently joined by Natalie O’Shea, Analytics Consultant at BetterUp. In this hangout we discussed developing communities of belonging in data science, building a business case and so much more! (43:02) On building procedures and standards for your data science team: Focus on relationships first. Inevitably, what you define as the best practice in the first case, you’re not going to be doing that best practice in 6 months. Not always - there are certain things that will stay, and teaching your team to use Git branches and merge into main and those things are going to be pretty stable. But there are other more thorny questions that are really hard to build best practices around. For example, in our team, we support a lot of different organizations with different data analyses. Do we put everything into one big GitHub repo? Do we make a repo for every organization for every project? It’s always in development and it’s constantly iterating, so being comfortable with that is a key part of developing best practices, because it’s never going to be just like a set it and forget it. It’s going to be constantly in building relationships and those best practices together. We run an R users office hours every two weeks and I often don’t have an agenda or anything. It’s a great way to just first and foremost build community. But that’s also where people come to say, “I’m really struggling with this; how do you approach this?” And now we’re starting to build those best practices as we build out the whole Posit onboarding infrastructure. Our hope is that we have internal packages. I think building that into vignettes, like Confluence documents (I did see that Quarto is supporting Confluence documentation, which is awesome) Be comfortable with iterating and make sure that you’re always communicating with each other and building strong relationships. (52:06) On building a business case for data science tools: We did a survey of people in terms of how much time are people spending building a single slide deck? Where are all the places that you have to go? On average, you spend, say, 10 hours building this slide deck. Then hey, “if all of these data points were populated automatically for you, how much time would be remaining?” We used that and factored in people’s salaries and all of these things, and then came up with a rough estimate of the time-cost savings that building out this app would have. The app I’ve built now, I don’t think anyone would have been able to see that if I just started with that. Building MVP– minimally viable products - like little pieces of the pie is important. I started off doing parameterized reporting with R Markdown, and people were like, “That’s amazing!” But the problem is that people aren’t going to have the kind of flexibility on the end user side. So I’m like, “this is great - this is how we actually populate slide decks with data insights, but if you want the flexibility to change the branding, alter the wording, all of this stuff like, this is our next product.” Then you just kind of work your way up - where now I have a production-grade Shiny app that does all of this for you. Build in little chunks that get you there to your end result. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

RStudio on Amazon SageMaker

Working with analysis or a data set that exceeds the capabilities of your local workstation? One simple option for scaling up is RStudio on Amazon SageMaker. See how you can get started quickly. Link to learn more: https://docs.aws.amazon.com/sagemaker/latest/dg/rstudio.html

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Oscar Baruffa @ IDH | Hiring for skills you don’t have | Data Science Hangout

We were joined by Oscar Baruffa, Senior Analytics Manager at IDH. We discussed the “soft skills” side of data science and how we manage ourselves, our teams and our projects - along with hiring. Specifically at 34:14, Oscar talked about the difference in hiring to replace you versus hiring for skills you don’t have. “When you’re getting into a management position, at some point you need to start hiring people and a team around you. Hiring is actually very different depending on different points in your career. I thought that it had been the same, because I had done hiring before, even when I wasn’t a manager. There comes this split that I think takes some people by surprise the first time. So before you’re, let’s say, a “manager manager”, if you are growing in your career and you do happen to be hiring someone, you are hiring someone most likely to replace you - like a more junior version of yourself. You’re going to train them and you are an expert of some level, and you’re going to bring them up to your level or something like that. You feel, I suppose, in a way, quite secure in what it is you’re doing because you’ve already got much more knowledge than them and you are bringing them up to your level. You feel very confident in answering the questions and mentoring them and all of that. But it becomes very different if you’re starting to hire for skills you don’t have. For example, I don’t have a stats background. I don’t have a data engineering background. I don’t have even that much, actually, of an analyst background. I was a market analyst, not a data analyst, per se. When you start hiring these team members into your team, these are for skills I don’t have. It becomes much more difficult for me to also assess - what are they good at or not? I first started - like I think most people start - trying to learn as much data engineering and data science as I could to try and just stay at least up with them. But there’s no way that you can do that. It clicks and you’re like, OK, wait, my job is not to be the expert of all of the people in my team. I am the facilitator of their work. If I know more than them, that’s probably actually a problem. We can’t have me spend 10% of time on all of these different vast fields while still being the expert in everything. That’s not going to be great and that’s quite a different switch. I think it takes a bit of getting used to - to start not understanding bits of what it is. Then the tables turn a little bit. I’m in some ways the non-technical manager of technical people, although I’m kind of technical myself. They’ll talk about things, and I also have to be like, whoa, whoa. Then I start also clicking a lot of what my directors and execs feel like sometimes. I’m like, yeah, you’re getting into way too much detail. I can’t do anything with what you’re telling me. My role mostly is to try and not just prioritize work but try and help them uncover where there’s work that needs to be done. Or if there’s a dilemma, try and break down, well, what if we go this direction? How bad will it be if this happens? Or how good will it be if this happens? Can we weigh risk like this? So I can convey to them as well where things just aren’t a priority. Because no matter what they do or stress about here, it isn’t going to matter later. That’s quite a different ballpark. I think it probably happens the first time you hire someone that’s not a replacement for you, it’s an addition to your team. It can catch you by surprise.” ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

Barret Schloerke: Lessons Learned Testing 2500+ Shiny Apps Every Day

About the Talk: The Shiny team tests 2500+ different combinations of Shiny Applications, R versions, and operating systems to verify no feature regressions occur within the bleeding edge of the Shiny-verse. Over the past few years, we have learned a few lessons to keep our tests robust and honest. There is a non-zero chance that a test failure is not your fault. When working with CI systems, external influences such as installation failures or slower computing power will prevent tests from executing properly. A picture is worth a thousand tests but visual testing is known to have a non-zero false-positive rate. To address this, we have utilized threshold based image comparisons for flaky tests. We have found that it is best to test the minimal amount of content where possible. Changes in App package dependencies can produce unintended changes in expected outputs. These hard learned lessons have reduced unreliable test results allowing the Shiny team to quickly respond to feature regressions. Speaker’s bio: Dr. Barret Schloerke is a Software Engineer at Posit. He currently develops and maintains many R packages in the Shiny ecosystem at RStudio including shiny, reactlog, plumber, learnr, leaflet, and shinyloadtest. Dr. Schloerke received his PhD in Statistics from Purdue University under the direction of Dr. Ryan Hafen and Dr. William Cleveland, specializing in Large Data Visualization

Barret Schloerke, Shiny Team

Leveraging Pins with Posit Connect

Leveraging Pins with Posit Connect Ryan Johnson, Data Science Advisor at Posit You might find this helpful if: You have reports that need to be regularly updated so you want to schedule them to run with the newest data each week You reuse data across multiple projects or pieces of content (Shiny app, Jupyter Notebooks, Quarto doc, etc.) You’ve chased a CSV through a series of email exchanges, or had to decide between data-final.csv and data-final-final.csv You haven’t heard of pins yet! For some workflows, a CSV file is the best choice for storing data. However, for the majority of cases, the data would do better if stored somewhere centrally accessible by multiple people where the latest version is always available. This is particularly true if that data is reused across multiple projects or pieces of content. With the pins package it’s easier than ever to have repeatable data. Timestamps: 0:17 - install the pins package and load into your environment 0:32 - register a board 0:59 - connecting to your Posit Connect instance from Posit Workbench or RStudio IDE 1:43 - define the Connect instance as your board 2:01 - pin the mtcars dataset to your Connect instance 2:38 - a pinned dataset on Posit Connect 2:50 - reading a pinned dataset Additional resources: Example workflow that involves Quarto, pins, plumber API, vetiver and shiny: machine-learning-pipeline-with-vetiver-and-quarto/ Connect User Guide - Pins for R: https://docs.posit.co/connect/user/pins/ Connect User Guide - Pins for Python: https://docs.posit.co/connect/user/python-pins/ 9 ways to use Posit Connect that you shouldn’t miss: https://posit.co/blog/9-ways-posit-connect/ Learn more: If you haven’t had a chance to try Posit Connect before or you’d like to learn how your team can better leverage pins, schedule a demo with our team to learn more! https://posit.co/schedule-a-call/?booking_calendar__c=RSC_Demo On the last Wednesday of every month, we host a Posit Team demo and Q&A session that is open to all. You can use this to add the event to your own calendar: pos.it/team-demo

Hadley Wickham | {purrr} 1.0: A complete and consistent set of tools for functions and vectors

{purrr} has reached the 1.0 milestone, with new features like progress bars, improvements to the map family, and tools for list flattening and simplification. 0:00 Introduction 0:11 What is purrr? 00:32 What is functional programming? 03:08 Announcing purrr 1.0 03:58 Progress bars 05:18 Better error messages 07:18 New map function: map_vec() 09:58 New list_* functions 12:04 Flattening and simplification 17:40 Breaking Changes 22:34 How the tidyverse handles deprecation 24:41 An overview of functional programming 26:22 Closing, resources to help with deprecation, how to submit issues See more in the {purrr} 1.0.0 release blog post! https://www.tidyverse.org/blog/2023/03/tidyverse-2-0-0/

Hadley Wickham

purrr tidyverse tidyverse.org RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Dan Negrey @ MarketBridge | Creating a framework for consistent measurement | Data Science Hangout

We were joined by Dan Negrey, Director, Analytics at MarketBridge. At (15:11) we asked Dan about a tip for impacting the business with data science. So I think every business is going to have KPIs (Key Performance Indicators), and there’s going to be other metrics besides KPIs, things that lead into that. As crazy as it sounds, some organizations struggle to measure those and to do so in a consistent and repeatable way. Maybe they measure something that just comes from one person sitting at a desk, and they’ve done it for six years, and they leave. All of a sudden, who knows how they do that? Creating a framework for consistent measurement is huge for an organization. The measurement is consistent and the outcomes are measured consistently. Then taking action to improve those outcomes can be thought of as more reliable because the measurement process is consistent. So that would be one thing for sure. Another – on that note, is decision making. Every company makes decisions. A lot of us are here because we like to do this kind of work, but most of our companies exist because they like to make money, and they like to grow. So we find a balance between doing what we do to help our company to achieve their goals. Find ways to help your company optimize cost, reduce waste and increase growth. All of that is through measuring and looking at decisions that have been made in the past and thinking about how they could have been made differently. This could be through historical analysis or building models to help make those decisions more effectively. That’s a huge win for any organization. There was also lot of love for repeatable data with the pins package at this Data Science Hangout. Dan Negrey shared: “Pins has been a huge package that we’ve started using a year or so ago…if you’ve never used pins, it’s definitely worth checking out.” Helpful resources on pins: Pins for R: http://pins.rstudio.com/ Pins for Python: https://lnkd.in/ghmxiEHV Great repo that uses pins: https://lnkd.in/ezvBkav Workflow that involves Quarto, pins, plumber API, vetiver and shiny: https://lnkd.in/e6gnMXfD Link to Ryan’s video & stepping stones: https://lnkd.in/erR-Mjr9 Other resources: MarketBridge career page (with open data science roles): https://marketbridge.applytojob.com/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

Open Source in Pharma with Phil Bowsher

Why are open-source languages like R and Python becoming the default ways for pharmaceutical companies to complete clinical trials? Phil Bowsher sheds insight on this trend, including how we got here and what it means for major pharmaceutical companies moving forward. Phil Bowsher leads the Life Sciences practice at Posit and has helped almost every major pharma around the globe successfully adopt open source. Learn more at posit.co/pharma

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Embracing R and Python

Listen to Posit’s Chief Scientist Hadley Wickham talk about the future of Posit. Visit www.posit.co to learn more

Hadley Wickham

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

David Granjon & Bo Wang @ Novartis | User-friendly, self-serve tools | Data Science Hangout

We were recently joined by David Granjon and Bo Wang, Senior Data Science Experts at Novartis during the Appsilon Shiny Conference. We learned how their team designs production ready apps for clinical trials, from wireframing activities to automated deployments. How do you get feedback from users when you don’t hear from them directly? To know whether the applications are properly used, they use a tracker within Posit Connect. This is basically an application David developed using the Connect API. With the Connect API, he can see “who is accessing the application, which gives you some insights like which team is using it and how to target support.” If you develop an application for several teams, you can have some insight of which team is using the application the most. If you see one team is using the application 20 times more than another one, maybe you want to invest more people to provide two paths here. He supplemented this information by developing an R package, shinyHeatmap, to record in-app usage. “Each click is recorded to identify dead zones to refactor design,” he says. “If you have an app no one visits, maybe it’s poorly designed.” For more information on that Connect API: https://pkgs.rstudio.com/connectapi/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

R-Ladies Rome (English) - What’s new in the tidyverse - Isabella Velasquez

Welcome to R-Ladies Rome Chapter! What’s new in the tidyverse - Speaker: Isabella Velasquez In this video, Isabella will tell you about What’s new in the tidyverse, a suite of packages that’s revolutionized data wrangling, visualization, and analysis. Recently, Tidyverse has undergone some changes and updates to make it even more user-friendly and powerful. The changes to Tidyverse include new packages, updates to existing ones, and improvements in performance and functionality. Some of the most notable updates include enhancements to package dependencies, performance improvements for specific functions such as group_by(), and the addition of new packages such as ggplot2, readr and dplyr. You can find the latest news here: https://bit.ly/3z9BcMR To follow Isabella Velásquez: Twitter: twitter.com/ivelasq3 LinkedIn: linkedin.com/in/ivelasq/ Materials: GitHub repo: https://bit.ly/3LHVSmS Website: https://bit.ly/3M5gE03 The tidyverse blog: https://www.tidyverse.org/blog/

Isabella Velásquez

Nick Pylypiw @ Cape Fear Collective | Building up trust with stakeholders | Data Science Hangout

We were joined by Nick Pylypiw, Chief Data Officer at Cape Fear Collective. Nick shared that one thing he’s been successful at in his career is building up trust with stakeholders. How do you build that trust? First off, people like data and analytics if it confirms their preconceived biases and beliefs A lot of people are fans of analytics and data science if it’s confirming what they already believe. One of the things working in pricing and marketing analytics at Lowe’s, is if I went into a room with a bunch of people who are marketing lumber and I said, “Hey, the promotion that you did last week did really well.” They may think, yeah, we really like this guy. If I said, “Hey, but it missed these markets and these markets” …if it counters their beliefs then that’s where they start poking holes and wanting to find fault in it. Find some quick wins. What are some small projects we can work on together that are low stakes projects that get people to buy into it and see, “Ok, now I can see why there’s value in this way of thinking.” When that inevitable cognitive dissonance comes, then you’ve built up some trust and some capital with that partner. There has to be a boots on the ground connection. For the same reasons we talked about building trust and capital in a Fortune 500 company, if you think about the state in that same way, if you’re from the beach town and you try to go up into the mountains and tell them what they don’t know about their public health crises, you’re going to get run out of there. You still have to build these connections organically. The same way we talk about building capital in a big company - especially with this line of work, you’d be surprised at how much buy-in you can get if you show up and you actually know how to pronounce the town name and the county name correctly. Do you go into Beaufort, North Carolina and you say “Buford”. That’s South Carolina by the way. Spelled exactly the same, Beaufort in South Carolina and Beaufort in North Carolina. If you pronounce it the other way, you’ve lost them before even the first slides hit the screen because it’s just some outsider coming in here who knows nothing about us. Learn about their area (of work) One of the things that our data science team has done is when you’re learning about a new area, one of the first tasks that we start doing is putting together a deck of the five worst areas of poverty in this area. I want to know the five areas where there’s great employment parity or great unemployment rates, all these different things. Through that exploration, you understand, oh, there’s this town here and there’s this county and you learn about that area. So now when you are in a conversation, you can intelligently talk about that region. It doesn’t sound like you’re just some person looking at a chart and saying, you need to do this and this. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

Dara Straussman @ Stripe | Partnership-model when forecasting | Data Science Hangout

We were joined by Data Straussman, Data Science Manager at Stripe. Dara’s team focuses on forecasting, machine learning, and analytics in the Finance and Operations spaces. We were able to get a glimpse into their financial modeling methodology at Stripe after a question from Alan at 12:59 Question: How do you get buy-in from business stakeholders who historically would have been setting targets (like salespeople and their leaders) that the forecasting you’re doing seems like a realistic enough picture of the world? Answer: It’s an ongoing evolution for how we do this interaction because it’s very important to get the input from stakeholders. There might be a big sales deal coming down the pipeline that no statistical model will know about. There might be some product launch plans that we’re just not going to be able to learn from history. So we find that interaction very important. When we started doing the – we call it the statistical forecasting and the adjustments — it was all done in spreadsheets by finance folks. We did really have to prove our value and show how a statistical model will actually help. So our current system: We have the statistical model that’s all done by data science. We do backtesting and reports to be able to build confidence in each level of the hierarchy so that people can understand where do they expect accuracy, where do they expect less accuracy. We have a whole process with the finance and strategy team, where they make adjustments to the forecast and are pretty clear about where they are doing adjustments and how it’s affecting the total. Then we track both - the statistical versus the adjusted forecast. What we’ve learned over time is that making adjustments in certain ways or certain places is very important and very effective. We sort of encouraged the finance and strategy team to focus their efforts there. Whereas in other places, the statistical model is quite accurate, and it’s best to spend effort elsewhere. So that’s the baseline of how we think about it– again, ever evolving. We also have an embedded model so we sit with finance and strategy. We work really closely with them and really understand their problems. It’s very much a partnership-type model rather than a “throw a model over the wall” type model. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

Python in Posit Workbench | Launch in Your Native Development Environment

Posit Workbench makes it easier than ever to code in Python. Launch the development environment you prefer, including VS Code, Jupyter, and the RStudio IDE. Learn more at Posit.co/workbench Professional data scientists using both R and Python love Posit Workbench because of its enterprise-friendly benefits, including collaboration, centralized management, security, and commercial support

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Art Steinmetz, (formerly) @ Oppenheimer Funds | Communicating with Execs | Data Science Hangout

We were joined by Art Steinmetz, private investor and former Chairman, CEO and President of OppenheimerFunds. During our conversation, Art shared his perspective on open source data science and how these tools helped contribute to the company’s bottom line. He also shared a few tips on communicating with executives - from an executive’s point of view, included below: Two key points (20:31) Data visualization is the most powerful way to get a narrative across quickly. Don’t use a PowerPoint presentation when you should be using a memo that people can read in a narrative On point one: Spend time learning good visualization techniques. Even if you’re a math whiz and a coding whiz, understand good visualization. It’s an aesthetic issue. It’s an art. There’s lots of blogs, lots of literature that talk about good visualization and bad visualization. For example: Don’t use a linear scale when you should be using a log scale, simple stuff like that because I’ve seen so many bad charts. At best, you’re going to lose your audience quickly. At worst, your audience will draw the wrong conclusions and make bad decisions. One point two: The physicist Richard Feynman was on the Challenger disaster panel. One of the big takeaways in that inquiry was shortcomings of PowerPoint for technical work.. So the other point I would make is, don’t use a PowerPoint presentation when you should be using a memo that people can read in a narrative. All of you are familiar with the problem of seeing a PowerPoint slide. You’ve got landscape orientation and you’ve got this incredible amount of 12 point text filling up a slide. Sometimes you’re subjected to sitting in a conference room, having that thing with 2 minutes worth of text on a slide that you’re supposed to process. Of course, nobody looks at that at all as they’re listening to, hopefully, what the speaker is saying. It’s a complete waste of everyone’s time to create and display this giant PowerPoint, this massive quantity of text on a PowerPoint slide. So don’t use PowerPoint when a pre-read memo would do the job. And yeah, some people aren’t going to read the memo. But treat your audience with respect anyway and tell them you’re going to assume people have read the materials. Then if you’re going to have a PowerPoint or a slide deck: have three, four bullet points at most on a slide with compelling visualizations that tell your story. That’s going to earn you far more respect than showing that you’ve crammed your PowerPoint with a million words. Example of Shiny generating discussions and helping investors: (37:30) So for instance, at Invesco right now– one of their folks has created a Shiny app that if one of our customers (our brokers) shares their customer’s (end investor) portfolios with us - we can run an analysis on that to help them optimize their tax situation– when they should take capital gains, when they should take capital losses, what they should sell to realize a loss or a gain. Then if they want to keep the portfolio complexion the same, what should they then buy on the other side to keep the portfolio complexion– the overall risk or aggressiveness - the same. He built a Shiny app that does that. All of the Invesco salespeople can look at all of the advisors that they serve (in that Shiny app) and that generates discussions. Salespeople love to have opportunities to call their customers up and have an interesting discussion. To say, “look, here’s an issue you have that I think I can help you solve.” Then if the discussion goes further, with one click, they generate a detailed PDF report on exactly what the gains and losses are across the portfolio. And the salespeople just love that tool. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

Isabel Zimmerman - Practical MLOps for better models | PyData Global 2022

www.pydata.org Machine learning operations (MLOps) are often synonymous with large and complex applications, but many MLOps practices help practitioners build better models, regardless of the size. This talk shares best practices for operationalizing a model and practical examples using the open-source MLOps framework vetiver to version, share, deploy, and monitor models. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

Isabel Zimmerman

Data Science Hangout | Regis James, Regeneron | Achieving scalability & showing value of community

We were joined by Regis James, Senior Manager (Biopharmaceutical Data Science) at Regeneron Pharmaceuticals. Achieving scalability through standardization (19:04) In order to achieve scalability I do have to standardize things a bit. We use Confluence, so on the wiki that I’ve made, I have a lockdown page where I have some standardized messages that I send out to people at different phases of things. I do customize and read what people say about themselves and I’m thinking, “this could be useful here, that could be useful there.” One of the things that I do is– I have this whole ingestion pipeline that I maintain to identify new members of the community and who can help the community learn more about the blockers to current responsibilities they have for their job. All companies have a directory where you can learn about who exists at the company and see people’s job titles. I can look into the directory for data or data science or whatever and see if the people that come out in the results are already in my Microsoft Teams group because they could be potential new people who could benefit from being in the community. They might be able to do their job better and also help other people do their job better, if they can connect to others who have similar needs or can offer similar things. I do this on a semi-regular basis, so I can really help onboard people who have just joined the company or maybe moved into a data science type role. Then I reach out to these people, and use my standardized message like, “Hey, I just want you to know this group exists. This community was built just for people like you. This is what we do and here’s a link to the wiki that has the links of all the recordings” - because we have a growing library of all the conversations just like the Hangouts. I only send one email and ask people to let me know if they’re interested in it. I don’t spam people. Of the subset, who answer - sometimes it’s like 20% that respond, they say, “Yes, this is amazing. Thank you for letting me know. I just came to the company. I didn’t even know if there was a community.” I’m also doing it from my own perspective of when I joined the company. When they respond and then they say they’re interested, I add them to the Teams group, to the Active Directory group, and then forward the invitation to the ongoing gatherings every other Friday. In the invitation text I paste in, “Welcome to the group. Please introduce yourself in the introduction section" and include a link that takes them directly into not just the Team for the group, but the channel of introductions. Since doing this, I’ve gotten a lot higher percentage of people who have been added to the group to start introducing themselves and say what they work on. I see what people are working on, and integrate that with my understanding of what people need help with from the Shiny app (the poll for what talks people are interested in.) Then I reach out to them and say, “Hey, would you be interested in being one of the guests for an upcoming event?” I send them the link and they can see there’s no pressure because I’m a year out. I’ve got someone every other week. So it’s like, Hey, would this be something you’re interested in and then I am gently having a conversation over time. Sometimes people drop out and I just reach out to those people and ask if they would be able to switch. How do you show the value of community? (40:14) To prove the contribution, the benefit of a community – I use the words stuck and unstuck because that’s really the only thing the business, or any organization cares about. There’s a bunch of stuff that the organization has to do. There are obstacles in the way, they are stuck. How do they get unstuck? That’s what data science is for, really. You can argue that it’s also for going from stuck to unstuck. The non-computational people who have the power to bestow upon you promotions, more money, or more influence over the company to make other decisions, really care about the value of the institution. If you can show how the existence of that community propagates through the network that exists in the institution so that something goes from stuck to unstuck and you can mark that, you can measure it, then you can say this only happened because of me. If you’re a community that’s gathered around pulling thorns out of your colleagues arms, you can say, we pulled 15 thorns out or we taught another group how to pull 100 thorns out because we were force multipliers. If you say, “what do you hate about your job?" to the person with power that I need to prove the community’s value to, think about how you’re reducing those pain points for those people and enabling them to go from stuck to unstuck. Collect those things and showcase them. From the get go, you should be thinking about how to indicate success to leadership. To join future hangouts: pos.it/dsh (All are welcome! We’d love to see you!)

Keynote: Hadley Wickham - Embracing multi-lingual data science | PyData Global 2022

www.pydata.org RStudio recently changed its name to Posit to reflect the fact that we’re already a company that does more than just R. Come along to this talk to hear a few of the reasons that we love R, and to learn about some of the open source tools we’re working on for python. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

Hadley Wickham

Data Science Hangout | Namrata Shetty-Anderson, UPS Store | Professional development for data teams

We were joined by Namrata Shetty-Anderson, Advanced Analytics Manager at The UPS Store. Right from the beginning of the conversation with Namrata, we dove into the art of data storytelling (7:20) …In my mind, data science is as much a science as it is an art. To be able to tell your story, you could write hundreds of lines of code and you could build amazing models, but at the end of the day if you can’t translate it to a non-data person as a data story, all that is a waste. You’re not adding any value. Businesses don’t care about your code. My boss doesn’t care about my hundreds of lines of code. He wants value. So I call it the art of data stories. I actually took a course by Kevin Hartman from Google, which talks about this. One thing I learned from that course is that you want to start with a solution for the problem in a tool agnostic environment. Don’t start with your tool when the problem is posed in front of you because what happens is the tool is going to limit your ability to tell your story. If I say I’m going to use Power BI, then I’m going to be limited to the visuals that are available in Power BI. So what I started doing, literally, was use a blank page and a pen. What do I want my outcome to look like? Once I know what that story is going to look like on a blank sheet of paper, then I decide what tool I’m going to use. Now the tool is going to depend on a few things: How quickly do I need to turn this around? How complex is this task? How good am I with this tool? Those are the things you need to look at. People say, Python’s better than R, R is better than Python. No, Power BI is the way to go. Tableau is the way to go. No, everybody’s right and everybody is wrong. Your tool of choice will depend on the problem you’re trying to solve. Don’t fall in love with anything you’re doing. Today you’re doing it one way, you’re going to find a better way to do it as you keep learning. *The course mentioned above was Business Analytics, Communicating With Data by Kevin Hartman: https://www.coursera.org/learn/intro-business-analytics &/or https://elvtr.com/course/storytelling-with-data Namrata also shared thoughts on approaches to professional development and skill building - (22:58) Managing a data science team is different from managing a team that’s operations driven, product driven, or even a software engineering team. It’s different because problems that come to a data science team are not always well defined and the solution is not cookie cutter. There’s no right answer, there’s no wrong answer. You need a research oriented approach to what we do and a drive to keep learning. So here’s what we have been doing. My whole team has the option of picking their learning platform. Some pick Coursera, some pick a virtual summit or buy a book, whatever. You tell me what your best learning approach is and you can have it. The team is also encouraged to block off time on your calendar. X hours a week or X hours a month, whatever you like to focus on learning and put it on your calendar. No one’s going to bother you. Do we do it? Sometimes. They’ll block it off and sometimes won’t do it. It happens to the best of us. We have a week long group camp Every year we have kind of a week long group camp, as I call it, where we bring in a certified instructor (we’ve been focusing on R for the last two years at least) and just work on little projects with the team. For this week, the team is not bothered by anyone just to focus on learning. (more on how they block that time at 31:51) We started having monthly sessions. We started with best practice sessions where one of the team members would say, hey look, this is a project I worked on and here’s how I approach the problem. This helps someone that’s new on the team, someone that’s learning to get some exposure to it. You can learn everything online, you can learn from instructors but then when you have to apply it in the real world, it’s not a one to one translation. Real world data is not normally distributed. Real world data has noise in it. When we maxed out our best practice sessions, we started doing a micro assignment. My team member Liz puts together micro assignments for the team with real work data - like online reviews, customer data, sales data - and perform these operations in R. We put together a central repository Because my team does so many things, we have a repository where we have reference guides, best practices, and a lot of documentation. If someone new joins my team, I tell them you’re going to spend your first week just reading everything that’s on that repository. Documentation, process documentation plays a very important role and it allows us to cover for each other. We want to rely on processes that make our people’s lives easier. To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

Data Science Hangout | Emi Zambada, Nationwide Financial | Building a story for the C-suite

We were joined by Emi Zambada, Assistant Vice President, Annuity Distribution Intelligence at Nationwide Financial. In talking with Emi about data science in insurance technology and managing teams through digital transformations, we also learned more about building a story for the C-suite in our own organizations. (40:26) Storytelling is critical. I go back to thinking about how we speak to little kids. You need to speak their language. They are not going to speak your language. If you spoke in Shakespearean language to a kid, they’re not going to understand you and they don’t care. You’re going to lose them. Listen to your leaders. Listen to the earning calls or whatever ways that they present to the organization. Pay attention to the words they use. Pay attention to how they describe things and the metrics they share. Use the words they use. Words are important. You can say the same thing with different words. We talk about data foundation, but if you say data integrity - you might say, well data integrity and data foundation are the same. If they use the word data foundation, use that. Use their words so they can understand you. Keep it consistent. Think about the old times - and the old fashioned newspaper. The New York Times always looks the same. They have the same structure, the same format. The articles and the layout are similar every day. Imagine if they tweak things and move things around - you will spend a lot of energy and time trying to figure it out. It takes mental energy to figure out the format and the structure that you don’t care about. You want to read the news. All your energy is to understand the content of the news. The same thing with storytelling and dashboards. Find a way to use the words, the structure, and the metrics that they use and are familiar with so that they don’t spend time and energy trying to understand your dashboard - your story. For example: the metric that you’re tracking is going this way, we might need to take action on this. Be very specific. Don’t say a lot of things. Less is better. Be clear, concise, and very specific on the things that you really care about. Repeat the things you care about. Silence doesn’t mean they agree. Watch them, look at their expressions and repeat. A mistake that we can make is that we don’t recap what we talked about last month. You can’t walk into a meeting thinking that they remember everything that happened last month. They don’t. They have done so many things since then. Don’t feel offended, it’s normal. Go back to that and say, last time we spoke we did this. Now we’re going to do this and let’s bring it to the present. Speaker bio: Emi leads strategic business intelligence for Nationwide Annuity distribution by leveraging data, analytics and technology and partnering with senior leadership to identify trends and measure efficiency and effectiveness of distribution. Emi is recognized as a thought leader and was recognized by MIT Chief Data Officer Magazine´s “40 Under Forty Data Leaders in 2022” and is a recipient of the Hispanic Association on Corporate Responsibility’s “2022 Young Hispanic Corporate Achievers Award”. A proud Mexican immigrant, Emi serves as an advocate for minority groups in AI and tech. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

Embracing Open Source in Pharma || GSK || Posit

Many statistical and clinical programmers have spent their entire careers programming in one language. Major pharmaceutical companies like GSK are collaborating with each other to bring open-source into clinical workflows. Learn more at pos.it/pharma

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Posit Package Manager | Manage R and Python Packages Across Your Organization

Posit Package Manager is a repository management server to organize and centralize R and Python packages across your organization. Learn more here: https://posit.co/products/enterprise/package-manager/ Use it to provide full mirrors of CRAN, Bioconductor, and PyPI. Restrict access to potentially harmful public packages by curating your own custom repository with only the packages you need. Support air-gapped environments by providing offline access to your package repository

air RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Data Science Hangout | Yu Cao, Exeter Finance | Impacting business with data science

We were joined by Yu Cao, VP Data Science at Exeter Finance. Speaker bio: Data science professional, EMBA student, amateur biologist, rock’n’roll zealot. Not only viewing business problems from the perspective of data science, but also from the perspective of the business. Yu shared his thoughts on creativity as a data scientist, working with the business, growing in your career, and much more. A few snippets from our conversation on building relationships with the business. (15:32) It all started with networking. I work with a very analytical team, so a lot of the people there just want to code and build a fantastic model, not spend time networking with people from other business lines. But actually, this is not the right attitude. If you want to collaborate with other people, first you need to get to know them. You need to understand their business – and you need to become their friends. Usually I reach out to them and do a self-introduction to let them know what I do and what my team can do. Let’s go out for lunch, let’s grab a cup of coffee and talk for 15 minutes to see what kind of business difficulties you are having now. If you are a sales team, are you worrying about how to increase the volume of sales? If you are a loan origination team, are you worried about how to increase the capture rate? Let’s talk about that. Then I will see, how can I connect my data set knowledge with the problem you are facing now? I can see whether I can build a model to help you solve that problem you have. Usually, the collaboration starts with this. If you want to build a data science team from scratch (25:51): Figure out what your team can do for the business. If data science has little to do for the business, it will create very little value there and it doesn’t make sense for you to build a team. You probably need to go to a new company. If you determine that data science can provide value to the business, you need to do some public relations. You need to let everybody, all the leaders in the company, know and understand this. Reach out to the leaders in your organization and ask them to provide feedback for your work. Tell them “My goal is to build a data science team and become a data science leader in this company. What do I need to do to make this happen?” Everything starts from the people, the buy-in from other leaders. If you are a data scientist wanting to move into leadership, you need to show ambition to leadership. The job market for data science is hard. This is the right time for you to show the company that you want to invest in yourself - that you don’t want to just code and build models, but also increase your influence, learn more about the whole business, and contribute to the entire organization. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

Data Science Hangout | Satish Murthy, Janssen | Creating a validated environment for reproducibility

We were recently joined by Satish J. Murthy, Senior Manager, Pharma R&D IT at Janssen. During the hangout we dove into the topic of validation and what it actually means to have a validated environment. Snippets from our conversation with Satish: In regulated industries like pharma and finance, any time a health reporting agency comes in and asks for proof, we have to be comfortable in saying that what we ran a couple of days ago, weeks ago, months ago, or years ago – we need to be in a position to replicate that entire environment as is. So this goes through a formal validation/verification process. And this, in traditional parlance, is known as a GXP platform. [GxP is a general abbreviation for the “good practice” quality guidelines and regulations.] X will stand for clinical practice, manufacturing practice, lab practice, etc. So the verification validated environment is often referred to as a GxP environment. It is to build confidence for the end users that we serve in pharma. It is for folks who want to help deliver patient care, which is crucial. One of the key components of this is containerization. Because the ask in validation is to ensure repeatability and reproducibility, the only logical choice at this time is containers. We have to containerize. I have a responsibility for the R part of it so I’m going to concentrate a little bit more on that. First: Users would want to basically run through their studies using R. We are using the Posit Workbench as our launch pad. This is deployed on a traditional EC2 instance. This is a production platform but there is a logical grouping where our users would first want to come and test it in a development area, and then move it into the traditional QA and prod. The way they would first test in the development area is, number one, the entire Posit Workbench is containerized. We always try to keep up with the latest release of Posit. There are some unique challenges around it - mostly about the IQOQ, and the time that it takes. It’s not just Posit, there are other vendors [like Atorus] who are helping us with this effort. We have to go through a formal process of vetting, validating, and verifying. So all of this takes time. That’s one challenge that we have. Second: Because we have containerized, it gives our users the capability to test something that is locked down and be ensured that, again, from a repeatability perspective, containers are the only way that will help them with that. So first, we will go through defining the process and specifying the requirements as to what really needs to go into the container. For example, if our users have identified a set of workflows that they would want to help them with the clinical studies that they are looking into. Traditionally, it will map to a set of R packages - the packages that need to make it into this container. This is where we are using standard technologies and tools that are used in the industry. We have a Jenkins pipeline and we are using the Posit Package Manager to lock down versions of our packages that we want to go into the container. There are some business processes that are defined as to what should go into this container. Once we have defined, we run through the build process to create the container and we will verify it in the traditional Posit Workbench. Once our users are comfortable that, yes, this is the workflow, this fits our model and we don’t see any issues, that container is then locked. Then it moves into the QA/Prod area and that is what is deemed as a validated run. Anything that the users would try out in their development environment is a prerequisite, but it is not deemed as a validated run. It is only when it is run through the production that it is deemed as validated. In order to do that, all of these validated runs behind the scenes will run as a service account user. Behind the scenes, we also have the traditional EKS cluster where we scale up and launch these containers. _ Thank you to the Atorus team for their help in planning today’s session! Resources shared during the call: R Validation Hub: https://www.pharmar.org/ riskmetric: https://pharmar.github.io/riskmetric/ Atorus Resources: https://www.atorusresearch.com/resources/the-power-of-a-trusted-partner/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

Isabel Zimmerman - Holistic MLOps for better science | PyData NYC 2022

www.pydata.org Machine learning operations (MLOps) are often synonymous with large and complex applications, but many MLOps practices help practitioners build better models, regardless of the size. This talk shares best practices for operationalizing a model and practical examples using the open-source MLOps framework vetiver to version, share, deploy, and monitor models. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps

Isabel Zimmerman

Justin Elszasz @ City of Baltimore | Data governance to better serve citizens | Data Science Hangout

We were joined by Justin Elszasz, Chief Data Officer at the City of Baltimore. Justin shared about the collaboration happening among other US governmental data organizations and Michael Bloomberg’s theory of change that if you really want to make an impact on people’s lives, local government is the place to be. At the Federal level, you’ve got a very wide, broad impact but it is very hard to see this trillion dollar budget trickle down to see how it impacts someone’s lives. Local government is where that money eventually comes into your programs and projects. Justin shared two organizations he is involved in: Bloomberg Philanthropies City Data Alliance: https://lnkd.in/en_-f4nS Civic Analytics Network: https://lnkd.in/e2imBP3k As a group, we also shared portals for public data across various cities: ⬢ Baltimore: https://lnkd.in/eGm_AKZd ⬢ San Francisco: https://lnkd.in/e66UeK2w ⬢ LA: https://data.lacity.org/ ⬢ Chicago: https://lnkd.in/eMHpdb3P ⬢ Washington, DC: https://opendata.dc.gov/ ⬢ Toronto: https://open.toronto.ca/ ⬢ Ottawa: https://lnkd.in/e8yhMzMk Part of the hangout touched upon legacy systems in government and the City of Baltimore’s direction in regards to hardware and cloud infrastructure. Justin shared that: Chief Data Officer roles run the spectrum in terms of responsibilities, and where they are situated organizationally. I have a little bit less control over architecture. That said, yes there are tons of legacy applications and infrastructure to deal with. Luckily, like we have an IT department that’s on it right now, and has plans to mitigate a lot of that. As those things get replaced, we will only go up in terms of data quality. Just over the last 2 years, we have rolled out Workday as our new ERP (enterprise resource planning) system. That in and of itself, has addressed a lot of Finance and HR data quality issues. We’re making moves in that direction. Yes, there’s legacy systems but I challenge you to find a Chief Data Officer in another city who doesn’t reckon with that sometimes. As far as cloud goes, our GIS team is working hard on moving a lot of our GIS layers and infrastructure over to cloud with Esri - as well as our Baltimore open data platform. I think it’s picking your battles and where you’re going to get the most bang for the buck at the moment for us. I’m looking to pilot a couple of tools to move to the cloud. One example I’ll mention is that over the summer, we partnered with a group at Carnegie Mellon called Data Science for Social Good. You may know this already, but Baltimore has a lot of vacant buildings and when a building sits vacant for long enough, rooftops tend to become an issue. Back in January of last year, we had 3 firefighters killed when a rooftop was in a fully involved fire. We partnered with Carnegie Mellon to develop an AI tool that will detect collapsed roofs based on our aerial flyover imagery. Every year we get new flyover imagery, which means every year we’ll want to refine and update the model and get new predictions, so we can find any new buildings that might have rooftop issues. We can’t run that on someone’s local machine, so that’s a good opportunity to start pushing the bounds of what we can do and moving to the cloud. We’re looking for a couple of places to definitely move that way. Other resources shared in the chat: ⬢ An article on Data Engineers before Data Scientists by Keith McNulty: https://lnkd.in/evTpT7ee ⬢ A citizen data science analysis of Baltimore Waterbill system: https://lnkd.in/eXGm6Ek4 ⬢ Book recommendation - “Change: How to Make Big Things Happen”: https://lnkd.in/eVH4NHSC We were joined by Justin Elszasz, Chief Data Officer at the City of Baltimore. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

Posit Workbench | Build Data Products in R & Python Using Jupyter, VSCode, and RStudio IDE

More computing power. More possibilities. Less hassle. Posit Workbench is the preferred development environment for R and Python developers. Code in R. Code in Python. Develop in Jupyter, VSCode, and of course, the RStudio IDE. Build all of the data products you can imagine: Reports, Dashboards, Applications, and APIs, with more computing power and no additional strain on IT. For more information: Posit.co/products/enterprise/workbench

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Data Science Hangout | JJ Allaire, Posit PBC | Making data science more open and collaborative

We were recently joined by J.J. Allaire, Founder and CEO of Posit PBC. What made you so interested in open source initially? It was really that the first big software projects I worked on were proprietary software, and I found it disappointing that proprietary software products kind of get very tied up with the company that sponsors them – the fate of that company, the other products that that the company might offer, or who that company gets acquired by really end up affecting the fate of the projects. I also found that while some proprietary software is either cheap or low cost, there’s a certain impediment to adoption associated with the price. As a creative person, I want my work to be available as broadly as possible. I like to make the work available to as many people as possible, but also there’s a dimension of the durability of the work. Is the work going to be around in 30 years or 40 years? What are the things that would make it? So that’s what made open source appealing to me. What are you most excited about at Posit in the next year ahead? It’s going to be related to Quarto, of course, because that’s what I’m spending all my time on. I would say that the thing I’m excited about is that we recognized Quarto is really powerful and flexible, and easy to use for a certain subset of users who are very technical and very motivated, but we actually want to make Quarto available more broadly. So working on tooling that lets both technical and non technical people collaborate over documents, and also lets some less technical people participate in using Quarto. (You can sort of see some of this work in the visual editor that’s in RStudio) Those are the kind of things that I’m focused on for the next year, and I’m excited to see those getting realized. In hindsight, what is one of the best decisions that you ever made for your career or your education? I think this actually has a lot of commonality with talking to a lot of people about their careers. I think when I was about in my late 30s, I had been involved in both developing software and also starting companies. I really learned about myself, that the company part of things – the management and entrepreneurship – I really did not like it all. I didn’t enjoy it. So I kind of said, I’m happy to be involved in starting companies but I’m absolutely not going to do that part, and I’m going to have to find a partner or other people who are excited about doing that part. What I really want to do is focus on engineering and product development. It’s very easy as a company founder to get pulled into all kinds of other things and that’s what happened to me the first 2 times. Just being super clear about that, and saying I won’t even do it unless I can satisfy this condition. I think that’s pretty broadly applicable in that a lot of us accumulate a lot of responsibility in our careers. Some of it is necessary and important for a given role or company, but then eventually being clear about, what do I really like to do and really want to do? What do I feel like I should do or what is put upon me? I know a lot of people now who are in their 40s who have actually managed dozens of people and are like, yeah, I don’t really want to do that. I don’t ever want to have anyone work for me anymore. So I’m going to be really clear about that. I’m going to walk in, and people are going to try to give me people and try to make me a manager, and I’m not going to do it. I think it applies pretty broadly, but just knowing yourself and setting pretty rigid constraints about what you’re willing to do in the workplace and not. Everybody’s different, and both can be really rewarding. I know a lot of people who find management very rewarding, but I know a lot of people who find it really alienating. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

JJ Allaire

Hadley Wickham: Thank you, from Open Source at Posit

The thing that’s made RStudio (now Posit) so amazing, is the community. We love and value our open source community, and we’re so appreciative of everyone who has helped make Posit what it is today. And our community, is… YOU. You live and work on every continent. You use R for school or work or fun; answering questions that are important to you. You create and share thousands of resources to help others. You contribute to code and documentation on hundreds of open source projects. You’re involved in the community, sharing with each other in organizations and user groups around the world. And, of course, you write millions of lines of code. We thank you, and wish you all the best in the coming year!

Hadley Wickham

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Rich Iannone | What’s new and exciting in gt 0.8.0 | Posit

With the gt package, anyone can make wonderful-looking tables using the R programming language. Rich Iannone, maintainer of gt, shows what’s new and improved in gt 0.8.0! 00:00 Introduction 00:42 Find/Replace values with sub_values() 02:46 Find values and style them with tab_style_body() 05:00 Place a cell in your Quarto/RMarkdown doc with extract_cells() 07:13 Make numbers more readable with cols_align_decimal() 08:54 See column id info with tab_info() 11:03 Date and time formatting improvements For more details: • Demo script in this video: https://pos.it/gt8 • Read the blog post on gt 0.8.0: https://posit.co/blog/new-features-upgrades-in-gt-0-8-0/ • Learn more at https://gt.rstudio.com/ • See a full list of new features and improvements at https://gt.rstudio.com/news/index.html#gt-080

Rich Iannone

gt Quarto rmarkdown rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Data Science Hangout | Javier Orraca-Deatcu, Centene | Excel to data science to lead ML engineer

We were joined by Javier Orraca-Deatcu, Lead Machine Learning Engineer at Centene. Among many topics covered, Javier shared how his background in finance and consulting led to his interest in data science to automate some of his work - and how he helped get other data scientists together in his organization. (26:31) How did you organize and recruit people for the data science community group at Centene? So I sort of piggybacked from a general data science community chat that we had at the company. There were several hundred people on this of varying backgrounds and expertise levels so there was a lot of conversation happening. There was already a Python group that was meeting– I think every other month. So three weeks after I started, I got really excited about the possibility of potentially creating something similar for R users. It started by just trying to figure out who owns that already existing data science chat and see if they could help support the idea of creating an R user group, something to meet once a month or once every two months. At larger companies especially, getting that type of top-level executive stamp of approval and support can go a long way, especially if that individual is part of the already existing IT or data science function. At the time, I created a Blogdown site. For those of you who are familiar with R Markdown, Blogdown is a package that allows you to create static websites and blogs with R Markdown. Now with Quarto you can do the same thing and create websites. I love the syntax of Quarto. We had partnerships with Posit, so we were able to get some people to come in and do workshops as well. We also had reticulate sessions, where it was a co-branded Python & R workshop where we were looking at ways in which we can actually communicate between teams of different languages a lot easier. I had a great experience with it. Everyone was so collaborative and it was such a great way to see the excitement around what you could do with both R and Python. I think what started as 13 users the first month, jumped to about 100 - 125 monthly users on this monthly meetup. …And on the journey to machine learning engineer, what was the hardest part? (49:10): Because of SQL, I had a really good understanding of at least how tabular data could be joined and the different transformations that could be done to these data objects. I think I would have really struggled without that basic understanding. But having said that, I think the part where I really struggled at first was function writing. Function writing was not intuitive to me. Basic function writing was but in general, I found it to be very complicated and it took a solid three to six months of practice to feel actually comfortable with it. Even when I started building Shiny apps– basic Shiny is quite easy but large functions underpin the entirety of a Shiny app. Everything you do within Shiny is effectively writing functions. The process of learning Shiny and becoming more comfortable with Shiny was very difficult and something that just took a lot of repetitions but it all sort of played together. While people may think of Shiny more as a frontend type of system, it did make me a much better programmer in the way I thought about actual functions and function writing. Other things that I found hard, looking back, I’m sort of embarrassed to say this, was reproducibility of machine learning – being able to reproduce a code set and get the exact same predictions every time. I wasn’t quite sure why this wasn’t working or how to create these fixed views, setting a seed or whatever you need to do to ensure that someone else downstream could replicate your study or analysis and get the exact same findings themselves. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

Data Science Hangout | Christina Fillmore, GSK | Open source community collaboration in Pharma

We were recently joined by Christina Fillmore, Data Science Leader at GSK. Christina shared some lessons learned from the pharmaverse and some insight into the community building going on in pharma. For more information on the pharmaverse: https://pharmaverse.org/ “Pharmaverse: Packages for clinical reporting workflows” blog: https://posit.co/blog/pharmaverse-packages-for-clinical-reporting-workflows/ A few tips shared from Christina: I don’t personally believe you need to have a big splash in order to start. I think if you wait for a big, beautiful website and everything to be working before you try and get into anything you’re gonna just be waiting. Find some friends and start developing packages with them. It doesn’t have to be a huge number of people. I wasn’t necessarily around for the whole origin story of the pharmaverse but, part of it grew out of Michael Rimmler and Mike Stackhouse saying we should build some packages together and I built a package for them with Atorus. We built a package or two together, and then started having more conversations with other people. And all of a sudden there was a beautiful website, and things were starting to come together. To some extent, it’s just talking to people and starting to build stuff. Eventually you’ll have enough stuff that you will have your own “verse.” I highly recommend starting. When things aren’t standard, what do you do? And how do you start? Sometimes a nice starting place is a package that helps standardize stuff. Metadata is not really in a standard format in pharma. Everyone has their own way of holding this metadata. One of the first packages that I built as part of the pharmaverse was a package called Metacor. Part of the reason was that we wanted to build automation off metadata, but everyone held it in a different excel format typically, or it was in a database. It’s different for everybody so we needed to have a thing that we could all use. It’s the most boring package in the whole wide world, but that enabled additional audit automation to be run off. From there, we then created a different package called Metatools that does a lot of this automation. But the first step was getting that initial thing we could all agree on. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

Data Science Hangout | Eric Nantz, Eli Lilly | Innovation in clinical trials with open source

We were recently joined by Eric Nantz, Director at Eli Lilly and Company. Eric shared his view on the biggest changes in pharma over the past 12 years: A lot of change that happened was an initiative many years ago where we tried to minimize the time from a treatment’s discovery in the lab to when we can actually get that treatment out to patients to benefit from. To do that, we had to do a lot of things differently. There have been reorgs in terms of how trials are designed and more emphasis into other functionalities but within statistics, it actually empowered us quite a bit to push new cutting-edge algorithms for designing, for simulation, and to not just do the traditional analysis for a clinical outcome. The fact that we got this mandate to shorten the time that we’re in the research phase, then we had to really put all hands on deck to put out new solutions. That brought innovation out of necessity but it also opened the door for those in my group at the time to really think differently of how we’re leveraging R in the design space, and become one of the industry leaders in clinical simulation. We definitely have relied on a lot of the functionalities that my colleagues have made. I’m not going to come here and say I know all the ins and outs of every statistical method. That’s why we have a team. Everybody can specialize in their key areas. But we’ve all been able to pitch in and be able to bring that innovation to more novel designs that can shorten that time that a patient has to be in a trial. The industry itself still has a ways to go, but just that emphasis on getting these treatments out to patients sooner organically then brought some new innovations that we were doing in statistics and it really gave us a voice at the table that we could bring some really innovative change to how we were doing things in the past. So certainly that’s been one of the higher-level changes I’ve seen. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

Quarto with the Quarto Team | An Open-Source Chat

Join Al Manning, Carlos SchIidegger, & Charles Teague, members of the Quarto Team, as they take our questions. Quarto is an open-source tool for scientific and technical publishing. Create dynamic content with Python, R, Julia, and Observable. Author documents as plain text markdown or Jupyter notebooks. Publish high-quality articles, reports, presentations, websites, blogs, and books in HTML, PDF, MS Word, ePub, and more. Author with scientific markdown, including equations, citations, crossrefs, figure panels, callouts, advanced layout, and more. Key Resources: ⬡ Learn more and get started with Quarto at quarto.org Contact ⬡ Bug reports and feature requests - https://github.com/quarto-dev/quarto-cli/issues ⬡ Need help? Github discussions - https://github.com/quarto-dev/quarto-cli/discussions Introduction Videos for Quarto ⬡ Mine and Julia talk, https://www.youtube.com/watch?v=p7Hxu4coDl8 ⬡ Quarto Series, 1️⃣ Welcome to Quarto Workshop led by Tom Mock: https://www.youtube.com/watch?v=yvi5uXQMvu4 2️⃣ Building a Blog with Quarto led by Isabella Velásquez: https://www.youtube.com/watch?v=CVcvXfRyfE0&feature=youtu.be 3️⃣ Beautiful reports and presentations with Quarto led by Tom Mock: https://www.youtube.com/watch?v=hbf7Ai3jnxY&feature=youtu.be Timestamps 00:00:00 Introductions 2:55 Why open source? 6:20 Can we expect to see Quarto available to R-users via CRAN any time soon? 9:10 Quarto and Google Docs? 9:49 Lua filters/shortcodes. Advice for a good development environment for prototyping and debugging? 14:59 Is there a single documentation page for ALL the quarto-specific YAML options? https://quarto.org/docs/reference 16:15 Navigating Quarto’s documentation. 18:00 Is there something like Observable SQL cells on the roadmap? 20:10 Is there something closer to {bookdown} for Quarto? What is the best way to retain data and environment objects in a quarto book? Is there any path to enabling this? See Includes, https://quarto.org/docs/authoring/includes.html 24:20 Flexdashboard? Coming soon. 26:30 A big challenge in the adoption is that Quarto is competing with ipython notebooks for mindspace, what does the Quarto team think about that? Quarto and Jupyter Notebooks will hopefully be thought of as complementary to one another, with Quarto helping a lot with narrative, layout, and appearance for publication and sharing. 30:10 Where should I go to contact you about an issue? What if the issue isn’t just Quarto, say, Quarto + Jupyter? 31:50: What is the Quarto team hoping to see the community produce? Feedback, reporting in github issues. Quarto Extensions. 34:05 Custom styling, configuring grid options. Any tips or anything in the roadmap that will help users finetune the look and feel of their output? 38:40 Terminology question; what do we call a published Quarto doc? (or webpage, blog, etc.?) 40:00 How do I stay up to date with Quarto? Getting the latest release and learning about what is new? See what’s up on quarto.org. Look under get-started and under downloads for pre-releases

Isabella Velásquez

bookdown cli finetune flexdashboard Quarto RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Meetup | The Looma Project | Why I Stopped Worrying and Learned to Love the API

Why I stopped worrying and learned to love the API Led by Davis DeRodes, Director of Data Science at The Looma Project Link to slides: https://github.com/RStudioEnterpriseMeetup/Presentations/blob/main/Looma_LearnedToLoveAPIs_PositMeetup.pdf Source code: https://github.com/DavisDeRodesLooma/positconnect-demo-api Helpful resources: ⬢ Brief intro to APIs blog post: https://rigor.com/blog/what-is-an-api-a-brief-intro/ ⬢ Isabella Velasquez, Intro to APIs blog post: https://posit.co/blog/creating-apis-for-data-science-with-plumber/ ⬢ Plumber package: https://www.rplumber.io/ ⬢ Public APIs: https://github.com/public-apis/public-apis ⬢ Aviation API that Davis used: https://docs.aviationapi.com/# ⬢ Posit Connect: https://posit.co/products/enterprise/connect/ APIs are the ticket to getting easy access to new data sources and creating automated ways to share data externally. Even the most stats-oriented data scientist with just R and Posit Connect can do some super useful and cool things with APIs. This talk will go over some best practices and interesting use cases for querying, posting to, and creating APIs using R packages, Shiny, Plumber and the Posit Connect ecosystem. Speaker bio: Davis DeRodes is the Director of Data Science at the retail media start-up Looma whose work focuses on the intersection of human-centric content and sales. Davis’ focuses are full stack data science, causal inference, data quality, explainability and R. Davis is an ATL-ien who currently resides in Durham, NC and is an alumni of Columbia’s Data Science Institute. Please note this event will be live streamed through YouTube Live and the recording will be available at the same link immediately following the event. During the event you can ask questions through this link as well: pos.it/meetup-questions Full playlist of meetup recordings: https://www.youtube.com/playlist?list=PL9HYL-VRX0oRKK9ByULWulAOO5jN70eXv All upcoming events: pos.it/community-events

Data Science Hangout | Matt Dancho, Business Science | Why some companies fail at data science

We were recently joined by Matt Dancho, Founder at Business Science. Halfway into our discussion (29:27) we spoke a bit about why some companies fail at data science. Matt shares: I think that there’s going to be a new role here in the next couple of years - that companies are going to start adopting the role of the business scientist. You already have a business analyst. The more I think about the problem and the challenge with data science and why companies often fail with data science - it is not because they don’t have good people or problem solvers. It’s because they lose sight of the business-first mentality, where you need to be able to start with a problem in an organization, be able to work with many different levels of the organization (from executives to line managers to the people who are doing the work), and you have to work cross-functionally throughout the company. To me, this requires a heavy business acumen. This is more than knowing data and a few algorithms that I can throw at the data. No, that’s not how it goes. The most successful people that I’ve seen are the ones that are able to communicate effectively, that know what the target of the organization is, the objectives, and they can take that down and translate it across the organization. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

R at AstraZeneca: upskilling our workforce through education, experience, and exposure

We were joined on November 29th at 12PM EST by Gabriella Rustici & Guillaume Desachy, who shared their experience about the R journey AstraZeneca is currently on. Resources: ⬢ R @ AZ: Building a Community in the Pharmaceutical Industry Blog Post: https://www.rstudio.com/blog/building-a-community-in-the-pharmaceutical-industry/ ⬢ R in Pharma YouTube videos: https://www.youtube.com/c/RinPharma ⬢ Posit Pharma Site: https://posit.co/solutions/pharma/ Timestamps: 4:53 - Start of session 5:41 - Paradigm shift in the pharmaceutical industry (many people are multilingual) 6:40 - Profile of R users at AstraZeneca (varied across data science, clinicians, medical director) 8:28 - Meet the R&D Learning & Development Team 10:03 - 3E Framework: Education, Exposure, Experience 12:11 - Bridging the science community and data science audience 13:11 - We all learn differently (solutions that suit different needs & styles) 17:31 - Index of learning (self-led index, synchronicity index) 12:20 - Experiential Learning 20:47 - The community of R users at AstraZeneca 21:05 - The early days (April 2021) 21:52 - azTidyTuesday: a playground to hone data viz skills 24:15 - internal R conference 25:38 - R function of the month 27:04 - Lunch & LeaRn 28:20 - R @ AZ 10:1 29:44 - Communication expanded from internal social media to R @ AZ Monthly Newsletter 31:57 - Workshops with Posit 32:11 - AZRHotdesk - come with your questions and someone will help you solve it 35:02 - Wish list for 2023 38:38 - Start of Q&A section Abstract: The use of R continues to become more and more important at AstraZeneca. It is a true paradigm shift that we have embarked on! This shift has required upskilling our workforce to make them proficient R users. To do so, we are leveraging the 3Es of learning: education, experience and exposure. Learn more about their team’s Data Science Educational Program and how the team at AstraZeneca has built their own strong community of R users - where learning takes place through experience and exposure. Speaker bios: Gabriella is Data Science Learning Senior Director in Astrazeneca’s R&D Data Science & AI where she is responsible for developing a strategy for, and creating a centralised approach to, data science learning for R&D. Gabriella completed her PhD at the Wellcome Sanger Institute and previously run bioinformatics training programs at the University of Cambridge and the European Bioinformatics Institute, in the UK. She is passionate about designing, implementing and evaluating effective and scalable solutions to educate scientists and data science practitioners at all career stages. Guillaume is passionate about helping bring new medicines to patients by leveraging the power of statistics and precision medicine. Since October 2020, he has been doing so at AstraZeneca where he works as a Statistical Science Director. In addition, since March 2022, he have been leading a team of 15 collaborators focusing on building the community of R users at AstraZeneca, called R @ AZ. During the event you can ask questions anonymously through slido here as well: rstd.io/meetup-questions Blog post on R @ AZ, Building a Community in the Pharmaceutical Industry: https://www.rstudio.com/blog/building-a-community-in-the-pharmaceutical-industry/ Please note the recording of this session will be shared at the same YouTube Live link

Data Science Hangout | Alan Schussman, W. L. Gore & Associates | Data science is not one thing

We were recently joined by Alan Schussman, Data Team Leader at W. L. Gore & Associates, Medical Products. “Data science is not one thing.” This was a recurring theme among comments and conversations in today’s data science hangout with Alan. Data science is about problem-solving I don’t think specific technical skills are the only thing that gets you in the door. We have people who come from analyst backgrounds and tools backgrounds of all different kinds — and can be successful here. There’s an element of being competitive that is rooted in some core technical foundations. However, you can be really technical but not a good problem solver. We need people who can learn technical things but also know what solving problems looks like. If you’re inflexible in how you solve problems, you are not going to be successful. Thinking of interviews I’ve been part of, you see people light up when they explain a problem they solved with different kinds of cooperation and creativity. Seeing this is really fun and helps you stand out. Data science is about compromise There’s a whole lot of what we do in our work that has to do with satisficing — getting to a good enough solution that folks can work with. A lot of technical training is a little too black and white for being able to compromise about, “This is good data that my customers can work with, and I’ll come back and improve it for the next cycle.” You’ve got to be able to compromise. Data science is about communication You’ve got to be able to talk through problems and feel embedded with your customers, so that you’re a partner to them and not just a technical service. Data science is more than machine learning I don’t think we do a service to people the way we set them up for what the data science industry “tends to look like.” I don’t know if it’s the right expectation for people going through data science training to have their eyes on the machine learning engineer kind of career, for example. There aren’t a ton of them. The applications are pretty specific. We’re a pretty sophisticated business, and we’re trying to figure out what some of those things mean to us, and how to effectively use them. Don’t get me wrong, I’m really excited for us to be at that place but without the business need it’s not fair for me to tell people: “Come join my team and be a data scientist the way that the world says data science looks.” It’s going to be technical. It’s going to be interesting. It’s going to be rewarding, but it’s different from that other picture. There isn’t any reason we can’t make people really excited about that kind of work. It’s really complex and the problems to solve are really fun. The platforms we get to work with are cool, but it looks a little different. We’ve got a lot of runway for folks with a broad set of skills. We have to be realistic about what we promise the work is going to look like and when, so that we’re not bringing in people who have a very particular vision of data science. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

Data Science Hangout | Marcos Huerta, CarMax | Translating Academia Experience to Data Science

We were joined at the Data Science Hangout by Marcos Huerta, Manager of Data Science at CarMax. A few snippets from the conversation with Marcos at 36:40: What was the most effective asset that you had that led to your transition to your current position? ⬢ A willingness to just try and teach myself new things. In graduate school, I’d done a ton of data analysis, but it was all in an obsolete language that wasn’t going to help me. I had to teach myself Python and R. I think the openness to trying that was key. ⬢ I gave myself a project to figure out how to use Python and understand classes and object-oriented programming, which I did not understand 10 years ago. ⬢ I do think that my work experience – because I had done a lot of non-technical stuff – helped as well. I had this record of professional accomplishment that maybe wasn’t technical but people knew I could think and I had this track record. How do you do the mental folding to translate the many years you spent getting a PhD in astrophysics to a different position? ⬢ The first step for me was not to data science, it was to the science policy world. I think I always had this interest in politics and the government. That first transition came because of these talks at Rice that happened once a month about non-academic careers. Someone who had done science policy and worked at the National Academies of Science and as a Congressional Science Fellow came and gave a talk about what she had done with her physics or astrophysics degree. That really fascinated me. ⬢ From science policy, data science was more practical. There’s a ton of jobs at the entry level for science policy but as you start to work your way up I was running out of things to do. There become fewer and fewer job openings. ⬢ Turns out when I started itching that part of my brain again, I really enjoyed it. I enjoyed the Data Incubator. I enjoyed trying to do the swirl lessons in R, building some Shiny apps, etc. Once I got back into doing technical stuff, I found it was still very satisfying. *non-verbatim transcription, summary of a few insights ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

Data Science Hangout | Jessie Pluto, Comcast | Taking Initiative with an Idea

We were joined at the Data Science Hangout by Jessie Pluto, Senior Manager of EBI Strategic Analytics at Comcast. Question: As an analyst when you’re working with business partners, you’re just trying to deliver value and insight in a speedy way. So I’m trying to convince analysts to maybe take a bit of a step back and learn and implement something new that can help both them and their business partners in the future. Do you have any advice for how I, as a person trying to get these things up and running, can help to create more of that culture of let’s go ahead and take a step back and do something now that will help me in the long term versus just trying to check boxes as quickly as possible? Answer: It is a constant battle. I feel like it is the place where it requires some strategy because you can’t do it all the time. You have to really recognize when is the right time to pounce on an idea? You have to have the ideas first. Sometimes you have to sit on them for a little while and then you have to just be able to recognize, I think, that opportunity. It’s OK to make mistakes. Sometimes you push something too soon and it might hurt a little bit, but don’t let that kill your motivation. I hate PowerPoint. I hate copy and paste so much. I might have said copy and paste 80 times already. But I hate it. For a long time, some of the reports that I was doing– just to give you an example, Paramount is, like, 30 individual networks - and so sometimes we’re repeating views for 30 networks times five different views at the bare minimum. It’s too many slides, too much copy and paste. I had made a push for, oh, we should convert this to R Markdown or Shiny or something. And same thing– it’s like, well, our stakeholders don’t know how to use that and it’s a server. It’s very private and customer information. And COVID hits, and sports viewing kind of just went away. We had so many questions coming in about, what do we do? What’s going on? What are customers doing? What are they watching? All sports just kind of went completely off the air immediately. And I realized, this is my moment. I created (we were in Hadoop at the time, we’ve since moved but we were in Hadoop at the time) some shell scripts, bash scripts to run at certain days and scheduled it and created this pipeline that I had been messing around with for a while, created an automated R Markdown report. And basically, I was able to give the information that the business was asking for very, very quickly. And they learned how to use it because they really wanted it. And it would have been too much for my very small team to be reporting every single week on what is happening this week. It’d be a huge load on our team to do that every single week from scratch. So basically investing– and I will admit that I spent a little bit of night time on this project - but it was a passion project where I was like, all right, this is my moment to prove that this is a feasible solution, and show that– because we had so many questions coming in. This was one of a huge backlog of questions. I was able to show that, OK, this is done. We’ve automated it. We can use this for other things, for other questions that business owners want every single week and still have time leftover for those ad-hoc questions. Those one-off things that are really important, we’ll have time for that, too, and not miss the report. I think there’s just this strategy that you have to really always be thinking about of when is the right moment to maybe not necessarily ask for approval– to just do something and show the value. Hopefully, you have leaders who will support you in those decisions If it’s the right one and the right moment you can push some of the different tools that were not really supported in the beginning of my career here. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.posit.co LinkedIn: https://www.linkedin.com/company/posit-software Twitter: https://twitter.com/posit_pbc To join future data science hangouts, add to your calendar here: pos.it/dsh (All are welcome! We’d love to see you!)

With a little help from my friends: Tools for developing & deploying algorithms in the hospital

The Data Science and Advanced Analytics (DSAA) team at Unity Health Toronto has successfully deployed prediction models to the hospital. This would not have been possible without trial-and-error, open source tools, and help from our friends at Posit. In this talk, we will share the various tools and approaches used to develop and deploy an early warning system in the hospital. Speaker bio: Chloé Pou-Prom is a data scientist with the Data Science and Advanced Analytics (DSAA) team at Unity Health Toronto. The DSAA team uses high quality healthcare data in innovative ways to catalyze communities of data users and decision makers in making transformative changes that improve patient outcomes and healthcare system efficiency. Timestamps: 2:37 - Start of presentation 4:41 - Projects their team works on 7:39 - Team is split into 4 teams (data integration & governance, advanced analytics, product development, and support) 8:00 - Successful deployment example (CHARTwatch - early warning systems for detecting patient deterioration) 12:09 - How this was running before (one person’s laptop, no logging, no dev environment) 13:23 - Moving from before to now - What do you need for development? 13:40 - Connections to databases 16:25 - Reproducible environment 17:50 - Package based development 19:30 - Dev, staging, production environments 20:24 - What do you need for deployment? 30:21 - end of fire alarm break:) 33:07 - Authentication with Posit Connect (with hospitals Active Directory) 34:30 - Scheduling scripts on Posit Connect 35:35 - Receiving alerts when there’s downtime (internal package for monitoring applications) 38:07 - Using internally developed packages with Posit Package Manager 40:58 - Implementation plan 49:29 - Monitoring 54:25 - Summary of development and deployment tools needed 56:12 - Start of Q&A Helpful resources: ⬢ Slides: https://lnkd.in/ePEH5k6Z Tools for Development: ⬢ Database connections [https://lnkd.in/eFDHR4cF] ⬢ Reproducible environment that lets multiple people collaborate on a project: [Renv: https://lnkd.in/e_DHddeA] ⬢ Package-based development [Functions and utilities that you re-use: https://r-pkgs.org/] ⬢ development, staging, production environments [https://lnkd.in/ezczn2Zz] Tools for Deployment ⬢ Authentication: [Posit Connect: https://docs.posit.co/rsc/ ] ⬢ Downtime protocols ⬢ A secure way to download internally-developed packages [Posit Package Manager: https://docs.posit.co/rpm/] ⬢ Implementation plan ⬢ Monitoring [“Monitoring Model Performance”: https://lnkd.in/eSUgRYNM & “MLOps with vetiver in Python and R”: https://lnkd.in/efN4vcrg] About Data Science and Advanced Analytics at Unity Health Toronto: ⬢ https://unitynet.unity.local/departments-programs-services/corporate-services/data-science-and-advanced-analytics/ About CHARTwatch ⬢ “Preparing a Clinical Support Model for Silent Mode in General Internal Medicine”: https://lnkd.in/eJAWmYWk ⬢ “Implementing Machine Learning in Medicine”: https://lnkd.in/eFesPijY ⬢ “From Compute to Care: Lessons Learned from Deploying an Early Warning System into Clinical Practice”: https://lnkd.in/e-D7aDDf Want to join us at a future event? pos.it/community-events

Hannah Frick - “Censored: A tidymodels package for survival models”

10 November 2022. “Censored: A tidymodels package for survival models” Hannah Frick, Software Engineer and Statistician, RStudio. CANSSI Ontario Statistical Software Conference. Abstract: Survival analysis is an important field in modeling, and there are many R packages available that implement various models, from “classic” parametric models to boosted trees. While they cover a great variety of model types, they also come with considerable amounts of heterogeneity in syntax. The tidymodels framework is a collection of R packages for modeling and machine learning using tidyverse principles. It provides a consistent interface to a variety of modeling functions, along with tools for resampling, assessing performance, and hyperparameter tuning. The censored package now extends the model coverage of tidymodels’s parsnip package for survival analysis. It offers a tidymodels interface to parameteric survival models, (regularized) proportional hazards models and various tree-based models such as decision trees, boosted trees, and random forests for survival analysis. Additionally, it offers predictions of time to event, linear predictor, survival probability, or hazard in a tibble format consistent across different models

Hannah Frick

Posit Presents: R Around the World

From Kenya to Kentucky to outer space. Open source is changing the world. And so can you. Learn more at Posit.co

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

What is Posit Connect | Deploy All of Your R & Python Content

Posit Connect is our flagship professional product for data scientists that want to share their data products with other. Learn more at https://posit.co/products/enterprise/connect/ Whether you’re building a Shiny application, a Dash application, a Quarto report, or dashboard, you can host it on Posit Connect. Posit Connect supports all the languages that data scientists love, as well as the development environments they love to build in

Quarto Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Data Science in People Analytics | Led by Elizabeth Esarove, AT&T

People are the face, heart, and hands of a company. In people analytics, we analyze data to reveal actionable insights that provide evidence for decisions regarding employees, work, and business objectives. This talk will cover the use of data science for people analytics projects such as workforce planning, improving employee engagement, and retaining talent. Speaker bio: Elizabeth Esarove is a data scientist in People Analytics at AT&T. In her role, Elizabeth is part of a larger team focused on embedding data and analytics into the root of decision-making and transforming insights into actionable solutions that improve employee outcomes and drive business value. Timestamps: *Q&A timestamps listed further below 3:42 - Start of session 5:14 - What is People Analytics 6:26 - Opportunities for Data Science in People Analytics 7:10 - Using Predictive Models to Reduce Attrition 11:10 - Segmenting Your Population 18:55 - Communicating with Leaders 20:11 - Time Series Forecasting for Workforce Changes 24:41 - Analyzing Employee Survey Comments Helpful Resources Below: *more follow-up to come with a Q&A blog post in the works People Analytics Books Mentioned today: Handbook of Regression Modeling in People Analytics: with examples in R, Python and Julia by Keith McNulty https://lnkd.in/eBFgniFG Excellence in People Analytics: How to Use Workforce Data to Create Business Value by Jonathan Ferrar and David Green https://a.co/d/bJrMRuW People analytics books shared in a previous data science hangout: Predictive HR Analytics: Mastering the HR Metric: https://a.co/d/5Hx05mw Inclusalytics - How Diversity, Equity and Inclusion Leaders Use Data to Drive Their Work: https://lnkd.in/g48tdrMu Other links shared by Liz: Time Series Models Forecasting: Principles and Practice by Rob Hyndman and George Athanasopoulos https://otexts.com/fpp3/ Text Analytics Text Mining with R by Julia Silge & David Robinson https://lnkd.in/emawveZd Additional resources shared: R Gov Conference: https://lnkd.in/ePfN7jru (David Meza is presenting on the RStudio (Posit) Ecosystem as a Critical Part of NASA Analytics Capabilities) People analytics for getting to the moon | Data Science Hangout with David Meza, NASA: https://lnkd.in/eDirbgCF For LATAM and Spanish Speaking people, Sergio Garcia Mora shared the R4HR community which has developed lots of free access content: https://data-4hr.com/ John Kelly IV shared the Human Resources Science LinkedIn Group: https://lnkd.in/eEMpYAfk Adrian M. Pérez shared the People Analytics Handbook: https://lnkd.in/ecsWy-dA Data Science Hangout: pos.it/dsh All upcoming #Posit community events: pos.it/community-events Q&A Timestamps: *the following timestamps are approximate. 16:00 - What are the most important people analytics KPIs @ AT&T? Can you share how your team/HR acts on these predictions (for optimal policy) both experimentally and ethically? do you implement new policy in smaller groups? 23:00 - How have you validated the predictive models? Looking backwards, how precise were they? 25:00 - Do you work with your HRBPs to segment your population? 25:00 - What languages are you using to build your predictive models? 31:00 - Do you include demographic information (gender, race, age) in your models? 31:00 - Are your surveys anonymous? 32:00 - How would you get the ROI from HR attrition modeling? 34:00 - Are most data scientists from a Psychometrics background? 35:00 - Is there a kind of “critical mass” to apply People Analytics? (just for big companies?) 36:00 - Looking at positive / negative comments, do you quote verbatim comments in your reports? (e.g. “here is one of the very positive / very negative comments we received”) 37:00 - Do you use something like Snowflake to store and model your data? And do you deploy these models automatically or manually update them? 38:00 - R user here. How do you balance between people-ops focused analytics tools from outside vendors (often very expensive, but helpful) with custom in-house analytics (often time-consuming)? 41:00 - How much of your work is driven by HR leadership, by HR business leaders, or by the HR analytics team pushing modeling and insights to those groups? 42:00 - What was your journey into learning data science and getting into people analytics? 44:00 - Do you have a role in education business units? to improve their questions, etc.? 45:00 - What is the HR tech stack at AT&T? Does your team have a data engineer solely for people data since they’re more sensitive? 47:00 - How do you present your results? (an application, report, power point) and how important is it to learn other languages (javascript, css, sql)? If you were to start a people analytics team in a company (+1000), how do you start? 50:00 - Do you use an internal tool for surveys? Do you use thresholds to maintain anonymity? 53:00 - Does AT&T have remote workers? If so, does people analytics segment on remote vs hybrid vs on-site?

Julia Silge

RStudio is now Posit!

Our name changed. Our mission stayed the same. Everyone in the R community has contributed to the success of open source and the journey was made richer because of supporters like you. You have challenged and inspired us to create products that enable data scientists to generate knowledge and use that knowledge to create a lasting impact in their communities and organizations. Outside of the powerful tools we have created together, we are proud to be part of such a passionate, supportive, and diverse community of users from around the world. Over the last few years, we ruminated on how we can better enable and support the changing needs of our customers and community. The result is our rebrand to Posit. We are excited at the chance to bring what we all love so much about the R community to everyone. Finally, our new website posit.co is now live. Please check out our new home and let us know what you think!

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Open Source Chat - {gt} with Rich Iannone

Join Rich Iannone, maintainer of the {gt} package, as he takes questions from the community about the latest in {gt} v0.7.0, and building great looking data display tables with R. Key Resources: ⬡ Get started with {gt} - https://gt.rstudio.com Reach out: 38:48 - How do I ask Rich about {gt}, feature requests, bug reports, how to solve a problem via {gt}? Rich and the {gt} team would love to hear from you. ⬡ Feature requests & bug reports with GitHub Issues, https://github.com/rstudio/gt/issues ⬡ GitHub Discussions, https://github.com/rstudio/gt/discussions ⬡ Ask the community a question, https://community.rstudio.com/tag/gt ⬡ Follow {gt} on Twitter, feel free to reach out and ask questions, https://twitter.com/gt_package Timestamps Rich Iannone Introduction. 03:52 - Why {gt}? - What does {gt} bring to the table? Why so much effort into static, data display tables? 05:50 - Why open source? Why is {gt} open source and why have you dedicated your career to develop open source software? 08:30 - {gt} v0.7.0, Tell us about those new vector formatting functions in {gt}. Why did you include them? Could you show us some examples? {gt}’s vector formatting functions help you customize the styling, look and feel of your values. Converting the output values R gives you, and making them look exactly the way you want them to can be tricky. A lot of work was put into {gt} to give nice value formatting options. You can now access all these outside of a gt table; e.g. in text, in a plot, etc. 22:35 - Could you provide an example or two with the new styling function called opt_stylize()? What kinds of tables can you make with that? Can you extend that with your own tweaks? 28:15 - Can you make your own themes and share them? “How do I create my own custom theme for my table? A theme I can share with the rest of my organization?” 31:58 - What is the distinction between tab_options and the opt_* functions? Why would a function be in opt_* and not tab_options? 34:00 - sub_values() function, to find and replace certain values in your table. 36:50 - What is the current support for latex in {gt} at the moment? “Personally, I much prefer HTML, but for scientific publications, we are asked to provide a LaTeX file.” 42:50 - “In my work, I often produce A4 output in PDF, mainly with ggplot2 content. It would be nice to be able to combine ggplot + gt tables in a similar way {patchwork} works. Having the plot and the table next to it is very useful sometimes.” 44:30 - Interactive Tables with {gt}? 47:45 - “Any plans to make applying of same style to several columns easier? Unless I’m mistaken, the locations argument of tab_style requires one to specify an individual column. See here: https://gt.rstudio.com/reference/tab_style.html#examples." Yes, supply a vector of columns or use tidyselect functions. 49:15 - “Excel output with {gt}? Would be a huge improvement. I often have to produce tabular output that can be easily reused. Usually it means Excel tables. So far I have mainly done this with Python and openpyxl or PyWin32 (through COM). A simple solution in R would be great.” 50:20 - Support for additional output formats with {gt}? Excel, PowerPoint, etc.? 50:25 - {pointplank}, a package to methodically validate your data whether in the form of data frames or as database tables., https://rich-iannone.github.io/pointblank/. Check out the workshop materials at https://github.com/rich-iannone/pointblank-workshop 55:50 - “Are there ways to have grouped rows? I mean when repeated rows have same characters can we merge them to one?” 58:00 - “Is there an ability to add ‘battleship coordinates’ (e.g. column letters & row numbers) to a gt object? This is a standard for table across my org and I’ve been trying to figure out how to implement it.” 59:59 “Do you have suggestions or examples of building out & applying corporate formatting to gt tables (e.g. adding a company logo, company colors, etc.)?” 01:04:30 - “With PDF/LaTeX output for wide tables, it does not shrink the table.”

Rich Iannone

ggplot2 gt pointblank rstudio tidyselect RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Mine Çetinkaya-Rundel & Julia Stewart Lowndes | Hello Quarto: Share, Collaborate, Teach, Reimagine

What is Quarto? In this talk, in four acts, we will discuss how Quarto unifies and extends the R Markdown ecosystem. Through our work with NASA Openscapes, Duke University, and RStudio, we will share our experiences sharing, teaching, collaborating, and reimagining with Quarto. Full of demos with Quarto in RStudio and Jupyter and weaving together narrative and code, this talk will help you get the most out of Quarto regardless of your background with R Markdown, the computing language(s) you may use, and your tool of choice for authoring. https://quarto.org/ Session: Keynote

Mine Çetinkaya-Rundel

Quarto rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Joe Cheng | The Past and Future of Shiny | RStudio (2022)

Shiny is a package that makes it easy to create interactive web apps using R. It’s hard to believe, but it’s been ten whole years since the launch of Shiny! I’ll mark the occasion by sharing some stories about the creation of Shiny, the conditions that have made it a success over the years, and our plans for the near future. https://shiny.rstudio.com/ Session: Keynote

Joe Cheng

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

What’s New in {gt} 0.7.0?

gt 0.7.0 was just released. Rich Iannone, maintainer of gt, dives into the 7 new features added. For more details, ⬢ Read the blog post on gt 0.7 https://www.rstudio.com/blog/all-new-things-in-gt-0-7-0/. ⬢ Learn more about gt at https://gt.rstudio.com/. ⬢ Follow the gt twitter account, https://twitter.com/gt_package. 00:07 The new Word table output format,.docx output. 00:34 A whole new family of vector formatting functions (vec_fmt_*()) has been added. 01:03 Table presets/themes styling with the new opt_stylize() function. 01:50 The new tab_stub_indent() for superfine control over row label indentation (in the stub) 02:26 The new fmt_duration() function for formatting of time duration values. 03:32 An upgraded gtsave() that uses {webshot2},.png output looks better. 04:14 Accessibility enhancements for HTML table outputs

Rich Iannone

gt rstudio webshot2 RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

rstudio::conf(2022) All things R & RStudio

July 2022 in Washington DC

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

RStudio Pro Product Lightning Series Meetup ⚡️

Recording from our October 11th meetup: a lightning series with our RStudio Product Managers to hear what’s new, ask questions, and provide feedback. Lightning talks: 3:16 - Sharing Internal Packages with RStudio Package Manager 18:07 - Running RStudio workloads in the Cloud with Amazon SageMaker 40:02 - Content execution in Kubernetes with RStudio Connect Resources and links shared during the meetup: A Package Manager demo tutorial on GitHub: https://github.com/rstudio/package-manager-demo Remote API Quickstart: https://docs.rstudio.com/rspm/admin/getting-started/configuration/#quickstart-remote-cli Differences between RStudio Workbench and RStudio Workbench on SageMaker: https://docs.aws.amazon.com/sagemaker/latest/dg/rstudio.html#rstudio-differences RStudio Workbench release notes: https://www.rstudio.com/products/rstudio/release-notes/ Remote Content Execution with RStudio Connect and Kubernetes Conference talk: https://www.rstudio.com/conference/2022/talks/remote-content-execution-rstudio-connect/ Product Links: Package Manager, control and distribute packages throughout your organization: https://www.rstudio.com/products/package-manager/ Workbench, premiere development environment for data science professionals: https://www.rstudio.com/products/workbench/ Connect, easily share your insights: https://www.rstudio.com/products/connect/ Timestamps: 22:43 - Demo of RStudio Workbench on Amazon SageMaker Sharing Internal Packages with RStudio Package Manager | Presented by Joe Roberts Many know that RSPM can be used to mirror public packages from CRAN or PyPI, but it can also be used to share your private, internally developed packages. We’ll explore the latest features to make internal packages easier to deploy within your organization. Running RStudio workloads in the Cloud with Amazon SageMaker | Presented by James Blair RStudio Workbench on SageMaker enables users to “right-size” their environment for any given analysis. We’ll showcase how this flexibility enables users to effectively meet the workload demands of various analyses. Content execution in Kubernetes with RStudio Connect | Presented by Kelly O’Briant New and interesting ways to configure RStudio Connect: A quick introduction to off-host content execution in Kubernetes We’re looking forward to learning from you and hearing your feedback as well! Meetup recordings are always shared here: https://www.youtube.com/playlist?list=PL9HYL-VRX0oRKK9ByULWulAOO5jN70eXv

Data Science Hangout | Matthew Montero, Gen Re | Bringing a Vision to Life

We were joined at the Data Science Hangout by Matthew Montero, Head of Enterprise Data Services at Gen Re. At 4:23, Matthew started us off with sharing the time that focusing on a vision and defining the strategy around that really clicked for him “When I worked in pharmaceuticals, I had the pleasure of having a manager who was very focused on vision. So that’s when I really got a feel for once you have a vision and you can sell that vision, then you have the ability to build as big a team you need. You have to be able to back that up with either ways to improve the business or ways to even improve the organization as a whole. With the knowledge I gained through the experience of dealing with getting consultants, onboarding new hires, and defining the strategy for that vision, I moved on to my current company, Gen Re. It fostered from there, let me take this vision and further define the way I feel it should be at this company and build a full strategy around that. This was about figuring out, from day one when you get the data in ️ all the way to building applications that use that data ️ the self-service route of other people wanting to use that data for their analytical purposes. Let me build that strategy around there. Being able to both build out strategy and communicate that to executives, to different business units, the heads of different business units, moved me up relatively fast in a sense. It’s being very confident in that vision and in the strategy and even the principles around that as well. It’s really laying out the whole vision of how everything is going to be. You don’t go into the technicals yet of how you actually accomplish that, it’s just how you view what it all looks like. And that’s the first thing you got to sell because at least if you have a vision, you have something you can communicate to everyone. And then that’s when you break down now what’s that strategy to accomplish that vision because you can’t do all that in one day. It’s going to take some time. So you have to have individual steps or a strategy to accomplish that. When I was earlier in my career, my boss asked - “You know my vision. How are you going to make that vision happen?” Once I was given that, I sat down and actually really thought through that. That’s the moment it clicked in my head. Like, this is my next step. I want to be able to both define that vision, define the high level strategies to it, and then work with teams to actually make that happen. That was a couple years back and the time it really clicked for me.” ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio To join future data science hangouts, add to your calendar here: rstd.io/datasciencehangout (All are welcome! We’d love to see you!)

Model Monitors and Alerting at Scale with RStudio Connect | Adam Austin, Socure

Deploying a predictive model signals the end of one long journey and the start of another. Monitoring model performance is crucial for ensuring the long-term quality of your work. A monitor should provide insights into model inputs and output scores, and should send alerts when something goes wrong. However, as the number of deployed models increases or your customer base grows, the maintenance of your monitor portfolio becomes daunting. In this talk we’ll explore a solution for orchestrating monitor deployment and maintenance using RStudio Connect. I will show how applications of R Markdown, Shiny, and Plumber can unburden data scientists of time-consuming report upkeep by empowering end-users to deploy, update, and own their monitors. Timestamps: 2:01 - Start of presentation 3:43 - About Socure 6:12 - Model performance matters, deployment isn’t the end of the story 7:26 - What is a monitor? 8:55 - What is an alert? 11:00 - Monitor example 18:09 - Firing an alert from RStudio Connect 19:00 - Why monitor from RStudio Connect? 24:33 - How monitoring drives success at Socure 30:00 - Git-backed deployment in RStudio Connect 36:00 - Shiny app that their account managers see 46:00 - Architecture of a monitoring system 56:00 - Connect hot tip System-wide packages 57:00 - Why did we try Connect for monitoring 59:02 - Why do we keep using it for that:) Resources shared: Blastula package: https://github.com/rstudio/blastula connectapi package: https://github.com/rstudio/connectapi rsconnect package: https://rstudio.github.io/rsconnect/ Intro to APIs blog post: https://www.rstudio.com/blog/creating-apis-for-data-science-with-plumber/ Speaker Bio: Adam Austin is a senior data scientist and RStudio administrator at Socure, a leading provider of identity verification and fraud prevention services. His work focuses on data science enablement through tools, automation, and reporting

Plot Outputs in Shiny for Python || Winston Chang || RStudio

Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. You can try out Shiny for Python without installing a single thing… All in the browser. Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Winston Chang (@winston_chang)

Winston Chang

rstudio Shiny for Python Shiny shinylive RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Multiple Inputs in Shiny for Python || Winston Chang || RStudio

Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. You can try out Shiny for Python without installing a single thing… All in the browser. Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Winston Chang (@winston_chang)

Winston Chang

rstudio Shiny for Python Shiny shinylive RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Getting Started with Shiny for Python - in the browser! || Winston Chang || Posit

Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. You can try out Shiny for Python without installing a single thing… All in the browser. Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Winston Chang (@winston_chang)

Winston Chang

rstudio Shiny for Python Shiny shinylive RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Data Science Hangout | Mythili Krishnaraj, AXA XL | Platform Governance With a Shared Vision

We were joined by Mythili Krishnaraj, Global Delivery Lead - Pricing and Analytics Platform at AXA XL. Mythili provided a really helpful view into the IT/technology perspective and shared the steps to platform governance at AXA XL. At (24:14) Mythili shares some guidance on how their team manages 200 users: With onboarding of users, they are put into different AD Groups. Some users might only use Workbench and are not publishing content to Connect so they are put in different AD Groups. Something that was initially lacking for us is that we didn’t know who was successfully onboarding or when someone left and we needed to revoke access to reallocate the license. The governance was thought about as what can we do in terms of each steps? First, with the license: 1️⃣ How do you get them onboard? 2️⃣ How do you keep an audit trail of what they are doing? 3️⃣ How can we tell when they are leaving? One other consideration was - what if someone is not able to use the license for 5-6 months? We started putting a timeline around it. If they don’t use the platform for 6 months, we get in touch with them and find out whether they need the license. Otherwise, we take it back. The team has also started their own R training to support new users. We have helped them with a sort of Wiki to put together all of our R resources so that they know who to go to for a request, how to connect to a data source, etc. That sort of knowledge sharing was a requirement. In terms of governance, we have also segregated the environments now: we have separate development and production. In development, we also have staging and non-prod. When they are ready to publish a model, they come through a ticket. We go through it to see what they are deploying, which are the data sources, have they used any hard coded passwords, etc. There is also a validation framework which we are using. Are they using licensed libraries? Are they using proper packages? A few tips also shared at (17:06) for IT and data science teams to successfully work together: Help the IT team understand the value of what you’re doing. Maybe your eventual output takes a week, a month, or even six months. It’s very important for them to be able to understand that value of what you’re going to bring in. Explain to IT the flexibility that you need. Analytics and data science is all about the speed that comes with any platform. It’s always a two-way street. Let them know that you’re here to support the governance, security and stability as well. Say, “I’m going to be really supportive of your guardrails but at the same time I’m expecting XYZ from your team.” Collaboration is required. Each team knows how we work now and there are sometimes tradeoffs for both sides, but we are able to deal with that because we want to do what’s best for the business. This objective is the same both for technologists and the data science world. Where the conflict comes in is when there is no shared vision. To get that shared vision, both teams need to talk. This conversation also reminded me of the meetup with Gordon Shotwell at Socure on Creating Secure Systems for Growth: https://www.youtube.com/watch?v=UnLpB4IDpZU Follow-up blog post on that meetup above as well, “How Data Scientists and Security Teams Can Effectively Work Together” https://www.rstudio.com/blog/how-data-scientists-and-security-teams-can-work-together/ Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the weekly Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Beautiful reports and presentations with Quarto | Led by Tom Mock, RStudio

Quarto is a powerful tool for authoring reproducible computational documents in R, Python or Julia. Quarto can also help with sharing your results to business stakeholders across your company. This talk will provide an overview of Quarto’s implementation of revealjs for interactive presentations and HTML/PDF documents for static reports. Content website: rstd.io/quarto-reports Timestamps: 2:55 - Start of session 4:10 - Visual editor in RStudio 6:20 - Parameters to create different variations of a report 15:30 - Unified syntax across different output formats 18:01 - Pandoc fenced divs 20:10 - Tabsets 22:22 - Pandoc bracketed spans 24:30 - Footnotes 26:30 - Layout image inline with paragraphs / image into “gutter” column margin 29:23 - Hide all code 29:50 - Code tools (Fold code, source code) 34:12 - Code highlighting 37:03 - HTML Appearance 38:00 - Bootswatch themes 38:43 - PDF Articles 42:05 - Presentations (revealjs (HTML), PowerPoint (MS Office), beamer (LaTeX, PDF)) 45:06 - Creating slides 47:53 - Multiple columns 48:28 - Secret Tip (Alt + Click to Zoom in to a section) 49:24 - Absolute Position 51:04 - Presentation themes 52:44 - Footer/Logo 54:01 - Slide Background 57:01 - Custom classes 58:35 - End slide with helpful links (all shared here: rstd.io/quarto-reports) This meetup is Part 3 in our Quarto series: Part 1: Welcome to Quarto Workshop: https://www.youtube.com/watch?v=yvi5uXQMvu4 Part 2: Building a Blog with Quarto: https://youtu.be/CVcvXfRyfE0 For more about Quarto: quarto.org Resources discussed: Visual editor: https://quarto.org/docs/visual-editor/ Parameters: https://quarto.org/docs/computations/parameters.html Tabsets: https://quarto.org/docs/interactive/layout.html#tabset-panel Fenced Divs and Bracketed spans: https://quarto.org/docs/authoring/markdown-basics.html#divs-and-spans Footnotes: https://quarto.org/docs/authoring/footnotes-and-citations.html Figures and figure layouts: https://quarto.org/docs/authoring/figures.html#complex-layouts Code execution options: https://quarto.org/docs/computations/execution-options.html Code chunk format options: https://quarto.org/docs/reference/formats/html.html#code Code appearance: https://quarto.org/docs/output-formats/html-code.html#appearance Code highlighting light/dark: https://quarto.org/docs/output-formats/html-code.html#appearance Function links in code chunks with downlit: https://quarto.org/docs/output-formats/html-code.html#code-linking HTML Themes: https://quarto.org/docs/output-formats/html-themes.html PDF formatting options: https://quarto.org/docs/reference/formats/pdf.html#title-author PDF journal templates: https://quarto.org/docs/journals/templates.html Presentations: https://quarto.org/docs/presentations/index.html Revealjs Options: https://quarto.org/docs/presentations/revealjs/ Advanced Revealjs (absolute positioning, layout helpers like r-stack): https://quarto.org/docs/presentations/revealjs/advanced.html Revealjs themes and customizing: https://quarto.org/docs/presentations/revealjs/themes.html Revealjs footer & logo: https://quarto.org/docs/presentations/revealjs/index.html#footer-logo Inline span text formatting: Emil Hvitfeldt’s Slidecraft 101: Colors and Fonts, https://www.emilhvitfeldt.com/post/slidecraft-colors-fonts/ Meghan Hall’s Quarto Slides, https://meghan.rbind.io/blog/quarto-slides/ Andrew Heiss’ Quarto slides on APIs and webscraping with R, https://github.com/andrewheiss/2022-seacen Speaker bio: Thomas is the Customer Enablement Lead at RStudio, helping RStudio’s customers be as successful as possible. He is deeply involved in the global data science community, sharing tips on RStats Twitter (find him at @thomas_mock), as co-founder of TidyTuesday, a weekly Data Science learning challenge, and presenting on various Data Science topics on YouTube or at conferences. For upcoming meetups: rstd.io/community-events

Emil Hvitfeldt

Submitting Your Work to the Table Contest | 2022 Table Contest

Rich Iannone walks through how to submit a table to the 2022 Table Contest. He explains considerations for each field, and how to update & edit your entry afterwards. Learn more about the 2022 Table Contest at https://www.rstudio.com/blog/rstudio-table-contest-2022/

Rich Iannone

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

MLOps with vetiver in Python and R | Led by Julia Silge & Isabel Zimmerman

Many data scientists understand what goes into training a machine learning model, but creating a strategy to deploy and maintain that model can be daunting. In this meetup, learn what MLOps is, what principles can be used to create a practical MLOps strategy, and what kinds of tasks and components are involved. See how to get started with vetiver, a framework for MLOps tasks in R and Python that provides fluent tooling to version, deploy, and monitor your models. Blog Post with Q&A: https://www.rstudio.com/blog/vetiver-answering-your-questions/ For folks interested in seeing what data artifacts look like on Connect, we have these for R: ⬢ Versioned model object: https://colorado.rstudio.com/rsc/seattle-housing-pin/ ⬢ Deployed API: https://colorado.rstudio.com/rsc/seattle-housing/ ⬢ Monitoring dashboard: https://colorado.rstudio.com/rsc/seattle-housing-dashboard/ ⬢ Create a custom yardstick metric: https://juliasilge.com/blog/nyc-airbnb/ ⬢ End point used in the demo: https://colorado.rstudio.com/rsc/scooby Our team’s reading list (mentioned in the meetup) Books: ⬢ Designing Machine Learning Systems by Chip Huyen: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/ Articles: ⬢ “Machine Learning Operations (MLOps): Overview, Definition, and Architecture” by Kreuzberger et al: https://arxiv.org/abs/2205.02302 ⬢ “From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors” by Bayram et al: https://arxiv.org/abs/2203.11070 ⬢ “Towards Observability for Production Machine Learning Pipelines” by Shankar et al: https://arxiv.org/pdf/2108.13557.pdf ⬢ “The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction” by Breck et al: https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/aad9f93b86b7addfea4c419b9100c6cdd26cacea.pdf Web content: ⬢ How ML Breaks: A Decade of Outages for One Large ML Pipeline by Papasian and Underwood: https://www.youtube.com/watch?v=hBMHohkRgAA ⬢ MLOps Principles by INNOQ: https://ml-ops.org/content/mlops-principles ⬢ Google’s Practitioners Guide to MLOps by Salama et al: https://services.google.com/fh/files/misc/practitioners_guide_to_mlops_whitepaper.pdf ⬢ Gently Down the Stream by Mitch Seymour: https://www.gentlydownthe.stream/ Speaker bios: Julia Silge is a software engineer at RStudio focusing on open source MLOps tools, as well as an author and international keynote speaker. Julia loves making beautiful charts, Jane Austen, and her two cats. Isabel Zimmerman is also a software engineer on the open source team at RStudio, where she works on building MLOps frameworks. When she’s not geeking out over new data science techniques, she can be found hanging out with her dog or watching Marvel movies

Isabel Zimmerman, Julia Silge

Let’s Create a Quarto Doc & Share it on Quarto.Pub | Table Contest 2024

Rich Iannone creates a gt table with Quarto, and publishes it on quarto.pub. You’ll see how to set up your.qmd file in the RStuido IDE, preview your table, publishing it to Quarto Pub, some admin settings, and how to share your table made in Quarto to the world

Rich Iannone

gt Quarto RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Create & Publish a Quarto Blog on Quarto Pub in 100 Seconds | Quarto Pub

Thomas Mock, Quarto Product Manager, walks you through how to build a simple blog with Quarto and share it with the world on quarto.pub, all in less than two minutes. Quarto is the multi-language publishing system. It also allows you to publish executable code blocks to include R, Python, Julia, or Observable JS output in your blog posts (and many other formats). Quarto websites and blogs are particularly excellent ways to develop your technical skills and share your learnings with the world. Resources, ⬡ Creating a Quarto Blog, https://quarto.org/docs/websites/website-blog.html ⬡ Publishing to Quarto Pub, https://quarto.org/docs/publishing/quarto-pub.html ⬡ Customize your Quarto blog or Website. This example creates and deploys a simple Quarto blog template, but there are ways to customize and style your content. Isabella Velásquez walks through this in detail at the Sept 2022 meetup, https://youtu.be/CVcvXfRyfE0 ⬡ Learn more about Quarto at quarto.org. Requirements, To publish from the RStudio IDE, you’ll need to be working on a recent version of RStudio, v2022.07.1 or later. You may also work from Jupyter Labs, VS Code, or a notebook integrated with the Quarto CLI

Isabella Velásquez

cli Quarto rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Data Science Hangout | JD Long, RenaissanceRe | Empathy When Integrating with Other Tools

We were recently joined by JD Long, VP - Risk Management at RenaissanceRe. JD is a financial risk manager, analytical polyglot, and purveyor of colorful metaphors. As people have recently asked more about building communities, I’ll share this snippet here from 45:29 in his hangout. 🤝 I started the Chicago useR Group 13-14 years ago and the design pattern for that group was, I think, a really good one: 1️⃣ We had about four or five people who were going to get together, drink beer, and talk about R anyway, and what we effectively said was, “let’s get in the types of presentations the four or five of us would like to hear, and then invite other people.” And if they don’t show up, we don’t really care because we were going to drink beer and listen to this anyway because we’re interested in this. And that community grew wildly with me running it like as a benevolent dictator just getting the presentations I wanted to hear. 2️⃣ And then one of the things I observed is we were getting lots of new users of R. So I decided once a quarter we would have a beginner’s meetup– And at the beginner’s night, we would make sure we had two things: Topics appropriate for beginners: like grouping and summing, and getting environments set up or updating packages. All the normal stuff people have friction with. Focus on that. Then we would make sure we had–I would call it now like a learner salon or something. We had a handful of people who were more experienced who said, I will sit at a table and anybody that brings their computer with questions, I will answer. We would have really senior people with lots of experience there doing Q&A, and people could bring their own problems, literally eat pizza, drink beer, and look at code. Those beginner nights were super helpful. So anyway, when people ask about building communities, I recommend those two things. Resources shared in the chat: ► Esquisse package: https://github.com/dreamRs/esquisse ► Shinyuieditor: https://rstudio.github.io/shinyuieditor/ ► Shiny Developer Series episode 7 (talking about esquisse): https://shinydevseries.com/interview/ep007/ ► Another “bridge” tool for business analytics is radiant: https://radiant-rstats.github.io/docs/ ► Norm Conf: https://normconf.com/ ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio To join future data science hangouts, add to your calendar here: rstd.io/datasciencehangout (All are welcome! We’d love to see you!)

RStudio Sports Analytics Meetup: NFL Big Data Bowl 2022 Winners discuss the Math behind the Path

RStudio Sports Analytics Meetup: NFL Big Data Bowl 2022 Winners discuss the Math behind the Path Led by Robyn Ritchie, Brendan Kumagai, Ryker Moreau, Elijah Cavan Helpful Links: Kaggle: https://www.kaggle.com/code/robynritchie/punt-returns-using-the-math-to-find-the-path Github: https://github.com/ritchi12/punt_returns_using_the_math_to_find_the_path Optimal Path Generator Shiny app: https://rstd.io/meetup-shiny-app Related blog post, Tips for Getting Started With the NFL Big Data Bowl: https://www.rstudio.com/blog/tips-for-getting-started-with-the-nfl-big-data-bowl/ Timestamps: 3:02 - Start of presentation 4:07 - Meet the team 5:25 - Quick overview of 2022 NFL Big Data Bowl subject 7:00 - Finding the best path to the end zone 9:12 - Penalized Expected Arrival Time (PEAT) 13:10 - Take the PEAT to find the safest route (A-star algorithm) 15:11 - Comparing the optimal path to the observed path 18:30 - Key R functions for this project 23:37 - Optimal Path Generator Shiny app demo 33:38 - Comparing optimal paths (risk/reward) 35:44 - “Aiming for the side won’t provide” 37:54 - The precision of a good decision 38:38 - How do you have a good submission for the big data bowl 40:40 - Elements of a good big data bowl submission Talk Abstract: Simon Fraser University has been a force in the NFL’s Big Data Bowl for years. Focusing on punt returns, this team used tracking data to develop an algorithm to find the optimal path for a successful return, quantify player formations on the field and predict yards remaining on any frame of the return. This year’s winning team will discuss their entry, the R code behind the optimal path and advice for this year’s entrants. Speaker Bio: Robyn, Brendan, Ryker and Elijah are all graduate students in statistics at Simon Fraser University in Vancouver, Canada. They each have a passion for sports analytics and enjoy applying their stats knowledge to the game. Q&A Timestamps: *rough estimate:) 15:00 - Do you think being in a general Statistics graduate program was beneficial to your work? As opposed to more specific like “Data Science” or “Sports Analytics”? 19:00 - Did you calculate PEAT at just a single point in time (at catch), or at multiple points during the return? 30:00 - Is it possible to share the code for future reference? 43:00 - What were the biggest challenges when going through the data? 45:00 - In terms of all your data inputs, do you see this extending to performance data like expected speed, fatigue etc., perhaps from practice etc.? 46:00 - At any point during the project, did you feel like you had hit a cul-de-sac or impasse? 48:20 - How do you get started working with player tracking data? 56:00 - Sounds like you were running the code locally? Were there any specific roadblocks to offloading that to the cloud, or was that just not really necessary? Upcoming community events: rstd.io/community-events Feedback & Suggestions: rstd.io/meetup-feedback

Data Science Hangout | Tiger Tang, CARFAX | Quantifying the Hours Saved

We were joined by Tiger Tang, Manager, Data Science at CARFAX. Tiger (Chongtai) Tang is dedicated to building the Data Science team specializing in NLP and forecasting. He is a big fan of Shiny and he has a passion for the Data Science community. We recommend checking out Tiger’s 2022 RStudio Conference Talk as well, “Saving 1000 hours with RStudio:” https://www.rstudio.com/conference/2022/talks/saving-1000-hours-rstudio/ How do you sell RStudio in your workplace? ️ Build a work automation process How do you build an automation process? 1️⃣ Look at a typical report & identify all the manual steps 2️⃣ These manual steps can usually be tied into 3 portions: getting the data, wrangling/analysis/visualization, and communication 3️⃣ You can replace these portions with R code, with the help of various R packages What are the three types of automation? 1️⃣ Attended automation - reports that still need human involvement: use R code in RStudio 2️⃣ Unattended automation - don’t need human input, but need to happen at the same time: use RStudio + RStudio Connect 3️⃣ Hybrid - combination of the previous 2, human input will come from the stakeholder & they can kick off processes to get answers: use Shiny + RStudio Connect Ok, time to sell it to decision makers What are the benefits? Reproducibility Less human error Cost benefit (hours saved) Why weren’t they interested when you shared these? The benefits listed above are great for selling to an R user who is concerned about the day to day workflow, but not decision makers who are more concerned about ROI. Update your strategy in highlighting the benefits: Cost benefit (hours saved) - If we go through with this we might be able to save 1,000 hours per year Less human error Reproducibility (as a free add-on travel insurance) It’s still the same benefits, just in a slightly different order. Start with one that does not require too much context to understand. What do you need to do the actual automation? Understand the current process and document it. This includes understanding: ⬢ the business reasons ⬢ the occurrence (daily, weekly, ad-hoc) ⬢ how you will communicate ⬢ how often to update the process so it will not become obsolete ⬢ you are not always the original report owner, so you need to know when to stop and call for additional help. From this you will understand the complexity, impact, and stability & can help you decide which project to start with as well. Top 3 recommendations for automation: 1️⃣ Always start with components: For ex, if you have a process that involves: SQL, Excel, and Outlook - code them individually because the same team will need this again and you can reuse the code. 2️⃣ Test, Test, Test: Capture all the scenarios possible. 3️⃣ Be practical and stay on target: Not everything needs to be fully automated. It’s not about building something cool with R but building something impactful with R. Structure for you: 1️⃣ Identify tasks in your workplace 2️⃣ Build a proposal with the benefits that matter to decision makers in your workplace 3️⃣ Build a requirement doc that identifies the right task to start with 4️⃣ Code by component and do plenty of tasks, while staying on target 5️⃣ Share the progress from time to time Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Posit Meetup | Ryan Derickson, VA | Collecting & Acting on Employee Engagement with Shiny

Led by Ryan Derickson, Veterans Affairs Shiny Executive Dashboards in VA 1:46 - Start of presentation 3:34 - The data used for the application 5:18 - Executives need to know how to help and what to watch out for (AES Exec Dashboard briefs ~2,000 people) 9:08 - Shiny app (Exec dashboard) demo 12:20 - Storytelling within the application 19:22 - Q&A: Is the url available? This is an internal app for VA behind our firewall, so external users aren’t able to access 19:33 - Q&A: My employer would like their data source reference daily, is Shiny the recommended method? A: We have large single efforts (annual survey) rather than continuously updated data, but I’ve seen really effective use cases of that. It depends a lot on how the data is stored and that you can access it through API or database. 20:24 - Q&A: Is there any concern from IT regarding open source tools? A: There is inertia to overcome. You need executive sponsorship to be able to say why the ultimate outcome matters. IT cares about security and maintainability, not the details of the app. A leader who has sway with IT leadership can help them in seeing the value. In VA, there is a really detailed process to go through to get on our federal Azure space, which is slow but you do get there eventually. 21:51 - Q&A: What has been your experience with R and VA? 23:22 - Q&A: Are there limitations on the packages used? 23:44 - Employee engagement & burnout communicated through the app 29:01 - Q&A: How many people are using the app at one time? 30:15 - Q&A: Is the UI done with bslib or javascript and css? 30:49 - Q&A: Where can we learn more about the click and drill-down interface function? 41:20 - Q&A: How long did it take to get the app up and running? 43:22 - Q&A: What was the process for designing the UI? 45:38 - Q&A: What is the return on investment for the VA? 47:00 - Q&A: What would you recommend for learning Shiny? 50:27 - Q&A: For other public sector employees making the case for open source, is there other advice you have? Abstract: Data analysis and visualization is becoming increasingly accessible, yet the difficulty in translating data into action often remains persistent. VA has adopted an integrated approach to collecting & acting on employee engagement data, with a (robust) Shiny app as the centerpiece for the executive-focused portion of that approach. This talk will cover that approach, demonstrate the app, and discuss organizational and service improvements that have followed. Speaker Bio: Ryan Derickson leads VA’s quantitative approach to improving employee engagement, primarily through feedback and related outcome data

Communicating the value of data science | Led by Merav Yuravlivker

Communicating the value of data science Led by Merav Yuravlivker, CEO & Co-Founder of Data Society Four years ago, Merav sent out a small survey to better understand the challenges of data professionals. It quickly spread and hundreds of responses poured in from Frustrated Data Scientists, who didn’t feel understood or have the support they needed. From that, Data Society released the Data Science Communicator Toolkit to help alleviate their pain points. Now, they’ve relaunched the survey to see how they’re doing today - what’s better, what’s worse, and what’s next. Merav will release the preliminary results of the updated survey and talk through how data professionals today can bridge the communication gap to foster better insights and increase their impact. Survey link: https://bit.ly/3pxrejT Data Science Communicator Toolkit: https://www.rstudio.com/champion/business-case (under the section that says build your case with a presentation) Speaker bio: Merav Yuravlivker is the CEO and co-founder of Data Society. She and her team train up thousands of professionals every year across Fortune 500 companies, large organizations, and federal agencies to help them use data better and solve tough challenges. Merav is passionate about delivering the impactful combination of education and data to transform how industries run today. What is a Champion Chat? The champion chat is an informal meetup where we can ask questions to the community and share best practices with each other for advocating for data science at your organization. During these calls, we may also feature a short presentation from a community member who has championed data science and has a few tips to share with us all. *Please note, any presentation portion of this meetup would be recorded, but the open discussion/ Q&A will not. Who? All are welcome - no matter your role/industry/experience No need to register for anything It’s always okay to join for part of a session You can just listen-in if you want You can ask anonymous questions too! Helpful info on advocating for data science: rstudio.com/champion

Data Science Hangout | Ivonne Carrillo Domínguez, Bixal | Transitioning to data engineering

We were joined by Ivonne Carrillo Domínguez, Data Engineering Manager at Bixal. Ivonne is passionate about storytelling and empowering data professionals to jump to the cloud. Here’s a snippet (55:00) including a few thoughts that both Ivonne and Brittany shared at the hangout with regards to some people being overlooked for data engineering roles based on their experience: When I am hiring people for a data engineer position - I focus more on the programming skills. If you are a good engineer, you will learn Spark or MapReduce in time. You will end up using certain tools, but in my opinion I wouldn’t say no to someone because of a specific technology. If you find a good candidate, you can teach them or mentor them in specific frameworks. When I started my first job as a data engineer, I didn’t know about big data or Spark. I had a lot of experience with Java and that’s why they hired me. I think knowing how to program is more important - and that’s the philosophy I use. I think what they are looking for when they ask about certain tools is if you have experience with concurrency or parallel jobs. Sometimes you need to think a bit differently with distributed computing frameworks, but if you start playing around and reading about it, it will also help give you an opportunity to land a job. Sometimes when you’re working through a third party like a hiring recruiter, they may not know all of the things that are needed for the role. They’re given the job description and they may not know “I really need someone who has this skill, but if they don’t have this particular one - I’m willing to train on that.” The recruiter might not know that information and this may even change depending on how long the role is open for. What’s best for the candidate in that case is to get as close to the actual hiring manager as possible. Give them your resume and let them know your experience because sometimes they’ll look at your resume and would absolutely love to give you an interview, where the recruiter might say, “oh, you don’t meet X percent of the job description, therefore I’m not going to pass it on.” This is one of the disconnects between having a third party do that interfacing. Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | Adam Bly, System | Decentralizing decision making

We were joined by Adam Bly, Founder & CEO at System. Adam asks, why haven’t we seen it all connected yet? Primitives for leaders: (12:12) Adam shared how he learned from Daniel Ek, the founder of Spotify - the importance of decentralizing as much of the decision making as possible to increase speed, autonomy, and ownership regardless of the size of a team. This puts a great deal of emphasis on the primitives that you feel are important for every leader in the company and then for their teams to really embrace that, understand and make sure people really get it. This empowers teams to be agile, to be squads, and go off and do their thing. We put a great deal of emphasis at System on a set of first principles that govern how we think about the product and technology, and then tensions that shape any major decision. We put a great deal of emphasis on our values and talk a lot about those and use those as primitives when we’re making product decisions or technology decisions. We also put a great deal of emphasis on the use of data broadly in the company to inform our decision making. By really equipping everybody with those three things that we talk about from day one onward through professional development, 1:1s, and everything at System, it allows for a high degree of ownership, autonomy, and distributed decision making around the company. Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Posit Meetup | Afshin Mashadi-Hossein, Bristol Myers Squibb | Framework for Data Collaboration

Led by Afshin Mashadi-Hossein, Sr Principal Scientist at Bristol Myers Squibb Github link: https://github.com/amashadihossein/daapr Other pharma use cases: rstudio.com/champion/life-science RStudio for Clinical Reporting: rstudio.com/solutions/pharma Chat with RStudio: rstd.io/chat-with-rstudio Abstract: For data science teams, data preparation takes substantial investment of time, data science expertise and subject matter proficiency. However, as the name implies, data preparation is typically viewed merely as a means to an end, encouraging creation of expensive but often single-use and fragile elements in data analysis workflows. Rather than seeing data preparation as an obstacle to be removed, we propose a framework that recognizes the time and expertise invested in data preparation and seeks to maximize the value that can be derived from it. Viewing analysis-ready data as a multi-purpose, modularly built product that should lend itself to collaborative development and maintenance, the framework of Data-as-a-Product (DaaP) aims to remove barriers to version tracking and collaborative data development and maintenance. Specifically, the framework, which is entirely implemented in R, enables joint code and data versioning based on git, standardizes metadata capture, tracks R packages used, and encourages best practices such as adherence to functional programming and use of data testing. Collectively, the patterns established by the DaaP framework can help data science teams transition from developing expensive, single-use “wrangled” datasets to building maintainable, version-controlled, and extendable data products that could serve as reliable components of their data analyses workflows. Bio: Afshin is a data scientist who is passionate about putting engineering and computational tools to work to realize the potential of biomedical data in service to human health

BioC 2022 - Hello, Quarto!

Mine Çetinkaya-Rundel, PhD., Professor of the Practice at Duke University, Data Scientist and Professional Educator at RStudio, Inc., gives her keynote presentation at the Bioconductor Conference 2022. Dr. Cetinkaya presents Quarto, an open-source scientific and technical publishing system built on Pandoc. Dr. Mine began her presentation by introducing Quarto and her personal experience using and teaching it to others. She then continued by giving an overview of the R Markdown ecosystem which includes packages like xaringa, Distill, Blogdown, Rmarkdown, and more. She explained the use of Quarto with these packages along with some other Quarto highlights. Afterward, Dr. Mine gave her first demo of Quarto which included setting up and sharing some handy features. Dr. Mine also gives another demonstration on publishing with Quarto and an overview of the features of collaboration. Dr. Mine then gives a second demonstration of collaborating and teaching with Quarto. To wrap up the presentation, Dr. Mine shared about reimagining open source and the work done by Openscapes and their mission for open practices accelerating data-driven solutions, and increasing diversity, equity, inclusion, and belonging in science. The presentation ended with a questions and answers session from the audience. Main Sections 0:00 Introduction 4:29 Quarto! 6:12 Share 10:57 The R Markdown ecosystem 11:59 Quarto highlights 13:40 Demo of Quarto 21:40 Demo Quarto Publishing 22:58 Quarto rundown 25:23 Collaborate 28:43 Demo 2 34:07 Teaching with Quarto 40:39 Reimagine 44:03 Q&A and Resources More Resources Bioconductor Conference Site: https://bioc2022.bioconductor.org/ BioC2022 Github: https://github.com/Bioconductor/BioC2022 Main Site: https://www.r-consortium.org/ News: https://www.r-consortium.org/news Twitter: https://twitter.com/Rconsortium LinkedIn: https://www.linkedin.com/company/r-consortium

Mine Çetinkaya-Rundel

Intro to Functional Data Analysis - Part 2 | Matthew Malloure, Dow Chemical

RStudio Meetup: Functional Data Analysis (Part 2) Led by Matthew Malloure, Dow Chemical Link to slides: https://github.com/MatthewMalloure/RStudioMeetup_FDA Intro to Functional Data Analysis (Part 1) https://youtu.be/nA9fVOCD8yM Timestamps: 3:00 - Start of presentation 7:50 - Recap, what is functional data analysis (FDA)? 12:00 - Why do we need FDA? 16:40 - Initial step in FDA applications - smoothing 22:21 - What made you first interested in FDA? 25:38 - What is your decision framework for which basis function to choose? 27:50 - Screening additives compared to control 41:27 - Specific FDA problems can pop up at work and I’m not sure which analysis is right. Is there a recommended resources for selecting approaches? 44:40 - Can you define “functional” as it is used in the FDA context? 49:30 - Functional regression 58:50 - Alternative modeling approaches Abstract: The primary purpose of this presentation is to continue the introduction of functional data analysis methods that was kicked-off during the March RStudio Energy Meetup by Santiago Rodriguez. During that session’s Q&A, two specific methods were frequently mentioned: Functional Principal Components Analysis (FPCA) and Functional Regression. In this talk, both methods are introduced in more detail, applied to a simulated example from the chemical industry, and compared to their univariate/multivariate analogues. Though no code will be shown in the presentation, commented R code used to produce all data, analyses, and figures will be provided. Speaker Bio: Matt is an associate research data scientist supporting new product development within the Packaging and Specialty Plastics business at The Dow Chemical Company. His specialty areas include functional data analysis, Bayesian hypothesis testing, computational statistics, and experimental design. Prior to joining Dow he earned a BS in Statistics and MS in Biostatistics at Grand Valley State University and a PhD in Statistics from Texas A&M University

Data Science Hangout | Lindsey Dietz, Federal Reserve Bank | Focus on the impact of the output

We were joined by Lindsey Dietz, Stress Testing Production Function Lead at the Federal Reserve Bank of Minneapolis. Lindsey leads a team of quants & data scientists implementing and analyzing stress testing models in the Federal Reserve System. She loves diving into the nitty gritty of data and using R code to better understand the statistical dynamics we observe in the banking sector. Loved kicking off the session with tips for communicating technical results to the business. ⬢ It’s key to think of pretty much every audience as a non-technical audience, even when you have people you know are technical. It’s best not to assume that your audience ever knows the things you know because it’s unlikely that anyone will be as deeply immersed in a problem as you are. ⬢ Focus on the impact of the output first. I like to tell my team that what you might think is the smallest piece of work, someone else is going to think that is magic. I was actually just telling someone this earlier today. They were talking about the coding they had done and I said, “you know, the thing that is going to really impress people is that you generated this automated piece of documentation from your code basically for free.” ⬢ I remember someone at the RStudio Conference talking about “minimizing time to magic” and that is really the impact driver for most people. ⬢ Focus on the impact in your regular words and remove the jargon. ⬢ Trust yourself that you’ve done all the checking, the assumptions, and the technical details. Write that down for yourself and have a good appendix or technical paper to go with your work. ⬢ Non-technical audiences may ask you questions that you didn’t even think about that are really helpful in being proactive for future analysis. ⬢ Practice your translation skills, even if you know they’re not going to understand your world and you’re not necessarily going to understand theirs. For decision makers - bottom lines are important, right? How does this save us time, how does it save us money? These are key concepts that you should keep in mind for non-technical spaces. Resources shared: Stress Testing Publications: https://lnkd.in/egXfajBF Info on Stress Testing data submitted by banks: https://lnkd.in/eqh5AckU Where to download some useful public bank data: https://lnkd.in/eRiceXaU Upcoming R Ladies meetups around the world! https://lnkd.in/dzcpRT-D Upcoming R user groups around the world as well! https://lnkd.in/esNku4Ei Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Welcome to Quarto Workshop! | Led by Tom Mock, RStudio

Welcome to Quarto 2-hour Workshop | Led by Tom Mock, RStudio Content website: https://jthomasmock.github.io/quarto-2hr-webinar/ FULL Workshop Materials (this was from a 2-day workshop): rstd.io/get-started-quarto Other upcoming live events: rstd.io/community-events Double-check: Are you on the latest version of RStudio i.e. v2022.07.1 or later? Packages used: tidyverse, gt, gtExtras, reactable, ggiraph, here, quarto, rmarkdown, gtsummary, palmerpenguins, fs, skimr ️ Pre-built RStudio Cloud with workshop materials already installed: https://rstudio.cloud/content/4332583 For follow-up questions, please use: community.rstudio.com/tag/quarto Timestamps: 7:16 - What is Quarto? 8:28 - How does R Markdown work? 9:40: Quarto, more than just knitr 13:56 - Quarto can support htmlwidgets in R and Jupyter widgets for Python/Julia 14:18 - Native support for Observable Javascript 19:28 - Quarto in your own workspace (Jupyter Lab, VSCode, RStudio) 20:26 - RStudio Visual Editor mode 23:30 - VS Code YAML 26:02 - Quarto for collaboration 26:55 - How do you publish Quarto? (Quarto Pub, GitHub Pages, RStudio Connect, Netlify) 28:44 - What about Data Science at Work? 29:59 - Formats baked into Quarto (basic formats, beamer, ppt, html slides, advanced layout, cross references, websites, blogs, books, interactivity) 32:13 - What to do with my existing.Rmd or.ipynb? 33:16 - Why Quarto, instead of R Markdown? 40:50 - Text Formatting 41:30 - Headings 41:51 - Code (also merging R and Python in one document) 43:29 - What about the CLI? 44:55 - Navigating in the terminal 57:56 - PART 2: Authoring Quarto 1:00:22 - Output options 1:04:46 - Quarto workflow 1:12:06 - Quarto YAML intelligence 1:13:20 - Divs and Spans 1:22:13 - Figure layout 1:34:40 - Code chunk options 1:41:00 - Quarto and R Markdown (converting R Markdown to Quarto) This 2-hour virtual session is designed for those who have no or little prior experience with R Markdown and who want to learn Quarto. Want to get started with Quarto? Install RStudio v2022.07.1 from https://www.rstudio.com/products/rstudio/download/#download - this will come with a working version of Quarto! Webinar materials/slides: https://jthomasmock.github.io/quarto-2hr-webinar/ Workshop materials on RStudio Cloud: https://rstudio.cloud/content/4332583 What is Quarto? Quarto is the next generation of R Markdown for publishing, including dynamic and static documents and multi-lingual programming language support. With Quarto you can create documents, books, presentations, blogs or other online resources. Should I take this? As with all the community meetups, everyone is welcome. This will be especially interesting to you if you have experience programming in R and want to learn how to take advantage of Quarto for literate data science programming in academia, science, and industry. This workshop will be appropriate for attendees who answer yes to these questions: Have you programmed in R and want to better encapsulate your code, documentation, and outputs in a cohesive “data product”? Do you want to learn about the next generation of R Markdown for data science? Do you want to have a better interactive experience when writing technical or scientific documents with literate programming? For more info on Quarto: quarto.org

Reproducible Publications with Julia and Quarto | J.J. Allaire | JuliaCon 2022

Quarto is an open-source scientific and technical publishing system that builds on standard markdown with features essential for scientific communication. The system has support for reproducible embedded computations, equations, citations, crossrefs, figure panels, callouts, advanced layout, and more. In this talk we’ll explore the use of Quarto with Julia, describing both integration with IJulia and the Julia VS Code extension, as well as areas for future improvement and exploration. For more info on the Julia Programming Language, follow us on Twitter: https://twitter.com/JuliaLanguage and consider sponsoring us on GitHub: https://github.com/sponsors/JuliaLang Resources Slides of the presentation: https://jjallaire.github.io/quarto-juliacon-2022/ Contents 00:00 Welcome 00:22 Overview 01:11 What is Quarto? 06:20 Simple Example 07:28 Code Cells 08:11 Rendering Pipeline 09:04 Creating Citations 09:41 Creating Cross References 10:50 Callouts 11:01 Advanced Page Layout 11:54 Diagrams 12:08 Output Formats 15:54 Quarto and Julia - Prerequisites 16:33 Basic Workflow 17:28 IJulia 18:35 Performance Considerations 21:04 Why IJulia? 22:14 Tools - VS Code 23:18 Tools - Text Editors 23:49 Thank You S/O to https://github.com/nelsonpray for the video timestamps! Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/JuliaCommunity/YouTubeVideoTimestamps Interested in improving the auto generated captions? Get involved here: https://github.com/JuliaCommunity/YouTubeVideoSubtitles

Wrangling data for a Shiny app in Python || Michael Chow || Posit

Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Michael Chow (@chowthedog) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Michael Chow

rstudio Shiny for Python Shiny shinylive RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Rstats Open Source Oss Reticulate Pyshiny Shiny for Python Jupyter Michael Chow Tony Pelleriti Posit

Shinywidgets - An Overview || Carson Sievert || RStudio

Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. Shinywidgets lets you use ipywidgets in Shiny for Python applications. We called it ipyShiny during development, but we’re launching as Shinywidgets! Learn more about how to integrate them into your Shiny for Python apps.. Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Carson Sievert (@cpsievert) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Carson Sievert

rstudio Shiny for Python Shiny shinylive RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Gt Lazy Evaluation Tidymodels Statistics Debugging Rstats Open Source Oss Reticulate Shiny for Python Pyshiny Ipyshiny Carson Sievert Tony Pelleriti Posit

Shiny UI Editor Project Walkthrough || Nick Strayer || RStudio

The Shiny UI Editor is a dynamic drag-and drop interface to help you design beautiful Shiny apps. The Shiny UI Editor is a visual tool for building the UI portion of a Shiny application that generates clean and human-readable code. The goal of the Shiny UI Editor is to allow people to build the broad-level UI for their Shiny app without writing code. The editor is intended for those who may not be comfortable with the HTML-style code of Shiny’s UI functions or who simply don’t want to fiddle with sizes to get things laid out correctly. Learn more about the Shiny UI Editor here: https://rstudio.github.io/shinyuieditor/ And read up on GridLayout here: https://rstudio.github.io/gridlayout Content: Nick Strayer (@NicholasStrayer) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Nick Strayer

gridlayout rstudio Shiny shinyuieditor RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Rstats Open Source Oss Reticulate Drag and Drop Shiny Ui Shiny Ui Editor Nick Strayer Tony Pelleriti Posit

Shiny UI Editor Feature Tour || Nick Strayer || Posit (RStudio)

The Shiny UI Editor is a dynamic drag-and drop interface to help you design beautiful Shiny apps. The Shiny UI Editor is a visual tool for building the UI portion of a Shiny application that generates clean and human-readable code. The goal of the Shiny UI Editor is to allow people to build the broad-level UI for their Shiny app without writing code. The editor is intended for those who may not be comfortable with the HTML-style code of Shiny’s UI functions or who simply don’t want to fiddle with sizes to get things laid out correctly. Learn more about the Shiny UI Editor here: https://rstudio.github.io/shinyuieditor/ And read up on GridLayout here: https://rstudio.github.io/gridlayout Content: Nick Strayer (@NicholasStrayer) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Nick Strayer

gridlayout rstudio Shiny shinyuieditor RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Rstats Open Source Oss Reticulate Shiny Ui Shiny Ui Editor Drag and Drop Nick Strayer Tony Pelleriti Posit

Shiny Programming Practices || Joe Cheng || Posit

Have you ever wanted to sit down and talk with Joe Cheng, the creator of Shiny and CTO of Posit (RStudio) and ask him how he approaches programming? Look no further - we’ve got that conversation for you right here! Shiny makes it easy to build interactive web apps straight from R or Python. You can host standalone apps on a webpage or embed them in Markdown-style documents or build dashboards. You can also extend your Shiny apps with CSS themes, htmlwidgets, and JavaScript actions. Learn more about Shiny: https://shiny.rstudio.com/ Check out Shiny for Python: https://shiny.rstudio.com/py/ Explore our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Joe Cheng (@jcheng) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Joe Cheng

rstudio Shiny for Python Shiny shinylive RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Rstats Open Source Oss Reticulate Pyshiny Shiny for Python Posit Joe Cheng Tony Pelleriti

Hey Shiny Team, what are some of your biggest learnings from 2022? || Shiny Developers || RStudio

BIG THINGS happened on the Shiny team in 2022! Our team built out a new Shiny UI Editor, Shiny for Python, and Shiny for Python in the browser using WebAssembly. So we asked some of our Developers what their biggest learnings have been from building these products! Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Content: Winston Chang (@winston_chang), Carson Sievert (@cpsievert), Nick Strayer (), Michael Chow (@chowthedog) Producer: Jesse Mostipak (@kierisi) Video editing + motion design: Tony Pelleriti (@TonyPelleriti)

Carson Sievert, Michael Chow, Nick Strayer, Shiny Team, Winston Chang

rstudio Shiny for Python Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Tidymodels Statistics Debugging Rstats Open Source Oss Winston Chang Nick Strayer Carson Sievert Michael Chow Shiny for Python Shiny Ui Editor Posit Drag and Drop Pyshiny Tony Pelleriti

Hello, World! A Quick Tour of Shiny for Python || Carson Sievert || Posit

Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Carson Sievert (@cpsievert) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Carson Sievert

rstudio Shiny for Python Shiny shinylive RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Rstats Open Source Oss Reticulate Shiny for Python Getting Started Carson Sievert Tony Pelleriti Posit

Getting Started with {shinytest2} Part I || Example + basics || RStudio

00:00 Introduction 00:48 Overview of the demo Shiny app 03:00 Running record_test() 04:44 Results from record_test() 07:18 A note on.png files created during testing 08:52 Debugging with shinytest2 09:32 Using app$view() to open a visual representation of a headless browser Part 2 - Exporting values: https://youtu.be/7KLv6HdIxvU Part 3 - Using shiny.testmode: https://youtu.be/xDxa_mDwN04 Manually testing Shiny applications is often laborious, inconsistent, and doesn’t scale well. Whether you are developing new features, fixing bug(s), or simply upgrading dependencies on a serious app where mistakes have real consequences, it is critical to know when regressions are introduced. shinytest2 provides a streamlined toolkit for unit testing Shiny applications and seamlessly integrates with the popular testthat framework for unit testing R code. shinytest2 uses chromote to render applications in a headless Chrome browser. chromote allows for a live preview, better debugging tools, and/or simply using modern JavaScript/CSS. By simply recording your actions as code and extending them to test the more particular aspects of your application, it will result in fewer bugs and more confidence in future Shiny application development. Read up on shinytest2 here: https://rstudio.github.io/shinytest2/ Learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) Motion design and editing: Jesse Mostipak (@kierisi) Theme song: Brad PKL by Blue Dot Sessions ( https://app.sessions.blue/browse/track/113507 )

Barret Schloerke

chromote rstudio Shiny shinytest2 testthat RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Shinytest2 Barret Schloerke

Getting Started with {shinytest2} Part 3 || Using shiny.testmode in {shinytest2} || RStudio

00:00 Introduction 00:15 Testing production apps Part 1 - Getting started: https://youtu.be/SS1Na3c8lhk Part 2 - Exporting values: https://youtu.be/7KLv6HdIxvU Manually testing Shiny applications is often laborious, inconsistent, and doesn’t scale well. Whether you are developing new features, fixing bug(s), or simply upgrading dependencies on a serious app where mistakes have real consequences, it is critical to know when regressions are introduced. shinytest2 provides a streamlined toolkit for unit testing Shiny applications and seamlessly integrates with the popular testthat framework for unit testing R code. shinytest2 uses chromote to render applications in a headless Chrome browser. chromote allows for a live preview, better debugging tools, and/or simply using modern JavaScript/CSS. By simply recording your actions as code and extending them to test the more particular aspects of your application, it will result in fewer bugs and more confidence in future Shiny application development. Read up on shinytest2 here: https://rstudio.github.io/shinytest2/ Learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) Motion design and editing: Jesse Mostipak (@kierisi) Theme song: Brad PKL by Blue Dot Sessions ( https://app.sessions.blue/browse/track/113507 )

Barret Schloerke

chromote rstudio Shiny shinytest2 testthat RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Barret Schloerke Shinytest2

Getting Started with {shinytest2} Part 2 || Exporting values || RStudio

00:00 Introduction 00:29 Exporting reactives 03:28 Using exportTestValues() Part 1 - Getting started: https://youtu.be/SS1Na3c8lhk Part 3 - Using shiny.testmode: https://youtu.be/xDxa_mDwN04 Manually testing Shiny applications is often laborious, inconsistent, and doesn’t scale well. Whether you are developing new features, fixing bug(s), or simply upgrading dependencies on a serious app where mistakes have real consequences, it is critical to know when regressions are introduced. shinytest2 provides a streamlined toolkit for unit testing Shiny applications and seamlessly integrates with the popular testthat framework for unit testing R code. shinytest2 uses chromote to render applications in a headless Chrome browser. chromote allows for a live preview, better debugging tools, and/or simply using modern JavaScript/CSS. By simply recording your actions as code and extending them to test the more particular aspects of your application, it will result in fewer bugs and more confidence in future Shiny application development. Read up on shinytest2 here: https://rstudio.github.io/shinytest2/ Learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) Motion design and editing: Jesse Mostipak (@kierisi) Theme song: Brad PKL by Blue Dot Sessions ( https://app.sessions.blue/browse/track/113507 )

Barret Schloerke

chromote rstudio Shiny shinytest2 testthat RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Shinytest2 Barret Schloerke

Data visualization and plotting with Shiny for Python || Carson Sievert || RStudio

Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Carson Sievert (@cpsievert) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Carson Sievert

rstudio Shiny for Python Shiny shinylive RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Rstats Open Source Oss Reticulate Shiny for Python Pyshiny Carson Sievert Tony Pelleriti Posit

An Interview with Winston Chang: Building a Wordle App with Shiny for Python || RStudio

Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Winston Chang (@winston_chang) + Jesse Mostipak (@kierisi) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Winston Chang

rstudio Shiny for Python Shiny shinylive RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Rstats Open Source Oss Reticulate Shiny for Python Pyshiny Wordle Winston Chang Jesse Mostipak Tony Pelleriti Posit

A Beginner’s Guide to Shiny for Python || Winston Chang || Posit

Shiny makes it easy to build interactive web applications with the power of Python’s data and scientific stack. Learn more about Shiny for Python: https://shiny.rstudio.com/py/ Check out our interactive Shiny for Python examples: https://shinylive.io/py/examples/ Content: Winston Chang (@winston_chang) Producer: Jesse Mostipak (@kierisi) Editing and Motion Design: Tony Pelleriti (@TonyPelleriti)

Winston Chang

rstudio Shiny for Python Shiny shinylive RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Rstats Open Source Oss Reticulate Shiny for Python Pyshiny Wasm Posit Winston Chang Tony Pelleriti

Data Science Hangout | Rebecca Hadi, Lyn Health | Transparent & Visible Work

We were joined by Rebecca Hadi, Head of Data Science at Lyn Health. Rebecca is passionate about improving healthcare using data science and machine learning. At 19:11, Rebecca shared her approach to communication with transparent and visible work. Here are 4 things they do on their team to create transparent & visible work: Starting off with an open Data Science channel (different from their team channel) that’s open to the company. For example, if we do an analysis for the sales team - we will post it in that channel and tag the sales team. That way anyone in the company can see the work. We keep conversations in the channel, rather than DMs. A lot of times someone else may be interested in that work. We also have a Google sheet that we call our insight repository. We have that pinned to our channel so when we post analysis, we also put them in our insight repository with one row per analysis. For example: in-patient mortality model, a headline, the link to the PDF, and the GitHub link. The insight repository has been very well received as a place that someone can start when they have a question. I also host a weekly data science prioritization meeting and we use Monday as our project management tool. This has been really helpful because in the past I’ve had some challenges with balancing requests from our clinical team versus sales team and understanding the priority. It can be hard to be that person in the middle. It’s a lot more productive when we can all have that conversation live. The sales team might say, “I need this for XYZ meeting” and you might have to say, “Ok, you know that’s going to push off these additions to the clinical dashboard.” Then the clinical team can weigh in, “We’re okay with that because of XYZ reason” I’m also a really big advocate of a monthly all-employee call and I try to get air time there pretty frequently. Initially it was level setting on “this is what data science is and how my team operates.” That has been successful for setting this idea of, “you are going to ask me something and I’m going to ask you the why behind it.” This helped set the precedent ahead of time where I’m not challenging an idea, but it will help my team produce better work if we understand why you care about this and how you are going to use it because that gives us context in decisions we need to make throughout the process. A few resources shared too: Javier shared: A resource that I just came across last week (def not something I do daily). if you’re wanting to collect / manipulate JSON data with R, Tom Mock has a super-easy-to-follow writeup with several different approaches: https://lnkd.in/gwGxJpcW Luke shared: Data is Plural newsletter is a decent off-the-beaten-path dredger of data sources: https://lnkd.in/grRGqQRA Rebecca shared this book on management, The Hands-Off Manager: https://lnkd.in/gg2TU2mB Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | Alec Campanini, Walmart | Using Shiny to make business decisions

We were joined by Alec Campanini, Senior Manager, Merchandising Operations Omnichannel Business Analytics at Walmart. Alec loves to standardize and scale data in a targeted and meaningful way without sacrificing speed of development. (5:12) - Alec shared an awesome example of the way their team is using Shiny at Walmart: Their team’s main scope is “rest of market” - trying to figure out every item that exists in the rest of the market, no matter if it’s in the store, online, through TikTok trends, social listening, whatever it may be. We have a lot of market share data, sales information, pricing over time, and bestseller trends that are popping up. We do a lot to find out where merchants would be lacking inside of a brand. Today I own a Shiny app (which has been alive for about a year and a half now) that is pushed to all merchandising. We have about 12,000 users on it today across all of the merchants and merch-ops. What does it do? Starting off it was insights on 28-30 different retailers in the e-commerce space. With so many items, there are a lot of different contracted data sets coming in. The biggest thing is normalizing all of those to the Walmart hierarchy. We need to have some way to tell a merchant that something from the rest of the market on a different website makes sense for them to look at. Our Shiny app and workflow allows us to quickly identify what percentage we have of a certain brand and drill down into things like: What are the top items being sold? Do I want stable items or trending items? Do I want something that I’ve never seen before? Do I already have the item but need to increase what I’m giving the customer? Do we need to lower the price? Is it shipping too slow? Maybe Amazon has 10 images for the item and we only have 5? An example of this Shiny app helping make business decisions: We released a line of new golf tech products for Father’s Day that came from the Shiny app We had a lot of the sports & fitness merchants find out that we sell a lot more golf technology rather than golf balls online. These are merchants that used to be in the store, but they got into the e-commerce space as a new merchant. They think the trends are going to be the same in-store as online, but they found a different trend inside of our app and were able to launch a whole slew of things this past week, which was really cool. Other timestamps: When making the case for code-first data visualizations over BI tools (25:58) Packages shared in the chat: bs4Dash: https://lnkd.in/gPUBDW72 Rhandsontable: https://lnkd.in/gtYedxzY Bigrquery: https://lnkd.in/gtPM6Pn2" Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | David Meza, NASA | People analytics for getting to the moon

We were joined by David Meza, AIML R&D Lead, People Analytics at NASA. We had a great chat about people analytics for getting to the moon and beyond Here are a few snippets: What areas do you focus on in people analytics? (5:19) A lot of our focus is around natural language processing, graph algorithms, and graph databases. We create a knowledge graph & apply various algorithms to be able to deliver answers. An example of this is around our skills and competency capabilities. Trying to get back to the moon and onto Mars, we need to identify the various skills and competencies that we have in our individuals. From a human perspective, that’s very labor intensive. We’re trying to use some of the concepts I’ve mentioned to be able to extract NASA-specific competencies from individuals or people applying for jobs to see how they align to our workforce. We’re starting to develop pipelines and methods to say: • We’ve got gaps here • Or here’s your career path • Here’s your training path • Here are the number of individuals we have that meet 70% of our skills and we need to upscale them to the next 30% People analytics can be broken into so many different areas: • Attrition • Time to hire • Recruitment capabilities • Learning/ development With regards to skills for people analytics: • NLP is a good tool to have because a lot of the data we have is unstructured. We need to be able to pull from that. • Understanding various classification models, how you’re grouping individuals & understanding various metrics within the HR area. How much of people analytics is data science and predictive modelings versus reports and business intelligence? Right now everybody does a little bit of everything When I first got here, they were primarily focused on reporting and creating some type of visualization Over the last couple of years, we’ve been trying to break into a group that focus on reports in general and a group looking more at the data analytics & modeling, creating the various algorithms that we can utilize for extracting skills and competencies or looking at our attrition models, for example. Ideally, I would break it down to 5-6 visualizers, 10 data scientists and 4-5 data engineers or architects. Resources shared: Joe shard an example of Shiny at NASA: https://lnkd.in/gRJjFX6B David shared Predictive HR Analytics: https://lnkd.in/gPPjjzPZ David shared Inclusalytics: https://lnkd.in/g48tdrMu Rachael shared the rstudio::conf workshop for admins: https://lnkd.in/eB9FZt-c Rachael shared the RStudio workshop for people analytics: https://lnkd.in/e4DdhFAV George shared an article on writing a resume for AI: https://lnkd.in/g3cF_vtD Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Programming Games with Shiny || Roll the Dice: with Quosures! || RStudio

00:00 Introduction 03:44 The pain of copy + paste 07:28 Going on a helper function adventure! 18:09 Ready for rlang 28:17!! + enquo() 37:57 Benefits of the rlang approach 38:46 Embracing the embrace operator 41:33 Visualizing what’s happening using reactlog You’ve most likely used Shiny to build a web app that displays data, but you can also use Shiny to build games! In this video series, Jesse and Barret pair program simply games in Shiny as a way to uncover and explore new features. Read up on the embrace operator here: https://rlang.r-lib.org/reference/embrace-operator.html Learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) and Jesse Mostipak (@kierisi) Animation, motion design, and editing: Jesse Mostipak (@kierisi) Theme song: Hakodate Line by Blue Dot Sessions ( https://app.sessions.blue/browse/track/111291" )

Barret Schloerke

reactlog rlang rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Viz Ggplot Technology Coding Connect Shiny Package Manager CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Quosures Embrace Metaprogramming Tidy Evaluation Reactlog Rlang Jesse Mostipak Barret Schloerke

Posit Meetup | Jake Riley, Children’s Hospital of Philadelphia | Translating Facts to Insights

RStudio Healthcare Meetup: Translating facts into insights at Children’s Hospital of Philadelphia Led by Jake Riley, data analyst at The Children’s Hospital of Philadelphia Abstract: {headliner} is a new R package to add dynamic, insightful text to plots and reports. {headliner} generates useful talking points that users can string together using {glue} syntax. This makes it easy to write an informative sentences without adding a lot of technical debt to a project. Learn how to get started with {headliner} and ways we have used it at The Children’s Hospital of Philadelphia. Speaker Bio: Jake Riley is a data analyst at The Children’s Hospital of Philadelphia. He is the author of several R packages related to data visualization and automated exploratory analysis. You can find his published work [simplecolors] and [shinyobjects] on CRAN with more packages on the way. Timestamps: 0:49 - Start of talk 1:25 - Dashboards focused on facts vs. insights 2:56 - What’s a good title for a chart? 5:09 - Intro to headliner package 7:41- using glue() under the hood 14:04 - helpers for working with data frames: compare_conditions() 18:41 - using ggtext 21:27 - example using pixar_films 23:40 - how they’ve used it at CHOP 28:05 - Next steps for headliner package 29:32 - Start of Q&A session Questions: 29:32 - Can you use any package you want in your organization? 31:13 - How do you load previous datasets to compare to current datasets? 32:48 - When you mentioned a front page on RStudio Connect (with the headlines), what is that? 33:25 - Is anyone using this for manuscripts at CHOP now? 36:24 - What has the adoption of R or Python been within the hospital analytics team? 37:28 - My manager is very leery of R because of technical depth. Any suggestions for convincing her of R’s value? 42:22 - How does CHOP use R for non-clinical analysis? 43:36 - How do you train new people to use R? 46:28 - How do you compare last week’s analysis to this week’s? 49:37 - Were there any major challenges in creating the hospital’s internal package? Resources/links shared: Jake’s LinkedIn: https://www.linkedin.com/in/jake-riley-70736a3/ headliner package: https://github.com/rjake/headliner waldo package: https://www.tidyverse.org/blog/2020/10/waldo/ Examples of R in Life Science & Healthcare: https://www.rstudio.com/champion/life-science Chris Bumgardner’s talk on building an R-based analytic practice at Children’s Wisconsin: https://youtu.be/pHZ8dsc0PhY simplecolors package to generate hex codes using uniformly named colors: https://rjake.github.io/simplecolors/ R Packages book by Hadley Wickham & Jenny Bryan: https://r-pkgs.org/ Meetup Links: Future events: rstd.io/community-events-calendar If anyone’s interested in speaking at a future meetup, we’d love to hear from you too! rstd.io/meetup-speaker-form

Hadley Wickham, Jenny Bryan

Data Science Hangout | Tanya Cashorali, TCB Analytics | Saving millions with a Shiny app

We were joined by Tanya Cashorali, CEO & Founder at TCB Analytics. Tanya is passionate about playing with data but using it for good causes whenever possible. Energized by working with ambitious people on difficult problems. I’m a huge advocate of rapid prototyping data products using Shiny and can talk about it for hours! What was your biggest win for a client? (23:30) We’ve had a lot of wins moving someone from Excel into a reproducible R pipeline and then displaying all the results in Shiny. One example is a pharmaceutical company that was analyzing clinical trial data super manually, generating reports in Excel and putting together a PowerPoint weekly. It probably took about 20 hours of multiple people’s time. We made a Shiny app and that turned this into pretty much no time at all. They sent us the data and it was updated monthly. They were able to then take that Shiny app to senior management and clinical trial managers to make decisions based on data very quickly, helping them understand which clinical trial sites were having problems. Another similar one at another pharmaceutical company was focused on drug manufacturing, where there are a lot of things that can go wrong. When there’s a contaminant in a batch, it can basically cause millions and millions of dollars in company loss because they have to shut down manufacturing completely until they identify the problem. This involves going up and downstream of these different drug products to identify the issues. It was taking a team of 5-10 people sometimes 6 months to identify the problem. Meaning for 6 months, you’re not able to manufacture drugs. We built a Shiny app that built out a D3 directed graph. This enabled one person to go and type in the drug compound and see everything up and downstream. It took that one person now maybe a week or several days to identify the problem now. That’s an instance where you could say this is literally millions of savings from a Shiny app. To me that just shows the power of R and the ability to really streamline manual processes. Resources shared: Rachael shared Libby’s officerDemo: https://github.com/LibbyHeeren/OfficerDemo Daren shared the xaringan package for producing super impressive presentationshttps://github.com/yihui/xaringan Tanya shared Jesse’s Twitter as a great resource for learning Shiny: https://twitter.com/kierisi Javier shared xaringanthemer: https://pkg.garrickadenbuie.com/xaringanthemer/ and xaringanExtra: https://github.com/gadenbuie/xaringanExtra Tanya shared Hugo’s podcast: https://hugobowne.github.io/ Tanya’s Twitter: https://twitter.com/tanyacash21 Tanya’s presentation on “How to Hire and Test for Data Science Skills” https://tcbanalytics.com/2017/11/07/tanya-cashorali-presents-strata-2017-nyc-hire-test-data-skills/ Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Enabling Citizen Data Scientists at Dow Chemical with Posit Academy

Led by James Wade, Associate Research Scientist at Dow Chemical Timestamps: 2:46 - Start of presentation 5:25 - Goal: “apply science and engineering technical expertise along with data science tooling to innovate in the materials science arena.” 6:36 - What does citizen data science mean? 8:05 - Data science as an interdisciplinary endeavor - looking to build a community of innovators 9:30 - Translating data to decisions 11:03 - Guidelines for success (data organizations, data access, data analysis, value preservation) 13:30 - Welcoming new users in an approachable, collaborative, and secure workspace with RStudio Team 14:25 - Making sure you can rapidly deploy your insights to others 16:25 - What is RStudio Academy? 20:55 - What do you need for academy? (Academy learners: 5-7 per cohort, cohort mentors from RStudio & your group, and a project - the closer to your work the better) 22:15 - Who is a good candidate? 23:55 - Who might not be the best candidate? 26:00 - What makes a good cohort? (similar work group, time zone, and skill level) 27:27 - Feedback (Are they still using the content they learned? 16 out of the 17 survey respondents were still writing code 6 months after) 31:42 - Community building (want to have a landing zone for people to continue to learn) 32:31 - RStudio Academy success story at Dow 35:30 - Start of Q&A portion Questions: 36:00 - How do you help someone who knows coding would be useful but can’t motivate to take 5 steps back to take 10 steps forward? 37:55 - How can more advanced users participate in developing curriculums? 39:44 - Does Academy also teach good coding style and version control? 41:00 - If you’re trying to “sell” Academy to the individual who would fill the group mentor role, what level of commitment and bandwidth do they need to have? 42:13 - Is the type of data you work with relevant to the work you do at Dow? or random / set datasets regardless of which company you’re with? 43:00 - What other ways of teaching R have you tried (or considered) at Dow? How does Academy compare? 44:55 - What is the duration of RStudio Academy? 46:15 - Can you have multiple cohorts go through at the same time? What if we want to up-skill hundreds of people? 48:20 - How did you find out who might be interested and get the word out? 50:08 - Advertising that you help learners up-skill in coding seems like a good way to set your company apart from others, are you hiring? 51:25 - After the RStudio Academy 10 week training is the Academy team still available for questions, support or consult? 53:01 - Is Academy only for R? 53:38 - How can a data science student participate in RStudio Academy? ( https://www.rstudio.com/conference/2022/workshops/intro-to-tidyverse/ ) 54:44 - How do you collaborate with others outside of Dow? 57:20 - How does RStudio Academy handle sensitive data? 1:00:20 - Do you have statistics on how many graduates are still using R? Abstract: In chemistry and materials science research, data is messy, unstructured, and scattered. Solving this problem requires researchers to deeply embed within data generation and analysis workflows. We are on a multi-year journey to equip scientists and engineers with guidance and tools to extract insights from their data. To this end, we have developed a set of 15 guidelines designed to move our organization toward a collaborative, reproducible work process in a dynamic data-diverse environment. In this talk, I will share our lessons from this journey learned through teaching, community building, and collaboration with a particular focus on the integration of language agnostic RStudio tools, products, and programs. I will especially be focusing on our experience with RStudio Academy. Speaker Bio: James is a research scientist working in the chemicals manufacturing industry as part of a research and development team. James applies materials characterization and data science with a special interest in sustainable materials design to develop new capabilities for research. His current focus is on augmenting materials characterization innovations with statistical analysis, machine learning, and data visualization. For more information on RStudio Academy: rstudio.com/academy Link to speak with RStudio: rstd.io/chat-with-rstudio

RStudio Team

Rich Iannone || New features in {gt} 0.6.0! || RStudio

00:00 Introduction 00:18 sub_missing() 03:51 Markdown formatting in sub_missing() 04:51 sub_zero() 07:34 sub_small_vals() 13:08 sub_large_vals() 16:25 final thoughts A new version of the R package {gt} has been released! We are now at version 0.6.0 and there are now even more features that’ll make your display/summary tables look and work much, much better. Let’s run through some of the bigger changes and see the benefits they can bring! New functions for substituting cell data We now have four new functions that allow you to make precise substitutions of cell values with perhaps something more meaningful. They all begin with sub_ and that’s short for substitution! sub_missing() (formerly known as fmt_missing()) Here’s something that’s both old and new. The sub_missing() function (for replacing NAs with… something) is new, but it’s essentially replacing a function that is old (fmt_missing()). The missing_text replacement of “—” is actually an em dash (the longest of the dash family). This can be downgraded to an en dash with “–” or we can go further with “-”, giving us a hyphen replacement. Or, you can use another piece of text. If you’re using and loving fmt_missing(), it’s okay! You’ll probably receive a warning about it when you upgrade to {gt} 0.6.0 though. Best to just substitute fmt_missing() with sub_missing() anyway! sub_zero() The sub_zero() function allows for substituting zero values in the table body. sub_small_vals() Next up is the sub_small_vals() function. Ever have really, really small values and really just want to say they are small? With sub_small_vals() we can reformat smaller numbers using the default threshold of 0.01. Small and negative values can also be handled but they are handled specially by the sign parameter. Setting that to “-” will format only the small, negative values. You don’t have to settle with the default threshold value or the default replacement pattern (in small_pattern). This can be changed and the “x” in small_pattern (which uses the threshold value) can even be omitted. sub_large_vals() Okay, there’s one more substitution function to cover, and this one’s for all the large values in your table: sub_large_vals(). With this you can substitute what you might consider as too large values in the table body. Large negative values can also be handled but they are handled specially by the sign parameter. Setting that to “-” will format only the large values that are negative. You don’t have to settle with the default threshold value or the default replacement pattern (in large_pattern). This can be changed and the “x” in large_pattern (which uses the threshold value) can even be omitted. Final thoughts We are always trying to improve the gt package with a mix of big features (some examples: improving rendering, adding new families of functions) and numerous tiny features (like improving existing functions, clarifying documentation, etc.). It’s hoped that the things delivered in gt 0.6.0 lead to improvements in how you create and present summary tables in R. If there are features you really want, always feel free to: File an issue: https://github.com/rstudio/gt/issues ) Talk about your ideas on the Discussions page: https://github.com/rstudio/gt/discussions Learn more about the gt package here: https://gt.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Rich Iannone (@riannone) Motion Design & editing: Jesse Mostipak Music: Nu Fornacis by Blue Dot Sessions https://app.sessions.blue/browse/track/98983

Rich Iannone

gt rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Coding Server Pro Shiny Rmarkdown CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Gt 0.6.0 Rich Iannone Gt Update Sub_missing Sub_small_vals Sub_large_vals Sub_zero Tables

Tom Schenk & Bejan Sadeghian | Making Microservices Part of Your Data Team

Making microservices a part of your data science team Led by Tom Schenk & Bejan Sadeghian at KPMG Timestamps: 1:09 - Start of presentation 4:00 - Challenges and trade-offs of a growing team (how I stopped worrying about hiring) 8:14 - What are microservices? (help separate out the different layers of an app) 9:36 - Hosting other web technologies on RStudio Connect (ex. React) 12:25 - Simple Hello World example of microservices 16:00 - Reason to separate out logging 17:15 - How to design & plan microservices (moving from a monolithic Shiny app) 17:51 - Challenges to getting started with microservices 21:02 - How do you address getting started? (domain driven design) 23:17 - Applying cloud design patterns 25:37 - Separation of development duties 27:22 - Addressing any risks that come with microservices 29:17 - Considering costs and benefits 30:11 - Microservices in action: demo of KODA app (making changes to the organization) 36:21 - PowerBI interacting with the same microservice from Connect 38:30 - Growing teams face a trade-off of complexity and simplicity (KPMG’s path) Questions: 43:28 - Can you use a Shiny front end together with a microservice backend? 44:09 - Do you hire separately for back-end data science development and front-end Shiny UI development? 46:00 - Are all microservices managed by a centralized unit? 47:02 - Who can access RStudio Connect in your organization? 48:29 - When you decided to go the microservice route, what was your first step? 50:47 - What roles are you hiring for? 52:20 - Might you suggest some web service servers that host R-based or Python services? 53:33 - Are apps build with microservices as responsive as those that adopt a monolithic architecture or do microservices introduce a lag? 55:09 - Can you show the back-end response data through developer tools? 56:45 - Can you speak more about the logging microservice? Did you build it ground-up or did you adopt an off-the-shelf package or app? Abstract: Whether or not you’ve heard of microservices architecture, you may want to know how microservices can help you scale R-based applications across an enterprise. As data science teams—and their applications—grow larger, teams can experience growing pains that make applications complex, difficult to customize, or challenging to collaborate across large teams. This meetup will discuss what microservices are, how it compares to Shiny, how it can help a data science team, and how you can deploy microservices using your RStudio Connect environment. This meetup will help you understand several key items: • The basic concept of microservices and benefits, such as making your code modular, domain-driven design, and reducing the complexity of application development, and facilitate larger development teams. • How to use the Plumber package to deploy APIs as part of a microservices architecture. • How you can work with front-end development teams using their preferred framework (e.g., React, Angular, Vue) using RStudio Connect. We will show a widely-used application built using a microservices architecture and hosted in RStudio, including before-and-after comparisons to show the strengths of a microservices framework leads to a better-looking and better-functioning application. Our team will discuss the journey and growth to arrive at the new approach to make development easier within a quickly growing group. Speaker Bios: Tom Schenk Jr. is a researcher and author on applying technology, data, and analytics to make better decisions. He’s currently a managing director at KPMG. He has previously served as Chief Data Officer for City of Chicago. Bejan Sadeghian is a director of analytics at KPMG and leads data science development, which spans from advanced analytics to machine learning engineering. For upcoming events: rstd.io/community-events-calendar Info on RStudio Connect: https://www.rstudio.com/products/connect/ To chat with RStudio: rstd.io/chat-with-rstudio

Data Science Hangout | Travis Gerke, PCCTC | Tips for Very Remote Work

We were joined by Travis Gerke, Director of Data Science at The Prostate Cancer Clinical Trials Consortium. Travis is an enthusiastic proponent of the use of R for data science in the clinical trials space and an advocate for productive remote work environments. Tips for very remote work Ok, first things first - how do you ensure you have internet? I use a 5g Netgear hotspot and have redundant phone plans with T-Mobile, AT&T, and Verizon. It depends where we are and what kind of service is going to be best, but I’ve never had a challenge streaming videos or with Zoom. If you do get a hotspot, $20 boosters can be tremendously helpful too and help funnel the signal into your hotspot a bit better. If you want to geek out on this, the best resource is rvmobileinternet.com. Even in the remotest places we’ve been, I was still able to maintain Zoom meetings. On a week where I am furthest from civilization, I will plan ahead and focus more on learning and heads down work. It’s actually kind of good it gives you a little bit of space from the usual grind of meetings.. How do you balance the time you can be available for conversations or when you’re traveling It’s something that one has to be very intentional about and communicate broadly. I do work odd hours, because I’m West Coast now and most of my colleagues are East Coast. I try to wake up early and stick to their schedule. It really comes down to communication. I will let people know if I’m traveling and block time on my calendar. I think async is the future of most work environments for data science. I’d recommend checking out content from Chris Herd on Twitter too: https://twitter.com/chris_herd If I send an email at a time like 2am, I make sure to communicate that if I’m sending an email, it doesn’t mean I expect anyone to see or respond at that time. A few people mentioned putting something in an email signature to let people know. Here’s an example: “I work on a flexible work schedule and across a number of time zones so I’m sending this message now because it works for me. Feel free to read, act on or respond at a time that works for you.” What tools are there for async work to work through something complicated without meetings? A few ideas and tips shared from the group: We lean into github whenever we can. The process of writing down where you’re stuck and/or how you solved something is good. When working with non-data scientists, sometimes you just have to have the meeting and that’s fine. It can be a lot more efficient that way but the drawback is that you don’t end up documenting. Writing things down reduces those institutional knowledge silos. People mentioned using: Slack, video messages on Slack, Teams, Loom for sharing video screen shares, snagit, Discord, Fuze, Zoom whiteboard Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | Alice Walsh, Pathos | Improving an Interview Experience

We were joined by Alice Walsh, PhD, VP of Translational Research at Pathos. Alice works in drug development, where she is excited about the potential of computational research to yield breakthroughs for patients. Loved that Alice also asked this question back to the audience: How do I make an interview a good experience for a candidate? Or have you had any nightmares that’d be helpful to share? A bunch of thoughts shared from the group: ⬢ I’ve had way more success not giving a technical interview, and having the technical interview be more of a discussion where I’m not even asking them to whiteboard anything or it’s just talking. ⬢ If I asked them, “how do you develop a shiny app”, I’d much rather someone tell me I’ve never developed a shiny app in my life but I use R Markdown every day. That tells me a lot about their ability to actually jump in and learn something new and their transparency. ⬢ I’m much more interested in the process. How do people approach a problem and solve challenges that they encounter versus the specific project they worked on because they’re not going to work on that project ever again with me. It’s going to be a new project so they will need to learn something anyway. ⬢ I’ve had success hiring from meetups or hackathons. Seeing people here and the way they problem solve gives you a lot of insight about these individuals. ⬢ My company actually does do a technical interview and we give candidates a data set while they’re on site or in a Teams meeting. We give them an hour to see what sorts of insights they find with a few very specifically directed questions. What we’re often looking for is not someone to have perfect answers to those questions - it’s really about understanding how they looked at the data set, what other information they want, and what do you wish you had more time to do. You get to see how people work through something and it’s okay if they don’t have a perfectly polished presentation. ⬢ I’ve had a nightmare interview that became a pop quiz on R stuff. What are all the packages in the tidyverse (and at the time I didn’t use tidyverse I was base R) ⬢ A nightmare one that sticks in my head was, please describe in detail the differences between Python 3 and Python 2. ⬢ I think, “this is something I would Google” is a valid answer sometimes because even if I don’t know this, I know where to find it and am really confident in my ability to Google this. ⬢ Honestly if I ask somebody a question and they said this is something that I know I could find the answer to, that would be a perfect answer to me. Not knowing but knowing where to find the information great. ⬢ I went on 3 interviews and they each had a technical part where for every single one of them the answer was: dynamic programming. They must have gone somewhere and decided that was the algorithm to ask about. I found that a bit ridiculous because it wasn’t relevant to what they were working on and it was off-putting. Now, when I interview people I try to make it more of a conversation around the data and what we might actually be doing. ⬢ I have an optional take home: “here’s a data set, take an hour and tell me something. Use whatever tools you want: Excel, R, Python, an abacus.” The key thing I want to see is a written output of what you did. I’m still on the fence, though, because I know a lot of people are anti-technical component. I’m still trying to figure out if it is really helping us make the best hiring decisions. Also wanted to share a link to a job posting on Alice’s team! She has simplified to just one posting, but they have a couple of openings. They are hiring both for more experienced scientists and folks transitioning from academic research and no drug development experience: https://www.indeed.com/viewjob?t=senior+computational+biologist+data+scientist&jk=204065f326f6e803&_ga=2.142078395.801277873.1653586940-640826182.1637348361 Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

{gt} Table Battles || Eurovision || RStudio

00:00 Introduction 00:07 Jesse’s gt table, with a focus on flag emoji and interactivity via a Shiny app 09:50 Rich’s gt table, with a focus on CSS and embedded animations Code: https://github.com/kierisi/rstudio_videos/tree/main/gt/table-battles Learn more about the gt package here: https://gt.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Rich Iannone (@riannone) & Jesse Mostipak (@kierisi) Motion Design & editing: Jesse Mostipak Music: Gemeni City by Blue Dot Sessions https://app.sessions.blue/browse/track/113567

Rich Iannone

gt rstudio Shiny RStudio Data Science Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Rich Iannone Jesse Mostipak Table Battles

Data Science Hangout | Lindsey Clark, Healthcare Bluebook | Measuring success of data science

We were joined by Lindsey Clark, Director of Data Science at Healthcare Bluebook. Evolving and Leading a Data Science/AI First Organization. One of the topics of discussion (33:46) was measuring the success of a data science team and calculating ROI. How do you measure success for a data science team? Lindsey shared a few tips from her experience at two different organizations: One measure of success was the conversion rate of projects. How many projects come to us and what percent are actually converted to an actual deployment? Our target was 5% which may sound low, but the idea was that we want to fail fast. It can be like the drug discovery mentality - if things aren’t going to work, we want to know early on. We also have targets around the number of specific models we wanted - what we called our data science portfolio library. They didn’t need to be really fancy machine learning, but a lot of them were business logic sort of ranking models. Another measure was product value and financial targets around that. Could we pinpoint that our model went into the data product and it resulted in more clients contracting with us or greater revenue because we were able to upsell a data product due to another feature we added. In my position now, we have recently been acquired so we are working on the success measures and what that looks like. We do utilize the Outcomes and Key Results (OKR) Strategy from the Measure What Matters book. We plan OKRs quarterly and try to plan projects that are really no longer than three month increments in order to keep things going and satisfy the business. Data Science ROI can of course be a difficult thing to measure. Data science can be a capital expense at times and sometimes you have to look at this team that’s going to do high risk projects as research. Things are going to fail and that’s okay. We’re willing to invest the money for our team and absorb that cost for what we potentially get out of the team in terms of deployed solutions, specific insights, and research that are valuable to the business. Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Using Python with RStudio Team

Using Python with RStudio Team Led by David Aja, Solutions Engineer at RStudio 00:30 - Overview of RStudio Team - RStudio Workbench, RStudio Connect, RStudio Package Manager 2:38 - Use case of RStudio Package Manager with Python packages 4:29 - RStudio Workbench (ability to use RStudio, Jupyter Notebook, JupyterLab, VS Code) 5:48 - VS Code running on RStudio Workbench 11:50 - Deploying a Streamlit app to RStudio Connect 14:00 - Pause for questions 18:55 - Jupyter on RStudio Workbench 19:41 - Extension in Jupyter that gives you push-button publishing from RStudio Workbench 20:35 - Publishing with source code vs. publishing only the finished document 21:10 - Hiding the code that is generated when publishing Jupyter to RStudio Connect 23:30 - Creating a custom url in RStudio Connect for your content 24:30 - Adding viewers / collaborators to your content in RStudio Connect 25:18 - Scheduling your Notebooks to run repeatedly 25:54 - Stepping back to describe the differences between RStudio open-source IDE and RStudio Workbench *please note that this meetup will cover our enterprise product, RStudio Team but all who are interested in joining are welcome! Many Data Science teams today are bilingual, leveraging both R and Python in their work. While both languages have unique strengths, teams frequently struggle to use them together: ⬢ Data Scientists constantly need to switch contexts among multiple environments. ⬢ Data Science Leaders wrestle with how to share results consistently and deliver value to the larger organization, while providing tools for collaboration between R and Python users on their team. ⬢ DevOps engineers and IT Admins spend time and resources attempting to maintain, manage and scale separate environments for R and Python in a cost-effective way. Join David Aja in this meetup, which will highlight how other data science teams are able to solve these problems with RStudio Team by: ⬢ Combining R and Python in a single data science project. ⬢ Launching and managing RStudio, Jupyter Notebooks, JupyterLab, and VS Code from the RStudio Workbench environment ⬢ Sharing Jupyter Notebooks, Python APIs via Flask, Dash, Streamlit, Bokeh, FastAPI, Shiny, R Markdown, etc. with the business through RStudio Connect. ⬢ Controlling and distributing Python and R packages with RStudio Package Manager. A few helpful links shared and mentioned during the call: ⬢ Examples David used: https://lnkd.in/g8bdbj7Q ⬢ Example usage patterns of Using Python with RStudio: https://lnkd.in/gek3BhgW ⬢ Helpful place for questions about using Python in RStudio: https://lnkd.in/gWRV2rbG ⬢ Model Management with Python example: https://lnkd.in/gyX2YVvi Product / Conference Questions: ⬢ More info on RStudio Team: https://lnkd.in/gv4YQj2G ⬢ If you’re starting to champion data science at your company: rstudio.com/champion ⬢ If you’d like to chat about RStudio Team: rstd.io/chat-with-rstudio ⬢ RStudio Conference: https://lnkd.in/dcrz79y ⬢ System Admin Workshop at RStudio Conference: https://lnkd.in/eB9FZt-c

RStudio Team

Dr. Uli Muellner & Nick Snellgrove | Shiny modularization, Leaflet for R and Leaflet JS extensions

Shiny modularization, Leaflet for R and Leaflet JS extensions Under the Hood of the Aotearoa Road Trip App with Epi-interactive App: https://rshiny2.epi-interactive.com/apps/AotearoaRoadTrip/ Abstract: Come along with Epi-interactive for a tour through the design and development of their 2021 Christmas R Shiny App - the Aotearoa Road Trip! We will start off with a short history of Epi-interactive’s Christmas Tools, followed by a demonstration of the 2021 Edition and a discussion about its design and concept. Then we will go under the hood into the Shiny / JavaScript code backend which makes it run, such as Shiny modularisation, Leaflet for R and Leaflet JS extensions such as MovingMarker, and some Shiny tricks we picked up along the way. Speaker Bios: Dr Uli Muellner is Epi-interactive’s Director (IT & Information Design) and has been leading the company’s data visualisation and user interface work over the last decade. After various leadership roles in IT and online learning he co-founded Epi-interactive and built up the company to where it is today. As an advocate of open source technology, he has been the driving force behind the company’s innovative and user-centric approaches and methods. Nick Snellgrove is a Full-Stack Developer at Epi-interactive. Along with being fluent in R and Shiny development techniques, he has a particular passion for spatial data visualisations, User Experience (UX) development and XR technology

Programming Games with Shiny || Roll the Dice || RStudio

00:00 Introduction 01:40 Rolling with eventReactive( ) 06:26 Reducing eventReactive( ) to reactive( ) + isolate( ) 16:23 Combining reactive( ) and bindEvent( ) 20:11 Reviewing our reactives 21:23 Writing a function to de-duplicate dice rolls You’ve most likely used Shiny to build a web app that displays data, but you can also use Shiny to build games! In this video series, Jesse and Barret pair program simply games in Shiny as a way to uncover and explore new features. Read up on tabset panels here: https://shiny.rstudio.com/reference/shiny/0.14/tabsetPanel.html Learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) and Jesse Mostipak (@kierisi) Animation, motion design, and editing: Jesse Mostipak (@kierisi) Theme song: Hakodate Line by Blue Dot Sessions ( https://app.sessions.blue/browse/track/111291" )

Barret Schloerke

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate EventReactive Isolate Reactive BindEvent Barret Schloerke Jesse Mostipak

Veerle van Leemput | Analytic Health | Optimizing Shiny for enterprise-grade apps

Can you use Shiny in production? A: Yes, you definitely can. Link to slides: https://github.com/RStudioEnterpriseMeetup/Presentations/blob/main/VeerlevanLeemput-OptimizingShiny-20220525.pdf Packages mentioned: ⬢ shiny: https://shiny.rstudio.com/ ⬢ pins: https://pins.rstudio.com/ ⬢ plumber: https://www.rplumber.io/ ⬢ blastula: https://github.com/rstudio/blastula ⬢ callR: https://github.com/r-lib/callr ⬢ shinyloadtest: https://rstudio.github.io/shinyloadtest/ ⬢ shinycannon: https://github.com/rstudio/shinycannon ⬢ shinytest2: https://rstudio.github.io/shinytest2/ ⬢ feather: https://github.com/wesm/feather ⬢ shinipsum: https://github.com/ThinkR-open/shinipsum ⬢ bs4Dash: https://rinterface.github.io/bs4Dash/index.html Timestamps: 2:44 - Start of presentation 5:41 - What qualifies as an enterprise-grade app? 10:46 - UI first / user experience / prototyping 13:20 - Separating code into separate scripts and creating code that’s easy to test 17:15 - Golem 19:28 - Functionize your code 20:50 - Rhino package, framework for developing enterprise-grade apps at speed 22:33 - Infrastructure, how do you bring this to your users? (lots of ways to do this. They do this with R, pins, plumber, rmd, blastula, and Posit Connect on Azure) 31:17 - Optimizing Shiny (process configuration, cache, callR, API, feather) 47:35 - Testing your app (shinyloadtest and shinycannon) 50:23 - Testing for outcomes (shinytest2) 52:15 - Monitor app performance & usage (blastula, shinycannon, usage metrics with Shiny app) Questions: 57:38 - What’s the benefit of using pins rather than pulling the data from your database? 59:30 - Are there package license considerations you had to think about when monetizing shiny applications? 1:00:45 - Do you use promises to scale the application? (they use CallR) 1:01:49 - For beginners, golem or rhino? 1:02:50 - The myth is that only Python can be used for production apps, what made you choose to use R? 1:05:12 - Is feather strictly better than using JSON? 1:06:38 - Where do you see the line between BI (business intelligence) and Shiny for your applications? 1:08:36 - Any tips for enterprise-grade UI development? Making beautiful apps (bs4Dash app) 1:10:25 - Have you found an upper limit for users? 1:12:19 - Any tips for more dynamic data? (optimizing database helps here) 1:13:50 - Where do you install shinycannon? (on our development Linux server) 1:15:00 - Can you share other resources or examples of code? (Slides here with resources: https://github.com/RStudioEnterpriseMeetup/Presentations/blob/main/VeerlevanLeemput-OptimizingShiny-20220525.pdf ) For upcoming events: rstd.io/community-events-calendar Info on Posit Connect: https://www.rstudio.com/products/connect/ To chat with Posit: rstd.io/chat-with-rstudio

Data Science Hangout | Wayne Jones, Shell | Thinking Empathetically & Using Your Initiative

We were joined by Wayne Jones, Principal Data Scientist at Shell. Wayne has been using R since its first inception and is passionate about open source solutions. During the hangout with Wayne, he brought up the point of thinking empathetically when communicating. Here are a few other tips shared among the group: 1️⃣ If you spot someone who you think is really good at communicating and you’re impressed by them - buddy up. Tell them you like the way they did that, can you give me a few tips and hints? 2️⃣ With data science, you can focus on a very small area sometimes and get obsessed by the details. Step back, step back, and step back some more so that when you’re presenting a problem you give the bigger picture and narrow into the data science details. 3️⃣ If you think more empathetically, this will also go a long way. Instead of thinking about it from a data science standpoint, put yourself in the customers or client’s shoes. With more empathy, you make yourself a better consultant. 4️⃣ Try using your team meetings (scrum meetings for example) to practice presenting in front of people, even something for 5 minutes where you need to learn to be succinct about it. 5️⃣ Think about when you were a child and bringing something in for show and tell. When they get to bring something they really care about, they are thinking more about the toy that they brought in rather than presenting. 6️⃣ There are lots of R meetup groups that are always looking for volunteers to speak at events! Lots of opportunities to practice there! 7️⃣ Learn about everyone else’s role so that you can talk their language - “BE RELEVANT” to your teams Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

{gt} Table Battles || Crosswords || RStudio

00:00 Introduction 00:34 Rich’s gt table, with a focus on creating audio within a table 07:28 Jesse’s gt table, with a focus on sentiment analysis Learn more about the gt package here: https://gt.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Rich Iannone (@riannone) & Jesse Mostipak (@kierisi) Motion Design & editing: Jesse Mostipak Music: Nu Fornacis by Blue Dot Sessions https://app.sessions.blue/browse/track/98983

Rich Iannone

gt rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Sentiment Analysis Audio Rich Iannone Jesse Mostipak

Posit Package Manager || Link Your Package Manager Repo to Your RStudio IDE || Posit

In this video Jeremey Allen walks through connecting Posit Package Manager to Workbench for fast and secure access to the organization’s R and Python libraries. Posit Package Manager is a repository management server to organize and centralize packages across your team, department, or entire organization. Get offline access to CRAN, PyPI, and Bioconductor, share local packages, restrict package access, find packages across repositories, and more. Experience reliable and consistent package management, optimized for data science. Learn more about Posit Package Manager here: https://www.rstudio.com/products/package-manager/ Got questions? Check out the Posit Package Manager Frequently Asked Questions page: https://docs.rstudio.com/rspm/admin/getting-started/faq/

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Katie Masiello || Build a Codenames app using {pins} and Shiny! || RStudio

00:00 Introduction 00:05 Project outline 03:56 Create a codename generator (using RMarkdown) 09:35 Publish to RStudio Connect 10:38 Create a Shiny app 18:15 A little bit of troubleshooting 18:18 Ta-da! Learn more about the pins package here: https://pins.rstudio.com/ Learn more about Shiny here: https://shiny.rstudio.com/ And learn more about RStudio Connect here: https://www.rstudio.com/products/connect/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Katie Masiello (@katieontheridge) Animation, motion design, and editing: Jesse Mostipak (@kierisi) Theme song: Contrarian by Blue Dot Sessions ( https://app.sessions.blue/browse/track/64281 )

rmarkdown rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Pins Codenames Katie Masiello

RStudio’s {pins} package: what it is, how it works, and what it can do for you! || RStudio

00:00 Introduction 00:09 What is the pins package? 01:49 pins - not just for RStudio Connect! 02:31 pins use cases 04:47 How to use pins instead of final_final_01_noreallyfinal.xls 06:37 How do pin boards work? 08:55 Getting started with pins 10:42 Versioning with pins at the board or pin level 11:47 pins and caching 12:13 Things you shouldn’t pin 14:00 Major functions in the pins package 17:21 Using pin_upload( ) and pin_download( ) 19:52 pins and Google Cloud 21:27 pins and modelops with the vetiver package Learn more about the pins package here: https://pins.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Katie Masiello (@katieontheridge) and Jesse Mostipak (@kierisi) Animation, motion design, and editing: Jesse Mostipak (@kierisi) Theme song: Contrarian by Blue Dot Sessions ( https://app.sessions.blue/browse/track/64281 )

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Pins Katie Masiello Jesse Mostipak

Posit Pharma Meetup: R for Clinical Study Reports & Submission | Yilong Zhang

Presenter: Yilong Zhang, PhD During the meetup you can ask questions here: rstd.io/pharma-meetup-questions (anonymously or with your name) Abstract: The use of open-source R is evolving in drug discovery, research, and development for study design, data analysis, visualization, and report generation in the pharmaceutical industry. It is critical to enable the ability to produce tables, listings, and figures (TLFs) and submit results to the regulatory agency using R. We developed R packages (r2rtf, pkglite) and reference book ( https://r4csr.org/ ) to simplify the workflow for an organization to complete those tasks. Based on the proposed workflow, the first pilot project has been successfully submitted to FDA by the R consortium R submission working group ( https://rconsortium.github.io/submissions-wg/). Bio: Yilong Zhang is a biostatistician at Meta (and was previously at Merck). He has worked with a group of statisticians and programmers to demonstrate the capability of using R for regulatory work. Other research interest include statistical methods in study design, missing data, and survival analysis with more than 25 papers published in peer-reviewed journals. Before joining Merck, he earned Ph.D. degree in Biostatistics at New York University. Helpful links: Use cases & insights from pharmaceutical industry leaders: https://www.rstudio.com/champion/life-science R/Pharma Conference: https://rinpharma.com/ R Validation Hub: https://www.pharmar.org/ Link to speak with RStudio: rstd.io/chat-with-rstudio Link to slides: rstd.io/pharma-meetup-slides

Data Science Hangout | Michael Chow, Posit | Exploring Team Structure w/ Data Scientists & Engineers

We were joined by Michael Chow, Data Scientist and Software Engineer at RStudio. Michael also previously led a team at the California Integrated Travel Project. On this week’s hangout there were a lot of thoughts shared on structuring a data science team from both Michael and the broader group: ⬢ Jacqueline Nolis also shared thoughts on this on a data science hangout that there were virtues to different ones, but ended up sold on the decentralized model where data scientists are embedded in teams: https://youtu.be/CcPE29bYGVo?t=325 ⬢ Michael agreed that data scientists and analysts should be sitting with the teams that they’re pushing out reports for. Otherwise, I would be trying to send people into those teams to figure out their priorities. ⬢ A data scientist should work with a Project Manager or whoever’s leading the team to push up metrics but also help change the roadmap. ⬢ It leaves a tricky question of where data engineers should be and how they should interact with the team. Today data engineers are often doing more tooling empowerment, so it can be okay to have them a bit more centralized and connect to the data scientists to enforce best practices or enable new pieces for them. ⬢ I think a nice model is for data scientists/analysts to live in the teams and data engineers to be like spokes of a wheel where then the data scientists connect with them and work closely to enforce better best practice and enable new important things. ⬢ Tatsu shared that in thinking of the structure, it’s also important to find your translators and to use the power of feedback. Reach out to those people to start to put that feedback into action. ⬢ George shared that insurance companies have come from a really traditional landscape where they have lots of actuaries working on lots of excel spreadsheets and there can be a lack of knowledge sharing and tool sharing. This is where the data science element comes in. To me, within the organization, you need to have this team which is a mini-spoke if you will, because they are central to the actuarial team. If they are too far removed and they’re back with the IT team, you end up with the old problems because they may not get the business concept communicated back. It’s all about getting enough skills, so they can get stuff done, especially proof of concepts. Maybe after that you can take a step back and then start to look at the centralized model again. ⬢ A central team can help converge to what they see as best practice, but if you’re pushing out something new, exploring a new line of work or area it can be important to set the data engineer there to actually do whatever they need to. Make sure that the converging doesn’t stifle creativity or prevent a team from doing the right thing. ⬢ Manny jumped in to share the perspective from data science being with IT as well, data science is a new field for their company (in real estate) and there’s an identity of where does data science fall. The IT team is fantastic and they’re very structured. Data science is so fluid and creative and non structured at the moment, so you kind of have to look at where it actually should fall. please note that some of the points above are summarized and not 100% actual quotes. Resources shared: ⬢ Tatsu shared in the chat, a few projects that Michael is working on: vetiver: https://vetiver.tidymodels.org/articles/vetiver.html, siuba: https://github.com/machow/siuba ⬢ Libby shared a helpful tip on creating a 2 minutes YouTube video with a cover letter, to get the attention of a hiring manager ⬢ Javier shared an example Shiny app used in an interview: https://javierorraca.shinyapps.io/Bloomreach_Shiny_App/ ⬢ Michael mentioned David Robinson’s screencasts: https://www.youtube.com/channel/UCeiiqmVK07qhY-wvg3IZiZQ ⬢ Michael mentioned an article on “What data scientists really do according to 35 data scientists”: https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists ⬢ Rachael shared a blog post link where Jacqueline Nolis talked about team structure as well: https://www.rstudio.com/blog/building-effective-data-science-team-answering-your-questions/#Structure ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout ► View the Data Science Hangout site here: rstudio.com/data-science-hangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

Michael Chow

Programming Games with Shiny || Guess the Number || RStudio

00:00 Introduction 00:35 Setting up our app UI 06:19 Using observeEvent() 10:02 Writing an if else statement as part of our feedback mechanism 13:40 Testing our app and deciding which bugs to fix first 14:20 Check yourself before you req() yourself 17:10 Using tabset panels to control what the user sees You’ve most likely used Shiny to build a web app that displays data, but you can also use Shiny to build games! In this video series, Jesse and Barret pair program simply games in Shiny as a way to uncover and explore new features. Read up on tabset panels here: https://shiny.rstudio.com/reference/shiny/1.5.0/tabsetPanel.html Learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) and Jesse Mostipak (@kierisi) Animation, motion design, and editing: Jesse Mostipak (@kierisi) Theme song: Hakodate Line by Blue Dot Sessions ( https://app.sessions.blue/browse/track/91291 )

Barret Schloerke

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Stringr Tidyr Purrr Github Data Wrangling Odbc Plumber Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Pgws Programming Games With Shiny Barret Schloerke Jesse Mostipak

Kelly O’Briant | Building a business case for data science & advocating for analytic infrastructure

3:10 - Start of Kelly’s presentation 5:05 - Identifying where you are in your organizational culture when asking for change 9:30 - What is analytic infrastructure: the how, where, and with what that goes into your daily data science work 10:55 - Production looks different at different organizations 12:20 - Don’t get caught up in the hype - there is no perfect deployment pipeline 14:00 - Tactical metrics for communicating devops - code deployment lead time 15:55 - Your analytic infrastructure is what enables you or teams to deliver value through decreasing that code deployment lead time 20:49 - What is an analytic administrator / R Admin? (A data scientist who onboards new tools, deploys solutions, support existing standards. They work closely with IT to maintain and scale analytic environments. They influence others in the organization to be more effective, and are passionate about making data science a legitimate analytic standard) 24:04 - Building a business case (introducing rstudio.com/champion) Join us for future champion chats the 4th Monday of every month (not the date I said in the recording) rstd.io/champion-chats Let’s admit it. Getting to use the tools you want sometimes needs a little convincing. We’ve had the opportunity to meet so many wonderful people from the community. People that: organize meetups in their own communities, spend time after-hours teaching their co-workers, solve business problems at work with Shiny, write blog posts to help others, and so much more.. People are doing amazing things with data science, yet so many are still in a position where they are not able to make the case for it at their own organizations. Hear from Kelly O’Briant on how you can start to navigate the internal process and advocate for great analytic infrastructure as a data scientist. Whether you’re getting pushback about using open-source, being told to use a BI tool instead, or just unable to find the other data scientists at your company - we want to help make this process less frustrating. Come hang out and chat with us! *Please note, only the presentation portion of this meetup was recorded, the open discussion and Q&A was not. Resources shared this week: ⬢ rstudio.com/champion ⬢ John shared a typology of organisational cultures: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1765804/pdf/v013p0ii22.pdf ⬢ Kelly shared The Phoenix Project book: https://www.amazon.com/Phoenix-Project-DevOps-Helping-Business-ebook/dp/B078Y98RG8 and The Unicorn Project book: https://www.amazon.com/gp/product/B07QT9QR41 ⬢ Kelly shared Benjy’s talk on conveying data science findings to non-experts: https://www.youtube.com/watch?v=UUIda4-CSXQ ⬢ Nick shared: The pkgdown package can be used to build informational web pages about R packages, which can themselves be deployed to RStudio Connect: https://pkgdown.r-lib.org/ ⬢ Kelly shared Nathan Stephens RViews blog on Analytics Administration for R: https://rviews.rstudio.com/2017/06/21/analytics-administration-for-r/

Data Science Hangout | Tegan Bunsu Ashby, Brooklyn Nets | Showing the Difference You’re Making

We were joined by Tegan Bunsu Ashby, Senior Software Developer, Brooklyn Nets. Tegan works at the intersection of data and design, building applications and visualizations to empower front office decision making and help win basketball games. Towards the end of the hangout, there was a great question from Trevor: How do you show that you are making a difference? One of the joys and terrors of being in a data intensive role for a team is that you can’t point to oh, we did X or we recommended Y and now we have an NBA championship. It has to be - does analytics and investment in data at a league level indicate that you’re going to have sustained success? The investment in analytics departments and the success of teams that have been able to do so is indicative of how teams are evaluating the value of that data. It’s really nice to say, but we also need to hold ourselves accountable. When you’re using all this time and resources - you have to sit back and think, does this contribute to winning? Does it help us make better decisions? At certain pressure points, did we create an environment where it was easier to negotiate a trade or pull the trigger on drafting a certain player over another? Were we able to support that in a way that was actionable, returnable and implemented? You have to communicate that. Resources shared: ⬢ Tegan mentioned The MVP Machine book: https://lnkd.in/e5FAkxsm ⬢ Women in Sports Data Symposium + Data Hackathon: https://lnkd.in/eG8M54gH Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | Daren Eiri, Arrowhead General Insurance | Building a DS Playbook

We were joined by Daren Eiri, Director of Data Science at Arrowhead General Insurance. Daren is passionate about building creative solutions within the insurance industry using data, statistical modeling, and cloud technology. What do you do to ensure your ideas or solutions are well received by internal customers? Daren shared that they have a data science playbook for building out models. This generally follows the path of: initial kickoff, understanding what their business problem is, and if we do build out a solution - what is it going to look like? Frequently meet with the business stakeholders. Whenever you’re making major decisions or have questions about something, make sure that you reach out to them about that. Make sure that it’s part of their business workflow. If they’re adding this new feature, how is the business going to use it and continue using it so it doesn’t get left in the dark. In terms of making sure that they’re onboard, we talk about their data and what it looks like. This is a service to the team because they may not have looked at the data at that grand of a scale. They may be more used to looking at it quarter by quarter or year over year. We look at the data and provide the perspective that we’re seeing. We always ask for their hypothesis and what they think will lead to what we’re trying to predict. This helps us understand what their problem is and include their own ideas into the project as well. That results in some ownership on their side too because they are partnered with us. When we have the model built out, we evaluate it and see the accuracy. We provide them with several examples and the variation of that data to show the limitations of what that final product will look like. When you’re spending the time walking through the process and they see it working, this helps getting them onboard. Resources shared: ⬢ Javier shared this article on packages to help work with business stakeholders more comfortable with Excel: https://lnkd.in/eqXTP82c ⬢ Daren shared, The Making of a Manager by Julie Zhuo: https://lnkd.in/epjdHGgc Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

{gt} Table Battles || Digital Publications || RStudio

00:00 Introduction 00:32 Jesse’s gt table, with a focus on changing background cell color 07:11 Rich’s gt table, which uses three different tables to create a fixed-size scrollable gt table You can find the code for each table here: https://github.com/kierisi/rstudio_videos/tree/main/gt/table-battles/01_round-01_digital-publications Learn more about the gt package here: https://gt.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Rich Iannone (@riannone) & Jesse Mostipak (@kierisi) Motion Design & editing: Jesse Mostipak Music: Nu Fornacis by Blue Dot Sessions https://app.sessions.blue/browse/track/98983

Rich Iannone

gt rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Rich Iannone Jesse Mostipak TidyTuesday Table Battle

Data Science Hangout | Jennifer Listman, Statespace | Culture that Helps Avoid Burnout

We were joined by Jennifer Listman, Director of Research at Statespace. Jenny likes mining multidimensional datasets to find valuable unknown unknowns. One of the questions that had a lot of people sharing in the chat as well was: ““How do you ensure a work-life balance to avoid burnout?”” ️ A few thoughts: The culture of the team matters. Make it okay to take personal time in the middle of the week to go to doctors, take children somewhere, etc. If it’s a family member’s birthday, let people do what they need to help them celebrate that person. Having that freedom is important. As a manager, take time off yourself to set a good example. Managers have to be really careful and be on top of that with your employees. Understand who has to take responsibility for certain things and if they are at risk of experiencing burnout. A good manager recognizes when the team needs a break is key. Know when they’ve had a crazy week & let them allocate mental health days accordingly Especially for companies that have nebulous working hours and lack of specific vacation time, ensure that people are taking vacation. HR may keep tabs on if people are taking enough time off, and will make people take off. Try quarterly check-ins to make sure that people are on-track to take enough time off. Some people make the choice to not have any work stuff on my phone at all or delete those apps from your phone on the weekend. Links shared about burnout ⬢ https://lnkd.in/g5GssAck ⬢ https://lnkd.in/g5DUVj8r Speaking of culture, Statespace is hiring! ⬢ Statespace Data Scientist (Remote) - Data & Analytics team: https://lnkd.in/ddxpp3Ep ⬢ Statespace Manager of Marketing Analytics and Data Science (Remote) - Data & Analytics team: https://lnkd.in/da-9J9aw ⬢ Statespace Biostatistician / Data Analyst, Digital Health (Remote) - Digital Health team: https://lnkd.in/d2ni-7vu Other resources shared: ⬢ Jenny shared a paper Statespace recently published (all of my analyses were done in R!): https://lnkd.in/dWFE-Cqx ⬢ Rachael shared Resume Review Club form: https://lnkd.in/gjtQDS9D ⬢ Antti shared a thread on R-related podcasts: https://lnkd.in/gTNtZhdg ⬢ Book on importance of sleep: https://lnkd.in/gUtWb8YF ⬢ Bala shared a link on keeping your mind young: https://lnkd.in/g9XiGmmb ⬢ Antti shared this post on LinkedInHardMode (content challenge): https://lnkd.in/giKWzeYh ⬢ Upcoming Shiny conference (April 27-29): https://shinyconf.com/ ⬢ Bala shared podcasts he enjoys: Datacast ( https://lnkd.in/gJtSURsh), AI Podcast ( https://lnkd.in/gVddYDT2 ) and Data Science Imposters ( https://lnkd.in/gMEUMUWN ) Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | Joseph Korszun, ProCogia | Encouraging People to Learn to Code

We were recently joined by Joseph Korszun, Manager of Data Science at ProCogia. There was a great discussion on mentorship and helping teach people to code. 🤔 How do you encourage people to learn to code? (15:27) ⬢ At every point in your career, you’ve written a formula in Excel so just lending that to translate into you know more usable scripts is helpful. ⬢ It starts with identifying what their determined career path is from their own perspective. Some people want to be more statistics oriented, or more ML engineering oriented. You have to understand what their focal point is. ⬢ Help identify someone’s pain point that they have right now and with that - what’s the quickest way to up-skill them in that? ⬢ Have people do some hands-on labs. This can be more beneficial than watching videos or reading textbooks and allows you to get into the code. You’re going to have errors along the way but that’s ok! ⬢ Let new coders know that it’s okay to google and copy/paste code. This can unblock new coders, so that you don’t have to code everything from scratch. Adapt existing code to your problem. ⬢ You can pair someone with somebody else who has that skillset to help mentor them. ⬢ Remember that junior colleagues often have a lot to teach to senior team members as well. Things are changing so quickly, and there are so many ways to do things. Interns may teach you something new technically and senior colleagues may then mentor them in the soft-skills. Resources shared: ⬢ Working with IT: https://lnkd.in/dzU-uzzW ⬢ Security courses here give practical tips for how to follow good security practices as an R programmer: https://lnkd.in/dmb2nmGV ⬢ Manager tools podcast: https://lnkd.in/dXY6hK7V ⬢ Breaking Math podcast: https://lnkd.in/dEJNu64F Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Jorge Valente Hernández Castelán | Algunas formas de ver a Uber en datos: Shiny en acción

*Please note, this meetup was hosted in Spanish. Algunas formas de ver a Uber en datos: Shiny en acción Jorge Valente Hernández Castelán App: https://lnkd.in/evpSjden ¿Cuál es el límite verdadero de la programación en R? Esta pregunta me rondó la cabeza cuando conocí este lenguaje por primera vez hace unos dos años. En aquel momento imaginé que era un lenguaje algo corto de posibilidades, que estaba orientado a estadística y, únicamente, obtener resultados de algunos cálculos. No pude haber estado más equivocado en aquel momento. Cuando comencé a trabajar con Dashboards, probando primero cosas simples como Flexdashboard, me di cuenta de que el presentar información que se encuentra dentro de una base de datos, archivo de excel, etc, no solo se limita a crear estadísticos y un simple gráfico. Las aplicaciones web hechas con shiny no solo presentan información estadística, te cuentan una historia mediante los datos, la cuál puede ser presentada de muchísimas maneras. La aplicación “Uber App” de hecho tiene ese objetivo: Contar mediante estadística y geografía una historia, conocer la lógica y la tendencia de los viajes en Uber dentro de la ciudad de Nueva York, pero ¿Cómo surgió la idea de hacer esta app? En realidad fue una experiencia interesante, ya que un día me encontraba visualizando ejemplos de apps hechas dentro de Python, por lo que una en específico me pareció bastante interesante y en ese momento me hice la pregunta: ¿Es posible realizar algo similar en R? La respuesta a la pregunta anterior fue la siguiente: No solo es posible, también es posible hacerla mejor, mucho mejor. Abstract in English: What is the true limit of programming in R? This question was on my mind when I first came across this language about two years ago. At that time I imagined that it was a language somewhat short of possibilities, that it was oriented to statistics and, only, to obtain results of some calculations. I couldn’t have been more wrong at the time. When I started working with Dashboards, first trying simple things like Flexdashboard, I realized that presenting information found in a database, excel file, etc, is not just limited to creating statistics and a simple graph. Web applications made with shiny not only present statistical information, they tell you a story through data, which can be presented in many ways. The application “Uber App” in fact has that objective: To tell a story through statistics and geography, to know the logic and the trend of Uber trips within the city of New York, but how did the idea of making this app come about? It was actually an interesting experience, since one day I was viewing examples of apps made within Python, so one in particular seemed quite interesting to me and at that moment I asked myself the question: Is it possible to do something similar in R? The answer to the previous question was the following: Not only is it possible, it is also possible to do it better

Alan Carlson | Robust, modular dashboards that minimize tech debt | RStudio

Robust, modular dashboards that minimize tech debt Presented by Alan Carlson, Snap Finance Abstract Dashboards can be complex but building them shouldn’t be! We’ve built a wrapper for developing production level dashboards that streamlines onboarding new developers and standardizes the initial infrastructure to mitigate tech debt. Now you and your team can spend more time developing insights and less time trying to spin up shiny code with {graveler}. Speaker Bio As the Tech Lead for the BI (Business Intelligence) team, Alan’s primary focus at Snap is researching, creating, and maintaining methods that help the rest of Snap’s BI Team in their work. From dashboards to visualizations to R code in general, he has built multiple packages and bookdowns that make BI easier to train and to use within the RStudio environment. Helpful Links: Blog Post: https://www.rstudio.com/blog/make-robust-modular-dashboards-with-golem-and-graveler/ Graveler package: https://github.com/ghcarlalan/graveler Environment variables: https://docs.rstudio.com/connect/user/content-settings/#content-vars Git-backed publishing: https://docs.rstudio.com/connect/user/git-backed/ If you’d like to join events live: colorado.rstudio.com/rsc/community-events Question about style guides: Tidyverse Style Guide: https://style.tidyverse.org/ Efficient R Programming book that Colin Gillespie wrote: https://csgillespie.github.io/efficientR/ Questions about RStudio Team: ⬢ RStudio Connect: https://www.rstudio.com/products/connect/ ⬢ Chat with RStudio about RStudio Team: rstd.io/chat-with-rstudio

RStudio Team

Data Science Hangout | Mike Smith, Pfizer | Building an R Center of Excellence

We were joined by Mike Smith, Senior Director, Pfizer R&D UK Ltd at the Data Science Hangout - a weekly, free-to-join open conversation for the data science community. If you’d like to join us live, you can add it to your calendar here: rstd.io/datasciencehangout Mike shared with us all that they are building up a Center of Excellence at Pfizer to help teams across the business build reproducible workflows and use analytics tools effectively & efficiently. What led to the creation of the CoE within Pfizer and how could we do something similar? Mike: ⬢ Last year before R/Pharma, we did a poll & found that 1,500+ colleagues had downloaded R. I wanted to service & build up that community to find out what other people are doing and share that. (2:45) ⬢ We’re a very decentralized disparate team, so there are subject matter experts (SMEs) throughout the organization. The Center of Excellence is focused on building connections between SMEs and helping the teams where there isn’t an SME available. ⬢ What we saw was that it’s hard to sometimes get an effective strategy across people in such a big company. We also saw that there were other places within the organization that wanted data science work but they didn’t have an R subject matter expert there. We want to be able to help them solve their problems and set them up with a proof of concept that they can tweak. 33:52 - Ok so how to do this? ⬢ Find out how many people are using the tools and who you could help. ⬢ Be that translator role between the business people who need solutions with the technical side - folks who are building things. Communicate the value: ⬢ We may have a bunch of people trying to write the same function or access the same data. We could solve this problem once and then make that into a package and serve that out to everybody and streamline their workflow for the future. ⬢ There’s a benefit in being able to solve problems strategically. We’re trying to build the lego pieces so that the next time we see a problem like this, we can use that. We can also offer this as a package or via something that allows other people to solve that problem for themselves. Talk to someone who has experience in this, other community builders ⬢ Doug Robinson helped start this at Pfizer because he had set-up something like this at Novartis before as well. Talking with someone who has done this before is really helpful because they have the experience of: who do we need to tell, what do we need to tell them, what’s our purpose for being, who do you have to speak to and convince. That has to be ready to go. Find a champion in leadership: ⬢ We went to the head of Statistical programming and said we’d like to do something like this. Fortunately, she was 110% supportive here. How did they phrase this CoE at Pfizer? ⬢ Check out this description from the job post: https://lnkd.in/g776nYVF Resources shared: Ethan shared: I saw on RStudio blog the other day the {sassy} system for SAS programmer transitioning to R: https://sassy.r-sassy.org/index.html Tatsu shared: For folks that have RStudio Connect and Tableau, there’s now a supported integration https://www.rstudio.com/blog/dynamic-r-and-python-models-in-tableau-using-plumbertableau/ Tatsu shared the Working with IT section of the champion site: https://www.rstudio.com/champion/working-with-it Mike’s Bandcamp: https://mikeksmith.bandcamp.com/ R Consortium Pharma Working Groups: https://www.r-consortium.org/projects/isc-working-groups R in Pharma Conference: https://rinpharma.com/ Upcoming Pharma meetup with Merck: https://youtu.be/RBVqKi3FV30 Question about style guides: Jesus shared: Tidyverse Style Guide: https://style.tidyverse.org/ Jesus shared: One guide overall guide on better clean R code is the contributing.md of the ggplot2 package: https://github.com/tidyverse/ggplot2/blob/main/CONTRIBUTING.md Sam shared: Efficient R Programming book that Colin wrote: https://csgillespie.github.io/efficientR/ Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | Erin Pierson, Charles Schwab | Advocating for the Role you Want

We were joined by Erin Pierson, Sr Manager of Trading Operations at Charles Schwab. There was a great conversation about networking within your organization on the hangout with Erin and also tips for the question: How do you have the conversation with your manager that…this isn’t the job that I was hired to do? 🤔 11:57 - ⬢ Talk to people and let them know what you’re capable of doing and what you’re interested in doing. Hopefully you have someone who’s in your corner that will help you get to where you want to be. ⬢ Start talking about what your skills are and what you had discussed your position being. ““You have technically hired me for this job and I’m not doing that job.”” ⬢ You have to have a little finesse about it and can’t be super negative but you can say, “You know, I’m really interested in doing X, Y, Z. I appreciate this job. I’m doing this and I’m learning this but I could also be doing more. I’m interested in doing more.” ⬢ Try phrasing it as what can I do for your leadership, even if it’s really more for you. ⬢ Make your manager aware of where you are and what you want to do. You need to have a good manager to have this happen but in some cases they may even be thinking about hiring someone for that role you actually want to do. ⬢ The bottom line is, if you’re not happy in your job you’re not going to stay in your job. They don’t want you to be unhappy because they don’t want to lose you. If you’re not happy, you’re not going to be doing good work. Be honest and let them know how you’re feeling about it. Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | Joe Gibson, de Beaumont Foundation | Collaboration Across a Team

We were joined by Joe Gibson, Senior Project Director at de Beaumont Foundation. At the hangout, there was a great conversation on building a code archive database/using snippets ️ 38:16 - Sharing a bit of the conversation below: Stephanie asked: How do you build a code archive database? I’ve worked places with Word documents and need something more user-friendly. Joe: Each time that we create a work product, we log into our database - it creates a new ID for that project and we add some basic information then create a folder for it. We then store it in our structure we have in our network to make it easier for people to find things. In addition to having the library of all our code, we have some folders that have handy code snippets. Steve: We’re developing an internal package for getting data and doing common tasks is a more standard way, which is nice. Nothing too fancy but it streamlines things. Ethan: If you’re using GitHub, Gist is also a great way to share snippets of code. I have a snippet that set up the header/documentation structure for a script. On of the first bits is library(tidyverse) Mike asked: Has anyone developed R snippets and distributed them across a team? I don’t know if people are familiar with the snippets within RStudio but they are cool because you can use template frameworks and it jumps you to the next thing you need to tailor for your own situation. It’s essentially a function. Tatsu shared: https://lnkd.in/gwWCB3T2 Javier: These are super helpful, Mike. I recently learned about them and was shocked. I have all kinds of snippets for myself and my team now. Jordan: I’ve saved snippets inside a core package and have a function that updates your RStudio snippets. Saved a snippet update gist here: https://lnkd.in/gEcmNViN requires a snippets folder in your package/inst. You can have this.onLoad() if you’re feeling lucky Resources shared: ◘ Rachael shared the new champion site: https://lnkd.in/gaHt_8Br ◘ Jen & Michelle shared the National Syndromic Surveillance Program Community of Practice: https://lnkd.in/gHE3C94s ◘ Joe shared Harold’s GitHub for NSSP projects: https://lnkd.in/gFRsezTM ◘ Joe mentioned, Mozilla Foundation to ensure the internet remains a public resource that is open and accessible to us all. https://lnkd.in/gbvdQXwH ◘ Rachael shared AI Inclusive https://lnkd.in/gt8cQUUX ◘ Cris shared Fairlearn to improve fairness of AI systems:: https://fairlearn.org/ ◘ Angela shared openscapes, helping teams develop collaborative practices that are more reproducible, transparent, inclusive, and kind: https://lnkd.in/gs-6_-ZA Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Rich Iannone || {gt} Intendo Game Data Project Walkthrough || RStudio

00:00 Introduction 00:11 Setting up our environment 01:21 Importing data 01:56 Data preparation using the tidyverse 14:12 Basic gt table 16:25 Specifying row order with row_group_order() 17:20 Formatting currency with fmt_currency() 18:10 Formatting missing values with fmt_missing() 18:55 Creating row groups with tab_options() 19:50 Relabel column names with cols_label() 20:41 Creating tab spanners with tab_spanner() 23:00 Creating a table title and subtitle with tab_header() 24:40 Aligning table title and subtitle with opt_align_table_header() 25:16 Creating a stubhead label with tab_stubhead() 26:00 Format all table cell text using tab_style() 27:25 Automatically format data color based on value using data_color() 30:45 Creating Markdown-friendly source notes using tab_source_note() 32:45 Creating Markdown-friendly footnotes using tab_footnote() 39:28 Adjust table column width using cols_width() 40:55 Adjust cell padding using opt_horizontal_padding() and opt_vertical_padding() 42:22 Change row group headers using tab_style() 43:40 Convert all table text to small caps using opt_all_caps() 43:58 Change all table text font using opt_table_font() 44:28 Changing table, table heading, footnotes, and source notes background color using tab_options() 46:41 Add a table “cap” at the top and bottom using table.border.top.width() and table.border.bottom.width() 47:23 Use multiline formatting with footnotes using footnotes.multiline() 47:34 Change line style using table_body.hlines.style() 47:55 Change table title and subtitle font sizes using heading.title.font.size() and heading.subtitle.font.size() 48:11 Checking out our final table! Code to recreate the table from the video: https://github.com/kierisi/rstudio_videos/blob/main/gt/rich-intendo-project-walkthrough/intendo-30032022.R Learn more about the gt package here: https://gt.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Rich Iannone (@riannone) Motion design and editing: Jesse Mostipak (@kierisi) Music: Nu Fornacis by Blue Dot Sessions https://app.sessions.blue/browse/track/98983

Rich Iannone

gt rstudio tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Rich Iannone

“A Stroke of Innovation,” a Posit Film

This snippet is from Posit’s short film, “A Stroke of Innovation,” detailing the incredible work done by the data science team at the City of Reykjavík. You can watch the whole film and read the entire story here: https://posit.co/about/customer-stories/iceland/

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Santiago Rodriguez | Intro to functional data analysis | RStudio Meetup

Energy Meetup - Intro to functional data analysis Presented by Santiago Rodriguez 2:49 - Start of talk 12:15 - First Q&A point 23:23 - Second Q&A point 31:56 - Third Q&A point 40:44 - Final Q&A point Abstract The focus of this talk is to introduce functional data analysis (FDA) and to showcase some of its applications in the utility space. A primary source of data in a utility are meter reads. This data is periodic and appears discrete, but energy consumption is continuous, which makes meter reads a perfect use case to apply FDA. The talk will highlight two applications: load profiles and segmentation. The talk will be non-technical - no math or code - because the goal of the talk is to persuade you to investigate FDA on your own. Speaker Bio Santiago is a data scientist in the marketing department at Consumers Energy, a Michigan-based, public utility. He focuses on data engineering, data science, and MLOps. Santiago has about a decade of experience working in the analytics space across energy, aviation, automotive, and contact centers. He has a bachelor’s in finance from Florida State University and a master’s in statistics from Texas A&M. Resources shared in the chat: FDA descriptive statistics blog post: https://lnkd.in/gZjauTdt Intro to FDA Blog Post: https://lnkd.in/gM5NCbHe Refund package is introduced in ‘Introduction to Functional Data’ by Kokoszka and Reimherr: https://lnkd.in/gd5Bt3tE Gaussian Process Regression Analysis for Functional Data by Shi & Choi: https://lnkd.in/gAig-AZa GAMs in R, free course - Noam Ross: https://lnkd.in/gput2QbK Jiguo Cao - Intro to FDA on YouTube: https://lnkd.in/ga4qfGDr Feedback: rstd.io/meetup-feedback Talk submission: https://lnkd.in/gJ7EUSCk If you’d like to find out about upcoming events you can also add this calendar: rstd.io/community-events Packages shared in the chat: Task view https://lnkd.in/gBqtuV2b FDA: https://lnkd.in/ge85i6UE Refund package: https://lnkd.in/gXGcT79t FDA.USC: https://lnkd.in/ggHdUaEe

Posit Presents: A Stroke of Innovation

Introducing “A Stroke of Innovation,” a short film made possible by a team of innovators looking to make the world a better place. Visit https://posit.co/about/customer-stories/iceland/ for the full web experience

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Programming Games with Shiny || Dragon Realm || RStudio

00:00 Introduction 00:05 Fun dragon facts 00:35 Describing the Dragon Realm game 01:20 Outlining our approach 04:38 Coding the basics of our app 10:15 Programming our action buttons 14:10 A note on coding objects “outside” of Shiny 15:27 Programming cave choice logic 20:29 Connecting action buttons to our consequences function 29:40 Creating separate pages using tabsetPanel() 39:35 Conclusion You’ve most likely used Shiny to build a web app that displays data, but you can also use Shiny to build games! In this video series, Jesse and Barret pair program simply games in Shiny as a way to uncover and explore new features. And because we know you’ll ask, Jesse is using the Woodland theme from the base16 palette. You can get it - and other themes - from the {rsthemes} package: https://github.com/gadenbuie/rsthemes Read up on tabset panels here: https://shiny.rstudio.com/reference/shiny/1.5.0/tabsetPanel.html Learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) and Jesse Mostipak (@kierisi) Animation, motion design, and editing: Jesse Mostipak (@kierisi) Intro music: RGift by Blue Dot Sessions ( https://app.sessions.blue/browse/track/91282 ) Theme song: Hakodate Line by Blue Dot Sessions ( https://app.sessions.blue/browse/track/91291 )

Barret Schloerke

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Dragon Realm Barret Schloerke Jesse Mostipak

Brad Lindblad | Professional Financial Reports with {rmarkdown} | Posit

GitHub: https://github.com/bradlindblad/pro_reports_talk Abstract: With finance there will always be a need for reports, and as long as there’s a need for reports, there will be R users who want to create them as lazily as possible. R Markdown lets us create incredibly customized and branded reports that can run automatically each month or day or whatever, and it all starts with the wonderful parameterizing features of R Markdown. In this lightning talk, we will work through a practical example of creating an income statement for a group of theoretical office branches. You will learn how to make a parameterized R Markdown report, organize your R Markdown files and even create a custom cover letter, all in R. Bio: Brad Lindblad is a data scientist located in Fargo, North Dakota. He is author of the tidyUSDA and schrute R packages, and specializes in geospatial data science and risk modeling. Brad is a frequent contributor to data science publications and loves creating new R users. This is a meetup recording from December 2020. For more information on how to join meetups live: rstd.io/community-events Links shared in the chat: Brad’s material/slides: https://github.com/bradlindblad/pro_reports_talk For anyone who’s new to R Markdown, this is a great reference guide and overview: https://bookdown.org/yihui/rmarkdown/ Pagedown package: https://github.com/rstudio/pagedown ETL example: https://solutions.rstudio.com/r/apps/twitter-etl/ More information on RStudio Connect: https://www.rstudio.com/products/connect/ To chat with RStudio about Connect: rstd.io/chat-with-rstudio

Data Science Hangout | Kristi Angel, Stitch Fix | How to Break into Data Science

On March 10th, we were joined by Kristi Angel, Data Scientist - Experimentation @ Stitch Fix. This week featured a lively discussion on breaking into data science: Try to get around the applicant tracking systems by networking - we can call it something different though. Utilize LinkedIn as a professional social media. Find people who are generating content that you find interesting. Engage and people will start to know who you are by your comments and feedback. This will help you build a reputation for yourself. Try to connect directly with the hiring manager. There are a lot of hiring managers advertising for roles on LinkedIn right now. Take a look at your resume and see if you can get some reviews from peers you trust and see if there’s a way that might be more effective. Your resume needs to tell your story in less than a 5 minute glance. A lot of us mentioned we would be willing to help review here - let’s think of ways to connect people through this group too:) For some people hitting HR roadblocks, where large businesses have decided that there are minimum requirements for education and experience and there is absolutely no budging from it - looking at smaller companies without those rules has helped lots of people I know. On the topic of interviewing and hiring people with degrees vs no degrees or not a relevant degree ️ A lot of us shared in the chat that we have different degrees than the job we’re doing today:) Looking past the degree is a really good idea in many cases because you need diversity of thought and background. If you want the company as a whole to have empathy for customers, it’s important to have a really varied set of life experience backgrounds. We still acknowledge, it can be hard because the degree and experience gives a little bit of a certification, especially for junior people. In startups especially, the time spent hiring can be a lot of drain on the organization so degrees are often used to quickly filter. If you’re on the side of the company interviewing, something that can also help is to make sure that all interview loops have a diverse representation. Links shared: Frank shared: Kahnaman’s book Noise has some great frameworks for interviewing and how we make decisions on candidates. One example, stack rank your candidates instead assessing each individually because (people) are typically bad at that: https://readnoise.com/ Melody’s team is hiring several Data Scientist positions and a Data Architect position. Here are a few links: https://lnkd.in/gf9nqqWV https://lnkd.in/gKwEcqC2 Data Architect position: https://lnkd.in/g3dRxKMG Liz shared: Manager Tools has a great podcast about resumes https://lnkd.in/gsRDKrqF" Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

Data Science Hangout | Stephen Bailey, Whatnot | From Academia to Industry

We were joined by Stephen Bailey, Data Engineer at Whatnot and former Director, Data & Analytics at Immuta at the Data Science Hangout. A few topics from the discussion: being the first data scientist working with real-time data bringing data science skills to data engineering getting people to feel ownership of data 49:43 - Towards to end of the hangout, there were a lot of thoughts on moving from academia to industry. Tatsu asked: As a fellow recovering academic, I’d love to hear your views of where we are in terms of the process for PhDs to go into industry in data related role? In psychology, there was minimal visibility into how to apply my skill set. A few thoughts from the discussion: ‣ With a PhD program, it’s almost like you have a relationship with the field and you’re very invested in the research. You’re part of the scientific community. One of the things that’s very jarring about leaving, is that it feels like a divorce - severing that relationship and that community. It is almost two different worlds. ‣ If anyone’s thinking about transitioning or doing something similar, talking to people and building relationships before that transition can really help. ‣ Intentionally think about branding yourself. ‣ The biggest thing in industry is that you can’t fall in love with a problem. It makes finding a job much harder. You have to fall in love with your position in some way, how you are solving problems. Apply the mindset of a scientist into this business context. ‣ The skillsets that everyone’s learning in graduate school - no matter what the subject area is, are very helpful. My biggest advice would be don’t be afraid to reach out to people (whether on LinkedIn or in community events.) Most people are very willing to talk to you about what their journey was no matter where they’re at in their career. Once you realize that, you will see that your room is much bigger. ‣Finding a mentor outside academia is really helpful. ‣ Certain departments within universities do a really nice job connecting with industry with the community and have great collaboration. How can that carry over to other institutions? How can companies better connect with colleges to help them understand how they can help more students? Other resources shared: Santiago shared: I watched this recently. It explains zero knowledge proofs: https://lnkd.in/g6iS-97i (source = Wired) Ian shared: PAWS is a very useful package for interacting with AWS: https://lnkd.in/g_Fh9t6X ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: https://www.addevent.com/event/Qv9211919 Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstu… Twitter: https://twitter.com/rstudio

Data Science Hangout | Mike Miller, Engine | Adjusting for Stakeholder Tendencies

We were recently joined by Mike Miller, Vice President and Data Science Team Leader at Engine. We dove deep into a conversation towards the end of the discussion regarding surveys (whether internal or to clients): Responder fatigue is a huge concern and something we deal with on a daily basis. ️ Keep it short, keep it focused. Make sure you’re asking people what you want to get out of it. It’s easy to go down a rabbit hole with data you’d like to see. Every time you add a question make sure it’s focused on your main objective. ️ Five questions, maybe 10 max. More than that you should be paying your respondents. ️ Double-barreled questions are bad - check for these. (questions composed of more than two separate issues or topics, which however can only have one answer.) ️ Write your question well. Think about the people answering this. Make sure the question is asking what you want people to respond to and that there’s no way for other people to interpret it differently. ️ Resources mentioned as well: Bryan’s meetup talk on Survey Design: https://lnkd.in/dCK_Ga8x Mentorship program Maisie shared: https://lnkd.in/gwPyH-Mr" Where to find more? ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Data Science Hangout site: rstudio.com/data-science-hangout ► Add the Data Science Hangout to your calendar: rstd.io/datasciencehangout Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio Twitter: https://twitter.com/rstudio

George Mount | R for Excel Users - First Steps | RStudio Meetup

Abstract: Excel’s built-in programming language has served as an entry point to coding for many. If you’re a data analyst steeped in Excel, chances are you could also benefit from learning R for projects of increased scope and complexity. This presentation serves as a hands-on introduction to R for Excel users: How R differs from Excel as an open source software tool How to translate common Excel concepts such as cells, ranges, and tables to R equivalents Example use cases that you can take and apply to your own work How to enhance Excel and Power BI with R By the end of this presentation, you will have a clear path forward for building repeatable processes, compelling visualizations, and robust data analyses in R. Speaker Bio: George Mount is the founder of Stringfest Analytics, a consulting firm specializing in analytics education and upskilling. He has worked with leading bootcamps, learning platforms and practice organizations to help individuals excel at analytics. George regularly blogs and speaks on data analysis, data education and workforce development. He is the author of Advancing into Analytics: From Excel to Python and R (O’Reilly). Link to George’s white paper “Five things Excel users should know about R”https://stringfestanalytics.com/five-things-r-excel/ Working group sign-up for those interested! Within many organizations Microsoft Excel is a preferred tool for working with data for non data analytics users. In order to build a data driven organization, source data and analytical models must be accessible to all data users (technical and non-technical) within their preferred tool. Let’s rally the R community to welcome Excel users into our data driven culture by building an Excel add-on to access data and models available within RStudio. If you’re interested in continuing this conversation and joining a working group, let us know! rstd.io/excel-r-community Links shared at the meetup! George’s GitHub/ Presentation Resources: https://github.com/stringfestdata/rstudio-mar-2022 Packages? Where to find them & recommendations: CRAN Task Views: https://cran.r-project.org/web/views/ Mark shared: for folks who primarily use excel to present formatted tables, the gt package is a great way to start doing this programmatically in R: https://gt.rstudio.com/ Ivan shared: In addition to regular Google, I’d recommend https://rseek.org/, given that the character ‘R’ is sometimes not search friendly:) Jeff shared: Fpp2 is great for forecasting and time series analysis - https://otexts.com/fpp2/ Floris shared: https://otexts.com/fpp3/ Ivan shared: If you’re into tidyverse, there’s an equivalent for time-series: https://tidyverts.org/ George shared: https://dplyr.tidyverse.org/ Ryan shared: This can be a helpful package for dynamically editing tables, like in excel https://github.com/DillonHammill/DataEditR Ryan shared: This is a great package for making and learning ggplot visualizations: https://cran.r-project.org/web/packages/esquisse/vignettes/get-started.html Other resources: Monaly shared: There is a R help group: r-help@r-project.org George shared: Helpful book/site on statistics: https://moderndive.com/ Ryan shared:Harvard has a good online source (free options) that has a number of classes, the following for stats: https://www.edx.org/professional-certificate/harvardx-data-science George shared: R for Data Science free book: https://r4ds.had.co.nz/ Fernando shared: big book of R https://www.bigbookofr.com/index.html Floris shared: Advanced R Book: https://adv-r.hadley.nz/ Pedro shared: The R for Data Science Slack channel is a great learning resource! r4ds.io/join (we just made a channel there called #chat-excel_to_r Ivan shared: For teams who are deeply entrenched in Excel (like my old team), this tool may be useful - https://bert-toolkit.com/. It allows running R code in.xls, so you can learn R while doing.xls:) Re: Glossary of terms: Ivan shared: inner_join() is like VLOOKUP in.xls. Dan shared: Here’s one cheat sheet (glossary of Excel to R) that I just found; https://paulvanderlaken.com/2018/07/31/transitioning-from-excel-to-r-dictionary-of-common-functions/ Extra Meetup Links Feedback: rstd.io/meetup-feedback Talk submission: rstd.io/meetup-speaker-form If you’d like to find out about upcoming events you can also add this calendar: rstd.io/community-events RStudio conference/submit a talk: https://www.rstudio.com/conference/ Recordings of all meetups: https://www.youtube.com/playlist?list=PL9HYL-VRX0oRKK9ByULWulAOO5jN70eXv

Mara Averick & Maya Gans | Data Visualization Accessibility | RStudio Meetup

2:55 - A11Y in R: Adapting Sarah L. Fossheim’s 10 dos and don’ts to keep in mind when designing accessible data visualizations | Maya Gans 30:11 - Adventures with {highcharter} and the Highcharts accessibility module | Mara Averick 58:07 - Q&A A11Y in R: Adapting Sarah L. Fossheim’s 10 dos and don’ts to keep in mind when designing accessible data visualizations | Presented by Maya Gans Abstract: This talk will use R based visualizations and walk through examples of accessibility considerations when making plots and applications. Speaker Bio: Maya Gans is a Data Visualization Engineer at Atorus Research where she develops custom applications using R and JavaScript. As an RStudio intern she designed TidyBlocks, a visual block based programming language. Maya also co-wrote JavaScript for Data Science. Maya uses ggplot2 and d3.js to create music related infographics for JamBase.com. When Maya’s not coding, she’s climbing mountains. Adventures with {highcharter} and the Highcharts accessibility module | Presented by Mara Averick Abstract: Lessons learned about accessibility in data visualization through using the {highcharter} R package and the Highcharts visualization library’s accessibility module. Speaker bio: Mara is a developer advocate at RStudio. She is the author of neither the highcharter package nor the Highcharts charting library, but enjoys using both to make interactive, accessible data visualizations. So many amazing resources shared yesterday on data visualization accessibility: Sarah L Fossheim’s blog - intro to designing accessible data viz: https://lnkd.in/dAAXfE35 Coblis - Color Blindness Simulator: https://lnkd.in/dJT-hJE4 Web Content Accessibility Guidelines (WCAG): https://lnkd.in/dJKmvFTq Color Contrast Accessibility Validator: https://color.a11y.com/ Google lighthouse (automated tool for improving quality of web pages): https://lnkd.in/d8xjSN5i A11y Project Checklist: https://lnkd.in/diFM_TBd Chartability (questions) for ensuring data visualizations, systems, and interfaces are accessible: https://lnkd.in/d_7wk3zx Accessible {highcharter} GitHub repo (Mara’s charts, and source.Rmds): https://lnkd.in/dhvBwQ-f Mara’s blog post series: https://lnkd.in/d9xz6VZ6 10 Guidelines for DataViz Accessibility by Øystein Moseng: https://lnkd.in/dS-XsKxw Accessible visualization via natural language descriptions by Alan Lundgard and Arvind Satyanarayan: https://lnkd.in/dpdN4skK DataViz Accessibility Advocacy and Advisory Group: https://lnkd.in/d336ACn3 Alt-texts: The Ultimate Guide by Daniel Göransson: https://lnkd.in/dsHcvPs2 JooYoung Seo’s Talk on non visual interactions with R packages: https://lnkd.in/dcid56BT Accessible Data Science for the Blind Using R: https://lnkd.in/dTWZbau8 Maya’s blog on skip links: https://lnkd.in/dFTYFxTk Twitter alt text: https://lnkd.in/d9bKqiPU Twitter account to follow: @alttextreminder Silvia Canelón’s blog posts: https://lnkd.in/drwbE2Rf Packages shared: ggpattern: https://lnkd.in/dyTBvvz4 gglabeler: https://lnkd.in/dumA8Um8 gghighlight: https://lnkd.in/d_m25j7x sonfiy: https://lnkd.in/dyPwHimP tuneR: https://lnkd.in/dWi2WZH8 brailleR package - https://lnkd.in/d_75cdnQ Caleb brought up a great point that visualizing data isn’t new, so it cam be helpful to look at adjacent disciplines to see ways people have solved these before as well. For example, cartographers have had really creative ways to make things visible. Sarah Belle, cartographer who makes fonts / typography really legible on maps: sarahbellmaps.com/belltopo-sans-font-by-sarah-bell/ Cynthia Brewer’s work on color palettes. https://colorbrewer2.org/# cartographers who use 3d printing for tactile maps: https://touch-mapper.org/en/

Shiny Usage Tracking in Posit Connect

🤔 Who actually used my Shiny app and for how long? ↗️ Is viewership increasing? Did my CEO use it?! Did you know? Posit Connect can record event-style usage information which is intended to answer questions like this and is accessed via the Posit Connect Server API. Please note: during the meetup you can ask questions here anonymously as well: rstd.io/connect-meetup-questions We will walk through several examples for getting started with the Posit Connect usage data designed to help you answer questions like: Who is visiting my content? What reports are most common? Has viewership increased over time? During/after the meetup, you can use the GitHub repo to create the dashboard in your own Connect environment as well and schedule the report to be distributed through email (with inline graphics). The goal for this code is that it is generic enough so that you can copy/paste it into your own R session and run it successfully. In order for the code to work in your environment, you need two pieces of information unique to your enterprise: Posit Connect’s server path A Posit Connect API Key We’d love to hear your feedback and learn how you have taken this API and created dashboards for your own organizations as well. Helpful Links: Follow-up thread on community.rstudio.com: https://community.rstudio.com/t/rstudio-connect-usage-data-thread-to-discuss-ideas-improvements/130581 Contribute to open-source examples: https://github.com/sol-eng/connect-usage Posit Connect Server API: https://docs.rstudio.com/connect/api/ Cookbook: https://docs.rstudio.com/connect/cookbook/ R Client: https://pkgs.rstudio.com/connectapi/ Python Client (mostly deployment): https://github.com/rstudio/rsconnect-python/ Cole’s slides: https://github.com/RStudioEnterpriseMeetup/Presentations If you ever have questions about Posit Connect or any of our professional products, you can use this link to chat with our team: rstd.io/chat-with-rstudio

Carson Sievert || Using tagQuery() from {htmltools} to modify HTML snippets in R || RStudio

00:00 Introduction 00:45 Motivating example - enabling front-facing camera as an input for fileInput() 01:55 Breaking down the return value of fileInput() 04:16 Design philosophy of fileInput() 07:27 tagAppendAttributes() overview 11:05 tagQuery() basics 12:00 Quick overview of the htmltools package 13:18 How tagQuery() is used to append attributes 20:54 How tagQuery() is used to append children 23:45 Using tagQuery() on an actionButton() Learn more about tagQuery here: https://rstudio.github.io/htmltools/articles/tagQuery.html Read up on tagAppendAttributes() here: https://shiny.rstudio.com/reference/shiny/latest/tagAppendAttributes.html And learn more about the htmltools package here: https://rstudio.github.io/htmltools/index.html Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Developer (@winston_chang) Animation, design, and editing: Jesse Mostipak (@kierisi)

Carson Sievert

htmltools rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Htmltools Carson Sievert Tagquery Tagquery()

James Blair || Getting Started with {plumbertableau} || RStudio

00:00 Introduction 01:19 Setting up the problem - capitalizing text with a custom function 02:18 Using Plumber to create an API for our function 04:08 Using Run API + Swagger from the RStudio IDE 05:44 Giving Tableau access to the function with PlumberTableau 09:16 Reviewing what we’ve done so far 09:47 Comparing results between Plumber and PlumberTableau 10:12 Overview of what PlumberTableau does 14:27 Centralized hosting with RStudio Connect 15:17 Looking at our API in RStudio Connect 18:14 How to access the deployed API from Tableau 21:03 Overview of RStudio Connect, Tableau, and PlumberTableau process 21:52 More in-depth example using sample sales data 22:36 Example with the Python equivalent of PlumberTableau, FastAPITableau 25:15 Overview of how these Tableau extension packages work 27:21 Setting up a connection between Tableau and RStudio Connect Read more about the plumbertableau package here: https://rstudio.github.io/plumbertableau/ And learn about the fastapitableau package here: https://rstudio.github.io/fastapitableau/ If you’re unfamiliar with Plumber, this Quickstart guide gives a good overview of the package: https://www.rplumber.io/articles/quickstart.html And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: James Blair (@Blair09M) Design and editing: Jesse Mostipak (@kierisi) Music: Borough by Blue Dot Sessions https://app.sessions.blue/browse/track/89821

plumber plumbertableau rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Plumbertableau James Blair

Data Science Hangout | Katie Schafer, Beam Dental | Building a Data Science Portfolio

We were recently joined by Katie Schafer, VP of Advanced Analytics at Beam Dental, to discuss the most important things going on with data science leadership. Great tips & resources shared for this question: What can I do to be able to present my skills and build a portfolio if I’m not able to share my work? Many people expressed that they are in this position. Katie: Is it possible to simulate something akin to some of the data you would use? For project portfolios, submitting the actual work is not something that I’ve seen often. In interviews, you need to be able to discuss the work. Describing the projects is really tough because you want to say something meaningful but you don’t want to be too specific. Having a role play conversation with yourself on questions you’re getting & practicing answers that are more generalized can be helpful. Take one specific problem, translate that to an abstract problem that you tackled with statistics and programming. Javier: I’ve been in a similar boat. There’s a package called synthpop: https://lnkd.in/g_paFGNB, which helps take actual data points and simulates them to make a completely synthetic data set. When I was interviewing for my job now I built a shiny app that I deployed to a free shinyapps.io. I styled the whole app to be consistent with their brand. I don’t attribute that alone to getting the job, but the interviewers were super impressed. The ability to change out the theme is so easy, so for anyone who’s trying to get a job this could be an easy way for you to have something that you can change whenever you interview with a new company. Rick: If you’ve ever worked with a designer - they have a template and they’re not just creating everything from scratch. If you have a nicely organized project as a data scientist, you’re showing your skills as a software engineer. Zac: I do a lot of personal projects because I don’t use R at work. For these projects I focus the topic on what skills I want to show off. I’m looking to start my career, so I write a blog & include reasons why I did something. I think this makes it a lot easier to share what I’ve done https://lnkd.in/gzhwuCqw Libby: For me with portfolio work, it’s been difficult because sometimes my contributions are one algorithm inside a larger shared project, so I stick to descriptions, talking about what I did and why I did it, what the result was. Larry: I describe the topic and present the deck. Sometimes I just make fake data, but talk about the steps I took. Katie: Some of my richest connections have also come from speaking. R-Ladies has been a great starting point for that. A lot of meetups have gone virtual so you can now join a chapter even if you don’t have one in your city. Jiwan: Maybe start with a 5 min lightning talk with a topic you’re excited about! Other resources shared during the chat: Prabha: Technical debt in ML systems - https://proceedings.neurips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf Rachael: R community explorer app: https://benubah.github.io/r-community-explorer/rugs.html Javier: GitHub for the {bslib} app, feel free to clone as you see fit: https://github.com/JavOrraca/bslib_demo_shiny Jobs shared during the chat: Niklas: Several data science roles at my company (sustainability SaaS). 2 data engineers, 1 data analyst, 1 data scientist - https://higg.com/careers/ Jill: We are hiring for a Data Analyst role here at Viemed. We are a healthcare company and we LOVEEE DATA. https://viemed.apscareerportal.com/jobs/1338665/apps/new?embed=1"

Data Science Hangout | Asmae Toumi, PursueCare | Data Science Team Culture

We were recently joined by Asmae Toumi, Director of Analytics at PursueCare, to discuss the most important things going on in data science leadership. There was a great discussion during this session starting from the question, “Is it ok to say that we are data analysts and data scientists interchangeably?” Javier: Some of the best data scientists have the title of financial modeler or senior actuary. Some of the best programmers and package developers I know, have the title of data analyst. I think it depends on the organization. We have these guidelines that are more of an ideological function of a data scientist vs data analyst but I think in practice it’s all over the place. Asmae: Jacqueline Nolis and Emily Robinson have good thoughts about this: https://jnolis.com/book/ (Short rules from the book I saw: An analyst creates dashboards and reports that deliver data. A decision scientist creates analyses that produce recommendations.) Mike: I think to anyone that is OUTSIDE of Data Science / Data Analytics, the distinction is not worth discussion. If you want funding you’re a Data Scientist. If you HAVE funding it doesn’t matter. Frank: No right answer (DS vs. DA)… only different ways to think about it at different times in different contexts. Jordan: I’ve usually been working in a ““Science”” (science/research) domain at companies. To this end, I’ve sort of been the ““Data”” Scientist, maybe not ““Data Scientist”” What about business intelligence? Asmae: I use data analyst and data scientist interchangeably. I think there is a meaningful difference there between a business analyst and a data scientist. What about data engineering? Asmae: We have 3 data engineers. Data scientists all want a data engineer. As a healthcare company, it’s complex to deliver the care that we do. With that complexity comes interfacing with different data vendors - different data sources so it’s really important to invest in data engineering so the reconciliation of that data across the many vendors is ensured. Data quality is also super important. Bryan: Data engineering is the foundation of it all — need a solid data engineer to do anything Falak: Data scientists understand the distribution of the data and figure out what class of algorithms would be suitable to handle a particular problem. Data engineers can solve the optimization problem. Other resources shared during the hangout: Allissa: An NIH prize challenge on maternal health that may be of interest to some - https://lnkd.in/gbtxTTcF Bala: Top 10 Roles for your Data Science Team: https://lnkd.in/g_SVzxDcAsmae Toumi, Director of Analytics at PursueCare, to discuss the most important things going on in data science leadership

Carson Sievert || Customizing Navigation Items in Shiny using {bslib} || RStudio

00:00 Introduction 00:15 Linking inside navbarPage 01:19 Replacing tabPanel with navbarPage, and navbarMenu 02:32 nav_spacer() 03:41 Adding header and//or footer content 04:07 Replacing tabsetPanel with navs_tab and navs_pill 04:32 navs_tab_card() and navs_pill_card() variants 04:40 Demo of all of the nav_*() functions The bslib R package provides tools for customizing Bootstrap themes directly from R, making it much easier to customize the appearance of Shiny apps & R Markdown documents. bslib’s primary goals are: To make custom theming as easy as possible. Custom themes may even be created interactively in real-time. Also provide easy access to pre-packaged Bootswatch themes. Make upgrading from Bootstrap 3 to 4 (and beyond) as seamless as possible. (Shiny and R Markdown default to Bootstrap 3 and will continue to do so to avoid breaking legacy code.) Serve as a general foundation for Shiny and R Markdown extension packages. (Extensions such as flexdashboard, pkgdown, and bookdown already fully support bslib’s custom theming capabilities.) You can read more about bslib here: https://rstudio.github.io/bslib/articles/bslib.html And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Carson Sievert (@cpsievert) Design & editing: Jesse Mostipak (@kierisi)

Carson Sievert

bookdown bslib flexdashboard pkgdown rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Bslib Carson Sievert

Carson Sievert || Developing Shiny Custom Themes in Real Time Using {bslib}| RStudio

00:00 Introduction 00:09 The magic of bs_theme_preview() 01:43 The interactive widget provided by bs_theme_preview() 02:12 Using Bootswatch themes 02:57 Using the interactive widget to adjust your theme in real time 03:38 Integration with Google Fonts 04:22 Thematic is enabled in bs_theme_preview() 04:45 DT tables is enabled in bs_theme_preview() 05:30 Going from the interactive widget to your R code 07:03 Using interactive theming on your own Shiny app 09:01 Interactive theming with R Markdown documents The bs_theme_preview() function launches an example shiny app via run_with_themer() and bs_theme_dependencies(). This is useful for getting a quick preview of the current theme setting as well as an interactive GUI for tweaking some of the main theme settings. Link to docs: https://rstudio.github.io/bslib/reference/bs_theme_preview.html You can read more about the bslib package here: https://rstudio.github.io/bslib/ And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Carson Sievert (@cpsievert) Design and editing: Jesse Mostipak (@kierisi)

Carson Sievert

bslib DT rstudio Shiny thematic RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Bslib Carson Sievert

Alok Pattani - Google | Sports Analytics Meetup | RStudio

Abstract: The increasing volume, variety, and velocity of sports data provides both great opportunities and challenges for data scientists working in sports. Using R with Google Cloud data science tools like BigQuery can help practitioners scale their analysis and impact in this “new era” of sports analytics. This presentation will include a demonstration of using R and Google Cloud together with an NCAA basketball data example, as well as a discussion of the application of such metrics and tools in the sports media and technology industries. Speaker Bio: Alok is a Data Science Developer Advocate at Google, where he shows how to use Google Cloud tools for data science, in sports and otherwise. He is a sports analytics expert and a long-time user of R and RStudio. Before joining Google, Alok spent 8 years at ESPN, where he was a founding member of their Sports Analytics team and contributed significantly to the use of analytical content across all media platforms. Alok is originally from Cheshire, CT and earned a BA/MA in statistics from Boston University. Alok’s Slides: https://lnkd.in/gxQUf8nV Packages shared: sportsdataverse: https://lnkd.in/g8UKAJgc wehoop: https://lnkd.in/grBgmc33 hoopr: https://lnkd.in/gRHNWV4j glmnet: https://lnkd.in/g4cuxYzs googleAuthRverse: https://lnkd.in/gf5fRgcC All of Mark Edmondson’s Google packages: https://lnkd.in/gmTpvMsY Other resources shared: Sports data: https://www.spotrac.com/ Analyzing NCAA Basketball with GCP: https://lnkd.in/gT-4vWwa 2020 Google Cloud March Madness Insights: https://lnkd.in/gEWd9xtu Alok’s presentation on Innovating the MLB Fan Experience through Data: https://lnkd.in/g9XmFKsy NFL Player Tracking Data Meetup recording: Analyzing Soccer Data with BigQuery: https://lnkd.in/gbbCGKaK Sports channel on the R for Data Science Online Learning Community Slack: r4ds.io/join # chat-sports_analytics

Carson Sievert || Custom Theming with {bslib} in Shiny and R Markdown using bs_theme() || RStudio

00:00 Introduction 01:15 Jumping right in with the theme argument in Shiny 01:31 Shiny’s classic navbarPage using bs_theme() 01:46 Specifying your Bootstrap version 02:31 Using the theme argument in R Markdown 03:17 Custom theming in R Markdown using bs_theme() 04:10 bslib templates provided by RStudio 05:33 Review of common arguments for the theme parameter 08:47 Tips for working with dark themes 10:34 Introduction to the thematic package, which styles plots 12:04 How thematic handles fonts 13:09 Using fonts with bslib in R Markdown 14:36 Moving a theme from an R Markdown document into a Shiny app 16:51 Setting warnings for contrast ratios 18:42 A quick tour of Bootstrap 4 and Bootstrap 5 Sass variables 20:35 A quick overview of writing custom HTML in Shiny 22:15 How bslib automatically handles color contrast ratios bs_theme() allows you to creates a Bootstrap theme object, where you can choose a (major) Bootstrap version, choose a Bootswatch theme (optional), customize main colors and fonts via explicitly named arguments (e.g., bg, fg, primary, etc), and customize other, lower-level, Bootstrap Sass variable defaults via …. You can read more about the bslib package here: https://rstudio.github.io/bslib/ And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Carson Sievert (@cpsievert) Design and editing: Jesse Mostipak (@kierisi)

Carson Sievert

bslib rstudio Shiny thematic RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Bslib Bs_theme() Bs_theme Carson Sievert

Barret Schloerke || Maximize computing resources using future_promise() || RStudio

00:00 Introduction 01:45 Setting up a multisession using the future package 02:05 Simulation using two workers 04:14 Simulation using 10 workers 05:20 What happens when we run out of workers? 05:35 How Shiny handles future processes like promises 07:16 Introduction to future_promise() 07:45 Demo of the promises package 09:21 Setting the number of workers 10:40 Demo of processing without future_promise() 14:11 Wrapping a slow calculation in a future() 14:53 Demo of processing using Plumber 16:25 Considerations on the number of cores to use 17:21 What happens if we run out of workers? 19:44 Decrease in execution times using future_promise() In an ideal situation, the number of available future workers (future::nbrOfFreeWorkers()) is always more than the number of future::future() jobs. However, if a future job is attempted when the number of free workers is 0, then future will block the current R session until one becomes available. The advantage of using future_promise() over future::future() is that even if there aren’t future workers available, the future is scheduled to be done when workers become available via promises. In other words, future_promise() ensures the main R thread isn’t blocked when a future job is requested and can’t immediately perform the work (i.e., the number of jobs exceeds the number of workers). You can read more about the promises package here: https://rstudio.github.io/promises/articles/shiny.html And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) Design and editing: Jesse Mostipak (@kierisi)

Barret Schloerke

plumber rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Promises Barret Schloerke Future_promise()

Nick Strayer || Part IV: Styling a Shiny Wordle App with CSS || RStudio

00:00 Introduction 00:44 Switching from verbatimTextOutput to uiOutput 01:42 Switching from renderText to HTML DOM elements 03:17 In-line styling with divs 07:30 Converting individual letters from block elements to adjacent grids with CSS grid 08:56 Adding CSS at the head of the UI variable in Shiny with tags$head (and wrapping with HTML!) 10:36 CSS targeting of the background color 12:24 Link: Complete Guide to CSS Grid 14:05 Moving text position within each individual div using CSS classes 16:48 Creating a gap between grid elements 17:13 Rounding border edges for letter grids 19:00 Formatting letter grid background color to indicate result “correctness” 21:30 Increasing font size 23:37 Updating the legend to use color, not text indicators 26:40 Adjusting padding to improve app aesthetic 28:08 Formatting the app UI with justified centering 31:56 Adjusting the text input and Go button 34:07 Why Flexbox is the right tool for this task 35:09 Exploring Flexbox Dev Tools in Chrome 39:14 Adjusting the colors of letter grids using Inspect Element 40:40 Making text bold with font-weight 41:04 Hint on how to approach formatting the keyboard In final installment of this four-part series, RStudio’s Nick Strayer walks through using CSS to stylize our Shiny Wordle app. Code + word list: https://github.com/wch/shiny-wordle Check out the full Shiny app here: https://winston.shinyapps.io/wordle/ You can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Nick Strayer (@NicholasStrayer) Animation, design, and editing: Jesse Mostipak (@kierisi) Music: Lakal by Blue Dot Sessions Wordle: https://www.powerlanguage.co.uk/wordle/

Nick Strayer

rstudio Shiny shinyapps RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate CSS Wordle Nick Strayer

Barret Schloerke || {reactlog} Rundown || RStudio

00:00 Introduction to Reactlog 00:44 Viewing Reactlog using an Old Faithful Shiny app 02:07 The Reactlog interface 04:31 Walking through a reactive graph with Reactlog 05:14 Downstream dependency invalidation in Shiny 06:43 How Shiny “grabs” data 09:41 How the Reactlog timeline works 10:46 Switching between idle states in Reactlog 11:58 Reactlog interactivity - clicking a single item 13:21 Reactlog with the Pythagoras Theorem app 15:45 Adding a UI and server value to add Reactlog to your Shiny app 18:05 Walking through the reactive graph using the Pythagorean Theorem app 21:07 Append-only behavior of Reactlog 21:18 Marking a time point in Reactlog 23:17 Using Reactlog to debug reactivity 26:55 Resetting our app and testing logic changes 28:01 Reactlog with a large Shiny app, CRANwhales 34:10 Freezing reactive values 36:19 Calculating click count in a Shiny app 37:10 Click the button, render the plot is bad - see why Shiny is an R package from RStudio that makes it incredibly easy to build interactive web applications with R. Behind the scenes, Shiny builds a reactive graph that can quickly become intertwined and difficult to debug. reactlog provides a visual insight into that black box of Shiny reactivity. After logging the reactive interactions of a Shiny application, reactlog constructs a directed dependency graph of the Shiny’s reactive state at any time point in the record. The reactlog dependency graph provides users with the ability to visually see if reactive elements are: Not utilized (never retrieved) Over utilized (called independently many times) Interacting with unexpected elements Invalidating all expected dependencies Freezing (and thawing), preventing triggering of future reactivity There are many subtle features hidden throughout reactlog. Here is a short list quickly describing what is possible within reactlog: Display the reactivity dependency graph of your Shiny applications Navigate throughout your reactive history to replay element interactions Highlight reactive family trees Filter on reactive family trees Search for reactive elements You can read more about reactlog here: https://rstudio.github.io/reactlog/articles/reactlog.html And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Barret Schloerke (@schloerke) Design & editing: Jesse Mostipak (@kierisi)

Barret Schloerke

cranwhales reactlog rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Reactlog Reactivity

Dmitri Adler & Merav Yuravlivker | The shift to data: Industry trends in finance | Posit

Posit Finance Meetup The shift to data: Industry trends from banks to hedge funds to federal agencies How has the finance industry shifted towards data in the past 5 years? Will all analysts need to program in Python in order to have a job in the future? Join Data Society co-founders Merav Yuravlivker and Dmitri Adler as they discuss the trends that they’re seeing in financial institutions from banks to hedge funds to federal agencies. By the end of the session, you’ll be able to speak about specific industry uses and walk away with concrete steps you can take to ensure you’re riding the data wave. Speaker Bios: Dmitri Adler is the Chief Solution Architect and co-founder of Data Society. He has deep expertise in building predictive models and algorithms for forecasting macroeconomic conditions, healthcare outcomes, trade flows, and business performance for a variety of government and private sector clients. Prior to starting Data Society, he advised the U.S. Treasury Department on the structure of the mortgage market after the financial crises while he was at J.P. Morgan and developed expertise in applying machine learning to financial modeling and investing. Dmitri has worked with large financial institutions and agencies to build custom software, assess financial risk, and integrate machine learning applications into their operations. Merav Yuravlivker is the Chief Executive Officer and co-founder of Data Society. She has deep expertise in developing effective professional development programs and assessments to maximize the capability of an organization and empower the workforce. Prior to starting Data Society, she built her career at educational institutions that include Teach for America, Kaplan, and the International Baccalaureate Organization. Over the past seven years, Merav and her team have saved organizations millions of dollars by incorporating data analytics skills and best practices that educate, equip and empower an organization’s workforce to achieve its goals and expand its impact. Data Society specializes in providing industry-tailored data science training and AI/ML solutions that enable Fortune 500 companies and government agencies to educate, equip and empower their workforce. Since 2014, the company has trained thousands of professionals with the skills needed to solve complex challenges, realize new opportunities, and take their careers to the next level. Data Society was recognized as an Inc. 5000 2021 fastest-growing company and named a top EdTech Company to watch by Forbes. For more information, visit www.DataSociety.com. Link to slides: https://github.com/RStudioEnterpriseMeetup/Presentations/blob/166452d28d61ef33faf8980c8f0f43426e72926b/The%20shift%20in%20data.pdf

Sjoerd Wierenga & Job Spijker | Public Health | Shiny in Production | Posit

R in Public Sector: Organizational & Technical Aspects of Shiny in Production with the Dutch National Institute for Public Health and the Environment. 00:00 - Introductions 2:47 - Organizational aspects of Shiny in production 32:52 - Technical aspects of Shiny in production 52:33 - Ask us everything / Open Discussion Questions: 29:00 - When you first introduced Shiny, what other tools were you comparing it to? How did you explain the difference to your leaders? 30:00 - What were the most important aspects of your prototype app to create buy-in? 52:33 - As Clusterbuster began to be used by more people, did you face any performance issues? How did you adjust your app to deal with more concurrent users? 56:10 - Can you say anything about the update frequency of the data? 57:15 - Which model was used to define the clusters? 58:23 - Did you ever consider not using a database? 1:01:50 - What’s the communication with the data engineering team? 1:03:51 - How often do you collect feedback from users and update your app? 1:05:10 - Was your data loaded into Docker in a form of some aggregates? How did you create them? 1:06:26 - What is the main advantage of keeping it all in R with Shiny? Did you feel at any point you were sacrificing simplicity? 1:08:14 - Did you use any specific methods to increase the performance of your app? Did you scope your data, or load it all in the global file? 1:12:03 - How did you make sure regions and users felt comfortable using your app? 1:13:25 - What types of businesses are hotbeds for covid clusters? Has this info informed policy changes? 1:14:50 - How did the data quality issues improve over the rollout? 1:16:47 - Did you use CI/CD? 1:17:38 - Did you have any functionality within your apps to send individual-level data to municipalities? 1:19:47 - For huge amounts of data, have you tested out different file types to store your data set within your containers? 1:20:54 - For people just starting to use Shiny, what is one piece of advice you would give them? Proof on Concept with fictitious data: https://rivm.shinyapps.io/clusterbuster/ Blog post from the team as well! https://www.rstudio.com/blog/how-the-clusterbuster-shiny-app-helps-battle-covid-19-in-the-netherlands/ Code-first blog post mentioned: https://www.rstudio.com/blog/code-first-data-science-for-the-enterprise2/ How the “Clusterbuster” app provides actionable information to 300 health professionals Presented by: Sjoerd Wierenga In this talk we want to give an overview of what it took to create the Clusterbuster from an organizational perspective. We will go into detail on how we got from an abstract question to an application that is user-friendly, safe, and valuable. Furthermore, we will offer a glimpse of what is yet to come, and where we see possibilities to turbocharge a more data-driven public policy approach. How to build a production shiny app within the context of public health governance. Presented by: Job Spijker This presentation goes into the more technical details about the production environment of the Clusterbuster application. We will show how we deployed the application, how we ensured security and mitigated the risks in case of a security breach, and how we organized our code for maintainability and refactoring. Presenter Biographies: Sjoerd Wieringa: As the son of two healthcare professionals, with a background in Public Administration, and a passion for technology, it is no surprise that Sjoerd Wierenga now works at the National Institute for Public Health and the Environment leading a team of highly skilled Data Scientists that created an application to support the battle against COVID-19. After having worked as a healthcare manager for several years, he decided he wanted to learn how to program. Which he has been doing now since 2016 in different capacities. Job Spijker: Job Spijker is a senior research and data scientist at the Dutch National Institute of Public Health and the Environment. He has a PhD in Earth Sciences with a focus on computational and statistical methods of spatial data. He is currently involved in projects about how the institute’s environmental and health data can be leveraged to create insightful actionable information to assist policy makers at local, regional, and national level

Data Science Hangout | Prabha Thanikasalam, Flex | Calculating ROI with the Business

The Data Science Hangout is a weekly, free-to-join open conversation for current and aspiring data science leaders. rstudio.com/data-science-hangout We were recently joined by Prabha Thanikasalam, Senior Director, Analytics and Supply Chain Solutions at Flex, to discuss the most important things going in data science leadership. There was a great conversation live and in the chat that followed this question. 𝐇𝐨𝐰 𝐝𝐨 𝐲𝐨𝐮 𝐬𝐭𝐚𝐲-𝐟𝐨𝐜𝐮𝐬𝐞𝐝 𝐰𝐡𝐞𝐧 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐨𝐧 𝐝𝐚𝐭𝐚 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬? (From two sides: when learning data science and also when working on projects for the business) Think about it as a marathon, not a sprint, you won’t be an expert in 6 months or a year. Build a good foundation over time by spending 45 minutes a day learning something. Don’t keep your email inbox open all the time. This won’t work all the time, but it helps! Try to separate the time you devote to learning new skills rather than “learning while doing” all the time. This can allow you to work faster with imperfect solutions and set aside learning a better way of doing it in dedicated time. Although it’s important to keep delivering quality analysis and product, it helps me move away from perfectionist tendencies. Getting “side-tracked” isn’t necessarily bad - sometimes we’re asked to solve the wrong question and find that out half way through Give yourself a deadline, or if you’re working with the business meet with them to discuss the success criteria and deadlines Encourage teams to explore rabbit holes and not be mindless “task completers.” Some of the best insights come out of curveballs we explore. Curiosity is also key to being a great data person Try using a pomodoro timer to keep focused time Check out Jacqueline Nolis & Emily Robinson’s book/companion podcast, “Build a Career in Data Science”, which talks about using projects as a motivator for learning new skills. Sometimes, it’s better to start with a problem you want to solve rather than start with a skill you want to learn! When starting out, try to use code as frequently as possible, and also find a project you can get really excited about. Passion can help with the late nights and tying in some of the side track thoughts to one topic or outcome. It’s helpful to realize there are still plenty of skills that require dedicated time to do more focused learning to exclusively understand the math, methodology, and technology. For example, with Natural Language Processing, starting with the technology and then moving onto the documentation can be an order that keeps you motivated. 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐬𝐡𝐚𝐫𝐞𝐝 𝐚𝐬 𝐰𝐞𝐥𝐥: Prediction Machines: https://lnkd.in/gSft3XRK & Measure what Matters: https://lnkd.in/g6xdSNci Building a Career in Data Science: https://jnolis.com/book/ Lessons learned from the smartest Software Engineer I’ve met: https://lnkd.in/gDScE7gH Inside Intel: https://lnkd.in/gKyTWZj4

Data Science Hangout | Ian Anderson, Philadelphia Flyers | Moving into Leadership & Managing a Team

The Data Science Hangout is a weekly, free-to-join open conversation for current and aspiring data science leaders. posit.co/data-science-hangout We were recently joined by Ian Anderson, Director of Hockey Analytics at the Philadelphia Flyers, to discuss the most important things going in data science leadership. One of the topics that there were a lot of thoughts on during this hangout was balancing that shift into leadership and maintaining technical skills. Question: 𝐀𝐬 𝐲𝐨𝐮 𝐦𝐨𝐯𝐞 𝐮𝐩 𝐭𝐡𝐞 𝐥𝐚𝐝𝐝𝐞𝐫 𝐢𝐧𝐭𝐨 𝐥𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐚𝐧𝐝 𝐦𝐚𝐧𝐚𝐠𝐢𝐧𝐠 𝐚 𝐭𝐞𝐚𝐦, 𝐚𝐫𝐞 𝐲𝐨𝐮 𝐰𝐨𝐫𝐫𝐢𝐞𝐝 𝐚𝐛𝐨𝐮𝐭 𝐥𝐨𝐬𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐭𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐜𝐨𝐝𝐢𝐧𝐠 𝐬𝐤𝐢𝐥𝐥𝐬? Here are a few of the thoughts shared live from Ian and the community: You make time for things that are important to you. If exercise and health is important to you, you make time for it. If coding is something that you want to continue to maintain, you can find the time for that. Think about blocking designated focus time to do your individual work Be curious. Office hours as a manager to answer questions from your group can also be an opportunity to learn new things/packages/methods together Organize opportunities for the team to share any topics that they would like (coding, techniques, etc.) In sharing topics, you can also try limiting the time to ~4min to share. Two benefits of this fast moving, high energy meeting: 1. People will inspire each other with passion and creativity 2. The team gets to practice communicating their ideas with precision. Becoming more comfortable with what it means to lead people, it’s about enabling them to be really successful. If you measure your success by other people’s success, it can also help rationalize that. If you can chop down barriers for them, then they will be more successful so that means the technical time I sacrifice enables them further. It’s still very relevant to keep your skills at some type of level just for street cred in your team. Bringing in new people, if you can talk that language it just really helps your team and you can have those conversations." ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstu … Twitter: https://twitter.com/rstudio

Winston Chang || Part III: Adding a Keyboard to a Wordle Shiny App || RStudio

00:00 Introduction 00:25 Setting up a keyboard 00:54 Using an HTML p tag to print out letter indicators 01:56 Back to our keyboard! 03:44 Setting up a search and replace 06:32 Removing letters using regular expressions 08:43 Making guesses a reactiveVal() 11:00 Avoiding an infinite loop with reactiveVal() In Part III of this four-part series, Winston walks through how to build a keyboard in a Shiny Wordle app. Code + word list: https://github.com/wch/shiny-wordle Check out the full Shiny app here: https://winston.shinyapps.io/wordle/ You can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Developer (@winston_chang) Animation, design, and editing: Jesse Mostipak (@kierisi) Wordle: https://www.powerlanguage.co.uk/wordle/

Winston Chang

rstudio Shiny shinyapps RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Wordle Winston Chang

Daniel Petzold || RStudio Team: Building and Sharing Jupyter Notebooks || RStudio

Learn more about RStudio Team here. https://www.rstudio.com/products/team/ Find the code for this example here. https://github.com/danielpetzold/space-tracker Read our blog post here. https://www.rstudio.com/blog/build-and-share-jupyter-notebooks-on-rstudio-team/ Timecodes 0:00 - Intro 0:07 - Build Jupyter Notebooks to analyze and visualize data 2:47 - Publish directly from RStudio Workbench to your content hub 5:13 - Share With Your Stakeholders on RStudio Connect Jupyter Notebooks are interactive documents for code, outputs, and text. However, they’re often stuck in data scientists’ local computing environments. Collaborating can be difficult and sharing can be tedious. To live up to their fullest potential, data science teams need a way to scale their development securely and efficiently — while providing stakeholders easy access to their output and visualizations. RStudio Team, made up of RStudio Workbench, RStudio Connect, and RStudio Package Manager, brings everything together to help data scientists create, reproduce, and share insights from their Jupyter Notebooks. Let’s dive into a real-life example by exploring data from NASA’s Center for Near-Earth Objects (NEOs). Daniel Petzold walks us through his data analysis and reporting. Want to explore the report yourself? Check out the published report on RStudio Connect here. https://colorado.rstudio.com/rsc/space-tracker/space_tracker.html On RStudio Workbench, you have a choice of editors: the RStudio IDE, JupyterLab, Jupyter Notebook, or VS Code. Choose your preference. From here, you can explore your dataset, embed HTML directly in your document, create visualizations, and more. Once you’ve run your analyses and created insightful visualizations, you want to be able to share them with your team. RStudio Workbench allows you to publish to RStudio Connect, the content platform from RStudio. You have multiple options: push-button deployment from Jupyter Notebook or using terminal commands from JupyterLab. It’s not enough to publish your work. Once on RStudio Connect, you can share with end-users. Make your analysis accessible to specific users or more generally with different authentication measures. In addition, you can schedule the document to run at a certain time and send out an email with refreshed data. Click the links below to learn more about these offerings. RStudio Workbench: https://www.rstudio.com/products/workbench/ RStudio Connect: https://www.rstudio.com/products/connect/

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tidyr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Jupyter JupyterLab Jupyter Notebooks RStudio Workbench RStudio Connect

Winston Chang || Part II: Handling Duplicate Letters in a Shiny Wordle App || RStudio

00:00 Introduction 00:52 Setting up the problem with duplicate letters 02:08 Coding the first pass for exact matches in the correct position 06:29 Re-evaluating how to approach the problem 12:28 Removing only one instance of a letter 13:56 Testing our code 14:54 Setting up the second pass 19:08 Scoping with a double arrow 19:52 Debugging with a browser() statement 21:28 Checking our code In Part II of this four-part series, Winston walks through how to handle duplicate letters when building your Shiny Wordle app. Code + word list: https://github.com/wch/shiny-wordle Check out the full Shiny app here: https://winston.shinyapps.io/wordle/ You can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Developer (@winston_chang) Animation, design, and editing: Jesse Mostipak (@kierisi) Wordle: https://www.powerlanguage.co.uk/wordle/

Winston Chang

rstudio Shiny shinyapps RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Wordle Winston Chang

Winston Chang || Part I: Build a Basic Wordle App with Shiny || RStudio

00:00 Introduction 00:12 What is Wordle? 00:36 The Wordle app we’ll build by the end of this four-part series 01:08 How to approach the problem 01:38 Word list (link to file below) 01:52 UI function with fluidPage() 02:24 Print out what player guesses using verbatimTextOutput() 03:36 Run app in Viewer Panel 04:04 Adding an action button with actionButton() 04:29 Using bindEvent() with actionButton() 06:02 Limiting guesses to words with five characters 07:40 Using req() and cancelOutput() 08:54 Incorporating the word list 10:13 Matching player guess to word list 11:06 Matching player guess to target word 13:50 Writing a function to match guess to target word with feedback 18:15 Checking word length between guess and target 23:02 Why we’re using intermediary functions 28:51 Printing formatted letter information In Part I of this four-part series, Winston walks through how to build a basic Wordle app using Shiny! Code + word list: https://github.com/wch/shiny-wordle Check out the full Shiny app here: https://winston.shinyapps.io/wordle/ You can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Developer (@winston_chang) Animation, design, and editing: Jesse Mostipak (@kierisi) Wordle: https://www.powerlanguage.co.uk/wordle/

Winston Chang

rstudio Shiny shinyapps RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Wordle Winston Chang

Capacity Planning for Microsoft Azure Data Centers | Using R & RStudio Connect

Capacity Planning for Microsoft Azure Data Centers | An Explainable Data Science Workflow using R & RStudio Connect | Presented by Paul Chang 2:12 - Start of presentation 47:43 - Start of Q&A session Thank you for watching! Here are a few helpful links: Link to Paul’s slides: https://lnkd.in/gh-hGScE More information on RStudio Connect: https://www.rstudio.com/products/connect/ How to open an Azure account: https://azure.microsoft.com/en-us/ Getting started with SAML authentication on RStudio Connect: https://support.rstudio.com/hc/en-us/articles/360022321494-Getting-Started-with-SAML-in-RStudio-Connect pins package: https://pins.rstudio.com/ plumber package: https://www.rplumber.io/ Upcoming events: rstd.io/community-events Chat with our team to start an RStudio Connect evaluation: rstd.io/chat-with-rstudio Abstract: The Long Range Capacity Planning team at Microsoft is responsible for producing plans for expanding Microsoft Azure Data Centers around the world. These are multi-billion dollar plans that enable the full suite of IaaS and PaaS cloud offerings for our customers, over a 5+ year time horizon. In this talk, we will present the data science software stack that we have built using RStudio Connect and Azure, for producing these data center capacity plans. We will discuss how RStudio Connect has empowered our data scientists to connect more directly with internal stakeholders and decision makers, and how RStudio Connect has enabled us to streamline our data science and business processes. Speaker Bio: Paul Chang, Senior Data & Applied Scientist, Microsoft Paul Chang is the Systems Architect of the Long Range Capacity Planning team for Microsoft Azure Data Centers. He received his Applied Math PhD from Simon Fraser University and has worked in a variety of fields including Applied Functional Analysis, Hydrogen Fuel Cell modeling, and A.I. Applications in Vehicular Traffic Engineering. He was also a software engineer in SQL Azure for a couple of years. Thank you for joining us! If you ever have suggestions or general feedback, please let us know! Here’s an anonymous google form: rstd.io/meetup-feedback We’d love to hear from you too! Here’s a talk submission form as well: rstd.io/meetup-speaker-form If you’d like to learn more about RStudio Connect: https://www.rstudio.com/products/connect/ If you’re just starting to advocate for data science in general or RStudio tools: rstudio.com/champion

What is Posit Workbench? || Posit

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Interoperability Serious Data Science Ggplot2 Github Data Wrangling Odbc Lazy Evaluation Tidymodels Statistics Debugging Programming Education Open Source Oss Reticulate RStudio Workbench Workbench Git

Rich Iannone || Making Beautiful Tables with {gt} || RStudio

00:00 Introduction 00:37 Adding a title with tab_header() (using Markdown!) 01:47 Adding a subtitle 02:48 Aligning table headers with opt_align_table_header() 03:48 Using {dplyr} with {gt} 06:03 Create a table stub with rowname_col() 07:35 Customizing column labels with col_label() 09:45 Formatting table numbers with fmt_number() 12:10 Adjusting column width with cols_width() 15:39 Adding source notes with tab_source_note() 16:55 Adding footnotes with tab_footnote() 18:55 Customizing footnote marks with opt_footnote_marks() 19:10 Demo of how easy managing multiple footnotes is with {gt} 23:41 Customizing cell styles with tab_style() 27:07 Adding label text to the stubhead with tab_stubhead() 28:15 Changing table font with opt_table_font() 29:25 Automatically scaling cell color based on value using data_color() With the gt package, anyone can make wonderful-looking tables using the R programming language. The gt philosophy: we can construct a wide variety of useful tables with a cohesive set of table parts. These include the table header, the stub, the column labels and spanner column labels, the table body, and the table footer. It all begins with table data (be it a tibble or a data frame). You then decide how to compose your gt table with the elements and formatting you need for the task at hand. Finally, the table is rendered by printing it at the console, including it in an R Markdown document, or exporting to a file using gtsave(). Currently, gt supports the HTML, LaTeX, and RTF output formats. The gt package is designed to be both straightforward yet powerful. The emphasis is on simple functions for the everyday display table needs. You can read more about gt here: https://gt.rstudio.com/articles/intro-creating-gt-tables.html And you can learn more about Shiny here: https://shiny.rstudio.com/ Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/ Content: Rich Iannone (@riannone) Design & editing: Jesse Mostipak (@kierisi)

Rich Iannone

dplyr gt rstudio Shiny tibble RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Rich Iannone

Isabella Velásquez | Building a Blog with R | RStudio

Building a Blog With R Presented by: Isabella Velásquez Here are a bunch of resources Isabella shared ⤵️ Slides from the presentation: https://lnkd.in/gqGFmHMf Internal Blog Example: https://lnkd.in/gaFPxN5F Other resources from the talk: https://lnkd.in/gjXxeMaa Distill Resources: 1️⃣ Distill for R Markdown: https://lnkd.in/gWsEBXfN 2️⃣ Building a blog with distill by Tom Mock: https://lnkd.in/gQiE8PC2 3️⃣ (Re-)introducing Distill for R Markdown: https://lnkd.in/gzidDpV2 4️⃣ The distillery: https://lnkd.in/gwDAg_7G 5️⃣ Postcards package: https://lnkd.in/geT6uB9t Blogdown Resources: 1️⃣ blogdown: Creating Websites with R Markdown: https://lnkd.in/gGQ-fCWw 2️⃣ Hugo Themes: https://themes.gohugo.io/ 3️⃣Hugo Apéro: https://lnkd.in/g8U9tfvq 4️⃣ A Blogdown New Post Workflow with Github and Netlify: https://lnkd.in/gYNwsKTm The R programming language is known for its applications to data science, but one of its best assets is the inviting community. Folks from around the world share their lessons learned, best practices, and code to support and inspire others. One tool that helps contribute to the thriving community is the blog. A blog is a wonderful opportunity to record your data stories, gain exposure for your expertise, and support others in their R journey. Thanks to the advancement of tools like R Markdown, you can quickly get up and running with a blog and focus on customization and style. In this talk, we will discuss possible reasons for creating a blog, the pros and cons of a blog, and how to decide on topics. We will then explore tools for creating your blog that make it easy to showcase your R skills, such as blogdown and distill. At RStudio, we are always looking for stories of how you are using R for your work, community, or for fun. If this talk inspires you to start writing, we would love for you to contribute to the RStudio blog: https://www.rstudio.com/blog/ Speaker Bio: Isabella Velásquez: Isabella is a content strategist, author, and active member of the R community. Currently, she works at RStudio as a Sr. Product Marketing Manager with the goal of driving engagement around all the awesome things happening at RStudio. In her previous role, she conducted data analysis and research, developed infrastructure to support use of data, and created resources to engage technical and non-technical audiences. She channels these experiences to illuminate what is possible with great products

Isabella Velásquez

Ralph Asher & Laura Darby Rose | R in Supply Chain Management | RStudio

Two talks! 3:24 - Start of meetup 3:24 - Intro to Supply Chain Design - Ralph Asher 33:38 - Forecasting Demand with R - Laura Darby Rose Intro to Supply Chain Design - Ralph Asher Abstract: COVID-19 has moved supply chain management from the back office to front-page news. And along with it, the discipline of supply chain design – the strategic evaluation of deciding where to locate manufacturing sites, warehouses, and other supply chain facilities – has gone from a little-known niche to a C-suite priority. In this talk, I will introduce the field of supply chain design to the R community. Drawing upon my decade of experience in supply chain design and R, I will give a short example of how to design a warehouse network to support future customer need. This example will be drawn directly from my experience in the corporate world and my consulting business. Speaker Bio: Ralph Asher is the founder of Data Driven Supply Chain LLC, a Minnesota-based consulting firm that uses data science and AI to evaluate, design, and optimize supply chains. ( www.datadrivensupplychain.com ) Prior to founding Data Driven Supply Chain, Ralph worked as an Operations Research Scientist in corporate supply chain functions for 8 years at Target, designing e-commerce supply chain networks, and at General Mills, designing warehousing networks. Ralph has used R for supply chain analytics for a decade and can be reached at ralph@datadrivensupplychain.com or via LinkedIn. Forecasting Demand with R - Laura Darby Rose Abstract: Over the course of about a year, Mallinckrodt Pharmaceuticals’ Specialty Generics division replaced an expensive SaaS (Software as a Service) with an R script and alternate forecasting process. Using an RStudio Enterprise solution, they have found a more flexible and cost-effective way to forecast demand and analyze data with R. This discussion will detail the process of replacing a SaaS with R, as well as challenges and next steps in the project. Speaker Bio: Laura Darby Rose is Manager of Demand at Mallinckrodt Pharmaceuticals, where she is responsible for statistical forecasting, forecast visualization, and forecast accuracy measurement for the Specialty Generics division. She has a M.A. in Economics from the University of Missouri-St. Louis, and enjoys using R and SQL for time-series analysis, creating Shiny apps, and data wrangling/cleaning. Are you a data scientist or data analyst working in supply chain management? Are you interested in joining a group of fellow practitioners and taking a leadership role in developing and promoting open source solutions in your field? Join us! At this meetup, we also proposed the formation of a community working group focused on developing and popularizing open source solutions for data scientists and analysts working in supply chain management. We’d love to start by creating a home – initially a website – which hosts resources for supply chain data scientists. More to come. Form to be part of the working group: rstd.io/supply-chain-community-org

An inclusive solution for teaching and learning R during the COVID pandemic

The COVID pandemic has shaken our teaching and learning approaches in many different ways all over the world. Nonetheless, it has also provided opportunities for bringing creativity into the classroom. In this talk, I will discuss how I have used RStudio Cloud in my teaching during the pandemic and how I capitalized on the opportunities that RStudio Cloud offers to deal with the crucial issues of software installation. Introducing RStudio Cloud in the units has allowed me to work effectively in an online environment to engage, motivate and empower students through their learning process while removing the troubles and hurdles of software installation which is generally particularly challenging in first-year cohorts without prior coding experience. I used RStudio Cloud in a data science introductory unit at Monash University and as a tool to present the usage of R and RStudio for reproducible reporting in another unit on Reproducible and Collaborative Practises. In the latter, I introduced RStudio Cloud during the first few weeks to get the students up to speed before transitioning to using R and RStudio Cloud in their own local machines while using the command line interface, Git, and GitHub as a version control tool for reproducible reporting. I will also discuss how I organized and managed the unit’s RStudio Cloud account so that my research associates were also an integral part of the unit delivery to ensure the success of the units. Read Dr. Menéndez’s guest post on the RStudio Blog: https://www.rstudio.com/blog/rstudio-cloud-an-inclusive-solution-for-learning-r/ Read more in the follow-up blog post: https://www.rstudio.com/blog/teaching-data-science-in-the-cloud/

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

To Our Community: Thank You | RStudio Open Source (2021)

This year, among many challenges, we have been so grateful for our community who have continued to show up in so many different ways. As the year comes to a close, we wanted to say thank you. We know folks have adjusted where they spend their time and energy, and we just want to say thank you for continuing to be here and learn together, wherever you are right now. Thank you to our maintainers. Our package maintainers not only contribute code, but also steward projects, welcome new contributors, and answer many questions. Thank you for continuing to set a positive tone and fostering a positive community for our contributors and users! Thank you to our contributors. Package contributors contribute code, ideas, conversation, documentation, and tests. You are the ones trying things out, figuring out what’s wrong, and sharing ideas and fixes. Thank you for pushing our code to grow and evolve and to help ever more people! Thank you to our educators. Educators work in classrooms, with friends and colleagues, and respond to email lists from someone around the world. Thank you for sharing your enthusiasm for data science with others and expanding the group of people who make sense of data with code! Thank you to everyone who’s used R to solve a problem, create aRt, write a blog post, share insights or any of the thousands of ways we can create with R. Thank you to all of you who have contributed in some way, no matter how big or small. Without you, there would be no point to writing open source code. Thank you for all the work you do in the world, using data to improve our lives and our knowledge of the world in such incredibly diverse ways. Music: Basketliner by Bitters, published on Blue Dot Sessions - https://app.sessions.blue/browse/track/81258 Editing & motion design: Jesse Mostipak

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Data Science Hangout | Chase Carpenter, Chicago Cubs | Advice for Getting your First Job in Sports

The Data Science Hangout is a weekly, free-to-join open conversation for current and aspiring data science leaders. An accomplished leader in the space will join us each week and answer whatever questions the audience may have. We were recently joined by Chase Carpenter, Director of Strategy & Analytics at the Chicago Cubs. The Cubs are also hiring for a Database Marketing Analyst: https://my1060wd.wd5.myworkdayjobs.com/en-US/Chicago_Cubs_FO/job/Chicago-Illinois/Analyst--Database-Marketing_R000445 A few key snippets from our conversation: 01:33 - Start of session 13:52 - Defining areas of analysis (scoping projects) 1. What’s the size of the prize? What’s the size of the problem? 2. Do we actually have data that can help understand or improve the problem? 3. When does that work need to be done? What’s the timing? 22:32 - How are models evaluated and over what time period 28:32 - What does the last mile look like? Delivering results back to the business 31:40 - Techniques for approaching ambiguity 36:28 - Finding quick wins to build relationships 41:49 - Perspective on getting your first job in sports analytics 50:15 - Adjusting sports models in a world after the pandemic 59:06 - Advice for aspiring data science leaders ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Add the Data Science Hangout to your calendar: https://www.addevent.com/event/Qv9211919 Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstu… Twitter: https://twitter.com/rstudio

Daniela Garcia & Julieta Nieva | R en la Administración Pública & R Markdown | RStudio

R en la Administración Pública & informes de técnicas psicométricas con R Markdown | RStudio Thank you to Sergio Garcia Mora for hosting this meetup. R en la Administración Pública: Cuando la Big Data te empuja a salir de la zona de confort Daniela Garcia Abstract: Muchas veces no tenemos motivación para programar debido a que el Excel lo resuelve todo fácilmente, sin embargo, cuando en mi trabajo nos solicitaron información detallada y actualizada sobre puestos y costos laborales de cada escuela de nuestra provincia (más de 2.000) era aprender o morir en el intento. En esta presentación veremos como a través de R automatizamos un reporte para toma de decisiones gubernamentales con gran precisión y escaso esfuerzo. Bio: Daniela García, Lic. en Relaciones Laborales, Analista de Datos de la Dirección General de Personal - Ministerio de Hacienda y Finanzas Corrientes, Argentina. Scrum Master en Starter - Consultora en Tecnología e Innovación. Colaboradora activa del Club de R para Recursos Humanos. https://www.linkedin.com/in/claudiadanielagarcia/ R Markdown aplicado a informes de técnicas psicométricas Julieta Nieva Abstract: Los psicólogos que realizamos investigación podemos tener algunas limitaciones a la hora de dar devoluciones personalizadas a los participantes voluntarios de nuestros proyectos. Poder analizar gran cantidad de datos, procesarlos rápidamente y poder ahorrar horas de trabajo manual, hicieron que R Markdown se convierta en nuestro aliado. En la presentación se explicará cómo utilizamos estas herramientas en el International Cognitive Research Consortium y cuáles fueron nuestros resultados. Bio: Julieta Nieva, Licenciada en Psicología. UX Researcher en Banco Galicia. Investigadora en ICRC. https://www.linkedin.com/in/julieta-nieva/

RStudio Connect | Cut down on the grunt work. Deliver insights more effectively with RStudio

You built a great data science product. Something your stakeholders want and have been asking for. But operationalizing that data product is hard. How do you deliver it to interested stakeholders in the format that they want with the specific insights that they need? And what if you need to deliver your data products to multiple stakeholders, all with slightly different requirements? How do you make sure that the data product is always up to date? And when the data is updated, how will your stakeholders know? This is why we built RStudio Connect. RStudio Connect is the best way for data science teams to curate and share data products with their organizations. It comes with all of the bells and whistles that professional data science teams typically need to turn their analyses into value. In this live event, we’ll do a deep dive into one of RStudio Connect’s most powerful features: scheduling and distributing data science products. This feature enables data science teams to take all of their data science products and insights and deliver them to everyone that needs them to make decisions. Always on time, in the format that they want, and as efficiently as possible. Specifically: we will show how a data scientist can deploy an R Markdown or Jupyter Notebook to RStudio Connect, then schedule the data product to update and get delivered to interested stakeholders. All without the traditional work associated with updating data pipelines and updating reports. Put simply: we think RStudio gives data scientists, data engineers, and business users superpowers, and we can’t wait to show it off to you

Leveraging the Cloud for Analytics Instruction at Scale: Challenges and Opportunities

Data science and programming languages like R and Python are some of the most in demand skills in the world. Students interested in analytics and professors facilitating curriculums deserve to use industry-leading tools to acquire these skills. However, it’s challenging to enable this experience in an educational setting, especially at scale. The traditional tools to facilitate learning analytics simply aren’t great. Students and professors often spend way too much time troubleshooting systems and software, things that are a complete waste of time and detract from the learning experience. Additionally, there are seemingly endless IT hurdles and requirements. That’s part of the reason we created RStudio Cloud, a brilliantly simple but powerful solution for teaching and learning analytics, especially at scale. RStudio Cloud solves many of the technical and financial challenges associated with teaching analytics. It’s also a joy to use for professors, students, and IT administrators. In this presentation, Dr. Brian Anderson will discuss the challenges and opportunities associated with leveraging the cloud to deliver analytics instructions at the undergraduate and graduate levels at scale. Our hope is that you walk away inspired to think about ways you can leverage RStudio and the Cloud to enhance your students’ experiences with learning analytics. Read more in the follow-up blog post: https://www.rstudio.com/blog/teaching-data-science-in-the-cloud/

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

James Blair | Using RStudio on Amazon SageMaker | RStudio

Using RStudio on Amazon SageMaker Presented by James Blair *please note that this meetup will share a new update about our professional product, RStudio Workbench. All are welcome, but just wanted to make sure to share that upfront:) https://www.rstudio.com/products/workbench/ Agenda: Presentation & Demo - 40 minutes Q&A with both RStudio & AWS SageMaker team - 20 minutes Amazon SageMaker helps data scientists and developers to build, train, and deploy machine learning models quickly by bringing together a broad set of capabilities purpose-built for machine learning. RStudio recently announced the release of RStudio on Amazon SageMaker, developed in collaboration with the SageMaker team. This brings a fully-managed RStudio Workbench IDE into the powerful SageMaker environment. With this functionality, Data Scientists can quickly get to work, spinning up their favorite development environment on SageMaker, choosing from a wide array of instance types as needed. They can get access to their organization’s data stored on AWS, as well as all of SageMaker’s deep learning capabilities. As a fully managed offering on Amazon SageMaker, this release makes it easy for DevOps teams and IT Admins to administer, secure, and scale their organization’s centralized data science infrastructure, using familiar AWS tools and frameworks. Here are James’ slides as well: https://github.com/blairj09-talks/rstudio-sagemaker-webinar Answers to the Q&A are shared in this blog post: https://www.rstudio.com/blog/using-rstudio-on-amazon-sagemaker-faq/

RStudio Team Deep Dive | In A Hosted Environment

You probably know that RStudio makes a free, open-source development environment for data scientists. It’s made with love and used by millions of people around the world. What you might not know is that we also make a professional platform, called RStudio Team. In this Live Session, Tom will walk you through our Rstudio Team Trial, where you can learn how to best test drive…. Scaling your data science work Seamlessly managing open-source data science environments Automate repetitive tasks Rapidly share key insights and data science products securely to your entire organization. And, optionally integrate some of your favorite open-source packages into the trial experience Leading organizations like NASA, Janssen Pharmaceuticals, The World Health Organization, financial institutions, government agencies and insurance organizations around the globe use RStudio’s professional products to tackle world-changing problems and we’re inviting you to learn how. You’ll learn how RStudio Team gives professional data science teams superpowers, with all of the bells and whistles that enterprises need. If you don’t have your own trial instance of Rstudio Team to follow along (not required), feel free to request yours here: https://www.rstudio.com/products/team/evaluation3/ Additional resources here: https://docs.google.com/document/d/1HGt7LSohhyxpCvETvVEFHugrdaSnTcZaXbI0jV5g9ok/edit?usp=sharing

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Leveraging R & Python in Tableau with RStudio Connect | James Blair | RStudio

Leveraging R & Python in Tableau with RStudio Connect Overview Demo / Q&A with James Blair Tableau combines the ease of drag-and-drop visual analytics with an open, extensible platform. RStudio develops free and open tools for data science, including the world’s most popular IDE for R. RStudio also develops an enterprise-ready, modular data science platform to help data science teams using R and Python scale and share their work. Now, with new functionality in RStudio Connect, users can have the best of both worlds. Tableau users can call R and Python APIs from Tableau calculated fields, getting access to all the power and analytic depth of these open-source data science ecosystems in real-time. For Tableau users, this makes it easy to add dynamic, advanced analytic features from R and Python to a Tableau dashboard, such as scoring predictive models on Tableau data. They can leverage all the great work done by their organization’s data science team and even call both R and Python APIs from a single dashboard. Data science teams can continue to use the code-first development and deployment tools from RStudio that they know and love. Using these tools, they can build and share R APIs (using the plumber package) and Python APIs (using the FastAPI framework). Speaker Bio: James is a Solutions Engineer at RStudio, where he focuses on helping RStudio commercial customers successfully manage RStudio products. He is passionate about connecting R to other toolchains through tools like ODBC and APIs. He has a background in statistics and data science and finds any excuse he can to write R code. A few other helpful links: Tableau Integration Documentation: https://docs.rstudio.com/rsc/integration/tableau/ Tableau / RStudio Connect Blog Post: https://blog.rstudio.com/2021/10/12/rstudio-connect-2021-09-0-tableau-analytics-extensions/ Embedding Shiny Apps in Tableau using shinytableau blog: https://blog.rstudio.com/2021/10/21/embedding-shiny-apps-in-tableau-dashboards-using-shinytableau/ James’ slides: https://github.com/blairj09-talks/rstudio-tableau-webinar

Building R packages with devtools and usethis | RStudio

Package building doesn’t have to be scary! The tidyverse team has made it easy to get started with RStudio and the devtools/usethis packages. This hour long presentation will walk you through the basics of R package building, and hopefully leave you prepared to go out and build your own package! Slides: https://colorado.rstudio.com/rsc/pkg-building/ Source Code: https://github.com/jthomasmock/pkg-building devtools: https://devtools.r-lib.org/ usethis: https://usethis.r-lib.org/ R Packages book: https://r-pkgs.org/index.html

devtools rstudio tidyverse usethis RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

RStudio Team Demo | Build & Share Data Products Like The World’s Leading Companies

You probably know that RStudio makes a free, open-source development environment for data scientists. It’s made with love and used by millions of people around the world. What you might not know is that we also make a professional platform, called RStudio Team. Learn How RStudio Team Can… Help you scale your data science work Seamlessly manage open-source data science environments Automate repetitive tasks And, rapidly share key insights and data science products securely to your entire organization. Timecodes 0:00 - Intro 4:18 - Hard truth of data science 10:22 - Serious Data Science 16:46 - Model management with R and Python 18:48 - Live Demo / RStudio Workbench 23:09 - RStudio support for Jupyter Notebooks 24:40 - Live Demo / RStudio Connect 28:01 - RStudio support for VS Code 30:05 - R and Python within RStudio 32:33 - Scale and share data science results 36:55 - Sharing previous versions of presentations 38:16 - Data Science team knowledge sharing 40:36 - Scheduling snd emailing data science content 43:55 - Live demo / RStudio Package Manager 48:09 - Data Science stories 49:37 - RStudio Team 52:59 - What makes RStudio different? 55:12 - Q/A - Learn More Leading organizations like NASA, Janssen Pharmaceuticals, The World Health Organization, financial institutions, government agencies and insurance organizations around the globe use RStudio’s professional products to tackle world-changing problems and we’re inviting you to learn how. You’ll learn how RStudio Team gives professional data science teams superpowers, with all of the bells and whistles that enterprises need. You can try RStudio Team free here: https://www.rstudio.com/products/team/evaluation2/ If you’d like to access presentation slides, sign up for future events, provide feedback and/or ask additional questions we’ve bundled everything together for you here: https://docs.google.com/document/d/1HGt7LSohhyxpCvETvVEFHugrdaSnTcZaXbI0jV5g9ok/edit?usp=sharing

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

GA Tech || “Communicating with 8 Million People through Shiny” || Posit

Georgia Institute of Technology faculty, scientists, GIS specialists, and graduate students launched a tool that provided real-time, localized information on the estimated risk of COVID-19 exposure by attending an event. In talking with their team it was clear that their empathetic perspective of the audience and communication-focus helped successfully share their insights with event planners, policy makers, various news outlets and individuals - adding up to ultimately over 8 million unique users around the world. Lessons learned from the GA Tech team about their experience building the COVID-19 Event Risk Assessment Planning Tool can apply to visualizations across many different industries and use cases - whether you are communicating to a handful of executives at your company or out to the world: As a Shiny developer, make sure that you have a specific question in mind. What is the problem that you have that your app is helping them solve? Think about who your audience is going to be and what they would use this for. For the COVID-19 Event Risk Assessment Planning Tool, this question was “what is the risk level of attending an event, given the event size and location?” View your audience through a lens of empathy. Think about metrics that people can really get a grip on and visualize. For example, the risk of attending a local event with 100 people in your own town vs. communicating this as cases per 100,000 people. If you want to communicate something that’s critical to the public, put it in the right terms. Balance the straightforwardness of your visualization. You don’t have to anticipate every single question. With every feature or piece of information included, ask yourself if this supports your overall point? Importance of continued communication. Keep the lines of communication open with your users. If you share a visualization, make sure that people have a clear way to contact you (email, Twitter, LinkedIn) with questions or feedback. Their team made an intentional effort to be available for local news particularly. They were responsive to the kinds of decisions people were making and adjusted the app to match their needs with event sizes for example. Check out their Shiny application here: https://covid19risk.biosci.gatech.edu/ Read the related blog post here: https://blog.rstudio.com/2021/09/14/how-do-you-use-shiny-to-communicate-to-8-million-people/

How to Prioritize Projects | Data Science Hangout Highlights

RStudio is joined by Moody Hadi, Manager of New Product Development and Financial Engineering at S&P Intelligence, to discuss how data scientists can become leaders within their organizations. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Data Science Hangout | Moody Hadi at S&P Global | Unlocking Business Value with Data Science

We want to help data science leaders become better. The Data Science Hangout is a weekly, free-to-join open conversation for current and aspiring data science leaders. An accomplished leader in the space will join us each week and answer whatever questions the audience may have. We were recently joined by Moody Hadi, Manager of New Product Development and Financial Engineering at S&P Intelligence ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstu … Twitter: https://twitter.com/rstudio

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

The Importance of Understanding Your Business Users | Data Science Hangout Highlights

RStudio is joined by Tori Oblad, Data Officer at WaFd bank, to discuss how data scientists can become leaders within their organizations. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Dr. Julia Silge | RStudio Voices | RStudio

Julia Silge recently sat down with Michael Demsko Jr for an interview, the first in a new Voices of RStudio PBC series. In this excerpt, Julia discusses where she sees the most value created in the data science lifecycle–and it’s not advanced machine learning models. Read the full interview at https://blog.rstudio.com/tags/rstudio-voices/

Julia Silge

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Measuring the Impact of Data Science | Data Science Hangout Highlights

RStudio is joined by Frank Corrigan, Director of Decision Intelligence, to discuss how data scientists can become leaders within their organizations. Watch the full recording: https://www.youtube.com/watch?v=KBs4b3Q2n8Y ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Teaching and learning with RStudio Cloud | RStudio

Learn about RStudio Cloud and most recent developments, particularly with respect to teaching with it. Slides are posted at https://rstd.io/tl-rscloud. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud # ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Cloud RStudio Cloud Teaching Remote Teaching Online Education

Make Sure You Communicate Value | Data Science Hangout Highlights

RStudio is joined by Tori Oblad, Data Officer at WaFd bank, to discuss how data scientists can become leaders within their organizations. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Data Scientists vs. Business Analysts | Data Science Hangout Highlights

RStudio is joined by Frank Corrigan, Director of Decision Intelligence, to discuss how data scientists can become leaders within their organizations. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Data Science Hangout | Frank Corrigan, Target | Understanding the Impact of Data Science

We want to help data science leaders become better. The Data Science Hangout is a weekly, free-to-join open conversation for current and aspiring data science leaders. An accomplished leader in the space will join us each week and answer whatever questions the audience may have. We were recently joined by Frank Corrigan, Director of Decision Intelligence at Target. 6:38 - Problem formation - trying to find the unknown unknowns and bringing them to the business 8:20 - Integrating your data science team into your company’s business objectives (ex. newsletter) 10:50 - What is the divide between a business analyst and a data scientist, in your eyes? How business analysts and data scientists differ 15:32 - What is the biggest mistake you’ve made in your role and what did you learn from this mistake? 19:15 - Onboarding new team members effectively 25:00 - The importance of motivating non-data scientists 26:42 - Resources for data scientists 32:30 - Challenges when using different tools across a data science team 49:28 - Analytical thinking vs critical thinking skills 57:30 - Embracing the 80/20 Rule & the importance of Focus Time 1:02:48 - Two frameworks to be more effective with stakeholders 1:06:47 - Rebranding to “decision intelligence” 1:08:35 - Quantitatively measuring impact from data science insights ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu ► Join the Data Science Hangout Live every Thursday from 12-1 ET: https://www.addevent.com/event/Qv9211919 Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

RStudio Cloud | Viewing Learning Work | Instructor View

As an Admin (or Moderator) of your course space, you and your fellow instructors have access to all projects in your space. You can open student projects from any projects listing, or to see all the projects of a given student, go to their profile page by clicking on their name from the Members page. Note that if you do open a student’s project while they also have it open, they will be temporarily disconnected from the project. You can also view how much time your students have spent using Cloud. Simply visit the Usage area of your space, where you can see aggregate usage data for the entire class, or for an individual student and their projects. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 # ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Cloud Teaching RStudio Cloud

RStudio Cloud | Tuning Resources | Instructor View

By default, each project is allocated 1 GB of RAM and 1 CPU, and can execute in the background for up to 1 hour. If your plan allows it, you can increase the memory, CPU or background execution time allocated to a project. To do so, open Project settings, go to the Resources panel and adjust the allocation. Note that copies of a project will inherit its resource settings. However, the effective allocation for the copy will be limited by the maxiumum allowed by the account that “owns” the new copy. The usage hours consumed by a project in a given amount of time depend on the resources allocated to the project, according to the following formula: (RAM + CPUs allocated) / 2 x hours. On disk, each project is allocated up to 20GB for its files, data, and packages. The maximum size of a file that can be uploaded to a project is 500MB. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 # ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Cloud Teaching RStudio Cloud

RStudio Cloud | Troubleshooting | Instructor View

Relaunch Project If you are working on a project, and it fails to load or becomes completely unresponsive, you can relaunch the project. Depending on the underlying problem, relaunching the project may fix it and let you continue. Allocate Additional Memory memory-gaugeRunning out of memory while working on a project may be the cause of a variety of errors. The project memory gauge will give you an idea of what percentage of available memory your project is currently using. The gauge updates roughly every ten seconds, so it may not show the exact usage at the current moment. Note that reclaiming unused memory is controlled by R and the operating system - you may see uncanny fluctuations in the gauge as the system manages memory. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 # ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Cloud Teaching RStudio Cloud

RStudio Cloud | Setting Up a Base Project | Instructor View

You can make all projects in a space begin with a default set of files and packages. You do this by defining a Base Project for the space. Create a new project and add any packages or files you want all projects created in the space to start with. Set the project’s access so that everyone in the space can view the project. Go to the Space Settings page and select the project as the Base Project. Once you select a project as your Base Project, it will no longer be included in the projects listing for the space. To access it, choose the Edit command from the Space Settings page. Changes to the Base Project are not retroactive. Changes will not be applied to any projects already created - the changes will only apply to future projects created via the New Project action. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 # ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Teaching Cloud RStudio Cloud

RStudio Cloud | Roles | Instructor View

Each member of a space is assigned a role to determine what they are able to do within the space. The available roles are: Admin: can manage users, view, edit and manage all projects and view space usage data. Moderator: can view, edit and manage all projects, and view space usage data. Contributor: can create, edit and manage their own projects. This is the default. Viewer: can view projects shared with everyone in the space. A viewer cannot create or save copies of projects. Members are assigned an initial role when they are invited to or join a space, but roles can be changed by an Admin at any time. When you invite an individual member to a Invitation Required space, you set the initial role in the Add Member dialog box. When someone joins via a sharing link, their initial role is set to the current Initial Role setting in the members options panel. Admins can update a user’s role via the role selector in the members list. Changing permissions lets you fine-tune the Contributor and Viewer roles. The permissions are: Contributors can see the members list: enables Contributors to see who can access the space. Contributors can make their projects visible to all members: enables Contributors to share their projects with everyone in the space. Contributors can change project resources: enables Contributors to change RAM and CPU settings on their projects. Viewers can see the members list: enables Viewers to see who can acess the space. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 # ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Cloud RStudio Cloud Teaching

RStudio Cloud | Reusing a Workspace | Instructor View

One approach to reusing a space is to remove the current cohort of students from the space, and reuse the same space with the next cohort. If you are an Admin of a space, you can remove any member from that space. Go to the Members area of the space. For the member you would like to remove, press the Delete icon. You will be prompted to confirm that you would like to remove them from the space, and to choose whether to leave their projects behind in the space, or move them to their personal space. You can also remove members programatically via the Cloud API using the rscloud package. Another option is to create a new space from your current course space using the Copy Space action. If you’d like to re-use materials from a space, e.g. to teach a new cohort of students, use the Copy Space command. All projects shared with everyone in the original space will be copied over to the new space. Members of the original space are not copied over - you will be the only initial member of the new space. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 # ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Teaching Cloud RStudio Cloud

RStudio Cloud | Project Types | Instructor View

A project is the fundamental unit of work on RStudio Cloud. It encapsulates your R code, packages and data files and provides isolation from other analyses. If you are familiar with projects in the desktop RStudio IDE, an RStudio Cloud project is the same thing, plus some additional metadata for access and sharing. To create a new project from scratch, simply press the New Project button from the Projects area. Your new project will open in the RStudio IDE. To create a new project from an existing git repository, press the down arrow on the right side of the New Project button, and choose ‘New Project from Git Repo’ from the menu that appears. Note that your git credentials need to be entered each time you create a new project and are only cached for 15 minutes by default. To create a Jupyter Project, press the down arrow on the right side of the New Project button, and choose ‘New Jupyter Project’ from the menu that appears. A new Jupyter project will be created and deployed. Once deployed, you will see the Jupyter hub tree view with a welcome.ipynb notebook that contains information about getting started with Jupyter. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 # ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Teaching Cloud RStudio Cloud

RStudio Cloud | Overview | Instructor View

ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 # ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Teaching RStudio Cloud Cloud

RStudio Cloud | Inviting Co-instructors and Teaching Assistants | Instructor View

After creating a space, go to the Members area to manage its membership. You can add members to your space in three ways: To add members one by one, choose the Invitation Required option, and send invitations to each person you’d like to add to the space via the Add Member button. Note that for security reasons, the invitation is good for 7 days from the time that it is sent. To allow many people to join the space, choose the Sharing Link option, copy the sharing link and then share that link with all the people you’d like to join the space, either via an email or by posting the link on a web page. Keep in mind that anyone with that link will be able to join your space. If you would like to disable a link you previously shared, choose Reset Sharing Link; this will prevent any additional people from joining the space using that link. Space membership can also be managed programatically. See API Access from R in the Advanced Topics section below for details. Note that you can switch the access option at any time - a common approach is to initially set the access option to Sharing Link, post the sharing link, then after all the people you want to join the space initially have done so, switch access to Invitation Only. The original sharing link will no longer allow additional people to join the space, but you can add any new members individually. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 # ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Teaching Cloud RStudio Cloud

RStudio Cloud | Invite Learners | Instructor View

As you did to invite your co-instructors, go to the space’s Members area to invite your students to your course space. The easiest approach is to enable access via a sharing link. Click on the Sharing link option in the Access section Set the Initial Role to Contributor Click on the Copy Sharing Link action You can then distribute the sharing link to all your students via a course web page or email. Once all your students have joined the space, you can either reset the sharing link (which will disable access via the previous sharing link), or set Access to “Invitation Required” to ensure that nobody joins the class later without your permission. See the Members section in Shared Spaces above for more details and alternate methods for adding students to your space. Note that each student must have their own RStudio Cloud account. When they attempt to access your course space for the first time, they will be prompted to log in, or to sign up for an account if they don’t already have one. If your organization is using SSO, account creation will happen automatically when they log in the first time. At this point, you and your students should be all set. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 # ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Cloud Teaching RStudio Cloud

RStudio Cloud | Exporting Projects | Instructor View

The contents of a project can be downloaded without opening the project. Press the Export action to the right of the project that you wish to download. A dialog box will appear showing the progress of your export. The process can take anywhere from a few seconds to a couple of minutes, depending on the size of the project, and how recently it was opened. Once the export is complete, press the Download button in the dialog box to download your project. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 # ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

RStudio Cloud | Creating an Assignment | Instructor View

A good way to create assignments for your students is to make a project for each assignment, and populate it with the files and packages you would like each student to have when they begin the assignment. When you are ready to reveal an assignment to your students, open the project and do the following: Click on the Project Settings button (the gear in the upper right) Select the Access panel Set the project access so it can be viewed by everyone in the space Check “Make this project an assignment” When the student opens an assignment project (either via the projects listing, or via a direct link to the project), RStudio Cloud will automatically make a copy for them. To convey this special behavior, assignments are displayed a bit differently, both for you and for your students. You can see the students’ copies of the assignment by clicking on the “View n derived projects” link that will appear with your original project. Note that changes you make to the original assignment will not be applied to any student copies already created. The changes will only apply to future copies of the assignment. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 # ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Cloud Teaching RStudio Cloud

RStudio Cloud | {rscloud} Package | Instructor View

You can access RStudio Cloud’s API to manage space members programatically using the rscloud package. You will need to create client credentials to use the package. To do so, click on your icon/name in the header to reveal the User panel, then click on Credentials. This will take you to the Credentials page of RStudio User Settings, where you can create and manage your client credentials. {rscloud} package repo: https://github.com/rstudio/rscloud ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 # ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Rscloud RStudio Cloud API Teaching Cloud

Posit Cloud | Creating a Shared Space | Instructor View

A shared space is an area where a group of people can collaborate together - only the members of a shared space can access the space and its contents. To create a shared space, go to the navigation sidebar (click the menu icon at the upper left if needed) and choose New Space, then follow the on-screen instructions. ABOUT RSTUDIO CLOUD: RStudio Cloud is a lightweight, cloud-based solution that allows anyone to do, share, teach and learn data science online. Analyze your data using the RStudio IDE, directly from your browser. Share projects with your team, class, workshop or the world. Teach data science with R to your students or colleagues. Learn data science in an instructor-led environment or with interactive tutorials. There is nothing to configure and no dedicated hardware, installation or annual purchase contract required. Individual users, instructors and students only need a browser to do, share, teach and learn data science. We will always offer a free plan for casual, individual use, and we now offer paid premium plans for professionals, instructors, researchers, and organizations. RSTUDIO CLOUD RESOURCES: RStudio Cloud https://rstudio.cloud RStudio Cloud Pricing plans https://rstudio.cloud/plans/instructor RStudio Cloud guide https://rstudio.cloud/learn/guide {rscloud} https://github.com/rstudio/rscloud VIDEO CREDITS: Monitor icon made by xnimrodx from flaticon.com Cloud icon made by Freepik from flaticon.com Tiny Putty Music from Blue Dot Sessions: https://app.sessions.blue/browse/track/52046 # ABOUT RSTUDIO: RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Teaching Cloud RStudio Cloud

Oliver Bridges - Smart DCC | Energy Meetup | RStudio

Navigating the Analytics Journey at Smart DCC Presentation by Oliver Bridges Abstract: This talk will highlight the journey we have been (and still are) on that took us from outsourcing our analytics, to running some basic SQL and using graphical-based visualization tools, to open-source RStudio, and eventually to RStudio Team. Our evolution has been shaped by the huge volumes of data and demands from our customers that really led us to RStudio. While none of us are defined as data scientists, rather data engineers, our whole team has been on this journey with us and we’re continually learning as we go. From creating our own packages, APIs, pins, and Shiny dashboards we have now created workflows that allow us the deal with 10s of millions of records of aggregated data. As you start (or continue) on your own journey, I’d love to help share what we’ve learned along the way. Bio: Oliver Bridges is the Head of Data Science and Analytics at Smart DCC, designing and now managing a team of 15. Ollie has 15 years of experience in telecommunication and 10 years in smart metering. He has worked all around the world, including Cape Town, Copenhagen, Sydney, Amsterdam, Brussels, Dublin, and the UK. Ollie lives in Cornwall UK, and has worked remotely since the start of the pandemic, but before that worked in Manchester (about 300 miles from my house). In his spare time, he loves sport and coding! Smart DCC’s mission is to help digitise Britain’s energy network, and make a critical contribution in the effort to achieve net-zero greenhouse gas emissions, and improve the nation’s connectivity

RStudio Team

Data Science Hangout | Tori Oblad, WaFd Bank | Getting Executives to Support Data Science

We want to help data science leaders become better. The Data Science Hangout is a weekly, free-to-join open conversation for current and aspiring data science leaders. An accomplished leader in the space will join us each week and answer whatever questions the audience may have. We were recently joined by Tori Oblad, Enterprise Data & Analytics Officer at WaFd Bank. Here are a few snippets from our conversation: 1:14 - Start of session 3:00 - How to build an internal data science community 11:40 - Showing the art of the possible 14:00 - How do you get others to lead topics and foster engagement? 26:17 - Writing starter scripts for new users 35:55 - When to use R or Python versus BI 36:38 - Building toy models in Excel to explain it to people / to build relationships with business 38:33 - Avoiding vendor lock-in, being technology agnostic 43:35 - How to build confidence with IT and compliance 49:15 - Working with business users and creating business value 53:21 - Getting business and executive support 1:22:30 - What data scientists should focus on when communicating with stakeholders: value ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

How to Improve Your Communication Skills | Data Science Hangout Highlights

RStudio is joined by Elaine McVey, VP of Data Science at The Looma Project, to discuss how data scientists can become leaders within their organizations. Watch the full recording here: https://www.youtube.com/watch?v=IkqItgPSPro&feature=youtu.be ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Kaija Gahm | greenT (Shiny Contest) | RStudio

Kaija Gahm: greenT Shiny app greenT is a Shiny app I built to explore grapheme-color synesthesia, a neurological phenomenon where letters/numbers (graphemes) are associated with colors in my mind. Whenever friends found out about my synesthesia, they would want to know what color their names were in my head, and it got to be challenging to explain. When I found myself manually coloring squares in a spreadsheet to demonstrate what I saw in my mind’s eye, I decided to build an app instead. The app lets you either set your own colors or see what mine look like. Then you can enter text, and it will display as a series of rectangles according to the colors you’ve selected. I also added a feature that mimics a Google form to collect survey responses from other people with synesthesia, so they can submit their color data if they want to. Bio: My name is Kaija (rhymes with Maya), and I’m an incoming PhD student in the ecology/evolutionary biology department at UCLA, where I’ll be studying movement ecology and behavior of vultures using social network analysis. I’ve been using R for about six years, and for the past year I’ve been working as a data manager for an ecosystem science nonprofit. I’m on the board of SORTEE, an organization focused on openness and reproducibility in ecology and evolutionary biology. I didn’t come from a computer science background, so I’m passionate about talking to people about incorporating R into their work in accessible and friendly ways. When I’m not at my computer, you can usually find me outdoors (hiking or birdwatching) or spending entire days in the kitchen making up recipes. I tweet and blog, mostly about R

How to Communicate Value | Data Science Hangout Highlights

RStudio is joined by Jonathan Regenstein, Head of Data and Quantamental Research at Truist Securities, to discuss how data scientists can become leaders within their organizations. Watch the full recording here: https://www.youtube.com/watch?v=pNTENrov020 ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

How to Speak to Executives | Data Science Hangout Highlights

RStudio is joined by Elaine McVey, VP of Data Science at The Looma Project, to discuss how data scientists can become leaders within their organizations. Watch the full recording here: https://www.youtube.com/watch?v=IkqItgPSPro ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Data Science Hangout | Jonathan Regenstein, Truist | Relationships with IT and Non-Data Scientists

We want to help data science leaders become better. The Data Science Hangout is a weekly, free-to-join open conversation for current and aspiring data science leaders. An accomplished leader in the space will join us each week and answer whatever questions the audience may have. We were recently joined by Jonathan Regenstein, Head of Data and Quantamental Research at Truist Securities. Working with IT and building relationships was a focus in our conversation with Jonathan and he included a few tips for building relationships with non-data scientist colleagues. Find a partner within the IT organization and talk to that person at least once a week. IT can help you communicate value proposition along the way as well. “It sounds crazy to say this in the world of data science, but relationship building was critical to what we did, especially at a bank. Thousands of request for new technology. There’s no way to avoid going through all the security scans and check marks that we have to go through. We want to make sure we have a good partner who is going to help us do that” 0:48 - Start of session 10:52 - How should data science leaders work with IT? 46:20 - How far out Data Science Leaders should be planning projects with IT 48:20 - How do you become a champion of data science within your organization? 1:02:11 - Your responsibility as a data science leader is to work cross functionally 1:04:17 - Data Science Leaders: Your business cares about the value, not how you got there. ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn:https://www.linkedin.com/company/rstudio-pbc Twitter: https://twitter.com/rstudio

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Data Science Hangout | Elaine McVey at the Looma Project | Communicating the Value of Data Science

The Data Science Hangout is a weekly, free-to-join open conversation for current and aspiring data science leaders. An accomplished leader in the space will join us each week and answer whatever questions the audience may have. We were recently joined by Elaine McVey, VP of Data Science at the Looma Project. 21:30 - How to approach experimentation and running tests 43:30 - How do you communicate the value of data science to executives 52:40 - Ways to improve your communication skills as a data scientist 57:15 - How to package insights to executives 1:01:03 - What data scientists get wrong when communicating insights ► Subscribe to Our Channel Here: https://bit.ly/2TzgcOu Follow Us Here: Website: https://www.rstudio.com LinkedIn: https://www.linkedin.com/company/rstu … Twitter: https://twitter.com/rstudio To join future data science hangouts, more info here: rstd.io/datasciencehangout

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Yu-Hung Chang, Phillip Lear & Brendan Scully | R in Manufacturing & Consumer Products | RStudio

Presentations from: Pratt and Whitney - Brendan Scully Kellogg Company - Phillip Lear AGCO - Dr. Yu-Hung Chang Abstract #1 In this talk, Brendan will showcase two of the tools developed at Pratt using R and hosted on RStudio Connect: a forecasting model for repair shop performance and a real-time dashboard for work management on the repair shop floor. In addition to these tools, he will talk about how to approach some common development and implementation challenges for decision analytics tools at large companies like Pratt. Bio: Brendan Scully is the Decision Analytics Strategy Lead for Pratt and Whitney’s aftermarket operations business. In this role, he is focused on how decision analytics tools can be used to improve the maintenance, repair, and overhaul of aircraft engines. Before this, Brendan helped other companies make the most of their data as an actuarial consultant at Milliman and Deloitte, and as data scientist at the African Leadership University. Abstract #2 Phillip will discuss his organization’s journey in transitioning a set of mixed analytic technologies to using a standardized analytic content platform. This talk will offer real life lessons learned in overcoming organizational barriers, getting stakeholders on board, and running a successful POC. Bio: Phillip is a Principal Data Scientist at the Kellogg Company working with global sales, marketing and ecommerce teams. He also teaches R to economics students at Grand Valley State University and sits on the Advisory Board for the Economics department. He lives in Kalamazoo (yes there really is a Kalamazoo!) with his wife Jamie and their two corgis, Penny and Winston. Abstract #3 Dr. Yu-Hung Chang is an Advanced Analytics Specialist at AGCO. She’s going to showcase an interactive app developed at AGCO using R and hosted on RStudio Connect, which provides comprehensive insights for global manufacturing quality analysis. The real-time dashboard provides not only forecasting failure rate based on different levels of parts, but also varieties of diagrams of cost and claims in different aspects. In this talk, Yu-Hung will share what are the challenges for development of analytics tools and how to establish some common development in agricultural manufacturing world. Dr. Yu-Hung Chang is an Advanced Analytics Specialist working for AGCO’s Global Field Quality group. Her multi-disciplinary background combines technical fundamentals with leading-edge research and big data applications, using the most advanced technologies. She has authored numerous articles, published in inter-disciplinary journals, covering topics from Statistics and Computational Physics to Aeronautics and Automotive

Meghan Hall & Mitch Tanney | R in Sports Analytics | RStudio

R in spoRts analytics! Presentations by Mitch Tanney & Meghan Hall Moving the Needle Toward Organizational Success (0:00 - 29:35) Extending R Markdown: from websites, slides, to PDFs, and more! (32:00 -53:00) Talk #1 Moving the Needle Toward Organizational Success (0:00 - 29:35) What You’ll Learn: Data analysis can and should influence decisions on the field, court, ice or pitch in the same way it impacts decisions in the boardroom. While the physical landscapes may vary, the objective to maximize the likelihood of successful organizational outcomes does not. Practical applications of data-driven, decision-making processes will serve as the framework for introducing attendees to the field of sports analytics. About the Speaker: Prior to joining RStudio in Customer Success, Mitch chased a professional football playing career and later founded and led analytics departments for the Chicago Bears and Denver Broncos. He was a member of the Super Bowl 50 World Championship team in Denver. Mitch graduated summa cum laude as a double major in Mathematics and Spanish from Monmouth College and completed an MBA with distinction from the University of Iowa. Talk #2 Extending R Markdown: from websites, slides, to PDFs, and more! (32:00 -53:00) About the Speaker: Meghan Hall is a data scientist at Zelus Analytics and is currently teaching a course on data visualization at Carnegie Mellon University. Meghan contributes to the public sports analytics community by helping beginners learn R as well as by writing and presenting on various aspects of hockey analysis as a member of Hockey-Graphs

Chris Bumgardner, Children’s Wisconsin || Healthcare Meetup || Posit

Cultivating an R-based Analytic Practice in Healthcare Supporting the advanced analytic needs of an active academic healthcare organization requires tools and practices that enhance the application of statistical and algorithmic approaches. To positively impact care, system operations, or even well-being at the community level, these tools need to support solutions that can be rapidly deployed and communicated as well as reproduced when studying longitudinal trends. At Children’s Wisconsin, we use R and Posit’s suite of tools to enable forecasting, modeling, and data mining among other data science activities. We communicate the results of our efforts using interactive applications built with Shiny as well as reports and push analytics created using RMarkdown. This talk will discuss how we have developed this capability and provide a few examples of the applications that have been created to support our vision that the kids of Wisconsin will be the healthiest in the nation. Agenda Children’s Wisconsin Introduction Data Science Tools and Supporting Infrastructure Example R-based Projects [Community: Missing Youth, System-wide: COVID-19 Response, Operational: Patient Placement Planning and Optimization] Challenges and Future Plans Speaker Bio: Chris Bumgardner leads the data science efforts at Children’s Wisconsin and works with teams across the health system to improve decision-making. He is focused on applying statistical methods to data sets large and small to discover and visualize insights that will help ensure Wisconsin’s kids are healthy, happy, and safe. Chris can often be found awake far too early thanks to an insubordinate rescue dog named Dutch. R in Healthcare Slack Group: https://join.slack.com/t/rinhealthcare/shared_invite/zt-sc7lc4k6-K9zb~kX826dOXMcaj~Wt~w RStudio Enterprise Community Meetup for future events: https://www.meetup.com/RStudio-Enterprise-Community-Meetup

Art Steinmetz | Open Source Data Science in the Enterprise | RStudio

Art Steinmetz is the former Chairman, CEO and President of Oppenheimer Funds. In this interview, Art gives his unique perspective on the value and suitability of open source, code-first data science for the enterprise. Timecodes 0:00 Intro 0:10 What are some of the advantages to adopting open source software within an organization? 2:23 Is open source software appropriate for enterprise-level data science? 4:29 How do you build support for open source software within an organization? 5:56 Do executives need to know how to code in order to manage data science teams? 7:56 How and why did you get started with R, and why? 9:55 Any other advice for an aspiring Data Science Leader? For a more in-depth view of Art’s perspective on Open Source Data Science in Investment Management, see this blog post https://blog.rstudio.com/2020/10/13/open-source-data-science-in-investment-management/ To learn more about RStudio’s professional products, and how they can help you scale, secure and operationalize your open source data science, see www.rstudio.com

Nicolas Nguyen - ZEISS | Supply Chain Management Meetup | RStudio

0 - 50:20 Presentation 50:20 - 56:52 Q&A Presentation by Nicolas Nguyen, Digital Supply Chain and Global S&OP Leader for Carl Zeiss Meditec Abstract: In demand & supply planning, we often need to calculate projected inventories and replenishment plans - sometimes for hundreds or thousands of SKUs, and through different levels of the distribution network. In the sales & operations planning (S&OP) process, we might need to run some scenarios to balance demand and supply to support sales: changing inventories plans, sales plans, delivery lead times, production plans, product mix, etc. Using Shiny, we can design simple, powerful, scalable, and reproducible apps for demand & supply planning as well as the S&OP process. In this talk, you will learn about real-life examples of web applications, which can be deployed in a few minutes to perform some popular demand & supply planning operations. __ Thank you to Nicolas for an awesome presentation on how he is using Shiny today. With all the interest from the community in this topic, we’d love to continue the discussion and connect us all through: Future meetups: https://www.meetup.com/RStudio-Enterprise-Community-Meetup/ R for Data Science Online Learning Community (“chat-supply_chain”): r4ds.io/join RStudio Community: community.rstudio.com

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Supply Chain Demand Planning Meetup

Managing COVID vaccine distribution in West Virginia | RStudio

With a little help from open source software Learn more: Data Driven West Virginia: https://business.wvu.edu/research-outreach/data-driven-wv DDWV PPE forecasting: https://wvutoday.wvu.edu/stories/2020/04/27/wvu-business-experts-partner-with-the-national-guard-to-forecast-ppe-needs DDWV inventory management system: https://wvutoday.wvu.edu/stories/2021/03/22/a-different-kind-of-science-wvu-chambers-college-data-scientists-propel-west-virginia-s-acclaimed-vaccine-strategy-with-digital-inventory-management-system West Virginia National Guard: https://www.wv.ng.mil/ Shiny: https://shiny.rstudio.com/ West Virginia leading nation at start of vaccine rollout: https://www.vox.com/first-person/2021/3/4/22313540/covid-19-vaccine-west-virginia In the United States, approximately 2.5 million doses of COVID vaccines are being delivered each day, and how these doses go from the manufacturer to a shot in someone’s arm varies by state, often with mixed results. But early on in the vaccine distribution process, one state led the pack in terms of using the majority of vaccine doses it had been allotted. That state? West Virginia. Part of what has made West Virginia successful is the creation of an inventory management system using Shiny, an open source framework for building interactive web applications. The system was built by Data Driven West Virginia, part of the John Chambers College of Business and Economics at West Virginia University, in collaboration with the West Virginia Army National Guard. Using Shiny has provided visibility into each component of the vaccine supply chain, leading to the creation of distribution plans that are able to quickly and efficiently match supply with demand, getting vaccines to the right people in the right location at the right time. RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Tidymodels Statistics Debugging Rstats Open Source Oss Data Driven West Virginia Katherine Kopp Brad Price Major Ryan Coss Covid Covid Vaccine Vaccine Distribution

Katherine Kopp | COVID vaccine distribution Shiny app walkthrough (mock data) | RStudio

Learn more: Data Driven West Virginia: https://business.wvu.edu/research-outreach/data-driven-wv DDWV PPE forecasting: https://wvutoday.wvu.edu/stories/2020/04/27/wvu-business-experts-partner-with-the-national-guard-to-forecast-ppe-needs DDWV inventory management system: https://wvutoday.wvu.edu/stories/2021/03/22/a-different-kind-of-science-wvu-chambers-college-data-scientists-propel-west-virginia-s-acclaimed-vaccine-strategy-with-digital-inventory-management-system West Virginia National Guard: https://www.wv.ng.mil/ Shiny: https://shiny.rstudio.com/ West Virginia leading nation at start of vaccine rollout: https://www.vox.com/first-person/2021/3/4/22313540/covid-19-vaccine-west-virginia To understand just how hard it is to get vaccines to the population, it helps to understand where it can go wrong. This starts with how vaccines are packed into containers. To fill up a container, Pfizer places 195 vials into a tray, and up to 5 trays into a single container. Moderna puts 10 vials into a small box, and then combines a minimum of 10 small boxes into a single container. In most states Pfizer and Moderna ship directly to the organization that will be administering the vaccine to the population. This could be a hospital, a pharmacy, or any place where trained professionals will be putting shots into arms. But what happens when a pharmacy receives a full container from Pfizer, 975 vials, but only needs 600? West Virginia has removed this complication by shipping directly to five hubs strategically located throughout the state. Within each of these hubs, containers of vaccine vials are broken down into smaller components and then either picked up or shipped directly to the hospital, pharmacy, or organization that will be administering the vaccine. These hubs are managed by the Joint Interagency Task Force (JIATF), a team of teams composed of public, private, and governmental organizations as well as the National Guard. The Joint Interagency Task Force is responsible for drawing up a weekly distribution plan for each hub, in alignment with CDC allocations, and matching vaccine supply with demand. By using a statewide system managed by a central organization, there’s a level of agility and fluidity that allows each hub to adjust to a variety of changes in order to maximize the number of vaccines that are being administered to the population each week. RStudio’s mission is to create free and open-source software for data science, scientific research, and technical communication to enhance the production and consumption of knowledge by everyone, regardless of economic means, and to facilitate collaboration and reproducible research, both of which are critical to the integrity and efficacy of work across industries. RStudio also produces RStudio Team, a modular platform of commercial software products that give organizations the confidence to adopt R, Python and other open-source data science software at scale, along with online services to make it easier to learn and use them over the web. Together, RStudio’s open-source software and commercial software form a virtuous cycle: the adoption of open-source data science software at scale in organizations creates demand for RStudio’s commercial software; and the revenue from commercial software, in turn, enables deeper investment in the open-source software that benefits everyone. Check out www.rstudio.com. Follow us on Twitter: https://twitter.com/rstudio Facebook: https://www.facebook.com/rstudiopbc/ And LinkedIn: https://www.linkedin.com/company/rstudio-pbc/

RStudio Team

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Rstats Open Source Oss Reticulate Data Driven West Virginia Covid Covid Vaccine Vaccine Distribution Katherine Kopp Brad Price Major Ryan Coss

Julia Silge | Monitoring Model Performance | RStudio

0:00 Project introduction 1:50 Overview of the setup code chunk 3:05 Getting new data 4:05 Getting model from RStudio Connect using httr and jsonlite 6:20 Bringing in metrics 9:45 Using the pins package 10:50 Using boards on RStudio Connect 13:30 Benefits of using pins 14:00 Visualizations using ggplot and plotly 17:00 Knitting the flexdashboard 18:10 Project takeaways You can read Julia’s blogpost, Model Monitoring with R Markdown, pins, and RStudio Connect, here: https://blog.rstudio.com/2021/04/08/model-monitoring-with-r-markdown/ Modelops playground GitHub repo: https://github.com/juliasilge/modelops-playground pins package documentation: https://pins.rstudio.com/ flexdashboard documentation: https://rmarkdown.rstudio.com/flexdashboard/ tidymodels documentation: https://www.tidymodels.org/

Julia Silge

flexdashboard httr rmarkdown rstudio tidymodels RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Modelops Julia Silge Model Performance Pins

Tom Mock | RStudio Connect in Production

https://rstudio.com/resources/webinars/rstudio-connect-in-production/ In part 2 of this 3 part series, Tom covers: Communicating results can be the most challenging part of Data Science: many insights never leave the laptops where they are discovered. In this webinar, we will show you how to use RStudio Connect to deploy your results in a production environment. You’ll learn how to automate publishing, schedule updates, and provide consumers with self-service access to your work. RStudio Connect is a revolutionary new way to host executable Data Science content. About Tom: Thomas is involved in the local and global data science community, serving as Outreach Coordinator for the Dallas R User Group, as a mentor for the R for Data Science Online Learning Community, as co-founder of #TidyTuesday, attending various Data Science and R-related conferences/meetups, and participated in Startup Weekend Fort Worth as a data scientist/entrepreneur

rstudio webinars RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Kelly O’Briant | Interactivity in Production | RStudio (2019)

https://rstudio.com/resources/webinars/interactivity-in-production/ In part 3 of this 3 part series, Kelly covers: Interactive products take your data science to a new level, but they require new coding decisions. This webinar will give you clear guidelines on when and how to add interactivity to your work. Here you’ll learn: when to use off-the-shelf interactive products like parameterized R Markdown and htmlwidgets, when to create bespoke interactivity with Shiny, how to make your Shiny apps as fast as possible, how to support interactivity in production, and much more. About Kelly: Kelly is Solutions Engineer for RStudio and also an organizer of the Washington DC chapter of R-Ladies Global. It’s an R users group for lady-folk and friends

rstudio Shiny webinars RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Garrett Grolemund | Reproducibility in Production | RStudio (2019)

https://rstudio.com/resources/webinars/reproducibility-in-production/ In part 1 of this 3 part series, Garrett covers the following: Computational documents offer limitless opportunities for your business. With them, your consumers can rerun your report with new parameters, apply your analysis to new data, or schedule future, automatic updates to your work—all with the click of a button. This is the first in a three part webinar series that will describe this new form of reproducibility. Here, we begin by showing you how to write executable R Markdown documents for a production environment. About Garrett: Garrett is the author of Hands-On Programming with R and co-author of R for Data Science and R Markdown: The Definitive Guide. He is a Data Scientist at RStudio and holds a Ph.D. in Statistics, but specializes in teaching. He’s taught people how to use R at over 50 government agencies, small businesses, and multi-billion dollar global companies; and he’s designed RStudio’s training materials for R, Shiny, R Markdown and more. Garrett wrote the popular lubridate package for dates and times in R and creates the RStudio cheat sheets

lubridate rstudio Shiny webinars RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Webinar Summary | Avoid Dashboard Fatigue | RStudio (2020)

0:00 Introduction 0:07 The Problem 1:05 The Solution 3:20 Real Life Success Stories 5:27 Demo (with code) Don’t have an hour to watch a webinar? We’ve made a summary video that covers the main points of our “Avoid Dashboard Fatigue” webinar from Sean Lopp and Rich Iannone. The full webinar covered: Data science teams face a challenging task. Not only do they have to gain insight from data, they also have to persuade others to make decisions based on those insights. To close this gap, teams rely on tools like dashboards, apps, and APIs. But unfortunately data organizations can suffer from their own success - how many of those dashboards are viewed once and forgotten? Is a dashboard of dashboards really the right solution? And what about that pesky, precisely formatted Excel spreadsheet finance still wants every week? In this webinar, we’ll show you an easy way teams can solve these problems using proactive email notifications through the blastula and gt packages, and how RStudio pro products can be used to scale out those solutions for enterprise applications. Dynamic emails are a powerful way to meet decision makers where they live - their inbox - while displaying exactly the results needed to influence decision-making. Best of all, these notifications are crafted with code, ensuring your work is still reproducible, durable, and credible. We’ll demonstrate how this approach provides solutions for data quality monitoring, detecting and alerting on anomalies, and can even automate routine (but precisely formatted) KPI reporting. Webinar materials: https://rstudio.com/resources/webinars/avoid-dashboard-fatigue/ About Sean: Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native. About Rich: My background is in programming, data analysis, and data visualization. Much of my current work involves a combination of data acquisition, statistical programming, tools development, and visualizing the results. I love creating software that helps people accomplish things. I regularly update several R package projects (all available on GitHub). One such package is called DiagrammeR and it’s great for creating network graphs and performing analyses on the graphs. One of the big draws for open-source development is the collaboration that comes with the process. I encourage anyone interested to ask questions, make recommendations, or even help out if so inclined!

Rich Iannone

blastula gt rstudio webinars RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Tom Mock & Shannon Haggerty | Theming Shiny and RMarkdown with {thematic} & {bslib} | RStudio

From rstudio::global(2021) Shiny X-Sessions, sponsored by Appsilon: this presentation covers the basics of how the thematic and bslib packages can be used to consistently style all the components of a shiny app at once. About Tom Mock: Thomas is involved in the local and global data science community, serving as Outreach Coordinator for the Dallas R User Group, as a mentor for the R for Data Science Online Learning Community, as co-founder of #TidyTuesday, attending various Data Science and R-related conferences/meetups, and participated in Startup Weekend Fort Worth as a data scientist/entrepreneur. About Shannon Haggerty: Shannon is on RStudio’s Customer Success team working with teams across the Life Sciences and Healthcare. In her free time, she likes to bake, hang out with her dogs, and explore new hobbies. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

bslib rmarkdown rstudio Shiny thematic RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Bslib Thematic

Pedro Silva | Styling Shiny with CSS & SASS and Speeding Up Shiny Apps | Posit

From rstudio::global(2021) Shiny X-Sessions, sponsored by Appsilon: in the first part of this talk I will discuss how to use CSS to give your application a fresh and unique look, while keeping your codebase clean and organized with SASS. During the second half I will discuss how to leverage Shiny update functions, proxy objects and JavaScript messages to speed up your dashboards. About Pedro Silva: Pedro has nearly a decade of experience combining frontend and backend technologies, and is an expert on augmenting R Shiny dashboards with CSS and JavaScript. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Appsilon

Jenny Bryan | Help me help you: creating reproducible examples | RStudio (2018)

What is a reprex? It’s a reproducible example. Making a great reprex is both an art and a science and this webinar will cover both aspects. A reprex makes a conversation about code more efficient and pleasant for all. This comes up whenever you ask someone for help, report a bug in software, or propose a new feature. The reprex package ( https://reprex.Tidyverse.org ) makes it especially easy to prepare R code as a reprex, in order to share on sites such as https://community.rstudio.com, https://github.com, or https://stackoverflow.com. The habit of making little, rigorous, self-contained examples also has the great side effect of making you think more clearly about your programming problems. Webinar materials: https://rstudio.com/resources/webinars/help-me-help-you-creating-reproducible-examples/ About Jenny: Jenny is a software engineer on the tidyverse team. She is a recovering biostatistician who takes special delight in eliminating the small agonies of data analysis. Jenny is known for smoothing the interfaces between R and spreadsheets, web APIs, and Git/GitHub. She’s been working in R/S for over 20 years and is a member of the R Foundation. She also serves in the leadership of rOpenSci and Forwards and is an adjunct professor at the University of British Columbia

Jenny Bryan

reprex rstudio tidyverse tidyverse.org webinars RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Reprex

Damian Rodziewicz | Scaling Shiny to Thousands of Users | RStudio

From rstudio::global(2021) Shiny X-Sessions, sponsored by Appsilon: in this talk I will discuss how to scale Shiny dashboards to thousands of users. About Damian Rodziewicz: Damian is one of the four co-founders of Appsilon. Before founding Appsilon he worked at Accenture, UBS, Microsoft and Domino Data Lab. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Appsilon

Dominik Krzemiński | Appsilon’s Guide to Working With Open Source Shiny | RStudio

From rstudio::global(2021) Shiny X-Sessions, sponsored by Appsilon: There is no need to praise Shiny for its influence on interactive data visualisation. As with many other technology stacks, Shiny could benefit from community contributions for the further development of the package itself and the growth of independent packages that add new features. In this talk, I present some of the most popular Shiny extensions and explain what are the ways to help with developing Shiny-related tools. About Dominik Krzemiński: Dominik is the Open Source Tech Lead at Appsilon where he enjoys contributing to open source tools, mainly in R and Python. He created shiny.i18n, shiny.semantic, and the TODOr package for R. He also participated in the Google Summer of Code, where he developed tools supporting neuroscience analyses. He’s also a fan of all kinds of board sports and capoeira. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Appsilon

Olga Mierzwa-Sulima | Best Practices for Developing Shiny Apps | RStudio

From rstudio::global(2021) Shiny X-Sessions, sponsored by Appsilon: Best practices for developing Shiny apps presentation covers organizing app’s code with modules and R6 classes, setting up development environment, and testing. About Olga Mierzwa-Sulima: Olga is experienced in production applications of analytical solutions, especially for FMCG companies. Recently she developed a price elasticity model for Unilever. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

R6 rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Olga Mierzwa-Sulima

Filip Stachura & Marek Rogala | Empowering Data Scientists to Build Spectacular Shiny Apps | RStudio

From rstudio::global(2021) Shiny X-Sessions, sponsored by Appsilon: in this talk, Appsilon’s CEO and CTO show their vision of challenges facing Shiny app authors and what is crucial to achieving success. They announce 3 key initiatives that Appsilon undertakes to empower data scientists to build spectacular Shiny Apps, including the {shiny.fluent} package. About Filip Stachura: Filip is a CEO and a Co-founder of Appsilon. He holds a double degree in Applied Mathematics and Computer Science from the University of Warsaw. He started his professional career at Microsoft in California. Passionate about data analysis, elegant visualisations and tackling hard algorithmic and analytical problems. About Marek Rogala: Marek Rogala is the CTO at Appsilon, where he drives innovation in R and Shiny as well as Machine Learning. He previously did software engineering at Google and at Domino Data Lab, where he worked on enabling data scientists to experiment and collaborate effectively.: Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Appsilon

Nathan Stephens | Best Practices for Administering RStudio in Production | RStudio (2019)

Most organizations are unfamiliar with the R programming language. As a result they often struggle to onboard and manage R in production. In this webinar we introduce the RStudio Quickstart which makes it easy to try RStudio professional products on your desktop for free. We also outline best practices for using R and RStudio in production. Webinar materials: https://rstudio.com/resources/webinars/best-practices-for-administering-rstudio-in-production/ About Nathan: Nathan has a background in analytic solutions and consulting. He has experience building data science teams, architecting analytic infrastructure, and delivering innovative data products. He is a long time user of R

rstudio webinars RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Nathan Stephens

Tom Mock | A Gentle Introduction to Tidy Statistics in R | RStudio (2019)

R is a fantastic language for statistical programming, but making the jump from point and click interfaces to code can be intimidating for individuals new to R. In this webinar I will gently cover how to get started quickly with the basics of research statistics in R, providing an emphasis on reading data into R, exploratory data analysis with the Tidyverse, statistical testing with ANOVAs, and finally producing a publication-ready plot in ggplot2. Use the code presented instantly on RStudio Cloud! RStudio Cloud: rstudio.cloud Webinar materials: https://rstudio.com/resources/webinars/a-gentle-introduction-to-tidy-statistics-in-r/ About Thomas: Thomas is involved in the local and global data science community, serving as Outreach Coordinator for the Dallas R User Group, as a mentor for the R for Data Science Online Learning Community, as co-founder of #TidyTuesday, attending various Data Science and R-related conferences/meetups, and participated in Startup Weekend Fort Worth as a data scientist/entrepreneur

ggplot2 rstudio tidyverse webinars RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Thomas Mock

Sean Lopp | Posit Investments in Pharma | Posit

From rstudio::global(2021) Pharma X-Sessions, sponsored by ProCogia: R/Pharma is an organization of R enthusiasts who work in the pharma and biotech industries. This presentation summarizes the group and presents some goals for 2021. More about Sean Lopp: Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Pharma Rpharma Biotech

Marly Gotti | Risk Assessment Tools: R Validation Hub Initiatives | Posit

From rstudio::global(2021) Pharma X-Sessions, sponsored by ProCogia: we will present some of the resources and tools the R Validation Hub has been working on to aid the biopharmaceutical industry in the process of using R in a regulatory setting. In the talk, you will learn about the {riskmetric} R package, which measures the risk of using R packages, and you will also see a demo of the Risk Assessment Shiny application, which is an advanced user interface for {riskmetric}. About Marly Gotti: Marly Gotti is a Senior Data Scientist at Biogen and a former RStudio intern. She is also an executive committee member of the R Validation Hub, where she advocates for the use of R within a biopharmaceutical regulatory setting. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Riskmetric Pharma Marly Gotti

Harvey Lieberman | R/Pharma | Posit

From rstudio::global(2021) Pharma X-Sessions, sponsored by ProCogia: R/Pharma is an organization of R enthusiasts who work in the pharma and biotech industries. This presentation summarizes the group and presents some goals for 2021. About Harvey Lieberman: Harvey Lieberman works at Novartis and has been a member of R/Pharma since 2017. For more on R/Pharma: https://www.pharmar.org/ Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Rpharma Pharma

Ellis Hughes | R Package Validation Framework | Posit

From rstudio::global(2021) Pharma X-Sessions, sponsored by ProCogia: in this talk I discuss the process developed for validating internally generated R packages at SCHARP (Statistical Center for HIV/AIDS Research and Prevention) - the R Package Validation Framework. I cover the elements of the framework and basics of applying it with some examples. By using tools native to the R package building infrastructure, validation can become an integrated part of your package development, improving the quality of both the package and validation. About Ellis Hughes: I am a statistical Programmer at Fred Hutch Cancer Research Center where I work on a team that evaluates potential HIV vaccine candidates. Having graduated from Washington State University with a degree in Bioengineering, I found a passion for programming in R. I now organize the Seattle UseR group, and enjoy building packages to automate my workflows. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Package Validation Ellis Hughes Vaccine Development

Nathan Stephens | Make PowerPoint Presentations with R Markdown | RStudio (2018)

Data scientists use R Markdown documents to create reproducible code that can be rendered in a variety of output types. Some of the most common output types include HTML, Word, and PDF, but new improvements make it possible to create PowerPoint presentations as well. PowerPoint presentations are still common currency for sharing insights in most organizations today. This webinar demonstrates how to create feature rich PowerPoint presentations from R Markdown and how to use these presentations to share insights, visualizations, Shiny apps, and more. About Nathan: Nathan has a background in analytic solutions and consulting. He has experience building data science teams, architecting analytic infrastructure, and delivering innovative data products. He is a long time user of R

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Nathan Stephens

Edgar Ruiz | Programación con R | RStudio (2019)

Hay ocasiones que, cuando trabajamos en un análisis en R, necesitamos dividir nuestros datos en grupos, y después tenemos que correr la misma operación sobre cada grupo. Por ejemplo, puede ser que los datos que tenemos contienen varios países, y necesitamos crear un modelo por cada país. Otro caso sería el de correr múltiples operaciones sobre los mismos datos. Estos casos requieren que sepamos cómo usar iteraciones con R. Este webinar se concentrará en cómo utilizar el paquete llamado purrr para ayudarnos a resolver este tipo de problema. Descargar materiales: https://rstudio.com/resources/webinars/programacio-n-con-r/ About Edgar: Edgar Ruiz es un Ingeniero de Soluciones en RStudio. Es el administrador de los sitios oficiales de sparklyr y de R para bases de datos. También es autor de los paquetes de R: dbplot, tidypredict y modeldb, y co-autor de el paquete dbplyr

Edgar Ruiz

dbplyr modeldb purrr rstudio tidypredict webinars RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Edgar Ruiz

Mike Garcia | R in Pharma: Intro to Shiny | Posit

Slides: https://garciamikep.github.io/rstudioglobal-2021-shiny-slides/slides.html#1 From rstudio::global(2021) Pharma X-Sessions, sponsored by ProCogia: in this introduction to Shiny app development, we begin with a quick review of visualization with {ggplot2} and then cover core concepts in app structure and reactive programming. After building several Shiny apps of increasing complexity, we wrap up with a demonstration of how to include your Shiny app in a dashboard using the {flexdashboard} package. About Mike Garcia: Mike is a Data Science Consultant with ProCogia, with a background in Biostatistics and experience in clinical trial design and public health research. If not geeking out on data with a cup of coffee and spreading his passion for R, he’s probably out enjoying the outdoors. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/ To hear more about how other major pharmaceutical companies are transitioning to open source data science you can watch talks from this year’s R in Pharma conference: https://www.youtube.com/@RinPharma/playlists At Posit, we have a dedicated Pharma team to help organizations migrate and utilize open source for drug development. To learn more about our support for life sciences, please see our dedicated Pharma page where you can book a call with our team. ( https://posit.co/solutions/pharma )

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Volha Tryputsen | R in Janssen Drug Discovery Statistics | Posit

From rstudio::global(2021) Pharma X-Sessions, sponsored by ProCogia: this talk discusses how R is utilized in the Janssen drug discovery statistics workflow. About Volha: Volha is the Principal Statistician in the Translational Medicine and Early Development Statistics (TMEDS) group in the Quantitative Sciences Department of Janssen R&D. Learn more about the rstudio::global(2021) X-Sessions: https://blog.rstudio.com/2021/01/11/x-sessions-at-rstudio-global/

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Pharma Janssen Drug Discovery

Posit Presents: Saving the Coral Reefs

Data scientists are changing the world. For a real-world view of how data scientists work to solve hard, complex, and valuable problems, watch Pim Bongaerts from the California Academy of Sciences speak about using data science to help save coral reefs. About Pim: Pim Bongaerts is the McCosker Chair of Aquatic Biology, Assistant Curator at the California Academy of Sciences, where he applies genomics and field ecology to his research into coral reef biodiversity and evolution

RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown CRAN Interoperability Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Pim Bongaerts California Academy of Sciences Coral Reefs Ocean Conservation

Lou Bajuk & Kevin Bolger | Why Data Science in the Cloud? | RStudio (2020)

As business and organizational needs expand, a centralized ecosystem such as the cloud is needed to securely store and access data, conduct analyses, and share results. We’ll share some examples of what it means to do data science in the cloud and discuss some problems that users may face along the way, and the solutions that RStudio products can provide. We’ll also discuss best practices for migrating to a cloud environment. What you’ll learn: What are the benefits of working in a cloud environment? What are the different cloud environments available? How do I learn which is the best fit for my organization? What should I consider when migrating my data science infrastructure to the cloud? Webinar materials: https://rstudio.com/resources/webinars/why-data-science-in-the-cloud/ About Lou: Lou is a passionate advocate for data science software, and has had many years of experience in a variety of leadership roles in large and small software companies, including product marketing, product management, engineering and customer success. In his spare time, his interests includes books, cycling, science advocacy, great food and theater. About Kevin: After finishing his education in the University of Limerick, Ireland – Kevin’s passion for data science was cemented. Focusing primarily on data analytics and modelling, he went on to spend the first years of his career working at a biopharmaceutical company, where he led the data team on multiple products. Since moving to Seattle with his Washington native wife, Kevin has spent his spare time enjoying the beautiful PNW and playing ‘hurling’, an ancient gaelic field sport with the Seattle Gaels. He now leads the Data Science team at ProCogia as the Director of Data Solutions – where he works with clients from Biotech to Telecom

rstudio webinars RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Forcats Rstats Open Source Oss Reticulate Lou Bajuk Kevin Bolger Cloud Based Data Science Cloud

Andy Nicholls & Michael Rimler | Using R to Drive Agility in Clinical Reporting | RStudio (2020)

The R language is used extensively throughout the pharmaceutical industry. But its use within the tightly regulated clinical reporting workflows has remained limited. GSK Biostatistics has embarked upon a journey to embed R as a primary statistical analysis tool for clinical reporting. Enabling R within a global department of over 600 Statisticians, Programmers and Data Scientists is challenging! It requires planning, patience, and a strong foundation that enables consistency across the enterprise. We invite you to learn more about how we achieved this at GSK. You’ll learn about our tidyverse-centric training program, a future-ready Working Area for R Programming (WARP) environment, and a leading-edge R for Clinical Reporting (R4CR) initiative. The goal: help embed R in every-day clinical reporting output. About Andy: Andy Nicholls has a long history with the R language and in Data Science, authoring the book ‘R in 24 Hours’. He is currently Head of Statistical Data Sciences within GSK’s Biostatistics department. One of his team’s main objectives is to embed the R language within Biostatistics; developing training materials, overseeing various adoption initiatives and provisioning a world-class environment for R. He is also the lead for the cross-industry R Validation Hub initiative that aims to support the use of R for regulatory work. About Michael: Michael Rimler is a clinical programmer in GSK Biostatistics and passionate about influencing the evolving role of open source technologies and data science capabilities on clinical data analytics. He is involved with numerous internal initiatives aimed at moving the organization in this direction, including leading the effort to fully integrate R into the clinical reporting process. Michael is a co-lead of the Open Source Technologies in Clinical Research PHUSE working group project, has chaired a PHUSE US Single Day Event on Data Visualization, and will serve as a co-chair for the 2021 PHUSE US Connect

rstudio tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Forcats Rstats Open Source Oss Reticulate Clinical Reporting GSK Pharma Andy Nicholls Michael Rimler

Hadley Wickham | testthat 3.0.0 | RStudio (2020)

In this webinar, I’ll introduce some of the major changes coming in testthat 3.0.0. The biggest new idea in testthat 3.0.0 is the idea of an edition. You must deliberately choose to use the 3rd edition, which allows us to make breaking changes without breaking old packages. testthat 3e deprecates a number of older functions that we no longer believe are a good idea, and tweaks the behaviour of expect_equal() and expect_identical() to give considerably more informative output (using the new waldo package). testthat 3e also introduces the idea of snapshot tests which record expected value in external files, rather than in code. This makes them particularly well suited to testing user output and complex objects. I’ll show off the main advantages of snapshot testing, and why it’s better than our previous approaches of verify_output() and expect_known_output(). Finally, I’ll go over a bunch of smaller quality-of-life improvements, including tweaks to test reporting and improvements to expect_error(), expect_warning() and expect_message(). Webinar materials: https://rstudio.com/resources/webinars/testthat-3/ About Hadley: Hadley Wickham is the Chief Scientist at RStudio, a member of the R Foundation, and Adjunct Professor at Stanford University and the University of Auckland. He builds tools (both computational and cognitive) to make data science easier, faster, and more fun. You may be familiar with his packages for data science (the tidyverse: including ggplot2, dplyr, tidyr, purrr, and readr) and principled software development (roxygen2, testthat, devtools, pkgdown). Much of the material for the course is drawn from two of his existing books, Advanced R and R Packages, but the course also includes a lot of new material that will eventually become a book called “Tidy tools”

Hadley Wickham

devtools dplyr ggplot2 pkgdown purrr readr roxygen2 rstudio testthat tidyr tidyverse waldo webinars RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Testthat Hadley Wickham

Mine Çetinkaya-Rundel | Teaching R online with RStudio Cloud | RStudio (2020)

RStudio Cloud is a lightweight and easy to set up / use solution to teaching R online, in the browser. In this webinar we will walk you through the steps of setting up your course on RStudio Cloud, highlighting the various functionalities for teachers and students. We will also discuss best practices and provide an opportunity for the audience to experience the setup first hand. Additionally, we highlight a suite of ready to use resources for teaching an introduction to data science and statistical thinking course using R. Webinar materials: https://rstudio.com/resources/webinars/teaching-r-online-with-rstudio-cloud/ About Mine: Mine Çetinkaya-Rundel is Professional Educator and Data Scientist at RStudio as well as Senior Lecturer in the School of Mathematics at University of Edinburgh (on leave from Department of Statistical Science at Duke University). Mine’s work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education as well as pedagogical approaches for enhancing retention of women and under-represented minorities in STEM. Mine works on integrating computation into the undergraduate statistics curriculum, using reproducible research methodologies and analysis of real and complex datasets. She also organizes ASA DataFest and works on the OpenIntro project. She is also the creator and maintainer of datasciencebox.org and she teaches the popular Statistics with R MOOC on Coursera

Mine Çetinkaya-Rundel

rstudio webinars RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown CRAN Interoperability Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Rayshader Plumber Blogdown Lazy Evaluation Tidymodels Statistics Programming Education Forcats Rstats Open Source Oss Reticulate Mine Çetinkaya-Rundel RStudio Cloud Teaching Online Remote Education Distance Learning

A quick tour of RStudio 1.4 | RStudio

HD version here: https://youtu.be/oCR_LB3H73M 0:00 Introduction 0:20 R Markdown Visual Editor 0:46 Insert citations in R Markdown 1:09 Python support in Environment pane 2:05 Python environment selection 2:25 Rainbow parentheses 2:43 Monospace font support 2:54 Support for multiple source columns 3:10 Command palette 3:27 Customize data and configuration storage (users and servers) 3:55 RStudio Pro edition features 4:08 Authenticate RStudio Server Pro using SAML 4:25 Project sharing with Launcher 4:48 Request a GPU with SLURM 5:00 Run Visual Studio Code sessions (beta) What’s new with RStudio 1.4: A visual markdown editor that provides improved productivity for composing longer-form articles and analyses with R Markdown. New Python capabilities, including display of Python objects in the Environment pane, viewing of Python data frames, and tools for configuring Python versions and conda/virtual environments. The ability to add source columns to the IDE workspace for side-by-side text editing. A new command palette (accessible via Ctrl+Shift+P) that provides easy keyboard access to all RStudio commands, add-ins, and options. Support for rainbow parentheses in the source editor (enabled via Options, then Code, then Display). New citation support that allows you to include document citations from your document bibliography, personal or group libraries, and several other sources. Integration with a host of new RStudio Server Pro features including project sharing when using Launcher, Microsoft Visual Studio Code support (currently in beta), SAML authentication, and local launcher load-balancing. Read more on our blog: https://blog.rstudio.com/2021/01/19/announcing-rstudio-1-4/

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Visual Editor

Greg Wilson | Teaching Online at Short Notice | RStudio (2020)

So here you are: you planned to teach your class or deliver your workshop in person, and now you have to do it online or not at all. Nobody is giving you time or money to make the change, and a hundred other things also need your attention. Where should you start, and what can you realistically hope to achieve? This one-hour webinar will present answers from people who have found themselves in this situation before, and will recommend a handful of techniques that you can apply right away. Webinar materials: https://rstudio.com/resources/webinars/teaching-online-at-short-notice/ About Greg: Dr. Greg Wilson has worked for 35 years in both industry and academia, and is the author or editor of several books on computing and two for children. He is best known as the co-founder of Software Carpentry, a non-profit organization that teaches basic computing skills to researchers, and is now part of the education team at RStudio

rstudio webinars RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Greg Wilson Teaching Remote Learning

Alison Hill & Desirée De Leon | How to Get Your Materials Online With R Markdown | RStudio (2020)

Full title: Sharing on Short Notice: How to Get Your Teaching Materials Online With R Markdown Educators create a lot of files for teaching- slides, exercises, solutions, assignments, data, figures- that all ultimately need to be shared with other people. Having a link for sharing your teaching materials can save you time and pain, but it is hard to get started if you’ve never shared your resources online before. In this webinar, we’ll give a tour of the R Markdown ecosystem for educators that you can start to use right away. We’ll show how it can help you make your teaching more shareable, reproducible, and resilient. About Alison: I studied psychology and quantitative methods, receiving my Ph.D. from Vanderbilt University (2008). For eight years, I was a professor and scientist at Oregon Health & Science University, where my research was funded by the National Institutes of Health, the Oregon Clinical and Translational Research Institute, and Autism Speaks. I have written numerous scientific journal articles and book chapters on autism and neurodevelopmental disorders. I have developed and delivered workshops, graduate-level courses, and curricula based on teaching R, the tidyverse, and literate programming. You can follow my current work for RStudio Education on GitHub. About Desirée: I am neuroscience PhD student at Emory University and also a former summer intern at RStudio. This past summer, I worked with Alison Hill to develop a handbook filled with practical advice and resources for educators who teach with R and RStudio. I enjoy spending my time on collaborative projects that involve coding, teaching, and illustration

rstudio tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Alison Hill Desiree De Leon Reproducibility

James Blaire & Barret Schloerke | Integrating R with Plumber APIs | RStudio (2020)

Full title: Expanding R Horizons: Integrating R with Plumber APIs In this webinar we will focus on using the Plumber package as a tool for integrating R with other frameworks and technologies. Plumber is a package that converts your existing R code to a web API using unique one-line comments. Example use cases will be used to demonstrate the power of APIs in data science and to highlight new features of the Plumber package. Finally, we will look at methods for deploying Plumber APIs to make them widely accessible. Webinar materials: https://rstudio.com/resources/webinars/expanding-r-horizons-integrating-r-with-plumber-apis/ About James: James is a Solutions Engineer at RStudio, where he focusses on helping RStudio commercial customers successfully manage RStudio products. He is passionate about connecting R to other toolchains through tools like ODBC and APIs. He has a background in statistics and data science and finds any excuse he can to write R code. About Barret: I specialize in Large Data Visualization where I utilize the interactivity of a web browser, the fast iterations of the R programming language, and large data storage capacity of Hadoop

Barret Schloerke

odbc plumber rstudio webinars RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate

Enabling Remote Data Science Teams | RStudio (2020)

Whatever happens in the coming months, remote work is here to stay. The goal of this webinar is to provide data scientists and data science team leaders with the knowledge and tools to succeed as a distributed team. Some of the topics we will cover include: Setting up a remote and collaborative R environment Version control and Scrum Using Shiny and RStudio Connect to share apps within and across teams How to improve the UI and appearance of Shiny dashboards How to scale Shiny dashboards to hundreds of users How to build and grow a remote data science team About Alex: Alex is a Solutions Engineer at RStudio, where he helps organizations succeed using R and RStudio products. Before coming to RStudio, Alex was a data scientist and worked on economic policy research, political campaigns, and federal consulting. About Olga: Olga is experienced in production applications of analytical solutions, especially for FMCG companies. Recently she developed a price elasticity model for Unilever. About Damian: Damian is one of the four co-founders of Appsilon. Before founding Appsilon he worked at Accenture, UBS, Microsoft and Domino Data Lab. About Pedro: Pedro has nearly a decade of experience combining frontend and backend technologies, and is an expert on augmenting R Shiny dashboards with CSS and JavaScript

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Forcats Rstats Open Source Oss Reticulate Version Control Scrum RStudio Connect Dashboards Remote Work

Roche & Novartis: Effective Visualizations for Data Driven Decisions || Posit (2020)

Effective visual communication is a core task for all data scientists including statisticians, epidemiologists, machine learning experts, bioinformaticians, etc. By using the right graphical principles, we can better understand data, highlight core insights and influence decisions toward appropriate actions. Without it, we can fool ourselves and others and pave the way to wrong conclusions and actions. While numerous solutions exist to analyze data, these often require many manual steps to convert them into visually convincing and meaningful reports. How do we put this in practice in an accurate, transparent and reproducible way? In this webinar we will introduce an open collaborative effort, currently undertaken by Roche and Novartis, to develop solutions for effective visual communication with a focus on reporting medical and clinical data. The aim of the collaboration is to develop a user-friendly, fit for purpose, open source package to simplify the use of good graphical principles for effective visual communication of typical analyses of interventional and observational data encountered in clinical drug development. We will introduce the initial visR package design which easily integrates into a typical tidyverse workflow. The package provides guidance and meaningful default parameters covering all aspects from the design, implementation and review of statistical graphics. Webinar materials: https://posit.co/resources/videos/effective-visualizations-for-data-driven-decisions/ About Charlotta: Charlotta is a computational biologist by training and works as a data scientist in the Personalized Healthcare department at Roche where she uses R to untap the wealth of information coming from healthcare data collected in real-world settings to support the development of new medicines. About Diego: Diego is a data scientist specializing in applied machine learning at Roche Personalized Healthcare since March 2019. He has developed models to perform various tasks and analyze diverse data sources. Currently, his main applications of interest are in onocology and clinico-genomics. About Mark: Mark is a methodologist supporting the clinical development and analytics department at Novartis. He has a focus on data visualization working on a number of internal and external initiatives to improve the reporting of clinical trials and observational studies. About Marc: Marc is a biostatistics group head at Novartis. He is interested in advancing the methods and practice of clinical development, for instance through effective use of graphics. https://graphicsprinciples.github.io/

tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Roche Novartis Pharma

Sean Lopp & Lou Bajuk | R & Python: A Data Science Love Story | RStudio (2020)

Many Data Science teams today leverage both R and Python in their work, but struggle to use them together. Data Science leaders and their business partners find it difficult to make key data science content easily discoverable and available for decision-making, while IT Admins and DevOps engineers grapple with how to efficiently support these teams without duplicating infrastructure. Even experienced data scientists familiar with both languages often struggle to combine them without painful context switching and manual translations. In this webinar, you will learn how RStudio helps organizations tackle these challenges, with a focus on some of the recent additions to our products that have helped deepen the happy relationship between R and Python: Easily combine R and Python in a single Data Science project using a single IDE. Leverage a single infrastructure to launch and manage Jupyter Notebooks, JupyterLab, VSCode and the RStudio IDE, while giving your team easy access to Kubernetes and other resources. Share and manage access to R- and Python-based interactive applications, dashboards, and APIs, all in a single place. Webinar materials: https://rstudio.com/resources/webinars/r-python-a-data-science-love-story/ About Lou: Lou is a passionate advocate for data science software, and has had many years of experience in a variety of leadership roles in large and small software companies, including product marketing, product management, engineering and customer success. In his spare time, his interests includes books, cycling, science advocacy, great food and theater. About Sean: Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native

rstudio webinars RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate

Sean Lopp & Rich Iannone | Avoid Dashboard Fatigue | RStudio (2020)

Data science teams face a challenging task. Not only do they have to gain insight from data, they also have to persuade others to make decisions based on those insights. To close this gap, teams rely on tools like dashboards, apps, and APIs. But unfortunately data organizations can suffer from their own success - how many of those dashboards are viewed once and forgotten? Is a dashboard of dashboards really the right solution? And what about that pesky, precisely formatted Excel spreadsheet finance still wants every week? In this webinar, we’ll show you an easy way teams can solve these problems using proactive email notifications through the blastula and gt packages, and how RStudio pro products can be used to scale out those solutions for enterprise applications. Dynamic emails are a powerful way to meet decision makers where they live - their inbox - while displaying exactly the results needed to influence decision-making. Best of all, these notifications are crafted with code, ensuring your work is still reproducible, durable, and credible. We’ll demonstrate how this approach provides solutions for data quality monitoring, detecting and alerting on anomalies, and can even automate routine (but precisely formatted) KPI reporting. Webinar materials: https://rstudio.com/resources/webinars/avoid-dashboard-fatigue/ About Sean: Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native. About Rich: My background is in programming, data analysis, and data visualization. Much of my current work involves a combination of data acquisition, statistical programming, tools development, and visualizing the results. I love creating software that helps people accomplish things. I regularly update several R package projects (all available on GitHub). One such package is called DiagrammeR and it’s great for creating network graphs and performing analyses on the graphs. One of the big draws for open-source development is the collaboration that comes with the process. I encourage anyone interested to ask questions, make recommendations, or even help out if so inclined!

Rich Iannone

blastula gt rstudio webinars RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Forcats Rstats Open Source Oss Reticulate Dynamic Emails Reporting Sean Lopp Rich Iannone

Lou Bajuk & Sean Lopp | RStudio: A Single Home for R & Python| RStudio

Many Data Science teams today are bilingual, leveraging both R and Python in their work. While both languages are tremendously powerful, teams frequently struggle to use them together. We’ve heard from our customers how even experienced data scientists familiar with both languages often struggle to combine them without painful context switching and manual translations. Data Science leaders and business stakeholders find it difficult to make key data science content easily discoverable and available for decision-making, and IT Admins and DevOps engineers grapple with how to efficiently support these teams. In this webinar, you will learn how RStudio helps Data Science teams tackle all these challenges, and make the Love Story between R and Python a happier one: Easily combine R and Python in a single Data Science project Leverage a single infrastructure to launch and manage Jupyter Notebooks and JupyterLab environment, as well as the RStudio IDE Organize and share Jupyter Notebooks alongside your work in R and your mixed R and Python projects This webinar will show examples of all these capabilities, and discuss the benefits of leveraging R and Python. About Lou: Lou is a passionate advocate for data science software, and has had many years of experience in a variety of leadership roles in large and small software companies, including product marketing, product management, engineering and customer success. In his spare time, his interests includes books, cycling, science advocacy, great food and theater. About Sean: Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Lou Bajuk Sean Lopp Webinar

Kevin Bolger & Oisin Bates | Architecting RStudio Products in the Cloud | RStudio (2020)

Part of RStudio’s Data Science in the Cloud Webinar Series. About Kevin: After finishing his education in the University of Limerick, Ireland – Kevin’s passion for data science was cemented. Focusing primarily on data analytics and modelling, he went on to spend the first years of his career working at a biopharmaceutical company, where he led the data team on multiple products. Since moving to Seattle with his Washington native wife, Kevin has spent his spare time enjoying the beautiful PNW and playing ‘hurling’, an ancient gaelic field sport with the Seattle Gaels. He now leads the Data Science team at ProCogia as the Director of Data Solutions – where he works with clients from Biotech to Telecom. About Oisin: Oisin is a data science and AWS Cloud Architect for ProCogia

rstudio RStudio Data Science Machine Learning Python Stats Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Forcats Rstats Open Source Oss Reticulate Kevin Bolger Oisin Bates Cloud Cloud Based Data Science Data Science Cloud

Alex Gold | Managing Packages for Open Source Data Science | RStudio

What You’ll Learn: With over 15,000 R packages on CRAN, over 230,000 on PyPI, and more arriving every day, the task of managing a package environment for data science in R and Python can be daunting. In this webinar, you will learn about the most current strategies and tooling for creating and maintaining a reproducible package environment. Whether you’re an individual data scientist or the administrator of an entire RStudio Team cluster, you’ll better understand how you can enhance your ability to work easily and safely with open-source data science packages. About Alex: Alex is a Solutions Engineer at RStudio, where he helps organizations succeed using R and RStudio products. Before coming to RStudio, Alex was a data scientist and worked on economic policy research, political campaigns, and federal consulting

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Alex Gold

Podcast | Not So Standard Deviations Episode 100 | RStudio (2020)

Featuring: Hilary Parker & Roger Peng In episode 100 of Not So Standard Deviations, the first ever episode prepared in advance, Hilary and Roger discuss creativity, its role in data science, and how it can be fostered through conversation. Also, follow up on coffee and oat milk. About Hilary: Hilary Parker is a Data Scientist on the styling recommendations team at Stitch Fix, a personal styling service that uses a combinations of human stylists and algorithmic recommendations to help people find what they love. At Stitch Fix, she focuses on what sorts of data to collect from clients in order to optimize clothing recommendations, as well as building out prototypes of algorithms or entirely new products based on new data sources. She is also a co-founder of the Not So Standard Deviations podcast, a bi-weekly data science podcast with Roger Peng that has over half a million downloads. Their topics of discussion include the R ecosystem, recent developments in the data science and statistics field, reproducibility and the “how” of how data scientists and statisticians work. Hilary recently authored the paper Opinionated Analysis Development based on discussions from the podcast. Prior to her career in the tech field, Hilary received her PhD in Biostatistics from Johns Hopkins School of Public Health. She lives at the San Francisco Zen Center with her partner, a Soto Zen Priest. In her free time, she enjoys exploring her home of 2 years, San Francisco. About Roger: Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health where his research focuses on the development of statistical methods for addressing environmental health problems. He is the author of the popular book R Programming for Data Science and nine other books on data science and statistics. He is also the co-creator of the Johns Hopkins Data Science Specialization, the Simply Statistics blog where he writes about statistics for the public, the Not So Standard Deviations podcast with Hilary Parker, and The Effort Report podcast with Elizabeth Matsui. Roger is a Fellow of the American Statistical Association and is the recipient of the Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to public health. He can be found on Twitter and GitHub at @rdpeng

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Hilary Parker Roger Peng

Panel: Career Advice for Data Scientists | RStudio (2020)

Featuring: Gabriela de Queiroz, David Keyes, Sydeaka Watson, and Jen Hecht This panel will be focused on how you build a career around R! Our panelists are all passionate about R and have each taken a different path to build a career around that passion. We’ll be touching on topics like the different stages of career growth and how you find a community you can go to for support. If you’re just getting started in your career, looking to make a transition, or interested in learning some great career-building skills, be sure to join us! About Gabriela de Queiroz: Gabriela de Queiroz is a Sr. Engineering & Data Science Manager at IBM where she manages and leads a team of developers working on Data & AI Open Source projects. Her team contributes to projects such as TensorFlow, PyTorch, Apache Arrow, Apache Spark, and several other open source projects inside IBM. She works to democratize AI by building tools and launching new open source projects. She is passionate about making data science available to everybody and is actively involved with several organizations to foster an inclusive community. She is the founder of AI Inclusive, a global organization that is helping increase the representation and participation of gender minorities in Artificial Intelligence. She is also the founder of R-Ladies, a worldwide organization for promoting diversity in the R community with more than 180 chapters in 50+ countries. About David Keyes: David Keyes is the founder of R for the Rest of Us, which teaches people and individuals to use R through online courses, custom trainings, and public workshops. With stops as an elementary school teacher, PhD social scientist, and program evaluator, David brings his unconventional career trajectory to his current role, helping those with who don’t think of themselves as typical R users embrace the power of R. About Sydeaka Watson: Dr. Sydeaka Watson is a native of New Orleans, Louisiana and currently lives in Dallas, Texas. She is Founder and Owner of Korelasi Data Insights, LLC and a Senior Data Scientist at Elicit Insights, LLC. In these roles, Sydeaka uses predictive analytics and visual tools to draw insights from diverse datasets. Sydeaka earned a Ph.D. in Statistics from Baylor University and has several years of teaching experience. In her 5 years as Research Assistant Professor in The University of Chicago Biostatistics Laboratory, she consulted with over 110 biomedical research teams in The University of Chicago Medical Center, specializing in statistical analysis and experimental design for clinical research studies. Her current research interests include applications of (1) image recognition for computer vision and (2) data science for social justice. Sydeaka currently serves as Organizer of the R-Ladies Dallas Chapter. She also volunteers in the Dallas chapters of Girls Who Code and Black Girls Code. About Jen Hecht: Jen Hecht is the VP of People Operations at RStudio. She was first introduced to R in 2013, as a non-programmer seeking better ways to manage analytical projects - a quest which was aided both by the RStudio toolchain and the welcoming support of R Ladies, R meetup groups, and other wonderful open resources. Ever since, she has been captivated by open data science tools and the communities that build them. Before joining RStudio in 2018, Jen held HR and People Analytics roles in a variety of industries, including financial services, biotech, and used record shops. Outside of work, Jen loves books and music of all kinds, and is a novice fly caster

rstudio tensorflow RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Data Science Career Career

Fernanda Viegas & Martin Wattenberg | Data, visualization, and designing AI | RStudio (2020)

Recent progress in machine learning has raised a series of urgent questions: How can we train and debug deep learning models? How can we understand what is going on inside a neural network? And, perhaps most important, how can we design systems that serve people best? We’ll show a series of examples from the People+AI Research (PAIR) initiative at Google–ranging from data visualizations for researchers, to tools for medical practitioners, to guidelines for designers–that illustrate how thinking carefully about data can lead to better tools, more effective design, and help humans and AI work together. About Fernanda: Fernanda Viégas and Martin Wattenberg co-lead Google’s PAIR (People+AI Research) initiative, part of Google Brain. Their work in machine learning focuses on transparency and interpretability, as part of a broad agenda to improve human/AI interaction. They are well known for their contributions to social and collaborative visualization, and the systems they’ve created are used daily by millions of people. Viégas and Wattenberg are also known for visualization-based artwork, which has been exhibited in venues such as the Museum of Modern Art in New York, London Institute of Contemporary Arts and the Whitney Museum of American Art. Their artwork has influenced contemporary design practice: for instance, the techniques in their wind map are now used by many major media companies to display the weather. About Martin: Fernanda Viégas and Martin Wattenberg co-lead Google’s PAIR (People+AI Research) initiative, part of Google Brain. Their work in machine learning focuses on transparency and interpretability, as part of a broad agenda to improve human/AI interaction. They are well known for their contributions to social and collaborative visualization, and the systems they’ve created are used daily by millions of people. Viégas and Wattenberg are also known for visualization-based artwork, which has been exhibited in venues such as the Museum of Modern Art in New York, London Institute of Contemporary Arts and the Whitney Museum of American Art. Their artwork has influenced contemporary design practice: for instance, the techniques in their wind map are now used by many major media companies to display the weather

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate AI Google Deep Learning

Alex Gold | Deploying End-To-End Data Science with Shiny, Plumber, and Pins | RStudio

It’s easier than ever to craft a complete R-centric data science pipeline thanks to packages like Shiny, Plumber, and Pins. In this talk, you’ll learn how to use R to bring your modeling and visualization work into production. You’ll walk away with recipes, tips, and tricks to deploy data, models, and apps to ensure your work is as impactful as possible. About Alex: Alex is a Solutions Engineer at RStudio, where he helps organizations succeed using R and RStudio products. Before coming to RStudio, Alex was a data scientist and worked on economic policy research, political campaigns, and federal consulting

plumber rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Alex Gold

ZJ | Easy larger-than-RAM data manipulation with {disk.frame} | RStudio

Learn how to handle 100GBs of data with ease using {disk.frame} - the larger-than-RAM-data manipulation package. R loads data in its entirety into RAM. However, RAM is a precious resource and often do run out. That’s why most R user would have run into the “cannot allocate vector of size xxB.” error at some point. However, the need to handle larger-than-RAM data doesn’t go away just because RAM isn’t large enough. So many useRs turn to big data tools like Spark for the task. In this talk, I will make the case that {disk.frame} is sufficient and often preferable for manipulating larger-than-RAM data that fit on disk. I will show how you can apply familiar {dplyr}-verbs to manipulate larger-than-RAM data with {disk.frame}. About ZJ: ZJ is a machine learning developer based in Melbourne, Australia. He regularly contributes to open source projects. He has more than 10 years of experience in banking before joining the tech sector. In his free time, he enjoys playing Go/Baduk/Weiqi

dplyr rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate ZJ Disk.frame

Wolfram King | Lifelong Learning with R Weekly | RStudio

R Weekly is a weekly newsletter with many great R blogs post, tutorials, and other formats of resources. R Weekly wants to keep track of these great things in the R community and make it more accessible to everyone. This is a warm and welcoming place. The team welcomes everyone who wants to contribute to the R community. In this talk I will cover these 6 topics: How to use the R Weekly website Why I created R Weekly How to Contribute to R Weekly How to release a new post How to join the team Learning from building the community About Wolfram: Wolfram King is the founder of the R Weekly project. He is an active member of the R community and has several popular R open-source projects on GitHub

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Wolfram King

Simon Couch | tidymodels/stacks: A Grammar for Stacked Ensemble Modeling | RStudio

Full title: tidymodels/stacks, Or, In Preparation for Pesto: A Grammar for Stacked Ensemble Modeling Through a community survey conducted over the summer, the RStudio tidymodels team learned that users felt the #1 priority for future development in the tidymodels package ecosystem should be ensembling, a statistical modeling technique involving the synthesis of multiple learning algorithms to improve predictive performance. This December, we were delighted to announce the initial release of stacks, a package for tidymodels-aligned ensembling. A particularly statistically-involved pesto recipe will help us get a sense for how the package works and how it advances the tidymodels package ecosystem as a whole. About Simon: Simon Couch is an R developer and statistics student at Reed College, where he is entering the final semester of his undergraduate degree. He co-authors and maintains R packages including broom, infer, and stacks, leads trainings and workshops as an RStudio-certified tidyverse trainer, and researches in algorithmic data privacy. He interned on the RStudio tidymodels team in summer 2020, and is currently applying to doctoral programs in statistics

Simon Couch

infer rstudio tidymodels tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Simon Couch Stacks

Richard Vogg | Examples of simulated datasets that bring value to a data-driven company | RStudio

Full title: How I became a Data Composer – examples of simulated datasets that bring value to a data-driven company How can I get the buy-in from business partners to use more advanced techniques? What can I do to make a data project involving several teams more efficient? And how can I train analysts who do not (yet) have access to sensitive data? A good data composer is skilled at creating suitable data quickly and efficiently. R has many functions and packages that help with simulating independent variables and composing those in a meaningful way. In this talk, I will share how I started creating data and how this skill helped me with solving some of the issues described above. Showing a few examples – of small, medium-sized, and large data composition – I want to encourage attendees to simulate data and enrich their data skillset. About Richard: Richard Vogg studied mathematics at TU Kaiserslautern, Germany, where he focused on statistics and obtained a Master’s degree. He worked as a Senior Business Analyst at Evalueserve in Chile for the last years, analyzing data for a major US bank. At the end of 2020, he moved back to Germany. Richard is a fan of applied statistics and storytelling with data. Outside of R, he enjoys playing the ukulele, trumpet, and didgeridoo

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Richard Vogg Simulated Data

Pamela E. Pairo | An easy and friendly way to build your multilingual website | RStudio

Having a personal website is a great way to share our experiences with other people, that also allows us to improve our communication skills and expand our networking groups. Besides, if the website is multilingual, the scope will be extended considerably by facilitating the exchange of ideas. I will give the key steps, some tips, and important considerations to bear in mind when creating a multilingual website using Blogdown, Hugo, and Netlify. Although having a multilingual website demands more effort, R enables us to build a website easily and keep it updated. I aim to help and encourage others to build their website to promote exchange experiences among people from different native languages. About Pamela: Pamela E. Pairo is a Ph.D. in Biological Sciences of the University of Buenos Aires, with expertise in community ecology. One of her research lines focuses on analyzing the impact of human activities on the diversity and composition of biological communities with particular interest in arthropods. In addition, she is interested in studying the spatio-temporal patterns of dengue disease in Argentina. She also is a teaching assistant in statistics at the Argentine University of Enterprise (UADE)

blogdown rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Pamela E. Pairo

Nicholas Pylypiw | Racial Equity Dashboard: Unpacking Systemic Inequity | RStudio

At Cape Fear Collective, we’re redefining what a town square looks like in our community, serving as a place where all people, organizations, and ideas can come together to effect real, lasting, and systemic change. By merging cutting edge data science with an emphasis on equity and the lived experience of our most marginalized communities, Cape Fear Collective supports Southeastern North Carolina’s front line organizations in combating poverty, racism, poor health and education outcomes, and socio-economic disparities. This talk is about how we bring that model to life through our Racial Equity Dashboard, from data sourcing, to modeling and, ultimately, action. About Nick: Nick is the Director of Data Science at Cape Fear Collective, a non profit which supports Southeastern North Carolina’s front line organizations in combating poverty, racism, poor health and education outcomes, and socio-economic disparities. Prior to CFC, he honed his data science and consulting skills in the marketing analytics space, transforming the way Fortune 500+ companies (Lowe’s Southwest Airlines, P&G, and many others) think about their customer strategy and value proposition. He lives in Raleigh, North Carolina

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Nicholas Pylypiw Dashboard

Michael Chirico | Making.pot-ery with R: Translations in R Packages | RStudio

The R community is globally distributed and R itself is available with messages in 14 languages. Adding translations for non-native English-speaking users of your package can ease their experience and empower them to build better things with less frustration (though please note that ““object of type ‘closure’ is not subsettable”” is equally inscrutable in all human languages). In this talk, I will cover translations in R packages – how to implement them, why to do so, and how to maintain them. This will summarize and extend learnings based on our experience adding Mandarin translations to data.table and culminating in the potools package. About Michael: Michael Chirico is a data scientist working on compute memory efficiency at Google. Before that he worked at Grab in Singapore and earlier got his PhD in Economics at the University of Pennsylvania. He is passionate about making tools to empower others who work with data (most of this energy is directed towards data.tab ≤) and loves learning languages (at various middling levels of proficiency in Japanese, Spanish, and Mandarin, with goals to learn Cantonese, Hokkien, Vietnamese and Bahasa)

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Michael Chirico

Marie Vendettuoli | Lessons learned developing a library of validated packages | RStudio

Full title: Towards an integrated {verse}: lessons learned developing a library of validated packages Developing R packages as a unified {verse} – a set of packages that work well together but with each focusing on individual tasks – is an efficient strategy to structure support for complex workflows. The ongoing challenge becomes managing the growth of related packages in a holistic manner. This is especially problematic in industries with a heavy emphasis on stability, for example if packages need to be validated prior to use in production. In this talk, I will discuss a paradigm for developing and maintaining validated R packages, emphasizing the following areas: Strategies for organizing packages to prevent excessive re-work Facilitating responsive, iterative development and Empathy for developer and user experiences About Marie: Marie Vendettuoli is a Senior Statistical Programmer at Statistical Center for HIV/AIDS Research and Prevention (SCHARP - https://www.fredhutch.org/en/research/divisions/vaccine-infectious-disease-division/research/biostatistics-bioinformatics-and-epidemiology/statistical-center-for-hiv-aids-research-and-prevention.html ) @ FredHutch. She holds a PhD from Iowa State University in Human Computer Interaction and started developing R packages for use within regulatory frameworks while working as a Data Scientist at USDA Center for Veterinary Biologics ( https://www.aphis.usda.gov/aphis/ourfocus/animalhealth/veterinary-biologics/sa_about_vb/ct_vb_about). Before discovering R, Marie worked in a CBER ( https://www.fda.gov/about-fda/fda-organization/center-biologics-evaluation-and-research-cber)-regulated laboratory. Her main interest is developing analytical infrastructure to facilitate scientific analysis for fellow data scientists working in a regulatory environment

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Marie Vendettuoli

Lucy D’Agostino McGowan | Designing Randomized Studies using Shiny | RStudio

This talk will walk through building a self-contained randomized study using Shiny and learnr modules. We will discuss building informed consent, the randomization process, demographic surveys, and R-based studies into a single online framework to allow users to seamlessly enroll and participate in randomized studies via a single URL. The talk will include both practical recommendations as well as technical code snippets. About Lucy: Lucy D’Agostino McGowan is an assistant professor of statistics at Wake Forest University, where she leads the WFU Data Science Lab. Her research focuses on causal inference, human-data interaction, and statistical communication

learnr rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Lucy D'Agostino McGowan

John Helveston | Using formr to create R-powered surveys with individualized feedback | RStudio

This talk demonstrates how the formr study framework extends the power and flexibility of R to surveys. Using R and RMarkdown code, researchers and teachers can use the formr platform to generate both simple surveys and complex studies with individualized feedback. The platform is built on a web-based application programming interface for R via OpenCPU, enabling complex features such as automated email and text message reminders, adaptive testing, graphical and interactive feedback, and integration with external data sources. In this talk, I introduce some of the formr basics and showcase two examples of how I have used it, including making conjoint surveys with randomized images and timed, randomized quizzes for my students. About John: John Paul Helveston is an Assistant Professor in the Engineering Management and Systems Engineering Department at the George Washington University. He studies technological change, with a particular interest in accelerating the transition to environmentally sustainable and energy-saving technologies. His research centers around how consumer preferences, market dynamics, and policy affect the emergence of critical technologies, such as electric vehicles and solar energy. He is an expert on China’s rapidly emerging electric vehicle industry as well as the critical relationship between the US and China in developing and mass producing low carbon energy technologies. He applies an interdisciplinary approach to research, with expertise in discrete choice modeling and conjoint analysis as well as interview-based case studies. He has conducted extensive fieldwork in China, collaborating with colleagues at Tsinghua university, Beijing Normal University, and China’s State Information Center on past projects. He is a fluent speaker of Mandarin Chinese and also an award-winning swing dancer. John holds a Ph.D. and M.S. in Engineering and Public Policy from Carnegie Mellon University and a B.S. in Engineering Science and Mechanics from Virginia Tech

rmarkdown rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate John Helveston Formr

Garrick Aden-Buie | xaringan Playground: Using xaringan to learn web development | RStudio

xaringan is a quirky package that extends R Markdown to create beautiful web-based HTML slides. Some of xaringan’s quirks come from the JavaScript library it uses, remarkjs, and some of it from the unusual naming scheme xaringan uses for its functions. But under this quirky exterior lies a powerful tool for learning and practicing web development, especially when combined with infinite_moon_reader() for immediate feedback. In this talk I’ll cover some basic web concepts that illustrate how fun and rewarding it can to learn HTML, CSS and JavaScript while building awesome slides in R Markdown. About Garrick: The workshop will be led by friend of RStudio, Garrick Aden-Buie, R developer and educator and Data Scientist at Moffitt Cancer Center, where he uses Shiny to enable and accelerate cancer research

Garrick Aden-Buie

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Garrick Aden-Buie Xaringan HTML

Dean Marchiori | A retrospective on a year of commercial data science projects in R | RStudio

Full title: How reproducible am I? A retrospective on a year of commercial data science projects in R Reproducibility is a critical aspect in science to enable trust & communication. In R, many tools exist to bring in the best practices of reproducibility into the hands of data scientists. However, outside of a research setting, how does reproducibility hold up in commercial data science projects? In this talk I take an honest retrospective of my own commercial R projects in the last year. I look at the various types of analyses completed, and which workflows were selected and why. Through this process we can learn how workflow choices may help in the short term but hinder in the long term. More importantly what can be done strike the balance between progress and perfection when doing data science in the wild? About Dean: Dean Marchiori is a Statistician based in Sydney, Australia. He currently works with Endeavour Energy as a Senior Data Scientist modelling bushfire and vegetation risk on the electricity network. Dean’s career started in finance as an equities trader before moving into advanced analytics, where he has worked with some of Australia’s largest organisations. His professional interests are in geospatial analysis, time series modelling and the R programming language. In 2019, Dean was named one of the top 10 analytics leaders in Australia by IAPA. He is also recognised as an Accredited Statistician with the Statistical Society of Australia. He holds a Bachelor of Science in Mathematics (awarded with University Medal) and a Masters degree in Applied Finance. Outside of work Dean enjoys bodysurfing, running and spending time with his wife and two boys

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Dean Marchiori

Danielle Smalls-Perkins & Dorris Scott | Reflections on creating the Mi-R community | RStudio

Full title: Your R is My R too: Reflections on creating the Mi-R community While the R community has made strides in increasing the representation and participation for women and users from underrepresented regions, there are still members of the R community that have expressed desires for a more inclusive space in addition to these strides. In addition, there are unique challenges that underrepresented R users experience in their respective workspaces or academic environments. In late February of 2020, Danielle Smalls-Perkins and Dorris Scott created Mi-R (Minorities in R) as a result of their various experiences both in and outside the R community. The purpose of this talk is to reflect on the challenges, highlights, and future directions of the first six months since the creation of Mi-R. About Danielle: Danielle Smalls-Perkins co-founded MiR Community with the hope that the R community would continue to encourage the inclusion and recognition of contributions made from R users of diverse backgrounds. She loves to use R for understanding and storytelling. Danielle currently works as a Senior Strategist in Google’s Trust and Safety Team. She advocates for model fairness, interpretability, and reducing harmful outcomes of algorithmic decision-making on vulnerable populations. About Dorris: Dorris Scott is the GIS Librarian and Social Science Data Curator at Washington University – St. Louis, where she provides consultation on projects that use geospatial data along with providing training in various GIS software, programming applications of geospatial data, and data management. She also serves as a liaison between Washington University Libraries and social science departments assisting faculty with their data needs such as data management and data curation. Dorris received her PhD in Geography from the University of Georgia, with a specialization in GIS applications for public health

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Dorris Scott Danielle Smalls-Perkins

Danielle Oberdier | How Content Makes the Data Go ‘Round | RStudio

What makes a successful data science community thrive across industries? A recent Aflac WorkForces Report showed that professionals who are engaged in a community within their industry are 70% more likely to be satisfied with their work. I believe anyone can and should create content about data. In this talk, I will direct your attention towards 1) the ways that content creation can lead to heightened data science opportunities 2) how to know which type/s of content mediums (podcasts, blogs, video) are right for you 3) how to leverage social media and networking connections to make your content reach the right audiences. I hope to inspire listeners to create their own content and online brands as resources for fellow R community members

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Danielle Oberdier

Colin Rundel | parsermd - parsing R Markdown for fun and profit | RStudio

parsermd is a new R package for parsing and programmatically interacting with R Markdown (Rmd) documents. This package implements a formal grammar for Rmd documents in C++ using Boost’s Spirit X3 library and provides additional user facing functions for the resulting abstract syntax tree. In this talk we will provide background on the structure and grammar of Rmd documents as well as discuss the ways in which the parsing of these documents enables a variety of automatable tasks. Specifically, we will focus on demonstrating how these tools can be used to provide automated feedback on student submissions in a statisical programming course. About Colin: Colin is a lecturer in Statistics and Data Science at the University of Edinburgh. He has been teaching statistics and data science courses, with a focus on computing and spatial modeling, for the last 8 years

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Colin Rundel

Chelsea Parlett-Pelleriti | Hands-on ways to remotely teach data science are invaluable | RStudio

Full title: With more learning occurring virtually or in hybrid mode, hands-on ways to remotely teach data science are invaluable With more learning occurring virtually or in hybrid mode, hands-on ways to remotely teach DS are invaluable. Guided simulation exercises in R allow learners to explore concepts deeply, on their own time, and with others. They can also experiment with the simulations, try out edge cases, and challenge their assumptions, leading to more fruitful discussions. The comparison between coefficient estimates in regular, LASSO, and RIDGE regression, or how PCA performs when data are related are great examples of concepts where guided simulations can encourage learners to build intuitive knowledge. This talk explores how to use simulation exercises in R to help learners explore DS concepts and provides examples. About Chelsea: Chelsea Parlett-Pelleriti is a PhD Candidate and full-time instructional faculty teaching Data Science at Chapman University. Her research centers around how we can use statistics and machine learning to improve the way we analyze behavioral data. In her free time, you can find Chelsea on Twitter making stats memes or statsTikTok’s. She also writes about statistics, machine learning, and using R for various blogs

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Chelsea Parlett-Pelleriti

Carson Sievert | Custom theming in Shiny & R Markdown with bslib & thematic | RStudio

Custom theming in Shiny and R Markdown often requires writing styling rules in both CSS and R. In particular, styles for HTML content (e.g., actionButton(), tabsetPanel(), titlePanel(), etc) derive from Bootstrap CSS, so customization is traditionally done by overwriting that CSS, which is difficult to do 100% correctly. The {bslib} package helps solve this problem by making it easy to customize (any version of) Bootstrap CSS defaults from R. However, this only solves part of the problem since CSS doesn’t necessarily effect output(s) rendered by R, such as plotOutput(). The {thematic} package helps solve this problem by providing auto theming of plotOutput()s (based on CSS) as well as a simple interface for styling any R graphic for any output format. About Carson: Carson is a software engineer at RStudio working on R packages such as shiny, shinymeta, and plotly. His book “Interactive data visualization with R, plotly, and shiny”, published by CRC Press, is also freely available online at plotly-r.com

Carson Sievert

bslib rstudio Shiny shinymeta thematic RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Carson Sievert Bslib Thematic

Athanasia M. Mowinckel | Make a package - Make some friends | RStudio

In 2017, I had never exposed my code to anyone other than a select few before, and I was terrified. I had some functions made from a colleagues script that I thought might be useful for others, and dared myself to make a package and push it to github. In stead of the dreaded ridiculing of poor code and development, people embraced the package and helped us make it better. Within just a couple of days, pull requests came from others to help us improve the code, implement tests, and improve documentation. I learned so much just by looking through the PRs and seeing how others worked. Rather than make me shy off development, the R neuro community’s positive feedback has helped me find a new interest and joy in developing tools. About Athanasia: Athanasia M. Mowinckel is a staff scientist at the Center for Lifespan Changes in Brain and Cognition, at the University of Oslo. She has a background on cognitive psychology, and uses R for almost everything. She goes by the nickname “Mo” (closer to ‘Mou’ than ‘Moe’), and is a member of the R-Ladies Global team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Athanasia M. Mowinckel Package Development

Alex Cookson | The Power of Great Datasets | RStudio

There are a few classic datasets, like mtcars, nycflights, or Titanic passengers. They’re okay, but they leave something to be desired for folks learning R: they’re kind of boring. There’s a big difference between “Okay Datasets” and “Great Datasets”. Great Datasets prompt you to exclaim, “That’s so cool!” They get your blood pumping and mind racing with questions you want answered. They give tremendous motivation to answer those questions. And in answering those questions, you’ll probably learn some R. I want you to curate Great Datasets. You’ll contribute to the richness of our community, you’ll learn some R yourself, and you’ll feel fantastic when someone finds your Great Dataset and exclaims, “That’s so cool!” About Alex: Alex Cookson helps the Customer Intelligence team at the Royal Canadian Mint make the most of their data. When he’s not working on A/B testing, recommendation engines, or exploratory data analysis at the Mint, he can be found participating in Tidy Tuesday or thinking up cool datasets to explore. And when he’s not doing that, he’s probably cycling around Toronto or doting on his two cats, Tom Tom and Ruby

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Package Development Alex Cookson

Alan Feder | Categorical Embeddings: New Ways to Simplify Complex Data | RStudio

When building a predictive model in R, many of the functions (such as lm(), glm(), randomForest, xgboost, or neural networks in keras) require that all input variables are numeric. If your data has categorical variables, you may have to choose between ignoring some of your data and too many new columns. Categorical embeddings are a relative new method, utilizing methods popularized in Natural Language Processing that help models solve this problem and can help you understand more about the categories themselves. While there are a number of online tutorials on how to use Keras (usually in Python) to create these embeddings, this talk will use embed::step_embed(), an extension of the recipes package, to create the embeddings. About Alan: Alan Feder is a Principal Data Scientist at Invesco, where he uses as much R as possible to solve problems and build products throughout the company. Previously, he worked as a data scientist at AIG and an actuary at Swiss Re. He studied statistics and mathematics at Columbia University. He is unreasonably excited to spread the word about categorical embeddings. Alan lives in New York City with his wife, Ashira, and two children, Matan and Sarit

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Alan Feder

Yanina Bellini Saibene | On programming, teaching, and building tutorials with learnr | RStudio

Full title: On programming, teaching, and building interactive tutorials with learnr Teaching R is part of my activities as a community organizer, an RStudio Certified Instructor, a conference chair, and a researcher. Since 2019, I use the learnr package to generate interactive tutorials to teach R synchronously and asynchronously. The addition of the Tutorials panel in RStudio IDE and the need for virtual classes made the use of this package even more interesting. In this talk, I will tell you how to generate interactive tutorials, how to add pedagogical tools to them, what other packages you can use with {learnr} and show multilingual examples. About Yanina: Yanina Bellini Saibene is a researcher at INTA (National Institute of Agricultural Technology) dedicated to applying data science to the agricultural sector and a professor in several regional specializations about Digital Agriculture and Data Analysis. Yanina is formally trained as a Licenciate in Information System with a Master degree in Data Mining and Knowledge Management. She is an active member of the R Community as an R-Ladies organizer and part of the R-Ladies Global Team. She also is a co-founder and co-chair of LatinR and part of the organizing team of useR!2020 and user!2021 (co-chair). She is also co-founder of MetaDocencia, a open, free, volunteer-lead, not-for-profit educational organization and part of the teams that translate educational and technical material to Spanish

learnr rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Yanina Bellina Saibene Learnr

Winston Chang | Making Shiny apps faster with caching | RStudio

Shiny’s 1.6 has a new function, bindCache(), which makes it easy to dramatically speed up reactive expressions and output rendering functions. This allows many applications to scale up to serve several times more users without an increase in server resources. Note: Shiny 1.6.0 isn’t yet on CRAN, but will be in the next few days. In the meantime, you can install it with: remotes::install_github(““ rstudio/shiny@rc-v1.6.0 "”) About Winston: Winston is a software engineer at RStudio. He holds a Ph.D. in psychology from Northwestern University and is the author of R Graphics Cookbook, published by O’Reilly Media

Winston Chang

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Winston Chang BindCache() BindCache

Vicki Boykis | Your public garden | RStudio

Vicki will discuss how that as people who can write code and analyze data, we have a lot of input and power over what our digital and work worlds looks like, and therefore can act as agents of change and repair. About Vicki: Vicki Boykis is a machine learning engineer at Automattic, the company behind Wordpress.com. She works mostly in Python, R, Spark, and SQL, and really enjoys building end-to-end data products. Outside of work she publishes the Normcore Tech newsletter ( https://vicki.substack.com ) and blogs at https://veekaybee.github.io/. In her “spare time”, she blogs, reads, and writes terrible joke tweets about data

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Vicki Boykis

Sophie Beiers | Trial and Error in Data Viz at the ACLU | RStudio

Visualizing data the “right” way requires many considerations — the topic, the quality of your data, your audience, your time frame, and the various channels of (sometimes conflicting) feedback you received. In this presentation, I’ll introduce some reflections on these considerations and ways I’ve incorporated feedback (or not) into my work as Data Journalist at the ACLU. Lastly, I’ll present some of the sillier trials and errors I’ve made that were arguably necessary to my process in creating effective data visualizations using R. About Sophie: Sophie Beiers works on the ACLU’s Analytics team as a Data Journalist where she analyzes and visualizes data for their lawyers’ legal arguments and for external advocacy pieces. Prior to her time at the ACLU, she received her master’s degree in Quantitative Methods in Social Sciences at Columbia University where she also TA’d at the Lede Program for Data Journalism. Before NYC, she kicked off her career in analytics in San Francisco at the education nonprofit “YouthTruth.” Sophie is a Bay Area native but currently lives in Portland, OR and enjoys running, hiking, and making pottery in her free time

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Sophie Beiers

Shirbi Ish-Shalom | Using R to Up Your Experimentation Game | RStudio

Have you ever cut an A/B test short? Maybe because of traffic constraints, your antsy boss, or early successful results. In reality, cutting your test short can be catastrophic, making your business decision no better than a coin flip. Learn some R-driven tips & tricks to get meaningful results quickly with a statistically rigorous methodology called sequential testing, an A/B testing enhancement my team employs at Intuit. Key Takeaways: What is sequential testing and how to use it. How to learn (and fail!) quickly by taking big metric swings How I used R to share my learnings & make them useful for anyone (even non-data scientists!) at my company About Shirbi: Shirbi Ish-Shalom is a human person

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Shirbi Ish-Shalom

Shelmith Kariuki | rKenyaCensus Package | RStudio

The rKenyaCensus package contains the results of the 2019 Kenya Population Census. The census exercise was carried out in August 2019, and the results were released in February 2020. Kenya leveraged on technology to capture data during cartographic mapping, enumeration and data transmission, making the 2019 Census the first paperless census to be conducted in Kenya. The data was published in four different pdf files (Volume 1 - Volume 4) which can be found in the Kenya National Bureau of statistics website. The data in its current form was open and accessible, but not usable and so there was need to convert it into a machine readable format. This data can be used by the government, non-governmental organizations and any other entities for data driven policy making and development. During the talk, I will explain the reasons behind development of the package, take you through the steps I took during the process and finally showcase analysis of certain aspects of the data. About Shelmith: Shelmith Kariuki is a Senior Data Analyst based in Nairobi, Kenya. She is an RStudio Certified Tidyverse trainer ( https://education.rstudio.com/trainers/), currently working as a Data Analytics consultant with UN DESA. She previously worked as a Research Manager at Geopoll, and as a Data Analyst at Busara Center for Behavioral Economics. She also worked as an assistant lecturer in various Kenyan universities, teaching units in Statistics and Actuarial Science. She has extensive experience in data analysis using R. She co-organizes a community of R users in Nairobi ( https://www.linkedin.com/feed/hashtag/nairobir/ ) and in Africa ( https://twitter.com/AfricaRUsers). One of the missions of her community work is to make sure that there is an increased number of R adopters, in Africa. She is very passionate about training and using data analytics to drive development projects in Africa

education.rstudio.com rstudio tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Shelmith Kariuki RKenyaCensus

Sean Lopp | R & Python: Going Steady | RStudio

While there has been a lot of excitement about the R and Python love story, there are still misconceptions that individuals, teams, or organizations must pick between R or Python. This talk will explain why this false choice exists, debunk the myths that cause teams to be stuck with only one tool, and clarify how data scientists can use both languages to be more effective. We will explore this love story’s blossoming relationship by looking at updates to RStudio’s packages and products that make it easier to develop and collaborate in R and Python. This talk is for individuals who want to uncover the benefits of multilingual data science, IT professionals who are skeptical their life can get better by supporting more languages, and data science managers interested in enabling their teams instead of forcing their data superheros to be subservient to particular tools. About Sean: Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Sean Lopp

Riva Quiroga | Learning to program in R with a “communicative approach” | RStudio

Full title: How to do things with words: learning to program in R with a “communicative approach” Textbooks for learning a new language always start the same: you learn to say hello, to introduce yourself, and some simple and useful sentences to communicate with others. In language teaching, this is called a “communicative approach”, and is based on the idea that learning a language successfully comes through having to communicate real meaning to real people. This is what I expected to find when I first tried to learn R seven years ago. Sadly, I got stuck in resources that started with definitions of abstract concepts and no real examples of how to say things with data. In this talk I will discuss the benefits of adopting a communicative approach and how to implement it when teaching/learning R, writing documentation, and writing code that will be read by other human beings. About Riva: I like to organize R related things, like meetups (RLadies Santiago & RLadies Valparaíso), conferences (LatinR, satRday Santiago), book translations (R4DS in Spanish), and data projects (#DatosDeMiercoles). I am an editor at The Programming Historian, and I am currently pursuing a PhD in Linguistics

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Riva Quiroga

Rika Gorn | From Zero to Hero: Best practices for setting up Rstudio Team in the Cloud | RStudio

Learn best practices for setting up the entire Rstudio team infrastructure - Server Pro, Connect, Package Manager from the perspective of a data scientist and for a data science audience - especially those who have never worked with servers, AWS, or bash. This talk will also be applicable to data scientists looking to start on an engineering project outside of Rstudio as well. I started out as a complete novice, & throughout my learning experience I noticed a distinct lack of resources for non-engineers. This talk will focus on best practices for AWS architecture and cloud formation, key security issues such as SSL and https, server configurations, deployment errors, and most importantly resources that are understandable for data scientists just getting into the data engineering or devops space. About Rika: Rika Gorn is the Manager of Business Intelligence at Spring Health - a mental healthcare tech start-up that provides comprehensive mental healthcare benefits. Previously, she worked on quality assurance for a mobile mental health team at Coordinated Behavioral Care, data analytics at Covenant House International, strategic management and evaluation at TCC Group, and program analysis at the Vera Institute of Justice. Rika received her Bachelors in Political Science from Hunter College and her Masters in Public Administration at the NYU Wagner School of Public Service. Rika is also a proud board member of R-Ladies NYC

RStudio Team

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate DevOps Rika Gorn

Nicole Kramer | A New Paradigm for Multifigure Coordinate-Based Plotting in R | RStudio

R is unparalleled in its ability to transform raw data into a wide array of beautiful graphics, all within the same environment. However, when it comes to complex, multi-paneled plots, users rely on 3rd party graphic design software to arrange plots. Here I present the new world of programmatic, coordinate-based multi-figure plotting in R. Employing grid Graphics and drawing from the paradigms of base plotting and ggplot2, I am developing a package that will revolutionize the way plots are laid out in R. Not only will individual plots be aesthetically customizable and tailored for speed, users will also be offered exquisite control over all aspects of page layout, plot placement, and arrangements. Come join me in changing how we plot in R! About Nicole: Nicole Kramer is a third year Bioinformatics and Computational Biology graduate student at the University of North Carolina at Chapel Hill. She works in the lab of Dr. Doug Phanstiel, where her and her colleagues use experimental and computational techniques to study human genomics. Prior to grad school, Nicole received her B.S. in Biological Engineering from MIT in 2018. When not doing science, you can find Nicole petting dogs, admiring giraffes, or knitting tiny animals!

ggplot2 rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Nicole Kramer

Neal Richardson | Bigger Data With Ease Using Apache Arrow | RStudio

The Apache Arrow project enables data scientists using R, Python, and other languages to work with large datasets efficiently and with interactive speed. Arrow is so fast at some workflows that it seems to defy reality–or at least the limits of R’s capabilities. This talk examines the unique characteristics of the Arrow project that enable it to redefine what is possible in R. The talk also highlights some of the latest developments in the arrow R package, including how you can query and manipulate multi-file datasets, and it presents strategies for speeding up workflows by up to 100x. About Neal: Currently Director of Engineering at Ursa Labs / RStudio. Previously led product and engineering at Crunch.io. Ph.D. in Political Science from the University of California, Berkeley

Neal Richardson

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Forcats Rstats Open Source Oss Reticulate Neal Richardson Apache Apache Arrow Big Data

Mine Çetinkaya-Rundel | Feedback at scale | RStudio

As enrollments in statistics and data science courses grow and as these courses become more computational, educators are faced with an interesting challenge – providing timely and meaningful feedback, particularly with online delivery of courses. The simplest solution is using assignments that are easier to auto-grade, e.g. multiple-choice questions, simplistic coding exercises, but it is impossible to assess mastery of the entire data science cycle using only these types of exercises. In this talk I will discuss writing effective learnr exercises, providing useful and motivating feedback with gradethis, distributing them at scale online and as an R package, and collecting student data for formative assessment with learnrhash. About Mine: Mine Çetinkaya-Rundel is Professional Educator and Data Scientist at RStudio as well as Senior Lecturer in the School of Mathematics at University of Edinburgh (on leave from Department of Statistical Science at Duke University). Mine’s work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education as well as pedagogical approaches for enhancing retention of women and under-represented minorities in STEM. Mine works on integrating computation into the undergraduate statistics curriculum, using reproducible research methodologies and analysis of real and complex datasets. She also organizes ASA DataFest and works on the OpenIntro project. She is also the creator and maintainer of datasciencebox.org and she teaches the popular Statistics with R MOOC on Coursera

Mine Çetinkaya-Rundel

gradethis learnr rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Mine Çetinkaya-Rundel Feedback Learnr Learnrhash

Michael Chow | Bringing the Tidyverse to Python with Siuba | RStudio

Last January I left my job to spend a year developing siuba, a python port of dplyr. At its core, this decision was driven by a decade of watching python and R users produce similar analyses, but in very different ways. In this talk, I’ll discuss 3 ways siuba enables R users to transfer their hard-earned programming knowledge to python: (1) leveraging the power of dplyr syntax, (2) options to generate SQL code, and (3) working with the plotnine plotting library. Looking back, I’ll consider two critical pieces that have helped me develop siuba: using it to livecode TidyTuesday analyses, and building an interactive tutorial for absolute beginners. About Michael: Michael Chow is a data scientist and learning researcher. He serves as a co-director at Code for Philly. In past lives, he worked on adaptive assessment tools in ed tech, and received a PhD in cognitive psychology from Princeton University

Michael Chow

dplyr plotnine rstudio tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Siuba Michael Chow Sql

Megan Beckett | Aesthetically automated figure production | RStudio

Automation, reproducibility, data driven. These are not normally concepts one would associate with the traditional publishing industry, where designers normally manually produce every artefact in proprietary software. And, when you have 1000s of figures to produce and update for a single textbook, this becomes an insurmountable task, meaning our textbooks quickly become outdated, especially in our rapidly advancing world. With R and the tidyverse in our back pocket, we rose to the challenge to revolutionize this workflow. I will explain how we collaborated with a publishing group to develop a system to aesthetically automate the production of figures for a textbook including translations into several languages. I think you’ll find this talk interesting as it shows how we applied tools that are familiar to us, but in an unconventional way to fundamentally transform a conventional process. About Megan: Megan Beckett is a Data Scientist at Exegetic Analytics, where she consults, develops and leads several analytical projects across a wide range of fields and industries. “Scientifically creative; creatively scientific.” This aptly describes her philosophy and approach in her work and life. Megan helped co-found and organises the Cape Town R-Ladies chapter and is a co-organiser of the satRday events in South Africa. She loves to paint, with her most recent work exploring the biodiversity of southern Africa, and running is her passion, whether on the road or the trail

rstudio tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Forcats Rstats Open Source Oss Reticulate Megan Beckett Publishing Automation Reproducibility Data Driven

Maya Gans | Starting an R Book Club: Cooking Up Friendships in Isolation | RStudio

Amidst a global pandemic there’s been one consistency in my life: every Tuesday a group of friends meet to discuss Hadley Wickham’s Advanced R. I crowdsourced interest using the R4DS Slack and the results were magical: a group of incredibly curious and generous people motivated to learn and teach one another emerged. The meetings evolved from a group of strangers giving timid presentations to a safe space where we share and improve upon personal applications. The 1 club has grown to 3 regional cohorts, and became a model for discussing other books too. This talk will go over the structure of our meetings in hopes of empowering others to start their own book clubs, showcasing a different way people can create and engage in communities. About Maya: I am a mycologist turned data scientist. I love statistics, data visualization, and all things JavaScript. I am currently an intern at RStudio designing a visual block-based programming language. I create music-related infographics for JamBase.com. When I’m not coding, I’m climbing tall mountains

Hadley Wickham

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Maya Gans Book Club Advanced R

Max Kuhn | What’s new in tidymodels? | RStudio

tidymodels is a collection of packages for modeling using a tidy interface. In the last year there have been numerous improvements and extensions. This talk gives an overview of additional tuning methods, new extension packages for models and recipes, and other features. About Max: Max Kuhn is a software engineer at RStudio. He is currently working on improving R’s modeling capabilities. He was a Director of Nonclinical Statistics at Pfizer Global R&D in Connecticut. He was applying models in the pharmaceutical and diagnostic industries for over 18 years. Max has a Ph.D. in Biostatistics. Max is the author of numerous R packages for techniques in machine learning and reproducible research and is an Associate Editor for the Journal of Statistical Software. He, and Kjell Johnson, wrote the book Applied Predictive Modeling, which won the Ziegel award from the American Statistical Association, which recognizes the best book reviewed in Technometrics in 2015. Their latest book, Feature Engineering and Selection, was published in 2019

Max Kuhn

rstudio tidymodels RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Max Kuhn

Matt Thomas & Mike Page | How the Tidyverse helped the British Red Cross respond to COVID | RStudio

Full title: Cognitive speed: How the Tidyverse helped the British Red Cross respond quickly to COVID-19 We will discuss the importance of cognitive speed, defined here as the rate in which an idea can be translated into code, and why the Tidyverse excels in this domain. We will demonstrate this idea in relation to a suite of tools we were required to rapidly develop at the British Red Cross in order to respond effectively to the COVID-19 pandemic. To do this, we will exhibit how elements of the unifying design principles outlined in the ‘tidyverse design guide - Tidyverse team’ relate to the notion of cognitive speed, giving specific examples for various design considerations. We believe this talk will encourage reflection on better design practices for future R developers, using the design principles of the tidyverse as the guiding beacon. About Matt: Dr. Matt Thomas is Head of Strategic Insight and Foresight at the British Red Cross. Matt’s team aims to help the Red Cross become more anticipatory and proactive by producing insights and tools including the Vulnerability Index ( https://britishredcrosssociety.github.io/covid-19-vulnerability/ ) and Resilience Index ( https://britishredcross.shinyapps.io/resilience-index/). He holds a PhD in Evolutionary Anthropology and, prior to joining the British Red Cross, was researching topics including reindeer herders in the Arctic, hunter-gatherers in the Philippines, and witches in China. Outside of work, Matt writes a column for an anthropology magazine ( https://www.sapiens.org/column/machinations/ ) as well as fiction. About Mike: Mike Page is a data scientist on the Strategic Insight and Foresight team at the British Red Cross. Here, he helps to develop a suite of open source tools and dashboards including the Vulnerability Index ( https://britishredcrosssociety.github.io/covid-19-vulnerability/ ) and Resilience Index ( https://britishredcross.shinyapps.io/resilience-index/). Mike is also the author of several R packages including mortyr and newsrivr. In his spare time you can find him rock climbing around the Alps

rstudio shinyapps tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Forcats Rstats Open Source Oss Reticulate Matt Thomas Mike Page Covid Covid-19 Pandemic Red Cross

Marcus Adams | Putting a GMP Shiny App into Production | RStudio

Full title: Not The App We Deserve. The App We Need: Putting a GMP Shiny App into Production In February 2020, the Digital Proactive Process Analytics (DPPA) group within Merck’s manufacturing division officially launched a Shiny app to automate the creation of Continuous Process Verification (CPV) reports into production. That’s right – the almighty, mysterious, coveted production. From a technical perspective, the app is nothing particularly special (except other than getting LaTeX successfully installed to support the use of R Markdown). Users enter a few parameters and out pops a PDF with a series statistical analyses of a product’s quality testing data. The R blogosphere is filled with examples of similar Shiny apps. What mattered was the app was in production, and furthermore it was approved for GMP use. This meant these reports could be submitted to the FDA and other regulatory agencies. This meant the data could be used to support product release decisions. This meant Merck’s engineers were about to save thousands of hours per year in compiling data, generating charts, and calculating summary statistics. This was the app manufacturing sites needed. Most of the work in getting this app into production was not implementing the top-level features. Sorry, no discussion of fancy statistical process control methods here. Instead this talk will discuss some of the many things the development team (none of which came from a software development background) needed to learn in order to create a robust, secure, and maintainable production application. About Marcus: Marcus Adams is an Associate Director, Engineering at the biopharmaceutical company Merck. He earned his BEng and MS in Chemical Engineering from the University of Delaware and Villanova University, respectively. His more than decade of experience at Merck spans the bio-pharmaceutical spectrum and includes experience in pre-clinical PK/PD modeling, product commercialization, in-line technology support, procurement, and vaccine distribution technology development. Currently, he works as a part of the Digital Proactive Process Analytics team, leveraging Merck’s Big Data Platform in the development of manufacturing information data models, report automation tools, and integrated-systems analysis applications. His professional interests include effective digital visualization, reproducible research/analysis, and convincing his coworkers of the diverse, flourishing world beyond Microsoft Excel

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Marcus Adams GMP

Malcolm Barrett | You’re Already Ready: Zen and the Art of R Package Development | RStudio

R packages make it easier to write robust, reproducible code, and modern tools in R development like usethis make it easy to work with packages. When you write R packages, you also unlock a whole ecosystem of tools that will make it easier to test, document, and share your code. Despite these benefits, many believe package development is too advanced for them or that they have nothing to offer. A fundamental belief in Zen is that you are already complete, that you already have everything you need. I’ll talk about why your project is already an R package, why you’re already an R package developer, and why you already have the skills to walk the path of development. About Malcolm: Malcolm Barrett is Clinical Research Data Scientist at Teladoc Health, an epidemiologist, and an R developer. He is also an organizer for the Los Angeles R Users Group. Malcolm is the author of several R packages, including ggdag and precisely. Previousy, he was an intern at RStudio and spent two years of service in AmeriCorps. In 2013 and 2014, while serving in AmeriCorps, Malcolm lived in the Zen Center of New York City, where he is still a student

rstudio usethis RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Malcolm Barrett Package Development

Kate Hertweck | R training and documentation for different levels of expertise | RStudio

Full title: Making the jump from learning to applying: R training and documentation for different levels of expertise How does someone make the leap from learning R to actively applying R in professional work? At what point (if ever!) do we get to call ourselves “experts in R? This talk explores what differentiates novice, practitioner, and expert R programmers, and how transitions between these stages occur. I’ll discuss the type of support required for R users to move from one level of expertise to the next, and how different types of training and documentation can support R users at each level. Understanding variable levels of education among R practitioners supports our own professional work, from collaborative coding to package development, and helps build a bigger, more inclusive R community. About Kate: Kate Hertweck is the bioinformatics training manager at Fred Hutchinson Cancer Research Center, where they develop and teach courses on reproducible computational methods as a part of fredhutch.io. Kate’s graduate training at University of Missouri in genomic evolution of plants was followed by a postdoctoral fellowship at the National Evolutionary Synthesis Center (NESCent) at Duke University, where they fell in love with R and began working exclusively in computational biology. Kate then spent four years as an assistant professor teaching bioinformatics, genomics, and plant taxonomy before transitioning to biomedical research training. Kate has been involved in The Carpentries, a non-profit organization that teaches reproducible computational methods, since 2014, serving as a leader in community governance since 2016. When not being an overenthusiastic instructor, Kate likes to spend their time doing fiber arts (knitting, crochet) and enjoying all things science fiction

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Kate Hertweck

Kara Woo | Always look on the bright side of plots | RStudio

Everyone who creates visualizations in R is bound to make mistakes that prevent their plots from looking as they should. Sometimes, these mistakes create beautiful “accidental aRt”, though other times they’re just plain frustrating. Either way, however, there’s something to be learned. This talk will draw on years of watching both the ggplot2 issue tracker and the @accidental__aRt twitter account to highlight some common plot foibles and explain what they can teach us about how ggplot2 works. About Kara: Kara Woo is a research scientist in data curation at Sage Bionetworks, where she builds tools to help researchers document and share their data. Kara is a core developer of ggplot2 and collects data visualizations gone beautifully wrong on a blog called accidental aRt

ggplot2 rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Kara Woo Accidental Art

JooYoung Seo | Accessible Data Science Beyond Visual Models | RStudio

Full title: Accessible Data Science Beyond Visual Models: Non-Visual Interactions with R and RStudio Packages Data science is full of vision-dominant practices, and most data scientists rely heavily on visual models. However, data science itself should require insight and computational thinking beyond what is just seen by eyes. JooYoung Seo, who is a blind data scientist and who was working for RStudio’s accessibility projects over the summer 2020, will talk about his experience with some non-visual techniques to interact with data. If you would like to know more about various ways of making data science accessible via R, and new accessibility features introduced in RStudio IDE and Shiny, his demonstration without sight will be thought-provoking. About JooYoung: JooYoung Seo is a Ph.D candidate in the Learning, Design, and Technology program at the Pennsylvania State University, and internationally certified accessibility professional whose research and development focuses on accessible computing for all. As an RStudio’s double-certified data science instructor (i.e., Tidyverse + Shiny), who is blind, he is committed to making data science ecosystem more accessible to people with and without dis/abilities using R. To this end, he has been actively contributing to R open-source projects including Shiny, RMarkdown, bookdown, and distill for accessibility, and interned on the RStudio IDE and Shiny team as an accessibility engineer in summer 2020

Shiny Team

bookdown rmarkdown rstudio Shiny tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate JooYoung Seo Accessibility

John Burn-Murdoch | Reporting on and visualising the pandemic | RStudio

John will discuss the lessons he’s learned reporting on and visualising the pandemic, including the world of difference between making charts for a technical audience and making charts for a mass audience. You’ll learn from his experience navigating the highly personal and political context within which people consume and evaluate graphics and data, and how that can help us better design and communicate with visualisations down the pipeline for the future. About John: John Burn-Murdoch is the Financial Times’ senior data visualisation journalist, and creator of the FT’s coronavirus trajectory tracker charts. He has been leading the FT’s data-driven coverage of the pandemic, exploring its impacts on health, the economy and wider society. When pandemics are not happening, he also uses data and graphics to tell stories on topics including politics, economics, climate change and sport, and is a visiting lecturer at the London School of Economics

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Forcats Rstats Open Source Oss Reticulate John Burn-Murdoch FT Financial Times Covid Covid-19 Pandemic Journalism

Jeroen Ooms | Monitoring health and impact of open-source projects | RStudio

At rOpenSci, we have come to realize that in order to help researchers get the most out of R, we need better tooling to monitor the quality, health, and impact of R packages. This applies both to our internal projects, as well as other packages in the R ecosystem. But what exactly makes a good R package? In this talk we discuss various aspects of open-source software that are not always immediately obvious, and that you may want to consider when depending on an R package. We identify several categories of indicators you could look for, ranging from the role in the dependency network, to expectations around maintenance and participation. Finally we introduce an ambitious new rOpenSci project called R-universe: an open platform, where we will experiment with showing metrics and other background information about packages, that may reveal something about the health and the impact of the project, and also facilitate discovery of other software. About Jeroen: Postdoc hacker for @ropensci at UC Berkeley

Jeroen Ooms

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Jeroen Ooms Ropensci

Javier Luraschi | Using pins with Python and JavaScript | RStudio

Last year, pins got released as a brand new R package to pin, discover and cache remote resources for R users. This package has matured to support many use cases; from caching remote URLs, and easily sharing datasets with other R users, to building automated pipelines. However, in order to truly collaborate in multi-disciplinary data-driven teams, one needs to consider how to collaborate beyond R. How can we share resources with designers and machine learning experts who happen to use different programming languages like Python and JavaScript? This talk will introduce the pinsjs project, a cross-language community project which has the goal of bringing pins to the broader open source community to enable rich workflows across larger data-driven teams. About Javier: Javier is the author of “Mastering Spark with R”, pins, sparklyr, mlflow and torch. He holds a double degree in Math and Software Engineer and decades of industry experience with a focus on data analysis. Javier is currently working on a project of his own; and previously worked in RStudio, Microsoft Research and SAP

rstudio torch RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Javier Luraschi JavaScript Pins

Irene Steves | The dynamic duo: SQL & R | RStudio

There’s a point in every data wranglers’ career in which their full dataset can no longer fit into just CSV files, and the journey to database-world begins. I reached this point about two years ago, when I transitioned from ecological research to the world of eCommerce fraud prevention. My calls to read_csv became scarcer as I came to rely more and more on databases. In this talk, I’ll demonstrate how I use R and SQL to access database tables, and how I incorporate both into my daily workflow, aided by features in RStudio IDE. I’ll also discuss our company’s “riskiconn” package for handling database connections and queries, which includes customizations to simplify day-to-day data querying. About Irene: Irene holds an M.Sc. in Ecology and a B.A. in Integrative Biology, through which she first discovered R and data science. Her interest in data led her to the Arctic Data Center at the University of California Santa Barbara, a summer internship at RStudio, and ultimately to the Research & Data Science department at Riskified, where she now explores the complex patterns of fraud in eCommerce. In her free time, she studies Hebrew through podcasts and dubbed kids’ movies

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Sql EDA

Hadley Wickham | Maintaining the house the tidyverse built | RStudio

Hadley will talk about how the tidyverse has evolved since its creation (just five years ago!). You’ll learn about our greatest successes, learn from our biggest failures, and get some hints of what’s coming down the pipeline for the future. About Hadley: Hadley Wickham is the Chief Scientist at RStudio, a member of the R Foundation, and Adjunct Professor at Stanford University and the University of Auckland. He builds tools (both computational and cognitive) to make data science easier, faster, and more fun. You may be familiar with his packages for data science (the tidyverse: including ggplot2, dplyr, tidyr, purrr, and readr) and principled software development (roxygen2, testthat, devtools, pkgdown). Much of the material for the course is drawn from two of his existing books, Advanced R and R Packages, but the course also includes a lot of new material that will eventually become a book called “Tidy tools”

Hadley Wickham

devtools dplyr ggplot2 pkgdown purrr readr roxygen2 rstudio testthat tidyr tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Hadley Wickham

Grant Fleming | Fairness and Data Science: Failures, Factors, and Futures | RStudio

In recent years, numerous highly publicized failures in data science have made evident that biases or issues of fairness in training data can sneak into, and be magnified by, our models, leading to harmful, incorrect predictions being made once the models are deployed into the real world. But what actually constitutes an unfiar or biased model, and how can we diagnose and address these issues within our own work? In this talk, I will present a framework for better understanding how issues of fairness overlap with data science as well as how we can improve our modeling pipelines to make them more interpretable, reproducible, and fair to the groups that they are intended to serve. We will explore this new framework together through an analysis of ProPublica’s COMPAS recidivism dataset using the tidymodels, drake, and iml packages. About Grant: Grant Fleming is a Data Scientist at Elder Research, co-author of the Wiley book Responsible Data Science (2021), and contributor to the O’Reilly book 97 Things About Ethics Everyone in Data Science Should Know. His professional focus is on machine learning for social science applications, model explainability, and building tools for reproducible data science. Previously, Grant was a research contractor for USAID

rstudio tidymodels RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Ethics Propublica Tech

Eric Cronstrom | How we made the switch: a case study on automating a complex report | RStudio

The Center for Charter Schools at Central Michigan University produces annual reports for about 60 schools. The reporting process used to be a cumbersome blend of many technologies. The Center used to use a blend of SQL, Excel, inDesign, and VBScript that would all culminate in a nice looking, branded report for each school. 2 years ago, staff turnover allowed the data team to rethink the process, having had experience in R from graduate work the team at the Center decided to go all in on R Studio and R Markdown for report production a mere 1 month before the reports were due to be published. This talk will be a case study of how we as an organization adopted RStudio tools to streamline a cumbersome process to fantastic results. About Eric: Eric is responsible for administering a wide range of day to day program functions associated with the performance and accountability of CMU partner schools. He ensures that the data the Center utilizes to evaluate school performance is accurate and stored within a sound data infrastructure. He also leverages his wide range of technical skills to lead the development and production of reports and respond to questions regarding school performance and demographic context. Prior to joining the Center, Eric was a database administrator for Central Michigan University Libraries. He has also served as a lecturer at Central Michigan University teaching courses in web design, database design and programming. He earned a master’s degree in information systems management and a bachelor’s degree in computer science from Central Michigan University. He is also pursuing an additional master’s degree in applied statistics at Central Michigan University

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate K12 Charter Schools Education

Emily Riederer | oRganization | RStudio

Many case studies demonstrate the benefits of organizations developing internal R packages. But how do you move your organization from individual internal packages to a coherent internal ecosystem? This talk applies the jobs-to-be-done framework to consider the different roles that internal tools can play, from unblocking IT challenges to democratizing tribal knowledge. Beyond technical functionality, we will explore design principles and practice that make internal packages good teammates and consider how these deviate from open-source standards. Finally, we will consider how to exploit the unique challenges and opportunities of developing within an organization to make packages that collaborate well – both with other packages and their human teammates. About Emily: Emily Riederer is an Analytics Manager at Capital One. Emily leads a team that is focused on building internal analytical tools and data products, including a suite of R packages and Shiny apps, and cultivating an innersource community of practice for analysts. Emily is an active member of the R community. In 2019, she co-organized satRday Chicago and the Chicago R unconference. You can find her {projmgr} R package on CRAN and her blog at emilyriederer.netlify.com. Previously, Emily earned degrees in Mathematics and Statistics at UNC Chapel Hill and worked as a research assistant in emergency department simulation and optimization

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate

Barret Schloerke | plumber + future: Async Web APIs | RStudio

plumber is an R package that allows users to create web APIs by decorating R functions using roxygen2-like comments. In the latest release, asynchronous code (using future or promises) may be inserted at any stage of a plumber route execution, enabling parallel processing using multiple workers. In this talk, I will go through how you can set up your own asynchronous plumber API to leverage your full computing potential. About Barret: I specialize in Large Data Visualization where I utilize the interactivity of a web browser, the fast iterations of the R programming language, and large data storage capacity of Hadoop

Barret Schloerke

plumber roxygen2 rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate API

Andrew Tran | The Opioid Files: Turning big pharmacy data over to the public | RStudio (2021)

Through a community survey conducted over the summer, the RStudio tidymodels team learned that users felt the #1 priority for future development in the tidymodels package ecosystem should be ensembling, a statistical modeling technique involving the synthesis of multiple learning algorithms to improve predictive performance. This December, we were delighted to announce the initial release of stacks, a package for tidymodels-aligned ensembling. A particularly statistically-involved pesto recipe will help us get a sense for how the package works and how it advances the tidymodels package ecosystem as a whole. About Andrew: Andrew is a data reporter on the rapid-response investigative team at The Washington Post who has analyzed how covid-19 has disproportionately impacted certain communities, the spread of opioids across the country, and the rise of right-wing violence. He shared in winning the Pulitzer Prize for Investigative Reporting in 2018. He’s an advocate for open data and reproducibility in journalism

rstudio tidymodels RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Andrew Tran Wapo

Allison Horst | R Art Lessons | RStudio

Art can be a welcoming bridge for learners and users to engage with and learn tools and skills in R. As RStudio’s first Artist-in-Residence, my goal has been to make the R landscape more welcoming for a broader community of users through engaging, didactic artwork. In this R, art, and heart-filled talk, I’ll share the motivation behind my R artwork and some lessons learned over the past year as Artist-in-Residence, including: Learning to embrace cute and credible artwork Art to help students engage with, learn and remember R skills Art for community building and support I hope this talk inspires viewers to use, create and share more artwork, so that together we can make the R landscape feel even brighter. About Allison: Allison Horst PhD teaches data analysis, statistics, and presentation skills at the Bren School of Environmental Science and Management (UC Santa Barbara). In addition to courses, she leads interdepartmental R/RStudio workshops for incoming graduate students, created and teaches an online R-refresher workshops for alumni, and is a co-founder of R-Ladies Santa Barbara. In 2018 she earned the student-selected Distinguished Teaching Award at the Bren School, and in 2019 was awarded the campus-wide Distinguished Teaching Award by the UCSB Academic Senate. For her graduate research, Allison studied toxicity of engineered nanoparticles in environmental microorganisms. She is also a landscape painter, illustrator and designer

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Allison Horst

Ahmadou Dicko | Humanitarian Data Science with R | RStudio

Humanitarian actors are increasingly using data to drive their decisions. Since the Haiti 2010 earthquake, the volume of data collected and used by humanitarians has been growing exponentially and organizations are now relying on data specialists to turn all this data into life-saving data products. These data products are created by teams using proprietary point and click software. The process from the raw data to the final data product involves a lot of clicking, copying and pasting and is usually not reproducible. Another approach to humanitarian data science is possible using R. In this talk, I will show how to seamlessly develop reproducible, reusable humanitarian data products using the tidyverse, rmarkdown and some domain-focused R packages. About Ahmadou: Ahmadou Dicko is a statistics and data analysis officer at the United Nations High Commissioner for Refugees (UNHCR) where he uses statistics and data science to help safeguard the rights and well-being of refugees in West and Central Africa. He has an extensive experience in the use of statistics and data science in development and humanitarian projects. Ahmadou was the lead of the OCHA Center for Humanitarian Data team for West and Central Africa and has worked with several humanitarian and development organizations such as IFRC, FAO, IAEA, OCHA. Ahmadou is a RStudio trainer ( https://education.rstudio.com/trainers/ ) and he is passionate about the R community. He is currently co-organizing the Dakar R User Group ( https://www.meetup.com/DakaR-R-User-Group/ ) and co-leading the AfricaR initiative ( https://africa-r.org/ )

education.rstudio.com rmarkdown rstudio tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Ahmadou Dicko Data for Good

Aaron Jacobs | Introducing xrprof: A New Way to Profile R | RStudio (2021)

Tracking down performance issues in R code usually means using R’s built-in Rprof() profiler or one of the packages built around it. But the changing nature of the R community (towards more deployed applications) makes local profiling workflows frustrating, which is why I have written a new profiler: xrprof. xprof is compatible with existing R tools, but unlike them it can be used to profile R code that is already running – in fact, it is designed to be safe to point at R code running ““in production””. xrprof also works seamlessly when R is run inside Docker, and can even be run in complex environments like Kubernetes clusters. Taking inspiration from the {jointprof} package, xrprof can also show function calls at the C/C++ level alongside those from R. This can be immensely useful for diagnosing problems in packages that make heavy use of compiled code. About Aaron: Aaron Jacobs is a Senior Data Scientist on the R&D team at Crescendo, a technology company in the sports betting space with a large internal R ecosystem. Prior to Crescendo he worked in Canadian public policy research. Aaron has a strong interest in the engineering side of data science and the emerging use of R “in production”. He is the author of several CRAN and GitHub packages, as well as xrprof – a new R profiling tool

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate

Tracy Teal | Teaching R using inclusive pedagogy: Carpentries workshops lessons learned | RStudio

Talk from rstudio::conf(2019) The Carpentries is an open, global community teaching researchers the skills to turn data into knowledge. Since 2012 we have taught 700+ R workshops & trained 1600+ volunteer instructors. Our workshops use evidence-based teaching, focus on foundational and relevant skills and create an inclusive environment. Teaching the Tidyverse allows learners to start working with data quickly, and keeps them motivated to begin and sustain their learning. Our assessment show that these approaches have been successful in attracting diverse learners, building confidence & increasing coding usage. Through our train-the-trainer model and open, collaborative lessons, this approach scales globally to reach more learners and further democratize data. About Tracy Teal: Executive Director of The Carpentries ( https://carpentries.org ) and co-founder of Data Carpentry ( http://www.datacarpentry.org ), a non-profit organization that develops and runs workshops training researchers in effective data analysis and visualization to enable data-driven discovery. Manages projects, operations and finances. Leads lesson development and volunteer coordination and is responsible for strategic and business planning

rstudio tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Tracy Teal

Sean Lopp | Announcing Posit Package Manager | RStudio (2019)

Posit Package Manager is the newest professional product that helps teams, departments, and entire enterprises organize and centralize package management. If you’ve ever struggled with IT to get access to a new (any?) R package, reproduce an old result, or share your code with others, Posit Package Manager can help! We’ll introduce the new product, discuss how R repositories can be used to solve problems and take a sneak peek at what is coming in 2019. VIEW MATERIALS https://github.com/slopp/rspm-rstudioconf About the Author Sean Lopp Sean has a degree in mathematics and statistics and worked as an analyst at the National Renewable Energy Lab before making the switch to customer success at RStudio. In his spare time he skis and mountain bikes and is a proud Colorado native. *Posit Package Manager, formerly known as RStudio Package Manager

rstudio Sean Lopp RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Mike K Smith | Using rmarkdown and parameterised reports | RStudio

My brain is lazy, shallow and easily distracted. Learn how I use notebooks to keep my present-self organised, my future-self up to speed with what I was thinking months ago, and also how I use parameterised reports to share results for both quantitative and non-quantitative audiences across multiple endpoints. I can update and render outputs for a variety of outputs from a single markdown notebook or report. I’ll show you how I organise my work using the Tidyverse, use child documents with parameterisation and also how this is served out to my colleagues via RStudio Connect. About Mike K Smith: I have 25 years experience of working in the Pharmaceutical industry (Pfizer), with more than 15 years working on modelling and simulation projects. I am a keen advocate of smarter drug development with a particular interest in Bayesian methods, dose-response, reproducible research and knowledge management. My particular expertise is in the use of simulation methodology to predict drug outcomes, find efficient trial designs, assess decision criteria and evaluate analysis methodologies. My current role at Pfizer is as specialist in computation and modeling solutions - evaluating and deploying new tools and training colleagues. I am an RStudio certified tidyverse trainer

rmarkdown rstudio tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Mike Smith

Mary Rudis | How R and Posit are revolutionizing Stats Education in Community Colleges | Posit

Talk from rstudio::conf(2019) There is no doubt that Posit has had an impact on how introductory statistics is taught in colleges today. When we consider the sheer dominance that giants like Texas Instruments, IBM, and Pearson Publishing have had in academic curriculum development, it’s no small wonder that tools like R and Python have been able to gain a foothold. Projects like DataCamp, ModernDive.com, “Introductory Statistics with Randomization and Simulation” courtesy of openintro.org, Wickham’s “R for Data Science” and Peng’s “R Programming for Data Science” are great resources for the student who has already some fundamental math or statistical background and has become comfortable around computing and applications-driven computational exercises. But many of us know that Data Science cannot simply be relegated to the privileged few that stumble into it by virtue of circumstance. My passion, and the purpose of my talk, is to provide educators with a digestible guidebook that would be appropriate for introduction to statistical concepts in high school, college, and under-resourced schools looking for ways to increase diversity in STEM. Organized in small, adaptable activities designed to be the amuse-esprit enticing both the timid and the skeptical to the proverbial banquet table that is Posit, this exploration into the world of statistics education should be of interest to a wide audience. My hope is to increase data literacy in real world context – with primary emphasis on descriptive statistics and distributions. About Mary Rudis: After graduating from Lehigh University in Bethlehem, PA with a degree in Mathematics, Mary began as a high school mathematics and computer science teacher, developing technology infrastructure for a small, private high school in Pennsylvania. Throughout her career, she brings innovative approaches and enjoys the role of trailblazer. Mary’s most recent accomplishments as math department chair included developing mathematics curriculum and coordinating engineering, bioengineering and data science degrees at Great Bay Community College in Portsmouth, NH. Mary’s primary interests are learning and instruction, developing data science curricula for two-year colleges and 4-year liberal arts colleges, and working with area high school students in STEM at the University of NH Tech Camp each summer

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Mary Rudis Education

Kelly Nicole Bodwin | Intro stats with R: Easing the transition to software for beginners | RStudio

Talk from rstudio::conf(2019) In this talk, we will present our approach to incorporating R and RStudio into a 10-week introductory statistics course for non-majors Cal Poly. Our primary contribution will be to share a series of Shiny Apps, created to ease students with no statistical or coding background into the philosophy of using programming tools to explore data. Our program was recently used in 3 sections of 35 students each this Fall, during which students were surveyed regularly for their reactions to the approach. We will demonstrate our new tools, discuss our successes and failures, share student-generated output, and summarize the results of our Fall survey. Kelly teaches at Cal Poly in San Luis Obispo, California, where she takes great joy in forcing statistical thought and R skills upon unsuspecting undergraduates. Her research is in clustering methods, digital humanities, and R tools for education. In her free time, she hikes and camps, plays board games, and tweets too much about R

rstudio Shiny RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Kelly Bodwin Beginner

Jesse Mostipak | R4DS online learning community: self-taught data science & DEI | RStudio (2019)

The first iteration of the R4DS Online Learning Community was created as an online space for learners and mentors to gather and work through the “R for Data Science” text in a collaborative and supportive environment. The creation of this group was inspired by my own success in transitioning to a career in data science coupled with the resources that I wanted to see in the R programming space. This talk will go through the learnings of creating an online learning space focused on R programming for data science, and how future iterations of similar groups can more proactively center on bringing about diversity, equity, and inclusion to data science spaces. About Jesse Mostipak: Molecular biologist turned public school teacher who eventually fell in love with non-profit data science. Harvard SDP, Datanaut, and perpetual #rstats noob

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate Jesse Mostipak

JD Long | Empathy in action: Building community of practice for analytics in a global corp | RStudio

Talk from rstudio::conf(2019) JD Long, Vice President of Risk Management & Data Philosophy at the global reinsurer Renaissance Re will share his experience with creating a “Community of Practice” for analytics inside of a global corporation. The theme of “empathy” will be recurring as he discusses how he worked to create a supportive learning environment focused on helping analysts “kick ass” regardless of their tool set. This means creating a community that’s supportive of Excel, SQL, Python, and, of course, R. About JD Long: I build models. And according to George E. P. Box, my models are wrong. My skill is understanding when and where my models are useful. I’m an experienced risk and data scientist with a background in insurance, reinsurance, market risk, and stochastic modeling. I’m the guy who can build a Monte Carlo model, help parallelize the model to run on Amazon’s cloud services and then stand in front of a general audience and put the work in context where everyone understands. My super power is thinking probabilistically, understanding risk, and communicating clearly. I have a history forming bridges between IT and business teams

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Forcats Rstats Open Source Oss Reticulate JD Long

Heather & Jacqueline Nolis | Push straight to prod: API development with R and Tensorflow | RStudio

Talk from rstudio::conf(2019) When tasked with creating the first customer-facing machine learning model at T-Mobile, we were faced with a conundrum. We had been told time and time again to deploy machine learning models in production you had to use Python, but our very best data scientists were fluent in building neural networks in R with Keras and TensorFlow. Determined to avoid double work, we decided to use R in production for our machine learning models. After months of work, wrangling our containers to meet cloud security compliance, and conforming to DevOps standards, we succeeded in creating a containerized API solution using the keras and plumber R packages and Docker. Today R is actively powering tools that our customers directly interact with and we have open sourced our methods. In this talk, we’ll walk through how to deploy R models as container-based APIs, the struggles and triumphs we’ve had using R in production, and how you can design your teams to optimize for this sort of innovation. About Heather Nolis: Heather Nolis is a founding member of the AI @ T-Mobile team, focusing the conversion of cutting-edge analyses to real-time, scalable data-driven products. She began her career in neuroscience but once realized how heavily that field relied on software built by other people, she pivoted - deciding to make software herself. You can find her @heatherklus on Twitter, where she speaks about diversity in technology, the ethical implications of data, and cats. About Jacqueline Nolis: Dr. Jacqueline Nolis is a co-founder of Nolis, LLC, a data science consulting firm. She has over a decade of experience using data to help companies including DSW, Union Bank, Microsoft, and Airbnb. She has a PhD from Arizona State University where her research focused on electric vehicle route optimization. For fun she likes to use machine learning for humor

plumber rstudio tensorflow RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate TensorFlow Tmobile

Jake Thompson | Branding and Packaging Reports with R Markdown | RStudio (2020)

The creation of research reports and manuscripts is a critical aspect of the work conducted by organizations and individual researchers. Most often, this process involves copying and pasting output from many different analyses into a separate document. Especially in organizations that produce annual reports for repeated analyses, this process can also involve applying incremental updates to annual reports. It is important to ensure that all relevant tables, figures, and numbers within the text are updated appropriately. Done manually, these processes are often error prone and inefficient. R Markdown is ideally suited to support these tasks. With R Markdown, users are able to conduct analyses directly in the document or read in output from a separate analyses pipeline. Tables, figures, and in-line results can then be dynamically populated and automatically numbered to ensure that everything is correctly updated when new data is provided. Additionally, the appearance of documents rendered with R Markdown can be customized to meet specific branding and formatting requirements of organizations and journals. In this presentation, we will present one implementation of customized R Markdown reports used for Accessible Teaching, Learning, and Assessment Systems (ATLAS) at the University of Kansas. A publicly available R package, ratlas, provides both Microsoft Word and LaTeX templates for different types of projects at ATLAS with their own unique formatting requirements. We will discuss how to create brand-specific templates, as well as how to incorporate the templates into an R package that can be used to unify report creation across an organization. We will also describe other components of branding reports beyond R Markdown templates, including customized ggplot2 themes, which can also be wrapped into the R package. Finally, we will share lessons learned from incorporating the R package workflow into an existing reporting pipeline. https://rstudio.com/resources/rstudioconf-2020/branding-and-packaging-reports-with-r-markdown/

ggplot2 rstudio Rstudio::conf(2020) Jake Thompson RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Max Kuhn | Total Tidy Tuning Techniques | RStudio (2020)

Many models have structural parameters that cannot be directly estimated from the data. These tuning parameters can have a significant effect on model performance and require some mechanism for finding reasonable values. The tune and workflow packages enable tidymodels users to optimize these parameters using a variety of efficient grid search methods as well as with iterative search techniques (such as Bayesian optimization)

Max Kuhn

rstudio tidymodels Rstudio::conf(2020) Max Kuhn RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Joe Cheng | Styling Shiny apps with Sass and Bootstrap 4 | Posit (2020)

Customizing the style–fonts, colors, margins, spacing–of Shiny apps has always been possible, but never as easy as we’d like it to be. Canned themes like those in the shinythemes package can easily make apps look slightly less generic, but that’s small consolation if your goal is to match the visual style of your university, corporation, or client. In theory, one can “just” use CSS to customize the appearance of your Shiny app, the same as any other web application. But in practice, the use of large CSS frameworks like Bootstrap means significant CSS expertise is required to comprehensively change the look of an app. Relief is on the way. As part of a round of upgrades to Shiny’s UI, we’ve made fundamental changes to the way R users can interact with CSS, using new R packages we’ve created around Sass and Bootstrap 4. In this talk, we’ll show some of the features of these packages and tell you how you can take advantage of them in your apps. Resources: https://speakerdeck.com/jcheng5/styling-shiny

Joe Cheng

Shiny shinythemes Rstudio::conf(2020) RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Joe Cheng

Eduardo Ariño de la Rubia | Value in Data Science Beyond Models in Production | RStudio (2020)

ML in production is one of the most obvious ways that data science organizations create value in business. However, these models are at the very end of a long story of how quantitative research changes and enhances organizations. In this talk I will discuss how I have found DS organization to be truly transformative outside of ML in the loop. Bio: Eduardo Ariño de la Rubia is a DS manager and educator. He loves R and RStudio. He has a Masters in Negotiation, Conflict Resolution and Peacebuilding, which is probably the most useful training he could have received

rstudio Rstudio::conf(2020) Eduardo Ariño De La Rubia RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Dewey Dunnington | Best practices for programming with ggplot2 | RStudio (2020)

The ggplot2 package is widely acknowledged as a powerful, dynamic, and easy-to-learn graphics framework when used in an interactive environment

ggplot2 rstudio Rstudio::conf(2020) Dewey Dunnington RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Alicia Schep | Auto-magic Package Development | RStudio (2020)

Vega-lite is a high-level grammar of interactive graphics implemented in Javascript; it renders interactive visualizations in the browser based on a JSON specification. In Python and Javascript, the Altair and vega-lite-api packages have demonstrated how the development of APIs to build Vega-Lite graphics can be partially automated based on the Vega-Lite JSON schema, which describes the required format for a Vega-Lite JSON specification. This talk will describe the development of the ‘vlbuildr’ package for building Vega-Lite specifications in R and the ‘vlmetabuildr’ package for building the ‘vlbuildr’ package. The ‘vlbuildr’ package seeks to provide a pipe-friendly, “R-like” functional interface for building up simple to complex specifications for Vega-Lite graphics, which can in turn be rendered as an HtmlWidget by the ‘vegawidget’ R package. Building such an API in a fully automated way from the Vega-Lite schema presents considerable challenges, so the approach taken here was to rely on partial automation. Human judgement dictates the basic contours of the API, such as what groups of functions to include and how various types of building blocks will go together. The part that is automated is filling in many details such as the different variants of a group of functions, the exact parameters needed for each function, and the documentation of those parameters – the parts that would be extremely tedious to port over!

rstudio Rstudio::conf(2020) Alicia Schep RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Desiree De Leon | Of Teacups, Giraffes, and R Markdown | RStudio (2020)

How do you make your R Markdown lessons feel friendly for learners you’ll never meet? How do you make it engaging so they sit and stay a while? How do you make it memorable so they come back to visit again? In this talk, I’ll share lessons learned from my experience of making a series of online statistics modules (co-authored by Hasse Walum) that feel accessible and fun– housed entirely in an R Markdown site, complete with a whimsical, illustrated narrative about teacup giraffes. I’ll show how adding good characters with your audience in mind, good design, and good play helped me make the most of HTML output. To help you get started, I’ll share resources that Alison Hill and I have developed–including a series of cookbooks and out-of-the-box templates– so that you will have a leg up on applying these ideas to R Markdown collections of your own

rstudio Rstudio::conf(2020) Desiree De Leon RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Namita Nandakumar | R + Tidyverse in Sports | RStudio (2020)

This talk will use a case study, most likely in hockey, to showcase the many ways in which R and the Tidyverse can be used to analyze sports data as well as the unique priorities and considerations that are involved in applying statistical tools to sports problems

rstudio tidyverse Rstudio::conf(2020) Namita Nandakumar RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Sports

Gordon Shotwell | Technical Debt is a Social Problem | Posit (2020)

Technical debt is a big problem for the R community. Even though R has excellent support for testing, documentation and packaging code it has the reputation that it is not suitable for production applications because data scientists don’t pay enough attention to technical debt within their codebases. Most people think of technical debt as an engineering problem. We choose to make our current work cheaper at the expense of needing to do more work down the road. But when you look closely at the root causes of technical debt they are almost always about interpersonal relationships. Developers have trouble empathizing with other users of their code and so don’t spend the time to make that code easy for future developers to use and understand. In this talk I argue that we should think about technical debt as a social problem because it gives us insight into why it’s so hard to pay back. I then provide a practical roadmap of how to introduce best practices into your data science team

Rstudio::conf(2020) Mr. Gordon Shotwell RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Ellis Hughes | Approaches to Assay Processing Package Validation | RStudio (2020)

In this talk I will discuss the steps that have been created for validating internally generated R packages at SCHARP (Statistical Center for HIV/AIDS Research and Prevention) and the lessons learned while creating packages as a team. Housed within Fred Hutch, SCHARP is an instrumental partner in the research and clinical trials surrounding HIV prevention and vaccine development. Part of SCHARP’s work involves analyzing experimental biomarkers and endpoints which change as the experimental question, analysis methods, antigens measured, and assays evolve. Maintaining a validated code base that is rigid in its output format, but flexible enough to cater a variety of inputs with minimal custom coding has proven to be important for reproducibility and scalability. SCHARP has developed several key steps in the creation, validation, and documentation of R packages that take advantage of R’s packaging functionality. First, the programming team works with leadership to define specifications and lay out a roadmap of the package at the functional level. Next, statistical programmers work together to develop the package, taking advantage of the rich R ecosystem of packages for development such as roxygen2, devtools, usethis, and testthat. Once the code has been developed, the package is validated to ensure it passes all specifications using a combination of testthat and rmarkdown. Finally, the package is made available for use across the team on live data. These procedures set up a framework for validating assay processing packages that furthers the ability of Fred Hutch to provide world-class support for our clinical trials

devtools rmarkdown roxygen2 rstudio testthat usethis Rstudio::conf(2020) Ellis Hughes RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Dr. Travis Gerke | UnicoRns are real | RStudio (2020)

Common advice from experienced data scientists to job-seekers is to avoid job postings that describe a “data science unicorn”: someone who has experience performing an unrealistically large array of technical and business-related job duties. Seeking a unicorn is viewed as a potential indicator that the company fails to understand their data science needs, and that new hires will not be poised for success due to lacking support and resources [Robinson & Nolis, 2019]. The R language, particularly when used with RStudio products, has evolved to enable production-level activities in the areas of data wrangling, reporting/dashboarding, database/software engineering, machine learning, and web application development. It is increasingly plausible that a data scientist will be able to efficiently perform a wide variety of job functions with experience only in a single language (R). Indeed, even entry level R users may tread into “unicorn” territory. Current standards for data scientist job descriptions and salaries do not accommodate this nuance, leaving both job-seekers and hiring managers unable to distinguish job requirements which should be read as warning signs from listings which are idyllic matches for the modern R unicorn. In this talk, we present data aggregated from several large compensation analytics companies which summarize current benchmarks for data science job descriptions and corresponding salary ranges. We then suggest job description language to target modern R users, considering both job duty compatibility and job post findability. These descriptions are presented with likely salary range pairings. Attention is given to deviations from traditional degree requirements, years of experience, and demands for multiple programming language literacy which may lack relevance for the R unicorn. Our overarching goal is to provide job description templates which encourage optimal matchmaking between R job seekers and organizations in need of their talents

rstudio Rstudio::conf(2020) Dr. Travis Gerke RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Brandon Farr | 15 Years of R in Quantitative Finance | Posit (2020)

Use of R in the investment industry is established and growing. This talk will discuss changes seen in 15 years of practice within asset management firms. I hope discussion of lessons learned and recommendations will benefit those currently in finance and those interested in hearing how the flexibility of R manifests in the financial world

Rstudio::conf(2020) Brandon Farr RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Quant

Kevin Ushey | renv: Project Environments for R | RStudio (2020)

The renv package helps you create reproducible environments for your R projects. With renv, you can make your R projects more: Isolated: Installing a new or updated package for one project won’t break your other projects, and vice versa. Portable: Easily transport your projects from one computer to another, even across different platforms. renv makes it easy to install the packages your project depends on. Reproducible: renv records the exact package versions you depend on, and ensures those exact versions are the ones that get installed wherever you go. In this presentation, I’ll introduce renv and some of its main workflows

renv rstudio Rstudio::conf(2020) Kevin Ushey Renv RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Evangeline Reynolds | Flipbooks | RStudio (2020)

Good examples facilitate accomplishing new or unpracticed tasks in a programmatic workflow. Tools for communicating examples have improved in recent years. Especially embraced are tools that show code and its resultant output immediately thereafter — the case of Jupytr notebooks and Rmarkdown documents. But creators using these tools often must choose between big-picture or narrow-focus demonstration; creators tend to either demo a complete code pipeline that accomplishes a realistic task or instead demonstrate a minimal example which makes clear the behavior of a particular function, but how it might be used in a larger project isn’t clear. Flipbooks help address this problem, allowing the creator to present a full demonstration which accomplishes a real task, and gives the viewer the opportunity to focus on unfamiliar steps. A set of flipbook building functions parse code in a data manipulation or visualization pipeline and then build it back up incrementally. Aligned superimposition of new code and output atop previous code and output makes it easy to identify how each code change triggers changes in output. The presentation will guide attendees in creating their own Flipbooks (with Xaringan slides) or mini Flipbooks (gif output)

rmarkdown rstudio Rstudio::conf(2020) Evangeline Reynolds RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Danielle Navarro | Toward a grammar of psychological experiments | RStudio (2020)

Why does a psychological scientist learn a programming language? While motivations are many and varied the two most prominent are data analysis and data collection. The R programming language is well placed to address the first need, but there are fewer options for programming behavioural experiments within the R ecosystem. The simplest experimental designs can be recast as surveys, for which there are many options, but studies in cognitive psychology, psychophysics or developmental psychology typically require more flexibility. In this talk I outline the design principles behind xprmntr, an R package that provides wrappers to the a javascript library (jsPsych) for constructing web based psychology experiments and uses the plumber package to call server side R code as needed. In doing so, I discuss limitations to the current implementation and what a “grammar of experiments” might look like

plumber rstudio Rstudio::conf(2020) Danielle Navarro RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Colin Gillespie | How to win an AI Hackathon, without using AI | RStudio (2020)

Anyone reading a newspaper or listening to the news is led to believe that AI is the solution to all problems. From self-driving cars to detecting disease to catching fraud, there doesn’t seem to be a situation that AI can’t tackle. Once “big data” is thrown into the mix, the AI solution is all but certain. But is AI always needed? Over the last eighteen months, Jumping Rivers has entered (and won) four Hackathons. All Hackathons were characterised with “big data” and the need to improve prediction. All Hackathons were won without using AI (or any sort of machine learning). This talk will focus on one particular competition around reducing leakage at Northumbrian Water. Using a combination of R, Shiny, and Tidyverse (and a few other tricks), we were able to demonstrate within the short Hackathon time frame that clear presentation of data to the front line engineers was more likely to reduce leakage, than simply providing vague estimates of a potential future leak

rstudio Shiny tidyverse Rstudio::conf(2020) Colin Gillespie RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Braulio Cuandon & Ana Alyeska Santos | Using R to Create Reproducible Engineering Test Reports

Talk from rstudio::conf(2020) Engineers at Biosense Webster, a Johnson and Johnson medical device company that specializes in diagnosing and treating cardiac arrhythmias, write multiple test reports to comply with FDA regulatory standards. These intricate reports require 36 hours of an engineer’s time on average, constraining the engineers from completing investigations and studies in a timely matter. Writing scripts in R that create reproducible reports can significantly reduce the time spent by an engineer creating these reports allowing them to do a much thorough investigation with a larger scope. Through Shiny, engineers could conveniently have their parameters and recorded data processed and stored in a database by accessing a web link and filling out the required information within a user-friendly interface. Upon the generation of the report, accurate and properly formatted test reports, compliant to both the company and FDA regulatory standards, are produced through Rmarkdown and knitr knitting all the outputs with complete data analysis tools such as normality plots and process capability measurements to a word document that follows company required headers, footers, and headings. The reproducible report creation shown in this report can be extended to other types of test reports and protocols. The pilot phase that has been conducted has shown that complete report production has been decreased from 36 hours to an hour

rmarkdown rstudio Shiny Rstudio::conf Rstudio::conf(2020) Braulio Cuandon Ana Alyeska Santos RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Shiny Rmarkdown CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Andrew Mangano | Growth Hacking - Product Analytics at Scale using R and RStudio | RStudio (2020)

Salesforce is not only a cloud software solution out of the box, but also a highly customizable platform that can be modified for a wide range of use cases. In addition to complexity, customer trust is our #1 company value and customer data privacy is abstracted from everyone outside of the customer. Product and Growth Analytics is an emerging field separate from business analytics and data science and focuses on building software product that improve user retention and engagement. Companies like Facebook and AirBnB have robust data science teams focused on product analytics. At Salesforce however, given the scale, customization, and privacy values, product data science is not so straightforward. Utilizing R and Rstudio tools for collaboration and reproducible analytics, the Data Intelligence team is able to solve complex problems at enterprise scale. This talk will preview anonymized predictive and growth analytics work while also highlighting how we work and collaborate cross platform and languages (Python via reticulate)

reticulate rstudio Rstudio::conf(2020) Andrew Mangano RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Ian Lyttle | Small Team, Big Value: Using R to Design Visualizations | RStudio (2020)

Many R users can feel isolated due to the prevalence of Python or Tableau at their institutions. This talk will focus on how we use R to develop reference implementations of visualizations (using ggplot2), and to develop corporate-themed color maps (using the colorspace package) to bring value to the entire institution. Color maps can be translated into variety of formats, for Tableau, Qlik Sense, d3, etc., and deployed independently from R. For visualizations, our goal is to translate ggplot2 objects to Vega-Lite specifications, using a package we are developing: ggvega. Vega-Lite visualizations are web-native, and are rendered independently from R. Specifications can be designed to be extensible to new data, allowing them serve as templates, to be deployed and updated for use outside of R. Of course, despite isolation within an institution, our work with the larger R open-source communities provides a foundation on which to build; in fact, we have a lot of company and are having a lot of fun

ggplot2 rstudio Rstudio::conf(2020) Ian Lyttle RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Mine Çetinkaya-Rundel | Making the Shiny Contest | RStudio (2020)

In January 2019 RStudio launched the first-ever Shiny contest to recognize outstanding Shiny applications and to share them with the community. We received 136 submissions for the contest and reviewing them was incredibly inspiring and humbling. In this talk, we shine a spotlight on the backstage: the inspiration behind the contest, the process of evaluation, what we learned about Shiny developers and how we can better support them, and what we learned about running contests and how we hope to improve the Shiny Contest experience. We also highlight some of the winning apps as well as the newly revamped Shiny Gallery, which features many noteworthy contest submissions. Finally, we introduce the new process for submitting your apps to the Shiny Gallery and, of course, to Shiny Contest 2020! https://rstudio.com/resources/rstudioconf-2020/making-the-shiny-contest/

Mine Çetinkaya-Rundel

rstudio Shiny Rstudio::conf(2020) Mine Çetinkaya-Rundel RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Thomas Lin Pedersen | Extending your ability to extend ggplot2 | RStudio (2020)

The ggplot2 package continue to be one of the most used frameworks for producing graphics in R. While being extremely flexible, the package itself can be constrained by the different types of graphic elements and statistic transformations available. Instead of continuing to add new features, the development in recent years have focused on making ggplot2 extensible by other packages, thus distributing development and maintenance. Despite the best of intentions, ggplot2 can feel daunting to extend, due unusual idiosyncrasies, a foreign object system, and a partly obscured rendering model. This talk intend to remove the mystery of extending ggplot2, by describing the basic ways that it can be extended and showcasing a couple of simple extensions that can be build with very little code. Lastly, it will include discussions of some best practices and gotchas that may come in handy when you start out

Thomas Lin Pedersen

ggplot2 rstudio Rstudio::conf(2020) Thomas Lin Pedersen RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Tyson Barrett | List-columns in data.table | RStudio (2020)

The use of list-columns in data frames and tibbles is well documented (e.g. Bryan, 2018), providing a cognitively efficient way to organize results of complex data (e.g. several statistical models, groupings of text, data summaries, or even graphics) with corresponding data. For example, one can store student information within classrooms, player information within teams, or analyses within groups. This allows the data to be of variable sizes without overly complicating or adding redundancies to the structure of the data. In turn, this can improve the reliability to appropriately analyze the data. Because of its efficiency and speed, being able to use data.table to work with list-columns would be beneficial in many data contexts (e.g. to reduce memory usage in large data sets). Herein, I demonstrate how one can create list-columns in a data table using the by argument in data.table and purrr::map(). I compare the behavior of the data.table approaches to the dplyr::group_nest() function and tidyr::unnest(), two of the several powerful Tidyverse nesting and unnesting functions. Results using bench::mark() show the speed and efficiency of using data.table to work with list-columns

dplyr purrr rstudio tidyr tidyverse Rstudio::conf(2020) Tyson Barrett RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Henrik Bengtsson | Future: Simple Async, Parallel & Distributed Processing in R | RStudio (2020)

Future is a minimal and unifying framework for asynchronous, parallel, and distributed computing in R. It is designed for robustness, consistency, scalability, extendability, and adoptability - all in the spirit of “developer writes code once, user runs it anywhere”. It is being used in production for high-performance computing and asynchronous UX, among other things. In this talk, I will discuss common feature requests, recent progress we have made, and what is the pipeline

rstudio Rstudio::conf(2020) Henrik Bengtsson RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Dr. Carson Sievert | Reproducible Shiny apps with shinymeta | RStudio (2020)

Shiny makes it easy to take domain logic from an existing R script and wrap some reactive logic around it to produce an interactive webpage where others can quickly explore different variables, parameter values, models/algorithms, etc. Although the interactivity is great for many reasons, once an interesting result is found, it’s more difficult to prove the correctness of the result since: (1) the result can only be (easily) reproduced via the Shiny app and (2) the relevant domain logic which produced the result is obscured by Shiny’s reactive logic. The R package shinymeta provides tools for capturing and exporting domain logic for execution outside of a Shiny runtime (so that others can reproduce Shiny-based result(s) from a new R session)

Carson Sievert

rstudio Shiny shinymeta Rstudio::conf(2020) Dr. Carson Sievert RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Ben Joaquin | Data Science in Meatspace | RStudio (2020)

“The Data Science community is dominated by folks doing amazing work with data that starts in and never leaves cyberspace. This talk is about best practices and playbooks for doing data science that involves meatspace (the opposite of cyberspace) and why R is such a great language for working with data that originated in the physical world. While the concrete examples in this talk will mostly come from the manufacturing space, where I have the most experience, I believe the themes are relevant to many meatspace workflows. We’ll talk through effective playbooks that can help you navigate common tasks throughout the life-cycle of a project. We’ll also weave in how R’s glorious package ecosystem, including Tidyverse, can be combined with other languages like python, and with enterprise products like RStudio Connect to great effect. Specifically, we’ll discuss practices in these areas: best practices for data collection in meatspace the importance of quantifying measurement system error collecting the correct data for training computer vision models the rarely discussed cost of maintaining models in production”

rstudio tidyverse Rstudio::conf(2020) Ben Joaquin RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Aymen Waqar | Building an iPad dashboard using plumber & RStudio Connect in Pharma | RStudio (2020)

As companies are becoming aware of the need to embrace data-driven solutions, R has gained a huge momentum over recent years. Getting the insights to users has become a very important factor of Data Scientist work. While our world has advanced there is a need to build not only web applications, but also applications on mobile that are available offline. We would like to share with you how within months we have gone from nothing to a production-ready application that handles 500 concurrent users in healthcare. There are plenty of challenges to solve including restricted environments, internal processes and users availability. We will show you how to overcome them and iterate fast, navigating through complex infrastructure and integrating with proxy architecture to serve applications to end users in compliant manner. With RStudio Connect and Plumber you can deploy a scalable REST API that can feed insights to your users. This allows you to go one step further and implement native applications for tablets and smartphones. With the right tools, mindset and priorities you can achieve personal success by introducing a digital transformation within your organization, starting with something as small as converting a business critical Excel file that is slow, difficult to edit and maintain, to a robust application. Step by step your organization will evolve and become empowered by your insights uncovering even more untapped potential

plumber rstudio Rstudio::conf(2020) Aymen Waqar RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Karl Feinauer | Using Jupyter with RStudio Server Pro | RStudio (2020)

This talk is for R admins who want to learn how to set up Jupyter notebooks on RStudio Server Pro. We’ll cover prerequisites, basic configuration, best practices for management, Jupyter Lab, and more

rstudio Rstudio::conf(2020) Karl Feinauer RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

David Smith | MLOps for R with Azure Machine Learning | RStudio (2020)

David Smith | January 31, 2020 Azure Machine Learning service (Azure ML) is Microsoft’s cloud-based machine learning platform that enables data scientists and their teams to carry out end-to-end machine learning workflows at scale. With Azure ML’s new open-source R SDK and R capabilities, you can take advantage of the platform’s enterprise-grade features to train, tune, manage and deploy R-based machine learning models and applications. In this talk, the attendees will learn how to: •Carry out ML workflows using the authoring experience of their choice, from no-code to code-first options that include Azure ML’s drag-and-drop visual interface for defining workflows and RStudio Server on the Data Science Instance, a hosted VM workstation, for using the Azure ML R SDK from the RStudio browser-based interface. •Use the Azure ML R SDK to manage cloud resources and train, hyperparameter tune, and log and visualize metrics for their models at scale on Azure compute. •Build ML Pipelines in R for defining and orchestrating reusable and reproducible ML workflows. •Deploy, manage, and monitor their R ML models and applications as web services on Azure Container Instance and Azure Kubernetes Service, with an emphasis on robust DevOps and CI/CD for orchestrating and streamlining their end-to-end data science development lifecycle

rstudio Rstudio::conf(2020) David Smith RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Ron Keizer | Building a Medical Device with R | Posit (2020)

The InsightRX precision dosing platform tailors in-patient drug doses to individual patients’ characteristics and biomarkers, leveraging pharmacological models of drug metabolism and drug effects. These models are implemented in R, exposed through APIs, and called from a cloud-based web application

Rstudio::conf(2020) Ron Keizer RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Neal Richardson | Accelerating Analytics with Apache Arrow | RStudio (2020)

The Apache Arrow project is a cross-language development platform for in-memory data designed to improve system performance, memory use, and interoperability. This talk presents recent developments in the ‘arrow’ package, which provides an R interface to the Arrow C++ library. We’ll cover the goals of the broader Arrow project, how to get started with the ‘arrow’ package in R, some general concepts for working with data efficiently in Arrow, and a brief overview of upcoming features

Neal Richardson

rstudio Rstudio::conf(2020) Neal Richardson RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Rstats Open Source Oss Reticulate Apache Arrow

Ryan Timpe | Learning R with humorous side projects | RStudio (2020)

What should you name a new dinosaur discovery, according to neural networks? Which season of The Golden Girls should you watch when playing a drinking game? How can you build a LEGO set for the lowest price? R is constantly evolving, so as users, we’re constantly learning. Over the past few years, I’ve found that working on side projects is great for hands-on learning - and for me, the more absurd the project, the better. Side projects provide a safe, low-stakes environment to learn new packages and methodologies before using them in work or in production. Sharing those projects can help publicize the package and increase its accessibility, benefiting both the original author and future users. In this talk, I’ll share my experiences with side projects for learning state-of-the-art data science tools and growing as an R user, including how one project helped me land my dream job

rstudio Rstudio::conf(2020) Ryan Timpe RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Jay Campanell | A high school student’s journey to bring R into the classroom | Posit (2020)

My 8th-grade capstone project introduced me to R. The project was a data visualization about breakfast tacos. I used R and other web-based tools. My lightning talk will focus on my experience about using R for class projects and getting support from my parents to help integrate R into the classroom. I will show how students can get started when they don’t know how to use R. I will talk about the project’s toolkit, which includes Posit Cloud, Google Sheets, Chromebook, measurement tools, my phone, and how R is being used in my school. A 5-minute presentation in our Lightning Talks series

Rstudio::conf(2020) Jay Campanell RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Javier Luraschi | Updates on Spark, MLflow, and the broader ML ecosystem | RStudio (2020)

Originally posted at https://rstudio.com/resources/rstudioconf-2020/updates-on-spark-mlflow-and-the-broader-ml-ecosystem/

rstudio Rstudio::conf(2020) Javier Luraschi RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Bryan Lewis | Parallel computing with R using foreach, future, and other packages | RStudio (2020)

Steve Weston’s foreach package defines a simple but powerful framework for map/reduce and list-comprehension-style parallel computation in R. One of its great innovations is the ability to support many interchangeable back-end computing systems so that the same R code can run sequentially, in parallel on your laptop, or across a supercomputer. Recent new packages like future package define elegant new programming approaches that can use the foreach framework to run across a wide variety of parallel computing systems. This talk introduces the basics of foreach and future packages with examples using a variety of back-end systems including MPI, Redis and R’s default parallel package clusters

rstudio Rstudio::conf(2020) Bryan Lewis RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Gergely Daroczi | Getting things logged | RStudio (2020)

One of the greatest strengths of R is the ease and speed of developing a prototype (let it be a report or dashboard, a statistical model or rule-based automation to solve a business problem etc), but deploying to production is not a broadly discussed topic despite its importance. This hands-on talk focuses on best practices and actual R packages to help transforming the prototypes developed by business analysts and data scientist into production jobs running in a secured and monitored environment that is easy to maintain – discussing the importance of logging, securing credentials, effective helper functions to connect to database, open-source and SaaS job schedulers, dockerizing the run environment and scaling infrastructure

rstudio Rstudio::conf(2020) Gergely Daroczi RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Dr. Sydeaka Watson | Neural Networks for Longitudinal Data Analysis | RStudio (2020)

Longitudinal data (or panel data) arise when observations are recorded on the same individuals at multiple points in time. For example, a longitudinal baseball study might track individual player characteristics (team affiliation, age, height, weight, etc.) and outcomes (batting average, stolen bases, runs, strikeouts, etc.) over multiple seasons, where the number of seasons could vary across players. Neural network frameworks such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) can flexibly accommodate this data structure while preserving and exploiting temporal relationships. In this presentation, we highlight the use of neural networks for longitudinal data analysis with tensorflow and keras in R

rstudio tensorflow Rstudio::conf(2020) Dr. Sydeaka Watson RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Rob Hyndman | How Rmarkdown changed my life | RStudio (2020)

Over the last few years, Rmarkdown seems to have taken over my life, or at least my written communication. These days I use Rmarkdown to maintain my website, write my blog, write textbooks, write academic papers, prepare slides for talks, keep my CV up-to-date, help my students write theses, prepare university policy documents, write letters, prepare exams, write reports for clients, and more. I haven’t quite got to the point of using it for shopping lists, but perhaps that’s my next Rmarkdown template. I will reflect on the journey in getting to this point, what I’ve lost and what I’ve gained. I will also speculate on what might be next in the Rmarkdownification of my life

rmarkdown rstudio Rstudio::conf(2020) Rob Hyndman RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Therese Anders | Peer review in data science courses | RStudio (2020)

Peer review enables instructors of large data science classes to provide substantive feedback to students beyond what is feasible with standard code review via automated grading and continuous integration. It facilitates peer learning, which is shown in literature to have positive learning outcomes, and can reduce the burden of grading by course staff. The ghclass package provides a suite of functions to manage courses via GitHub repositories. The package has recently been supplemented with the functionality to implement peer review. Developed during my 2019 summer internship with RStudio in collaboration with my mentor Mine Çetinkaya-Rundel, the peer review functions in ghclass interface with the GitHub API to create review repositories, move files between authors and reviewers, submit feedback, and collect grades. In this presentation, I will give a demonstration of the peer review functions in ghclass. A set of six functions allows instructors to 1) create a random review roster, 2) set up the review repository infrastructure within a GitHub organization, 3) move assignments from authors to reviewers, 4) collect grades, 5) return the feedback, and 6) obtain a rating of the review from the authors. I reflect on the pedagogy of implementing peer review in introductory data science classes and talk about lessons learned from a real-world test run of the package in the Fall semester 2019 at the University of Edinburgh, conducted by Mine Çetinkaya-Rundel. The presentation highlights ghclass as an R command-line based, open source, low profile, and powerful solution to enable peer review in classes ranging from a size of two to approximately 400 students. A 5 minute presentation in our Lightning Talks series

Mine Çetinkaya-Rundel

rstudio Rstudio::conf(2020) Therese Anders RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Frank Harrell | R for Graphical Clinical Trial Reporting | RStudio (2020)

For clinical trials a good deal of effort goes into producing both final trial reports and interim reports for data monitoring committees, and experience has shown that reviewers much prefer graphical to tabular reports. Interactive graphical reports go a step further and allow the most important information to be presented by default, while inviting the reviewer to drill down to see other details. The drill-down capability, implemented by hover text using the R plotly package, allows one to almost entirely dispense with tables because the hover text can contain the part of a table that pertains to the reviewer’s current focal point in the graphical display, among other things. Also, there are major efficiency gains by having a high-level language for producing common elements of reports related to accrual, exclusions, descriptive statistics, adverse events, time to event, and longitudinal data. This talk will overview the hreport package, which relies on R, RMarkdown, knitr, plotly, Hmisc, and HTML5. RStudio is an ideal report development environment for using these tools

rmarkdown rstudio Rstudio::conf(2020) Frank Harrell RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Dr. Tyler Morgan-Wall | 3D ggplots with rayshader | RStudio (2020)

Learn how a single line of code can transform your data visualizations into stunning 3D using the rayshader package. In this talk, I will show how you can use rayshader to create beautiful 3D figures and animations to help promote your research and analyses to the public. Find out how to use principles of cinematography to take users on a 3D tour of your data, scripted entirely within R. Leaving the 3D pie charts in the pantry at home, I will discuss how to build interpretable, engaging, and informative plots using all three dimensions

rstudio Rstudio::conf(2020) Dr. Tyler Morgan-Wall RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Rstats Open Source Oss Reticulate

Katie Masiello | Professional Case Studies | RStudio (2020)

The path to becoming a world-class, data-driven organization is daunting. The challenges you will likely face along the way can be thorny, and in some cases, seem outright impossible to overcome. How do you get teams that traditionally butt heads, such as IT and data science, to complement each other and work in unison? How can you efficiently scale the scope and reach of your data products as requirements change? Your time should be spent doing truly valuable work instead of updating charts and reports. How do you prevent the support structure behind your platform from toppling like a house of cards? Despite these challenges, we think that the end result is worth it: an organization that is equipped to make important decisions, with confidence, using data analysis that comes from a sustainable environment. We see this outcome every day

rstudio Rstudio::conf(2020) Katie Masiello RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Yihui Xie | One R Markdown Document, Fourteen Demos | RStudio (2020)

R Markdown is a document format based on the R language and Markdown to intermingle computing with narratives in the same document. With this simple format, you can actually do a lot of things. For example, you can generate reports dynamically (no need to cut-and-paste any results because all results can be dynamically generated from R), write papers and books, create websites, and make presentations. In this talk, I’ll use a single R Markdown document to give demos of the R packages rmarkdown, bookdown for authoring books ( https://bookdown.org ), blogdown for creating websites ( https://github.com/rstudio/blogdown), rticles for writing journal papers ( https://github.com/rstudio/rticles), xaringan for making slides ( https://github.com/yihui/xaringan), flexdashboard for generating dashboards ( https://github.com/rstudio/flexdashboard), learnr for tutorials ( https://github.com/rstudio/learnr), rolldown for storytelling ( https://github.com/yihui/rolldown), And the integration between Shiny and R Markdown. To make the best use of your time during the presentation, I recommend you to take a look at the rmarkdown website in advance: https://rmarkdown.rstudio.com

blogdown bookdown bookdown.org flexdashboard learnr rmarkdown rstudio rticles Shiny Rstudio::conf(2020) Yihui Xie RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Brendan Graham | Using R Bridge the Gap in Electronic Medical Record Reporting | Posit (2020)

Electronic Medical Records (EMRs) are a treasure trove of information, but tend to fall disappointingly short when it comes to visualizing and reporting data in a user friendly and intuitive manner. Building reports in an EMR can be a frustrating experience; the developer is at the mercy of how the data is stored within the EMR and the available EMR reporting tools can be bland and uninspiring. But reporting on data in the EMR doesn’t have to be this way! Combining the data-rich EMR with R’s robust reporting capabilities benefits both developers and consumers of data. This talk will describe how a cross-departmental project team uses an internal R package, RMarkdown reports scheduled via R Studio Connect, and an interactive flexdashboard app to quickly implement solutions to gaps in the reporting capabilities of the EMR. The flexibility of R relative to EMR reporting tools facilitates a design thinking approach to reporting allowing for more user input, customization and quick iteration. Furthermore, the web-based app we developed is able to be embedded within the EMR itself allowing for a more streamlined workflow

flexdashboard rmarkdown Rstudio::conf(2020) Brendan Graham RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Miriah Meyer | Effective Visualizations | RStudio (2020)

Originally posted to https://rstudio.com/resources/rstudioconf-2020/effective-visualizations/

rstudio Rstudio::conf(2020) Miriah Meyer RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Daniel Falbel | What’s new in TensorFlow for R | RStudio (2020)

TensorFlow is the most popular open-source platform for machine learning and it’s ecosystem is evolving incredibly fast. In this talk we will explore what’s new in TensorFlow 2.0 as well as how to build data pre-processing pipelines using the tfdatasets package and how to use pre-trained models with tfhub

Daniel Falbel

rstudio tensorflow tfdatasets Rstudio::conf(2020) Daniel Falbel RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Lionel Henry | Interactivity and Programming in the Tidyverse | RStudio (2020)

In Tidyverse grammars such as dplyr you can refer to the columns in your data frames as if they were objects in the workspace. This syntax is optimised for interactivity and is a great fit for data analysis, but it makes it harder to write functions and reuse code. In this talk we present some advances in the tidy eval framework that make it easier to program around Tidyverse pipelines without having to learn a lot of theory

Lionel Henry

dplyr rstudio tidyverse Rstudio::conf(2020) Lionel Henry RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Colin Fay | Production-grade Shiny Apps with golem | RStudio (2020)

Shiny is an amazing tool when it comes to creating web applications with R. Almost anybody can get a small Shiny App in a matter of minutes, provided they have a basic knowledge of R. As of today, we can safely tell that it has become the de-facto tool for web application in the R world. Building a proof-of-concept application is easy, but things change when the application becomes larger and more complex, and especially when it comes to sending that app to production—until recently there hasn’t been any real framework for building and deploying production-grade Shiny Apps. This is where ‘golem’ comes into play: offering Shiny developers an opinionated framework for creating production-ready Shiny Applications. With ‘golem’, Shiny developers now have a toolkit for making a stable, easy-to-maintain, and robust for production web application with R. ‘golem’ has been developed to abstract away the most common engineering tasks (for example, module creation, addition of external CSS or JavaScript file, …), so you can focus on what matters: building the application. And once your application is ready to be deployed, ‘golem’ guides you through testing, and brings you tools for deploying to common platforms. In this talk, Colin and Vincent will present the ‘golem’ package, first talking about the “why ‘golem’?”, then presenting the general philosophy behind this framework, and help you get started building your first Shiny App with ‘golem’

rstudio Shiny Rstudio::conf(2020) Colin Fay Golem RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Jared Lander | R: Then and Now | RStudio (2020)

R has changed a lot since the meetup was founded 10 years ago. Back then we were using base graphics (or lattice) and the apply family of functions and we didn’t have pipes. At the time there was an impressive 1800 packages on CRAN, now there are over 15,000 extending R’s reach far beyond its traditional domain of statistics and machine learning into publishing, website building and video generation. The community has grown and changed dramatically during that time, with the New York meetup alone going from 25 to over 10,000 members. During this talk we go through a then-and-now of R code and community to palpably see how everything has changed

rstudio Rstudio::conf(2020) Jared Lander RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

William Chase | The Glamour of Graphics | RStudio (2020)

I see a lot of ugly charts. This is to be expected as I work with a lot of academics and data scientists, neither of whom have been trained in how to design attractive charts. I myself produced many ugly charts during my years as a research scientist, when the design process basically came down to random tweaking until things “looked good”. If only I could go back and tell young inexperienced me that there was a better way. In this talk, I will present that better way–a series of design principles that can take any chart from drab to fab. Rather than applying these techniques willy nilly, I will show how they form a layered “Glamour of Graphics” that is structured and can be easily applied to any chart. This Glamour of Graphics has some simple implementations in ggplot, where we will replace geoms, aesthetics, and scales with typography, color, and layout. Finally, I will discuss why looks matter when it comes to charts, and how by following the Glamour of Graphics you can design charts that are more persuasive and more accurately perceived

rstudio Rstudio::conf(2020) William Chase RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Riva Quiroga | The development of “datos” package for the R4DS Spanish translation| RStudio (2020)

Originally posted at https://rstudio.com/resources/rstudioconf-2020/the-development-of-datos-package-for-the-r4ds-spanish-translation/

rstudio Rstudio::conf(2020) Riva Quiroga RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Nima Safaian | Rpanda trading simulation - from an idea to a multi-user shiny app | RStudio (2020)

The idea of rpanda commodities trading simulation was many years in the making. As energy trading professionals working in the industry, we had developed insights around how to make risk/reward market calls, and what skills make someone an exceptional commodities trader. Traders are one of the most expensive seats in terms of monetizing value from the assets. We developed rpanda as a simulated environment which replicates closely how real-life physical commodities trading works in order to assist talent development and selection, both in academics and enterprise. My co-founder and I did not know how to design production-ready software, but we always had used R/Shiny for market analysis in our corporate jobs. Rather than hiring expensive app developers, we decided to do it ourselves. We used Rstudio development stack such as Rstudio Connect and open source tools, like plumber to turn our idea into a production-ready app that is used by University of Alberta classes. In this presentation, we share our journey, technical challenges, and how we overcame them

plumber rstudio Shiny Rstudio::conf(2020) Nima Safaian RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Carl Howe & Greg Wilson | Data Science Education in 2022 | RStudio (2020)

More people are learning data science every day, and there are more ways for them to learn than ever before. To understand where we are and where we might be going, this talk looks at what data science education could look like two years from now: far enough away that we can dream, but close enough that we can only dream a little. We explore the balance between automated and collaborative learning, different ways to deliver different kinds of lessons to different kinds of people, and ways in which our tools and practices could improve

rstudio Rstudio::conf(2020) Carl Howe Greg Wilson RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Jeff Leek | Data science education as a public health intervention in E. Baltimore | RStudio (2020)

Originally posted at https://rstudio.com/resources/rstudioconf-2020/data-science-education-as-an-economic-and-public-health-intervention-in-east-baltimore/

rstudio Rstudio::conf(2020) Jeff Leek RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Lauren Chadwick | Meet You Where You R | RStudio (2020)

At RStudio, we wake up and go to bed thinking about the positive impact that open source work and data science has had and can have on the world. To maximize this impact, we find three areas of investment absolutely critical to ensure our open source community keeps up with the world’s changes and outlives us all: 1. Find ways to make R more approachable. 2. Enable teams of all types & sizes (educational, professional, etc.) to be able to leverage the work they’re doing in R, and effortlessly communicate that work to others. 3. Extend the language so our open-source community can continue to be at the forefront of innovation, no matter their preference of tool or language

rstudio Rstudio::conf(2020) Lauren Chadwick RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Jesse Sadler | vctrs: Creating custom vector classes with the vctrs package | RStudio (2020)

The base R types of vectors enable the representation of an amazingly wide array of data types. There is so much you can do with R. However, there may be times when your data does not fit into one of the base types and/or you want to add metadata to vectors. vctrs is a developer-focused package that provides a clear path for creating your own S3-vector class, while ensuring that the classes you build integrate into user expectations for how vectors work in R. This presentation will discuss the why and how of using vctrs through the example of debkeepr, a package for integrating historical non-decimal currencies such as pounds, shillings, and pence into R. The presentation will provide a step-by-step process for developing various types of vectors and thinking through the design process of how vectors of different classes should work together

rstudio vctrs Rstudio::conf(2020) Jesse Sadler Vctrs RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Rebecca Barter | Becoming an R blogger | RStudio (2020)

Blogging is an excellent way to learn, improve your communication skills, and gain exposure in the R and data science communities. In this talk, I will discuss how and why I started blogging, and why you should too. I will guide you through choosing topics, writing your blog using RStudio and blogdown, hosting it on netlify, and sharing your blog with the world. This talk is for you if you’ve wanted to start a blog on R, data science, or to showcase your data analyses, but don’t know where to start. Materials: github https://github.com/rlbarter/rstudio-conf-2020-blogger-slides slides (pdf) https://github.com/rlbarter/rstudio-conf-2020-blogger-slides/blob/master/Becoming%20an%20R%20blogger

blogdown rstudio rstudio-conf Rstudio::conf(2020) Rebecca Barter RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Mike K Smith | Learn to teach, for goodness sake | RStudio (2020)

Even though I’ve completed 4 marathons, you certainly shouldn’t come to me for a training plan on how to achieve your goals for any race you’re about to run. So why do we often turn to “experienced R users” to help us learn R or train an organization? The RStudio certified trainers have been taught modern, evidence-based teaching practices which they use in planning training sessions in order to help delegates achieve THEIR learning goals effectively in a given time-frame. My talk will illustrate some of these teaching concepts and how, by becoming a certified trainer, you can help others learn about R more effectively. A 5 minute presentation in our Lightning Talks series

rstudio Rstudio::conf(2020) Mike K Smith RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Kelly Bodwin | Course Material Creation in the R Ecosystem | RStudio (2020)

In this talk, I will introduce a suite of three packages designed to aid course material creation in R: {demoR} for displaying code in knitted R Markdown with custom highlighting and formatting; {shindig} for shortcuts to creating simple educational Shiny apps; and {curricular} for easy creation of syllabi, homework exercises, exams, etc. Together, we will explore how these new tools - in conjunction with other existing resources - have been used to create a clean and consistent ecosystem for my R-based Introductory Statistics course. I will share some metrics on student outcomes, as well as my own experiences with the advantages and challenges in building the course. A 5-minute presentation in our Lightning Talks series

rstudio Shiny Rstudio::conf(2020) Kelly Bodwin RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Katherine Simeon | Every voice matters: An analysis of @WeAreRLadies | RStudio (2020)

As a rotating curation, @WeAreRLadies is a twitter account that has a different curator (i.e., tweeter) each week with a mission to highlight female and minority genders and their work in R. So far, curators have tweeted from 18 different countries and represent a variety of domains and levels of R expertise, ranging from R novices to those developing their own packages. With 45 R-Ladies curators to date, the account has become a popular R-related twitter resource, gaining more than 13,000 followers in the past year and hundreds of interactions each week. This talk will present a text analysis and reflection on over a year of Twitter text data from @WeAreRLadies. As the founder and maintainer of this account, I witness firsthand the bidirectional relationship between one’s learning journey and their use of R. In this talk, I will attempt to quantify this through a text analysis that explores how one’s experiences learning and using R relates to how they talk (or tweet) about it. By analyzing tweet text as well as other metrics provided by twitter (e.g., number of likes, replies, and clicks), I will showcase different ways curators have engaged with the R Twitter community and explore how account engagement has changed as the number of curators and followers continue to grow. I will also discuss how curators’ different areas of expertise have resulted in tweets and discussions that both demonstrate the variety of tools available in R, and spotlight unifying ideas and best practices in R programming. Finally, I will reflect on lessons learned and future directions for @WeAreRLadies, as well as its contribution to the R-Ladies Global initiative. Overall, this talk will discuss how diverse perspectives of @WeAreRLadies curators have enriched the conversations in the R Twitter community by validating different learning journeys and by promoting and amplifying underrepresented voices. A 5-minute presentation in our Lightning Talks series

rstudio Rstudio::conf(2020) Katherine Simeon RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Jon Harmon | Learning by Teaching: Mentoring at the R4DS Online Learning Community | RStudio (2020)

I host a weekly R Office Hour on the R4DS Online Learning Community Slack. By doing so, I have learned more about R than I ever would have thought. Here I'll present concrete examples of how R users can participate in the R community to expand their skills. R users of all skill levels can develop their skills by helping one another learn. Committing to help people with their coding challenges leads to the exploration of answers in areas you might otherwise not examine. A 5-minute presentation in our Lightning Talks series

rstudio Rstudio::conf(2020) Jon Harmon R4DS RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Javier Luraschi | Datasets in Reproducible Research with ‘pins’ | RStudio (2020)

Open source code is an essential piece in making science reproducible. Tools like ‘rmarkdown’ and GitHub facilitate running and sharing outcomes with colleagues and with the broad scientific community at large. However, it is less clear what tools should be used to retrieve, store and share datasets; while it is possible to make datasets part of your workflows today, it is usually hard and we are often left with manually sharing or downloading links to datasets. Not only that, but it’s also hard to share or discover datasets. In this talk we will introduce for the first time the ‘pins’ package. A package designed to: pin, discover and share resources. Meaning that, you can use ‘pins’ to simplify your data science workflows by easily fetching resources from GitHub, Kaggle, CRAN and RStudio Connect. We will present a ‘pin’ as a generic resource that can contain tabular datasets like CSVs, unstructured data like JSON files, image archives as ZIP files and so on. This talk will be highly interactive showing you how to get started by installing ‘pins’ from CRAN, retrieve and cache resources, share and discover useful and fun data resources to improve and enhance your day-to-day workflows

rmarkdown rstudio Rstudio::conf(2020) Javier Luraschi RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Hunter Glanz | The Five Principles of Data Science Education | RStudio (2020)

In this talk, I will outline a unified philosophy of data science education, and provide tips and tools for implementing these principles in the classroom using R and RStudio. Although data science as a professional discipline is well-established, its pedagogy is still in a period of growth. Even within a single university, multiple data science courses may be offered across different departments leading to inevitable redundancy of efforts amidst rich domain-specific innovations. My experience as an instructor in many such courses has lead me to five principles that transcend domain, context, and choice of language: reproducibility, communication, version control, practical application, and data ethics. For each of these full-stack themes, I will share examples of how to leverage tools in R and RStudio to enhance learning. A 5-minute presentation in our Lightning Talks series

rstudio Rstudio::conf(2020) Hunter Glanz RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Dr. Amelia McNamara | Making a tidy dress | RStudio (2020)

After at least a year of dreaming about it, I finally produced the #rstats / #Tidyverse dress of my dreams. This involved designing fabric, getting it custom printed, making a pattern from an existing garment, and sewing the dress. I learned a lot of useful lessons during this project, including "do unit tests" (make a practice dress) and "document your work" (get your BFF to take pictures of you)

rstudio tidyverse Rstudio::conf(2020) Dr. Amelia McNamara RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Davis Vaughn | Sliding Windows and Calendars | RStudio (2020)

A number of R packages exist to make computing moving averages on a single numeric series straightforward. But generally “real” life is much messier than that! Try computing a moving average over a twenty-day sliding window when you have a time series with missing data. Oh! By the way, you should also skip over weekends when looking back twenty days. And you know that random holiday that your company celebrates that no one else does? Skip over that too. These are hard but realistic problems, and until now there has been a lack of tools necessary to solve them. In this talk, I’ll present two packages designed to tackle these issues, slide and almanac. slide is a package designed to perform arbitrary sliding window calculations. The simplest example of this would be a moving average. What makes slide unique is its support for sliding relative to an index, such as a date vector, which allows you to correctly compute the boundaries of that twenty day window. almanac is package for creating custom business calendars, and then adjusting dates relative to them. Inspired by lubridate, almanac allows you to shift dates by a set number of “business” days while respecting the weekends and holidays defined by a user-specified calendar. For example, shifting a Friday forward by 1 business day would land on a Monday, unless that Monday happened to be a holiday, in which case the next business day would actually be Tuesday. Together, slide and almanac provide the tooling necessary to solve the problem mentioned earlier. Additionally, because slide works with any arbitrary function, we can use the same procedure to compute rolling regressions, cumulative sums, and any other sliding computation. A 5-minute presentation in our Lightning Talks series

lubridate rstudio Rstudio::conf(2020) Davis Vaughn RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Colin Rundel | livecode: broadcast your live coding sessions from and to RStudio | RStudio (2020)

In this talk we will demonstrate livecode, a new R package for broadcasting code for live code demonstrations. This package implements a simple webserver (using httpuv) to dynamically publishes the content of a code file (i.e..R or.Rmd) as you edit it live. This enables your students to have near realtime access to your code as you write it. The broadcast file can be viewed with any webbrowser but the package is specifically designed to be used within RStudio leveraging its builtin viewer. This gives students have direct access to the shared code within the IDE, allowing direct copying into their own source files and/or the console and thereby improving their ability to interact and experiment with your code. A 5-minute presentation in our Lightning Talks series

httpuv rstudio Rstudio::conf(2020) Colin Rundel RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Caroline Ledbetter | Rproject templates to automate and standardize your workflow | RStudio (2020)

Many teams and organizations have tasks and structures that are standard across projects. Lack of consistency and documentation can lead to lost productivity when team members join collaborations or previous work is consulted by your future self. Setting up folder structures can be particularly tedious. This talk will demonstrate using Rstudio project templates as part of an organizational package to automatically setup file structures, establish git repositories and add standardized readme files. It will also show how including report templates for Rmarkdown files can lead to more consistent and professional reports. Project info can be optionally stored so that project information can be easily added automatically to the top of reports and included in snippets for code file headers. Creating standard, easy to implement documentation and procedures can be particularly effective in encouraging skeptical collaborators to use git and Rmarkdown. Organizational packages can also be a great place to house functions that are specific and common to an organizations needs. The talk will showcase this functionality using the CIDAtools package that we developed. While the CIDAtools package was developed to address issues that sometimes arise from the less structured environment of academia, the tools presented can be equally useful in an industry setting. A 5-minute presentation in our Lightning Talks series

rmarkdown rstudio Rstudio::conf(2020) Caroline Ledbetter RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Athos Damiani | Sound annotation with Shiny and wavesurfer | RStudio (2020)

We observed a huge improvements of Machine Learning tools but the main effort were to help at post annotated dataset step. We still struggle to build a trusty pipeline to make these annotations. The package wavesurfer brings to R users the ability to annotate audio files with ease and reliability, exploring the friendly user interface of Shiny to make this hard and laborious part of the project more joyful and efficient. A 5-minute presentation in our Lightning Talks series

rstudio Shiny Rstudio::conf(2020) Athos Damiani RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Amanda Gadrow | Lessons about R I learned from my cat | RStudio (2020)

Forming good development habits for R projects is pretty straight-forward if you follow the lessons I’ve learned from my cat, whose advice includes “be lazy”, “keep your claws sharp”, and “land on your feet”. Attendees of this talk will learn how to make life easier on colleagues and their future selves by using simple software engineering best practices to build their current projects. Each point will come with cat photos and code samples, the two best parts of the Internet!

rstudio Rstudio::conf(2020) Amanda Gadrow RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Jenny Bryan | Object of type ‘closure’ is not subsettable | RStudio (2020)

Your first “object of type ‘closure’ is not subsettable” error message is a big milestone for an R user. Congratulations, if there was any lingering doubt, you now know that you are officially programming! Programming involves considerably more troubleshooting and debugging than many of us expected (or signed up for). The ability to solve your own problems is an incredibly powerful stealth skill that is worth cultivating with intention. This talk will help you nurture your inner problem solver, covering both general debugging methods and specific ways to implement them in the R ecosystem

Jenny Bryan

rstudio Jenny Bryan Rstudio::conf(2020) RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Emily Riederer | RMarkdown Driven Development | RStudio (2020)

RMarkdown enables analysts to engage with code interactively, embrace literate programming, and rapidly produce a wide variety of high-quality data products such as documents, emails, dashboards, and websites. However, RMarkdown is less commonly explored and celebrated for the important role it can play in helping R users grow into developers. In this talk, I will provide an overview of RMarkdown Driven Development: a workflow for converting one-off analysis into a well-engineered and well-designed R package with deep empathy for user needs. We will explore how the methodical incorporation of good coding practices such as modularization and testing naturally evolves a single-file RMarkdown into an R project or package. Along the way, we will discuss big-picture questions like “optimal stopping” (why some data products are better left as single files or projects) and concrete details such as the {here} and {testthat} packages which can provide step-change improvements to project sustainability

rmarkdown rstudio testthat Emily Riederer Rstudio::conf(2020) RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Claus Wilke | Spruce up your ggplot2 visualizations with formatted text | RStudio (2020)

The ggtext package provides various functions to add formatted text to ggplot2 figures, both in the form of plot or axis labels and in the form of text labels or text boxes inside the plot panel. Text formatting can be achieved through a small subset of markdown, HTML, and CSS directives. Features currently supported include italics, bold, super- and sub-script, as well as changing font size, font family, and color. Basic support for adding images to formatted text is also available

ggplot2 rstudio Claus Wilke Rstudio::conf(2020) Ggtext RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Jim Hester | Azure Pipelines and GitHub Actions | RStudio (2020)

Open source R packages on GitHub often take advantage of continuous integration services to automatically check their packages for errors. This is very useful to catch things quickly, as well and increasing confidence for proposed changes, as the Pull Requests can be checked before they are merged. Travis-CI and Appveyor are the most popular current methods. However newer services, Azure Pipelines and GitHub Actions, show promise for being more powerful and simpler to configure and debug. I will discuss these services and demonstrate some of their capabilities and how to configure them for your own use in packages and reports

rstudio Jim Hester Rstudio::conf(2020) RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Larry Fenn | Journalism with RStudio, R, and the tidyverse | RStudio (2020)

The Associated Press data team primarily uses R and the Tidyverse as the main tool for doing data processing and analysis. In this talk, some of the technology behind the published stories will be showcased: - Using dbplyr to work off a hosted database containing 380 million opioid records to identify “pill mills”. - Using open-sourced AP style templates for R Markdown and ggplot to quickly produce graphics and reports off breaking news. - Using R Markdown and htmlwidgets to give reporters and editors interactive reports to identify reporting leads

dbplyr rstudio tidyverse Larry Fenn Rstudio::conf(2020) RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

George Kastrinakis | Building a data science pipeline for the FT with RStudio Connect | RStudio

Talk from rstudio::conf(2020) We have recently implemented a new Data Science workflow and pipeline, using RStudio Connect and Google Cloud Services. This has vastly decreased our pipeline complexity, allowing us to bring our models and products into scheduled production more quickly. In addition, our workflow, working closely together as a team on all projects on a regular two-week sprint cycle, has increased the range of projects we have been able to take on and complete. To detail some of the key lessons we’ve learned (and some of the difficulties!), we’ll walk you through one of our recent sprints, where we productionalised the generation of a suite of behavioural and demographic features so that they can be more easily plugged in to a range of models and used across the business by the FT’s platform and product teams

rstudio George Kastrinakis Rstudio::conf(2020) RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Sharla Gelfand | Don’t repeat yourself, talk to yourself! Reporting with R | RStudio (2020)

If you’re responsible for analyses that need updating or repeating on a semi-regular basis, you might find yourself doing the same work over and over again. The principle of “don’t repeat yourself” from software engineering motivates us to use functions and packages, the core of repetition in the R universe. For analyses, it can be difficult to know how to use this principle and move beyond “copying and pasting scripts and changing the data file and the object names and updating the dates and results in RMarkdown”, especially when there’s some element of human intervention required, whether it be for validating assumptions or cleaning artisanal data. This talk will focus on those next steps, showcasing opportunities to stop repeating yourself and instead anticipate the needs of and communicate effectively with your future self (or the next person with your job!) using project-oriented workflows, clever interactivity, templated analyses, functions, and yes, your own packages

rmarkdown rstudio Sharla Gelfand Rstudio::conf(2020) RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Ian Cook | Bridging the Gap between SQL and R | RStudio (2020)

Ian Cook | January 31, 2020 Like it or not, SQL is the closest thing we have to a universal language for working with structured data. Celebrating its 50th birthday in 2020, SQL today integrates with thousands of applications and has millions of users worldwide. Data analysts using SQL represent a large audience of potential R users motivated to expand their data science skills. But learning R can be frustrating for SQL users. One major frustration is the inability to directly query R data frames with SQL SELECT statements. Eager to use R for tasks that are not possible with SQL (like data visualization and machine learning), these users are dismayed to find that they must first learn an unfamiliar syntax for data manipulation. The popularity of the sqldf package (which automatically exports an R data frame into an embedded database, then runs a SQL query on it) demonstrates this frustration. But now there is a way to directly query an R data frame without moving the data out of R. In this talk, I introduce tidyquery, a new R package that runs SQL queries directly on R data frames. tidyquery is powered by dplyr and by queryparser, a new pure-R, no-dependency SQL query parser

dplyr rstudio Ian Cook Rstudio::conf(2020) Sql RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Winston Chang | Asynchronous programming in R | RStudio (2020)

Writing regular R code is straightforward: you tell R to do something, it does it, and then it returns control back to you. This is called synchronous programming. However, if you use R to coordinate threads, processes, or network communication, the regular model may be unable to do what you want, or it may only be able to do it with a significant performance penalty. In this talk I'll explain how asynchronous programming with the later package can handle these kinds of programming problems. I'll also show how to provide a synchronous interface for asynchronous code, so that users will have a simple, familiar way to use your code. Materials - github repo

Winston Chang

rstudio Winston Chang Rstudio::conf(2020) RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Nick Strayer | Stochastic Block Models with R: Statistically rigorous clustering | RStudio (2020)

Nick Strayer | January 31, 2020 Often a machine learning research project starts with brainstorming, continues to one-off scripts while an idea forms, and finally, a package is written to disseminate the product. In this talk, I will share my experience rethinking this process by spreading the package writing across the whole process. While there are cognitive overheads involved with setting up a package framework, I will argue that these overheads can serve as a scaffolding for not only good code but robust research practices. The result of this experiment is the SBMR package: a native R package written to fit and investigate the results of Bipartite Stochastic Block Models that forms the backbone of my PhD dissertation. By going over the ups and downs of this process, I hope to leave the audience with inspiration for moving the package writing process closer to the start of their projects and melding research and code more closely to improve both

Nick Strayer

rstudio Nick Strayer Rstudio::conf(2020) RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

J.J. Allaire | Open Source Software for Data Science | RStudio (2020)

Open Source Software for Data Science J.J. Allaire | February 1, 2020 Open-source software is fundamentally necessary to ensure that the tools of data science are broadly accessible, and to provide a reliable and trustworthy foundation for reproducible research. This talk will delve into why open source software is so important and discuss the role of corporations as stewards of open source software. I’ll also talk about how RStudio is structured and organized to pursue its mission of creating open source software for data science. About the speaker J.J. Allaire - JJ Allaire is a software engineer and entrepreneur who has created a wide variety of products including ColdFusion Open Live Writer Lose It! and RStudio

JJ Allaire

rstudio J.J. Allaire Rstudio::conf(2020) RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Kara Woo | Boxplots: a case study in debugging and perseverance | RStudio (2019)

Come on a journey through pull request #2196. What started as a seemingly simple fix for a bug in ggplot2’s box plots developed into an entirely new placement algorithm for ggplot2 geoms. This talk will cover tips and techniques for debugging, testing, and not smashing your computer when dealing with tricky bugs. VIEW MATERIALS https://github.com/karawoo/2019-01-17-rstudioconf About the Author Kara Woo Kara is a research scientist in data curation at Sage Bionetworks, where she helps other researchers document and share their data. She has previously worked as an information manager at Washington State University and at the National Center for Ecological Analysis and Synthesis (NCEAS), where she combined data management with fieldwork at a remote Siberian lake. Kara is an enthusiastic R programmer, and collects data visualizations gone beautifully wrong on a blog called accidental aRt

ggplot2 rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forecast Ggplot2 Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Kara Woo Tibble Rstats Statistics Debugging Programming Education

Kelly O’Briant | Configuration management tools for the R admin | RStudio (2019)

This talk will feature an introduction to configuration management tools for the Analytic Administrator. An analytic admin is someone who is invested in continually improving analytic infrastructure, advocates for best practices in data product deployments, and acts to adopt DataOps philosophies in their organization. One of the biggest challenges for an analytic admin can be figuring out how to help IT groups develop core competencies around the management of R tooling. When IT groups are unfamiliar with R, they might lean heavily on the analytic admin for guidance or resist adoption entirely. Data science teams that rely on delivering results through integrated R based solutions can get blocked when they lack the full support of IT. I’ll present a roadmap for how analytic admins can create custom teaching tools for introducing the R toolchain. Using these strategies, a dash of creativity, and a little bit of empathy, I hope you can get the IT buy-in you’ll need to make R a fully legitimate part of your organization. About the Author Kelly O’Briant Kelly is Solutions Engineer for RStudio and also an organizer of the Washington DC chapter of R-Ladies Global. It’s an R users group for lady-folk and friends

rstudio RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Kelly O'briant

Edgar Ruiz | Databases using R The latest | RStudio (2019)

Learn about the latest packages and techniques that can help you access and analyze data found inside databases using R. Many of the techniques we will cover are based on our personal and the community’s experiences of implementing concepts introduced last year, such as offloading most of the data wrangling to the database using dplyr, and using the RStudio IDE to preview the database’s layout and data. Also, learn more about the most recent improvements to the RStudio products that are geared to aid developers in using R with databases effectively. VIEW MATERIALS https://github.com/edgararuiz/databases-w-r About the Author Edgar Ruiz Edgar is the author and administrator of the https://db.rstudio.com web site, and current administrator of the [sparklyr] web site: https://spark.rstudio.com. Author of the Data Science in Spark with sparklyr cheatsheet. Co-author of the dbplyr package and creator of the dbplot package

Edgar Ruiz

dbplyr dplyr rstudio Edgar Ruiz RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats SparklyR

Jonathan McPherson | New language features in RStudio | RStudio (2019)

RStudio 1.2 dramatically improves support for many languages frequently used alongside R in data science projects, including SQL, D3, Stan, and Python. In this talk, you’ll learn how to use RStudio 1.2’s new language features to work more efficiently and fluidly in multi-lingual projects. VIEW MATERIALS https://github.com/rstudio/rstudio-conf/tree/master/2019/RStudio_1.2_Language_Features--Jonathan_McPherson About the Author Jonathan McPherson Jonathan is a software engineer at RStudio working on the IDE. In the past, he’s written Web applications at a nuclear site in the desert, exploratory information visualization systems at UC Davis, and features for flagship Office products and modern web applications at Microsoft

rstudio rstudio-conf RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Jonathan McPherson

Jeffrey Arnold | Solving R for data science | RStudio (2019)

While teaching a course using “R for Data Science”, I wrote a complete set of solutions to its exercises and posted them on GitHub. Then other people started finding them. And now I’m here. In this talk, I’ll discuss why I did it, and what I learned from the process, both what I learned about the Tidyverse itself, and what I learned from teaching it. About Jeffrey: I am formerly an Assistant Professor of Political Science of Political Science University of Washington and Core Faculty Member in the Center for Statistics and the Social Sciences, an Instructor of Political Science and QuanTM Pre-Doctoral Fellow at Emory University. I received Ph.D. in political science at the University of Rochester. Prior to graduate studies, I was a Research Associate/Economist in the Money and Payments Studies research group at the Federal Reserve Bank of New York

rstudio tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Jeffrey Arnold

Wes McKinney | Ursa Labs and Apache Arrow in 2019 | RStudio (2019)

Learn more about what’s happening at URSA labs at https://wesmckinney.com/archives.html

rstudio Wes McKinney RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Amelia McNamara | Working with categorical data in R without losing your mind | RStudio (2019)

Categorical data, called “factor” data in R, presents unique challenges in data wrangling. R users often look down at tools like Excel for automatically coercing variables to incorrect datatypes, but factor data in R can produce very similar issues. The stringsAsFactors=HELLNO movement and standard tidyverse defaults have moved us away from the use of factors, but they are sometimes still necessary for analysis. This talk will outline common problems arising from categorical variable transformations in R, and show strategies to avoid them, using both base R and the tidyverse (particularly, dplyr and forcats functions). VIEW MATERIALS http://www.amelia.mn/WranglingCats.pdf (related paper from the DSS collection) http://bitly.com/WranglingCats https://peerj.com/collections/50-practicaldatascistats/ About the Author Amelia McNamara My work is focused on creating better tools for novices to use for data analysis. I have a theory about what the future of statistical programming should look like, and am working on next steps toward those tools. For more on that, see my dissertation. My research interests include statistics education, statistical computing, data visualization, and spatial statistics. At the moment, I am very interested in the effects of parameter choices on data analysis, particularly data visualizations. My collaborator Aran Lunzer and I have produced an interactive essay on histograms, and an initial foray into the effects of spatial aggregation. I talked more about spatial aggregation in my 2017 OpenVisConf talk, How Spatial Polygons Shape Our World

dplyr forcats rstudio tidyverse RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Amelia Mcnamara

Tyler Morgan-Wall | 3D mapping, plotting, and printing with rayshader | RStudio (2019)

Long form discussion: https://www.tylermw.com/3d-printing-rayshader/

rstudio Tyler Morgan-Wall RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Panel | Growth & change of careers, organizations & responsibility in data science | RStudio (2019)

Hosted by Eduardo Arino de la Rubia, Instagram With: Hilary Parker, Data Scientist, Stitch Fix Karthik Ram, Data Science Fellow, UC Berkeley Angela Bassa, Director of Data Science, iRobot Tracy Teal, Executive Director, Carpentries About the Author Eduardo Arino de la Rubia Technologist and Data Scientist driven to create software that people use, find useful, and pleasant. From programming through architecture, from green field to maintenance, software is interesting technologically, socially, and intellectually. I enjoy contributing to the process, either through leadership or individual effort, of creating software that is deployed joyfully and is as bug free as possible

rstudio Eduardo Arino De La Rubia Data Scientist Director of Data Science Hilary Parker Karthik Ram Angela Bassa Tracy Teal RStudio Data Science Machine Learning Python Stats Data Visualization Data Viz Ggplot Technology Coding Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Jeroen Ooms | A preview of Rtools 4.0 | RStudio (2019)

Rtools is getting a major upgrade. In addition to the latest gcc, it now includes a full build system and package manager to build, install, and distribute external c/c++/fortran libraries needed by R packages. Thereby it bridges the long-standing gap between Windows and MacOS/Linux with respect to the availability of high quality, up-to-date system libraries. In this talk, we will show how to build and install system libraries with Rtools, and manage your Rtools build environment. It should be interesting both for Windows users as well as non-Windows package authors that are interested in reducing the pain of making things work on Windows. VIEW MATERIALS https://resources.rstudio.com/rstudio-conf-2019/a-preview-of-rtools-4-0 About the Author Jeroen Ooms Postdoc hacker for @ropensci at UC Berkeley

Jeroen Ooms

rstudio rstudio-conf Jeroen Ooms Rtools RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Emily Robinson | Building an AB testing analytics system with R and Shiny | RStudio (2019)

Online experimentation, or A/B Testing, is the gold standard for measuring the effectiveness of changes to a website. While A/B testing is used at tens of thousands of companies, it can seem difficult to parse without resorting to expensive end-to-end commercial options. Using DataCamp’s system as an example, I’ll illustrate how R is actually a great language for building powerful analytical and visualization A/B testing tools. We’ll first dive into our open-source funneljoin package, which allows you to quickly analyze sequential actions using different types of behavioral funnels. We’ll then cover the importance of setting up health checks for every experiment. Finally, we’ll see how Shiny dashboards can help people monitor and quickly analyze multiple A/B tests each week. VIEW MATERIALS http://bit.ly/rstudio19 About the Author Emily Robinson I work at DataCamp as a Data Scientist on the growth team. Previously, I was a Data Analyst at Etsy working with their search team to design, implement, and analyze experiments on the ranking algorithm, UI changes, and new features. In summer 2016, I completed Metis’s three-month, full-time Data Science Bootcamp, where I did several data science projects, ranging from using random forests to predict successful projects on DonorsChoose.org to building an application in R Shiny that helps data science freelancers find their best-fit jobs. Before Metis, I graduated from INSEAD with a Master’s degree in Management (specialization in Organizational Behavior). I also earned my bachelor’s degree from Rice University in Decision Sciences, an interdisciplinary major I designed that focused on understanding how people behave and make decisions

rstudio Shiny Emily Robinson AB Testing RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Karthik Ram | A guide to modern reproducible data science with R | RStudio (2019)

Resources: https://github.com/karthik/rstudio2019

rstudio Karthik Ram RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Hilary Parker | Cultivating creativity in data work | RStudio (2019)

Traditionally, statistical training has focused primarily on mathematical derivations, proofs of statistical tests, and the general correctness of what methods to use for certain applications. However, this is only one dimension of the practice of doing analysis. Other dimensions include the technical mastery of a language and tooling system, and most importantly the construction of a convincing narrative tailored to a specific audience, with the ultimate goal of them accepting the analysis. These “softer” aspects of analysis are difficult to teach, perhaps more so when the field is framed as mathematics and often housed in mathematics departments. In this talk, I discuss an alternative framework for viewing the field, borrowing upon the past work in other fields such as design. Looking forward, we as a field can borrow from these fields to cultivate and hone the creative lens so necessary to the success of applied work. VIEW MATERIALS https://www.slideshare.net/mobile/hilaryparker/rstudioconf2019l About the Author Hilary Parker I’m a Senior Data Analyst at Etsy, where I help product teams with data-driven development, via experimentation, opportunity sizing and impact analysis. I got my Ph.D. from the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health, working with Jeff Leek. I studied genomics and built tools to help researchers use genomic technologies in personalized medicine applications. I graduated from Pomona College in 2008, where I double-majored in Mathematics and Molecular Biology. True to my liberal arts upbringing, I’m a passionate teacher. Most notably, I taught an introductory Biostatistics class at the American University of Armenia (and kept a pretty cool travel blog along the way)

rstudio Hilary Parker Stitch Fix RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Angela Bassa | Data science as a team sport | RStudio (2019)

How do you data science as a team sport? Oftentimes a data scientific initiative starts with just a single, lonesome data scientist. But when that germ of a team is successful and starts expanding, should the team be embedded in other disciplines or should it be centralized into its own function? Where should it live in the organizational structure? Should you focus on recruiting senior data scientists or is there a benefit to attracting junior talent as well? And in terms of capabilities, should you hold out for unicorns or hire several specialists to get all jobs done? Data scientists need to work on almost every aspect of a business, so how should a team composition set the data science discipline up for success? Great data scientists have career options and won’t abide bad managers for very long: if you want to retain them, you’ll need to care about their work, connect it to the business, and design a diverse, resilient, high-performing team. Materials: https://github.com/angelabassa/rstudioconf-2019

rstudio Angela Bassa Ange Bassa IRobot RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

James Blair | Democratizing R with Plumber APIs | RStudio (2019)

The Plumber package provides an approachable framework for exposing R functions as HTTP API endpoints. This allows R developers to create code that can be consumed by downstream frameworks, which may be R agnostic. In this talk, we’ll take an existing Shiny application that uses an R model and turn that model into an API endpoint so it can be used in applications that don’t speak R. VIEW MATERIALS https://bit.ly/2TXfFR5 About the Author James Blair James holds a master’s degree in data science from the University of the Pacific and works as a solutions engineer. He works to integrate RStudio products in enterprise environments and support the continued adoption of R in the enterprise. His past consulting work centered around helping businesses derive insight from data assets by leveraging R. Outside of R and data science, James’s interests include spending time with his wife and daughters, cooking, camping, cycling, racquetball, and exquisite food. Also, he never turns down a funnel cake

plumber rstudio Shiny James Blair RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Ian Fellows | Don’t let long running tasks hang users: introducing IPC for Shiny | RStudio (2019)

Long running tasks in Shiny are not cancelable and typically lock the user interface while running. This talk introduces the ipc package, which helps you build dynamic applications when non-trivial computations are involved. ipc allows your Shiny Async workers to be cancelable and communicate intermediate results/progress back to the user interface. About the Author Ian Fellows Dr. Ian Fellows is a professional statistician with research interests ranging over many sub-disciplines of statistics. His work in statistical visualization won the prestigious John Chambers Award in 2011, and in 2007-2008 his Texas Hold’em AI programs were ranked second in the world. Applied data analysis is his passion. He is accustomed to providing insightful analysis and operationalizing these analyses in enterprise systems using a variety of programming languages and tools including R, Python, Java, MapReduce and Spark

rstudio Shiny Ian Fellows RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Eric Nantz | Effective use of Shiny modules in application development | RStudio (2019)

As a Shiny application grows in scale, organizing code into reusable and streamlined components becomes vital to manage future enhancements and avoid unnecessary duplication. Shiny modules are customized R functions that are easily reused multiple times within an application by avoiding namespace collisions and assist with organizing the code base. Like R functions, modules can be simple utilities or elaborate pieces with multiple inputs and outputs. While the process of creating a module is uncomplicated, application developers can quickly encounter challenges including communication among modules, defining logical compositions, and avoiding hidden state modifications. In this talk, we will introduce practical principles and techniques developers can leverage to address these issues head-on such as documenting modules, passing parameters and return values effectively between modules, and how nesting modules enables dynamic user interfaces with minimal overhead. VIEW MATERIALS https://rpodcast.github.io/rsconf-2019 About the Author Eric Nantz I have a broad background in statistics, computer science, and system administration which gives me a unique set of skills for using state-of-the-art technology and techniques to accomplish important and innovative data analyses. In my professional role as a statistician, I support the design and analyses of clinical trials evaluating treatments for auto-immune disorders. I also perform statistical analyses of specialized biomarkers utilizing cutting-edge statistical software such as R and high-performance computing infrastructures. I am also the creator, producer, and host of the R-Podcast. The R-Podcast is dedicated to helping those who are new to statistical computing develop their skills and confidence in using the free and open-source statistical computing package called R to get their data analyses done

rstudio Shiny Eric Nantz RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Hao Zhu | Empowering a data team with RStudio addins | RStudio (2019)

RStudio addins provide a mechanism to extend RStudio in various ways. Addins can interact with the RStudio IDE through RStudio API. It can also provide users a graphical interface with the power of Shiny. In practice, we found it very useful for enhancing or streamlining interaction with data and computing infrastructure. In this talk, we will demonstrate how our team develops and uses RStudio addins to empower our work. You will see some internal tools created to help us manage database connections, and an addin which helps us access external cloud computing resources. We will also show an example of using the addins in rcrossref and citr to download and manage citation and literature databases during rmarkdown document development. VIEW MATERIALS https://github.com/hebrewseniorlife/addin_demo About the Author Hao Zhu Hao is a data analyst and software developer working at the Hinda and Arthur Marcus Institute for Aging Research. He completed his training at Boston University School of Medicine in the program on Clinical Investigation. His interests include research reproducibility, data visualization and machine learning. At the Marcus Institute, he works with different teams on various topics, ranging from smartphone motion sensors to MRI images, and helps researchers understand their data by creating analytical reports and web applications. At the same time, Hao leads the development of R packages in the Biostatistics Core. He has contributed multiple R packages to the open source R community, such as kableExtra and memor. He also has a passion for teaching and has mentored several students at the Marcus Institute

rmarkdown rstudio Shiny Hao Zhu Addins RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Amanda Gadrow | Getting it right: Writing reliable and maintainable R code | RStudio (2019)

How can you tell that your scripts, applications, and package functions are working as expected? Are you sure that when you make changes in one part of the code, it won’t break something in another part? Have you thought deeply about how the consumers of your code (including Future You) will use it, maintain it, fix it, and improve it? Code quality is essential not only for reliable results but also for your script’s maintainability and your users’ satisfaction. Quality can be measured in part with targeted testing, and fortunately, there are several effective and easy-to-use code testing tools available in R. This talk will discuss some of the most useful testing packages, covering both concepts and examples. VIEW MATERIALS https://github.com/rstudio/rstudio-conf/tree/master/2019/Testing_R_Code--Amanda_Gadrow About the Author Amanda Gadrow Amanda is a software engineer with many years’ experience writing automated test frameworks for enterprise software. She started learning R when she joined RStudio in 2016, and has been basking in its glory ever since. Amanda leads the QA and Support teams, and spends a significant amount of time analyzing customer data to improve the products and optimize support. She is a co-organizer of R-Ladies Columbus, and an avid musician on the side

rstudio rstudio-conf Amanda Gadrow RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Thomas Lin Pedersen | gganimate live cookbook | RStudio (2019)

Animation of data visualisation is becoming increasingly popular both as an attention grabber on social media and as a way to tell small data stories. gganimate is a package that extends ggplot2 for making animations and provides a grammar of animation on top of the grammar of graphics. This talk will quickly introduce gganimate, and then dive into a series of different animation and show how they were made and how they could be changed or expanded. Slides: https://data-imaginist.com/slides/rstudioconf2019 4 Resources: https://resources.rstudio.com/rstudio-conf-2019/gganimate-live-cookbook 4 Discussion https://community.rstudio.com/t/gganimate-live-cookbook-thomas-lin-pedersen-rstudio-conf-2019l-video/24852

Thomas Lin Pedersen

ggplot2 rstudio rstudio-conf Thomas Lin Pedersen RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Alan Dipert | Integrating React.js and Shiny | RStudio (2019)

React.js is a thriving JavaScript library that eases encapsulating and sharing sophisticated component libraries. The React.js ecosystem is filled with components for doing everything from color selection (react-color) to animation (react-spring). While it’s always been technically possible to integrate React.js components with Shiny applications, it hasn’t always been particularly obvious how. To make it easier, we augmented the excellent reactR package with functions specifically designed to make it easier to create new htmlwidgets, inputs, and outputs based on React.js components. In this talk, I will further motivate and demonstrate these new tools and do my best to empower the audience to try them out. About the Author Alan Dipert Alan is a software engineer on the Shiny team at RStudio. In the past, he’s helped build web applications, reporting pipelines, and many things between. When he’s not working, Alan likes to spend his time reading or being with his family

Shiny Team

rstudio Shiny Alan Dipert Reactjs React.js JavaScript RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Kevin Kuo | Introducing mlflow | RStudio (2019)

We introduce the R API for MLflow, which is an open source platform for managing the machine learning lifecycle. We demonstrate each component of the platform–Tracking, Projects, and Models–and describe how they can be leveraged in practical data science workflows. About the Author Kevin Kuo Kevin is a software engineer working on open source packages for big data analytics and machine learning. He has held data science positions in a variety of industries and was a credentialed actuary. He likes mixing cocktails and studying about wine

rstudio Kevin Kuo MLFlow RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats API

Rich Iannone | Introducing the gt package | RStudio (2019)

With the gt package, anyone can make great-looking display tables. Though the package is still early in development, you can do some really great things with it right now! I’ll walk through a few examples that touch upon the more common table-making use cases. These will include features like adding table parts, integrating footnotes, styling/transforming table cells, using tables in R Markdown documents, and even including gt tables in email messages. VIEW MATERIALS https://github.com/rich-iannone/presentations/tree/master/2019_01-19-rstudio_conf_gt About the Author Rich Iannone My background is in programming, data analysis, and data visualization. Much of my current work involves a combination of data acquisition, statistical programming, tools development, and visualizing the results. I love creating software that helps people accomplish things. I regularly update several R package projects (all available on GitHub). One such package is called DiagrammeR and it’s great for creating network graphs and performing analyses on the graphs. One of the big draws for open-source development is the collaboration that comes with the process. I encourage anyone interested to ask questions, make recommendations, or even help out if so inclined!

Rich Iannone

gt rstudio Rich Iannone RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Jim Hester | It depends: A dialog about dependencies | RStudio (2019)

Software dependencies can often be a double-edged sword. On one hand, they let you take advantage of others’ work, giving your software marvelous new features and reducing bugs. On the other hand, they can change, causing your software to break unexpectedly and increasing your maintenance burden. These problems occur everywhere, in R scripts, R packages, Shiny applications and deployed ML pipelines. So when should you take a dependency and when should you avoid them? Well, it depends! This talk will show ways to weigh the pros and cons of a given dependency and provide tools for calculating the weights for your project. It will also provide strategies for dealing with dependency changes, and if needed, removing them. We will demonstrate these techniques with some real-life cases from packages in the tidyverse and r-lib. VIEW MATERIALS https://speakerdeck.com/jimhester/it-depends About the Author Jim Hester Jim is a software engineer at RStudio working with Hadley to build better tools for data science. He is the author of a number of R packages including lintr and covr, tools to provide code linting and test coverage for R

covr lintr rstudio Shiny tidyverse Jim Hester Software Dependencies Dependencies RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Jenny Bryan | Lazy evaluation | RStudio (2019)

The “tidy eval” framework is implemented in the rlang package and is rolling out in packages across the tidyverse and beyond. There is a lively conversation these days, as people come to terms with tidy eval and share their struggles and successes with the community. Why is this such a big deal? For starters, never before have so many people engaged with R’s lazy evaluation model and been encouraged and/or required to manipulate it. I’ll cover some background fundamentals that provide the rationale for tidy eval and that equip you to get the most from other talks. VIEW MATERIALS https://github.com/jennybc/tidy-eval-context#readme About the Author Jenny Bryan Jenny is a recovering biostatistician who takes special delight in eliminating the small agonies of data analysis. She’s part of Hadley’s team, working on R packages and integrating them into fluid workflows. She’s been working in R/S for over 20 years, serves in the leadership of rOpenSci and Forwards, and is an Ordinary Member of the R Foundation. Jenny is an Associate Professor of Statistics (on leave) at the University of British Columbia, where she created the course STAT 545

Jenny Bryan

rlang rstudio tidyverse Jenny Bryan RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Jesse Sadler | Learning and using the tidyverse for historical research | RStudio (2019)

My talk will discuss how R, the tidyverse, and the community around R helped me to learn to code and create my first R package. My positive experiences with the resources for learning R and the community itself led me to create a blog detailing my experiences with R as a way to pass along the knowledge that I gained. The next step was to develop my first package. The debkeepr package integrates non-decimal monetary systems of pounds, shillings, and pence into R, making it possible to accurately analyze and visualize historical account books. It is my hope that debkeepr can help bring to light crucial and interesting social interactions that are buried in economic manuscripts, making these stories accessible to a wider audience. VIEW MATERIALS https://github.com/jessesadler/rstudioconf-2019-slides About the Author Jesse Sadler I am an early modern historian interested in the social and familial basis of politics, religion, and trade. I received a Ph.D. in European History from UCLA in 2015 and have taught courses on cultural and intellectual history of early modern Europe and the Atlantic. My research investigates the familial basis of the early modern capitalism through archival research on two mercantile families from Antwerp at the end of the sixteenth and beginning of the seventeenth century. I am currently working on a manuscript that argues for the significance of sibling relationships and inheritance in the development of early modern trade. My manuscript places concepts such as patriarchy, emotion, exile, and friendship at the heart of the efficacy of long-distance trade networks and the growth of capitalism

rstudio tidyverse Jesse Sadler Learn to Code RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Caitlin Hudon | Learning from eight years of data science mistakes | RStudio (2019)

Over the past eight years of doing data science, I’ve made plenty of mistakes, and I’d love to share them with you – including what I’ve learned and what I’d do differently with some hindsight. This talk will cover mistakes made during analyses (including communication when delivering results) team and infrastructure mistakes, plus some advice for incoming data scientists. About the Author Caitlin Hudon My name is Caitlin Hudon and I am lead data scientist at OnlineMedEd, a startup in Austin. I have about eight years of experience doing data science-y things in a variety of industries including IoT, marketing, higher education, non-profits, and start-ups. I am also the co-founder of R-Ladies Austin, founder of the ‘ALL the Ladies in Tech’ quarterly happy hour here in ATX, and a member of the Fall 2017 NASA Datanaut class. Outside of data, I love tacos (especially trading taco spots in Austin), traveling (I’ve been to all 50 states and am working on the continents), the Cubs (including Will Ferrell’s Harry Caray impressions), and live music

rstudio Caitlin Hudon RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Miles McBain | Our colour of magic The open sourcery of fantastic R packages | RStudio (2019)

What does it mean to say software is, to quote one Twitter user, ‘so f***ing magical!’? In the context of our popular community hobby of rating and sharing R packages, the term ‘magic’ seems reserved for our most powerful expressions of visceral approval. Why is this? And what does it say about how we value software? Can this magical quality be quantified? We will consider these questions in examination of magical specimens, and in the process reveal the surprising depths at which notions of magic are embedded in the R zeitgiest. VIEW MATERIALS https://github.com/MilesMcBain/rstudioconf_talk About the Author Miles McBain As an Applied Statistician Miles combines a theoretical statistical knowledge and computing expertise to help organizations understand their core business and their customers. Miles is a hacker at heart, which he channels into regular contributions to the open source and open science communities. In addition to commercial projects Miles is always interested in small data/statistics consulting jobs for start-ups and non-for-profits that enable him to expand his applied experience in areas such as as A/B testing, experimental design, and statistical power analysis. He does this mainly for the thrill of learning new domains and the opportunity to meet fascinating people

rstudio Miles McBain RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Max Kuhn | parsnip A tidy model interface | RStudio (2019)

parsnip is a new tidymodels package that generalizes model interfaces across packages. The idea is to have a single function interface for types of specific models (e.g. logistic regression) that lets the user choose the computational engine for training. For example, logistic regression could be fit with several R packages, Spark, Stan, and Tensorflow. parsnip also standardizes the return objects and sets up some new features for some upcoming packages. VIEW MATERIALS https://github.com/rstudio/rstudio-conf/tree/master/2019/Parsnip--Max_Kuhn About the Author Max Kuhn Dr. Max Kuhn is a Software Engineer at RStudio. He is the author or maintainer of several R packages for predictive modeling including caret, Cubist, C50 and others. He routinely teaches classes in predictive modeling at rstudio::conf, Predictive Analytics World, and UseR! and his publications include work on neuroscience biomarkers, drug discovery, molecular diagnostics and response surface methodology. He and Kjell Johnson wrote the award-winning book Applied Predictive Modeling in 2013

Max Kuhn

parsnip rstudio rstudio-conf tensorflow tidymodels Max Kuhn Parsnip RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Gabor Csardi | pkgman A fresh approach to package installation | RStudio (2019)

The main goals of pkgman is to make package installation fast and more reliable. This allows new, simpler and safer workflows, such as separate package libraries for projects. In this talk, we will show the features that make pkgman fast, convenient and reliable. Features that make pkgman fast: * Concurrency: pkgman performs all downloads, package builds and installations concurrently by default. * Metadata and package cache: pkgman caches all metadata and all downloaded and locally built packages in its cache. * Lazyness: pkgman only downloads and installs packages if needed. Features that make pkgman convenient: * BioC and GitHub packages are supported seamlessly. * Informative UI. pkgman can lay out the installation/update plan, that the user needs to confirm. It returns data about downloads, builds, installations, etc. Features that make pkgman reliable: * Dependency solver. pkgman makes sure that you end up in consistent, working state of dependencies. * Private library: pkgman’s own dependencies do not affect your regular package library, and vice versa. pkgman does not load any packages from your regular library. About the Author Gábor Csárdi Gábor is a software engineer at RStudio, working in Hadley’s team on R infrastructure packages

Gábor Csárdi

rstudio Gabor Csardi Pkgman RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Brooke Watson | R at the ACLU Joining tables to to reunite families | RStudio (2019)

Last year, over 2500 immigrant children were separated from their family while in government custody. Information about their status is scattered across several government agencies, and throughout the national class-action lawsuit “Ms. L vs ICE,” the Analytics team of the ACLU has been using R to join, deduplicate, validate, and analyze it. Using specifics of this case, this talk will address common challenges arising from human-generated data in spreadsheets. With generalizable examples, I will discuss data tidying, standardization, deduplication, and validation using the tidyverse, janitor, assertthat, and other packages. Finally, I will share best practices for requesting useful data from non-quantitative subject matter experts. About the Author Brooke Watson I am a Data Scientist at the ACLU, where I use code and statistics to support civil rights litigation and advocacy. Previously, I worked in public health and disease research, most recently as a Research Scientist with the EcoHealth Alliance. I completed my Master’s degree in epidemiology from the London School of Hygiene and Tropical Medicine and swam for Tennessee’s Lady Vols as an undergrad

rstudio tidyverse Brooke Watson ACLU RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Joe Rickert | R Consortium initiatives in medicine | Posit (2019)

R / Pharma and R / Medicine, two new R Consortium sponsored conferences that appeared in 2018, are both grass roots, community-level manifestations of fundamental trends driving the growth of R. R / Pharma organized as an R Consortium working group by R users at several prominent pharmaceutical companies demonstrated that R has reached sufficient critical mass in Pharma to be recognized as a technology important enough to explore inter-company cooperation, while the addition of Genetech/Roche to R Consortium shows the commitment of upper level management to migrate from SAS to R. - At the other, non-corporate end of the spectrum, the R / Medicine conference was well attended by practicing clinicians coming to R, and in some cases statistics and data analysis, for the first time in order to move forward with the practice of data driven medicine. - In this talk, I will present some highlights from these conferences, and other related R Consortium initiatives, describe some of the R-based projects that illustrate the kind of work being done, and indicate what to expect this year. About the Author Joseph Rickert Joseph is Posit’s “Ambassador at Large” for all things R, editor of the R Views blog, and Posit’s representative on the R Consortium’s board of directors. Joseph came to Posit via Revolution Analytics and Microsoft where he was a data scientist, blogger and community manager. Before joining Revolution Analytics Joseph worked as a statistician for a small healthcare economics consulting firm, and before that he held a variety of technical, marketing and sales positions while working for startups that spanned multiple industries including government contracting, local area networks, disk drives and test equipment. Joseph studied Classics and Mathematics as an undergraduate at Franklin & Marshall College and earned an M.A. in Humanities and an M.S. in Statistics from the California State University

Joe Rickert Pharma Medicine RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Mark Sellors | R in production | RStudio (2019)

With the increase in people using R for data science comes an associated increase in the number of people and organisations wanting to put models or other analytic code into “production”. We often hear it said that R isn’t suitable for production workloads, but is that true? In this talk, Mark will look at some of the misinformation around the idea of what “putting something into production” actually means, as well as provide tips on overcoming the obstacles put in your path. VIEW MATERIALS https://rinprod.com/ About the Author Mark Sellors Mark is the Head of Data Engineering at Mango Solutions as well as the author of the ‘Field Guide to the R Ecosystem’. He has more than a decade’s experience working with analytical computing environments, DevOps and Unix/Linux. He uses his experience to help Mango’s customers transform their analytic capabilities to ensure they can make the most of their data. Mark and his team are at the forefront of the data engineering field, deploying high performance analytical environments using a wide range of tools, such as R, Python, Spark, and cloud computing. He is experienced in the complete product life-cycle from initial ideas and proofs of concept through to development, test, release and production

rstudio Mark Sellors RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Garrett Grolemund | R Markdown The bigger picture | RStudio (2019)

Statistics has made science resemble math, so much so that we’ve begun to conflate p-values with mathematical proofs. We need to return to evaluating a scientific discovery by its reproducibility, which will require a change in how we report scientific results. This change will be a windfall to commercial data scientists because reproducible means repeatable, automatable, parameterizable, and schedulable. VIEW MATERIALS https://github.com/garrettgman/rmarkdown-the-bigger-picture About the Author Garrett Grolemund Garrett is a data scientist and master instructor for RStudio. He excels at teaching, statistics, and teaching statistics. He wrote the popular lubridate package and is the author of Hands On Programming with R and the upcoming book, Data Science with R, from O’Reilly Media. He holds a PhD in Statistics and specializes in Data Visualization

lubridate rmarkdown rstudio Garrett Grolemund Pvalues RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Karl Broman | R qtl2 Rewrite of a very old R package | RStudio (2019)

For nearly 20 years, I’ve been developing, maintaining, and supporting an R package, R/qtl, for mapping quantitative trait loci (genetic loci that contribute to variation in quantitative traits, such as blood pressure) in experimental crosses (such as in mice). It’s a rather large package, with 39k lines of R code, 24k lines of C code, and nearly 300 user-accessible functions. In the past several years, I’ve been working on rewriting the package, to better handle high-dimensional data and more complex experimental crosses. This has been a good opportunity to take advantage of many new tools, including Rcpp, Roxygen2, and testthat. I’ll describe my efforts to avoid repeating the mistakes I made the first time around. VIEW MATERIALS https://bit.ly/rstudio2019 About the Author Karl Broman Karl Broman is Professor in the Department of Biostatistics & Medical Informatics at the University of Wisconsin–Madison; research in statistical genetics; developer of R/qtl (for R). Karl received a BS in mathematics in 1991, from the University of Wisconsin–Milwaukee, and a PhD in statistics in 1997, from the University of California, Berkeley; his PhD advisor was Terry Speed. He was a postdoctoral fellow with James Weber at the Marshfield Clinic Research Foundation, 1997–1999. He was a faculty member in the Department of Biostatistics at Johns Hopkins University, 1999–2007. In 2007, he moved to the University of Wisconsin–Madison, where he is now Professor. Karl is a Senior Editor for Genetics, Academic Editor for PeerJ, and a member of the BMC Biology Editorial Board. Karl is an applied statistician focusing on problems in genetics and genomics – particularly the analysis of meiotic recombination and the genetic dissection of complex traits in experimental organisms. The latter is often called “QTL mapping.” A QTL is a quantitative trait locus – a genetic locus that influences a quantitative trait. Recently he has been focusing on the development of interactive data visualizations for high-dimensional genetic data; see his R/qtlcharts package and his D3 examples

roxygen2 rstudio testthat RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Qtl2 Karl Broman

Barret Schloerke | Reactlog 2.0 Debugging the state of Shiny | RStudio (2019)

The revamped reactlog provides an updated visual display to traverse through the reactive behavior within your shiny application. Using live shiny applications, we will use reactlog’s directed dependency graph to find missing reactive dependencies in “working” applications and address suboptimal reactive coding patterns. Correcting these coding patterns will reduce the amount of calculations done by shiny and keep reactive objects from being created unnecessarily. VIEW MATERIALS http://github.com/schloerke/presentation-2019-01-18-reactlog About the Author Barret Schloerke I specialize in Large Data Visualization where I utilize the interactivity of a web browser, the fast iterations of the R programming language, and large data storage capacity of Hadoop

Barret Schloerke

reactlog rstudio Shiny Barret Schloerke Reactlog RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Mel Gregory | RStudio Cloud for education | RStudio (2019)

RStudio Cloud aims to take the friction out of doing data science with R, allowing students (and instructors) to skip over installation, setup, and IT challenges and go straight to the good stuff. In this talk, you will learn about RStudio Cloud, how it can streamline the learning process, and how to use it to facilitate teaching classes, workshops, and other learning situations. Additionally, we will highlight some best practices for using RStudio Cloud in an educational setting, and talk about other learning resources available. About the Author Mel Gregory Mel is a software engineer and has been for a while. She likes simplicity

rstudio Mel Gregory RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Darby Hadley | RStudio Job Launcher Changing where we run R stuff | RStudio (2019)

RStudio Job Launcher provides the ability to start processes within batch processing systems and container orchestration platforms. In this talk, we will explore what is possible when you have the ability to launch containerized R sessions including scaling, isolating, and customizing environments. We will review examples of launching ad-hoc jobs as well as dockerized R sessions in Kubernetes using the Job Launcher. About the Author Darby Hadley Darby is a QA engineer for multiple teams at RStudio. He has a passion for improving products, creating efficient processes, and helping people. Before joining RStudio he worked primarily in the video game industry

rstudio Darby Hadley Job Launcher RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Javier Luraschi | Scaling R with Spark | RStudio (2019)

This talk introduces new features in sparklyr that enable real-time data processing, brand new modeling extensions and significant performance improvements. The sparklyr package provides an interface to Apache Spark to enable data analysis and modeling in large datsets through familiar packages like dplyr and broom. VIEW MATERIALS https://github.com/rstudio/rstudio-conf/tree/master/2019/Scaling%20R%20with%20Spark%20-%20Javier%20Luraschi About the Author Javier Luraschi Javier is a Software Engineer with experience in technologies ranging from desktop, web, mobile and backend; to augmented reality and deep learning applications. He previously worked for Microsoft Research and SAP and holds a double degree in Mathematics and Software Engineering

dplyr rstudio rstudio-conf Javier Luraschi SparklyR RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Alex Hayes | Solving the model representation problem with broom | RStudio (2019)

The R objects used to represent model fits are notoriously inconsistent, making data analysis inconvenient and frustrating. The broom package resolves this issue by defining a consistent way to represent model fits. By summarizing essential information about fits in tidy tibbles, broom makes it easy to programmatically work with model objects. Combining broom with list-columns results in an especially powerful way to work with many model fits at once. This talk will feature several case studies demonstrating how broom resolves common problems in data analysis VIEW MATERIALS https://buff.ly/2FGKFkj About the Author Alex Hayes Alex is interested in how statistics can help people make better decisions. He’s active in the R and data science communities, particularly interested in improving interfaces to modeling sofware. In his free time, he tries to get outside to climb and bike

rstudio Alex Hayes Broom RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Edzer Pebesma | Spatial data science in the Tidyverse | RStudio (2019)

Package sf (simple feature) and ggplot2::geom_sf have caused a fast uptake of tidy spatial data analysis by data scientists. Important spatial data science challenges are not handled by them, including raster and vector data cubes (e.g. socio-economic time series, satellite imagery, weather forecast or climate predictions data), and out-of-memory datasets. Powerful methods to analyse such datasets have been developed in packages stars (spatiotemporal tidy arrays) and tidync (tidy analysis of NetCDF files). This talk discusses how the simple feature and tidy data frameworks are extended to handle these challenging data types, and shows how R can be used for out-of-memory spatial and spatiotemporal datasets using tidy concepts. VIEW MATERIALS https://edzer.github.io/rstudio_conf/2019/index.html About the Author Edzer Pebesma I lead the spatio-temporal modelling laboratory at the institute for geoinformatics. I hold a PhD in geosciences, and am interested in spatial statistics, environmental modelling, geoinformatics and GI Science, semantic technology for spatial analysis, optimizing environmental monitoring, but also in e-Science and reproducible research. I am an ordinary member of the R foundation. I am one of the authors of Applied Spatial Data Analysis with R (second edition), am Co-Editor-in-Chief for the Journal of Statistical Software, and associate editor for Spatial Statistics. I believe that research is useful in particular when it helps solving real-world problems

ggplot2 rstudio tidyverse Edzer Pebesma Spatial RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Irene Steves | Teaching data science with puzzles | RStudio (2019)

Of the many coding puzzles on the web, few focus on the programming skills needed for handling untidy data. During my summer internship at RStudio, I worked with Jenny Bryan to develop a series of data science puzzles known as the “Tidies of March.” These puzzles isolate data wrangling tasks into bite-sized pieces to nurture core data science skills such as importing, reshaping, and summarizing data. We also provide access to puzzles and puzzle data directly in R through an accompanying Tidies of March package. I will show how this package models best practices for both data wrangling and project management. VIEW MATERIALS https://github.com/isteves/ds-puzzles About the Author Irene Steves This summer I was an intern at RStudio, where I worked with Jenny Bryan to develop a series of coding challenges to cultivate and reward the mastery of R and the tidyverse. I was previously a Data Science Fellow at the National Center for Ecological Analysis and Synthesis (NCEAS), where I reviewed data submissions to a national repository for completion, clarity, and data management best practices. As a fellow, I also collaborated on a number of open science projects to improve access to Ecological Metadata Language (EML) and datasets in the DataONE network (see metajam, dataspice)

Jenny Bryan

rstudio tidyverse Irene Steves Puzzles RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Nic Crane | The future’s Shiny: Pioneering genomic medicine in R | Posit (2019)

Shiny’s expanding capabilities are rapidly transforming how it is used in an enterprise. This talk details the creation of a large-scale application, supporting hundreds of concurrent users, making use of the future and promises packages. The 100,000 genomes project is an ambitious exercise that follows on from the Human Genome Project - aiming to put the UK at the forefront of genomic medicine, with the NHS as the first health service in the world to offer precision medicine to patients with rare diseases and cancer. Data is at the heart of this project; not only the outputs of the genomic sequencing, but vast amounts of metadata used to track progress against the 100,000 genome target and the status and path of each case through the sample tracking pipeline. In order to make this data readily available to stakeholders, Shiny was used to create an application containing multiple interactive dashboards. A scaled-up version of the app is being rolled out in early 2019 to a much larger audience to support the National Genomics Informatics Service, with the challenge of creating a complex app capable of supporting so many users without grinding to a halt. In this talk, I will explain why Shiny was the obvious technology choice for this task, and discuss the design decisions which enabled this project’s success. VIEW MATERIALS https://github.com/thisisnic/rstudio-conf-2019 About the Author Nic Crane Nic Crane is a Data Scientist at Elucidata, and has formerly worked for Mango Solutions and IBM. She is passionate about learning and teaching all things data science

rstudio rstudio-conf Shiny Nic Crane Human Genome Project RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Carl Howe | The next million R users | RStudio (2019)

Many students believe that R is obscure, complex, and difficult to write. However, data from a new large-scale survey of R users conducted by RStudio shows that new R users are taking dramatically different learning paths from those who learned R as recently as 2 years ago, and these new learning paths are changing its perception. In this talk, we’ll present this new survey data, describe how new tools and techniques for teaching R can satisfy the demands of today’s R learners, and outline a vision for adding millions of new R users to our community. VIEW MATERIALS https://github.com/rstudio/learning-r-survey/blob/master/slides/Next-Million-R-Users.pdf

rstudio Carl Howe RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Tonya Filz | The resilient R champion | RStudio (2019)

Merriam-Webster defines resilience as the ability to recover from or adjust easily to misfortune or change. As a Customer Success Representative who works alongside data scientists using RStudio’s toolchain, I’ve had a front row seat to the challenges faced by data scientists as they aim to promote the use of RStudio’s toolchain in their organization. This talk will focus on effective strategies that have been used to overcome some of the most difficult organizational barriers that are faced by data scientists using R. Specific topics will include funding barriers, IT support, server space, the “open source mentality”, and political pressures within organizations. About the Author Tonya Filz Helping data scientists and data analysts succeed by leveraging RStudio to clean, visualize, analyze, and communicate conclusions to key business leaders

rstudio Tonya Filz Customer Success RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

David Robinson | The unreasonable effectiveness of public work | RStudio (2019)

In this talk, I’ll lay out the reasons that blogging, open source contribution, and other forms of public work are a critical part of a data science career. For beginners, a blog is a great accompaniment to data science coursework and tutorials, since it gives you experience applying practical data science skills to real problems. For data scientists at any stage of their careers, open source development offers practice in collaboration, documentation, and interface design that complement other kinds of software development. And for data scientists more advanced in their careers, writing a book is a great way to crystallize your expertise and ensure others can build on it. All of these practices build skills in communication and collaboration that form an essential component of data science work. Each also lets you build a public portfolio of your skills, get feedback from your peers, and network with the larger data science community. VIEW MATERIALS https://bit.ly/drob-rstudio-2019 About the Author David Robinson David is the Chief Data Scientist at DataCamp, an education company for teaching data science through interactive online courses. His interests include statistics, data analysis, education, and programming in R. David is co-author with Julia Silge of the tidytext package and the O’Reilly book Text Mining with R. He also the author of the broom, gganimate, and fuzzyjoin packages, and of the e-book Introduction to Empirical Bayes. David previously worked as a data scientist at Stack Overflow, and received a PhD in Quantitative and Computational Biology from Princeton University

Julia Silge

rstudio David Robinson Open Source Oss Blogging RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats

Earo Wang | Melt the clock Tidy time series analysis | RStudio (2019)

Time series can be frustrating to work with, particularly when processing raw data into model-ready data. This work presents two new packages that address a gap in existing methodology for time series analysis (raised in rstudio::conf 2018). The tsibble package supports organizing and manipulating modern time series, leveraging tidy data principles along with contextual semantics: index and key. The tsibble data structure seamlessly flows into forecasting routines. The fable package is a tidy renovation of the forecast package. It promotes transparent forecasting practices and concise model representations, to empower analysts tackling a broad domain of forecasting problems. This collection of packages form the tidyverts, which facilitates a fluent and fluid workflow for analyzing time series. VIEW MATERIALS https://slides.earo.me/rstudioconf19 About the Author Earo Wang I’m currently doing my Ph.D. on statistical visualisation of temporal-context data at Monash University, supervised by Professor Di Cook and Professor Rob J Hyndman. I enjoy developing open-source tools with R, and is the (co)author of some widely-used R packages including anomalous, hts, sugrrants, rwalkr and tsibble. My research areas invovle data visualisation, time series analysis, and computational statistics

rstudio Earo Wang Time Series RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss

Felienne | Explicit Direct Instruction in Programming Education | RStudio (2019)

In education, there is and has always been debate about how to teach. One of these debates centers around the role of the teacher: should their role be minimal, allowing students to find and classify knowledge independently, or should the teacher be in charge of what happens in the classroom, explaining students all they need to know? These forms of teaching have many names, but the most common ones are exploratory learning and direct instruction respectively. While the debate is not settled, more and more evidence is presented by researchers that explicit direct instruction is more effective than exploratory learning in teaching language and mathematics and science. These findings raise the question whether that might be true for programming education too. This is especially of interest since programming education is deeply rooted in the constructionist philosophy, leading many programmers to follow exploratory learning methods, often without being aware of it. This talk outlines this history of programming education and additional beliefs in programming that lead to the prevalence of exploratory forms of teaching. We also explain the didactic principles of direct instruction, explore them in the context of programming, and hypothesize how it might look like for programming

rstudio Felienne RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss

Yihui Xie | pagedown Creating beautiful PDFs with R Markdown and CSS | RStudio (2019)

The traditional way to beautiful PDFs is often through LaTeX or Word, but have you ever thought of printing a web page to PDF? Web technologies (HTML/CSS/JavaScript) are becoming more and more amazing. It is entirely possible to create high-quality PDFs through Google Chrome or Chromium now. Web pages are usually single-page documents, but they can be paginated thanks to the JavaScript library Paged.js, so that you can have elements like headers, footers, and page margins for the printing purpose. In this talk, we introduce a new R package, pagedown ( https://github.com/rstudio/pagedown), to create PDF documents based on R Markdown and Paged.js. Applications of pagedown includes, but not limited to, books, articles, posters, resumes, letters, and business cards. With the power of CSS and JavaScript, you can typeset your documents with amazing elegance (e.g., a single line of CSS, “tr:nth-child(even) { background: #eee; }”, will give you a striped table, and “border-radius: 50%;” gives you a circular element) and power (e.g., HTML Widgets). VIEW MATERIALS https://bit.ly/pagedown

pagedown rstudio Yihui Xie Pagedown RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss

Matt Dancho | Using R, the Tidyverse, H2O, and Shiny to reduce employee attrition | RStudio (2019)

An organization that loses 200 high-performing employees per year has a lost productivity cost of about $15M/year. This cost is massive, yet many organizations don’t know it exists. It doesn’t show up on a financial statement. Therefore, it goes unnoticed. This presentation showcases how several open source tools integrate to form a solution to the employee attrition problem. Specifically: (1) How the tidyverse enables problem identification through visualization. (2) How recipes + H2O can be combined to explain key relationships to attrition and predict employee attrition. (3) How Shiny can be used to create a powerful dashboard that empowers business leaders to make data-driven decisions across the organization

rstudio Shiny tidyverse Matt Dancho RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss

Hadley Wickham | vctrs Tools for making size and type consistent functions | RStudio (2019)

vctrs is a new package that provides tools (cognitive and computational) to ensure that functions behave consistently with respect to inputs of varying length and type. The end goal of vctrs is to be invisible to the end user of the tidyverse (simply enabling their predictions about function outputs to be more correct), but will help developers write functions that “just work”

Hadley Wickham

rstudio tidyverse vctrs Hadley Wickham Vctrs RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss

Claus Wilke | Visualizing uncertainty with hypothetical outcomes plots | RStudio (2019)

Uncertainty is a key component of statistical inference. However, uncertainty is not easy to convey effectively in data visualizations. For example, viewers have a tendency to interpret visualizations of the most likely outcome as the only possible one. Viewers may also misjudge the likelihood of different possible outcomes or the extent to which moderately rare outcomes may deviate from the expectation. One way in which we can help the viewer grasp the amount of uncertainty present in a dataset is by showing a variety of different possible modeling outcomes at once. For example, in a linear regression, we could plot a number of different regression lines with slopes and intercepts drawn from the range of likely values, as determined by the variation in the data. Such visualizations are called Hypothetical Outcomes Plots (HOPs). HOPs can be made in static form, showing the various hypothetical outcomes all at once, or preferably in an animated form, where the display cycles between the different hypothetical outcomes. With recent progress in ggplot2-based animation, via gganimate, as well as packages such as tidybayes that make it easy to generate hypothetical outcomes, we can easily produce animated HOPs in a few lines of R code. This presentation will cover the key concepts, packages, and techniques to generate such visualizations. VIEW MATERIALS: https://docs.google.com/presentation/d/1zMuBSADaxdFnosOPWJNA10DaxGEheW6gDxqEPYAuado/edit?usp=sharing

ggplot2 rstudio Claus Wilke RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss

Sigrid Keydana | Why TensorFlow eager execution matters | RStudio (2019)

In current deep learning with Keras and TensorFlow, when you’ve mastered the basics and are ready to dive into more involved applications (such as generative networks, sequence-to-sequence or attention mechanisms), you may find that surprisingly, the learning curve doesn’t get much flatter. This is largely due to restrictions imposed by TensorFlow’s traditional static graph paradigm. With TensorFlow Eager Execution, available since summer and announced to be the default mode in the upcoming major release, model architectures become more flexible, readable, composable, and last not least, debuggable. In this session, we’ll see how with Eager, we can code sophisticated architectures like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) in a straightforward way. VIEW MATERIALS https://github.com/skeydan/rstudio_conf_2019_eager_execution

rstudio tensorflow RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate Sigrid Keydana GAN VAE Deep Learning

Lionel Henry | Working with names and expressions in your tidy eval code | RStudio (2019)

n practice there are two main flavors of tidy eval functions: functions that select columns, such as dplyr::select(), and functions that operate on columns, such as dplyr::mutate(). While sharing a common tidy eval foundation, these functions have distinct properties, good practices, and available tooling. In this talk, you’ll learn your way around selecting and doing tidy eval style. Materials: https://speakerdeck.com/lionelhenry/selecting-and-doing-with-tidy-eval

Lionel Henry

dplyr rstudio Lionel Henry RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Joe Cheng | Shiny in production: Principles, practices, and tools | RStudio (2019)

Shiny is a web framework for R, a language not traditionally known for web frameworks, to say the least. As such, Shiny has always faced questions about whether it can or should be used “in production”. In this talk we’ll explore what “production” even means, review some of the historical obstacles and objections to using Shiny for production purposes, and discuss practices and tools that can help your Shiny apps flourish. About the Author: Joe Cheng is the Chief Technology Officer at RStudio. Joe was the original creator of Shiny, and leads the team responsible for Shiny and Shiny Server. GitHub: https://github.com/jcheng5 Materials: https://speakerdeck.com/jcheng5/shiny-in-production

Joe Cheng

rstudio Shiny Joe Cheng RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Tareef Kawaf | Welcome and the Posit Vision | Posit (2019)

From Posit President, TAREEF KAWAF About Tareef: Tareef Kawaf is a software startup executive and current president of RStudio, Inc., a Massachusetts-based company that develops both open-source and commercial software for the R statistical programming language. Prior to joining RStudio, Mr. Kawaf served as senior vice president of engineering and operations at Brightcove, Inc.. Over 8 years he helped Brightcove build and operate the second-largest online video platform, helping it grow from 0 to 92M in revenue and complete its initial public offering (IPO). Mr. Kawaf jointly holds a patent for the “Method and System for Dynamic Pricing,” issued in 2001 which is a core component of Oracle’s ATG Commerce solutions and helps retailers define sophisticated rules for couponing, discounting, and personalized commerce. Mr. Kawaf received his B.S. degree in Computer Science with a minor in Mathematics from the University of Massachusetts Amherst in 1994. He and his family currently reside outside of Boston, MA

rstudio Tareef Kawaf RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Jeff Allen | RStudio Connect Past, present, and future | RStudio (2019)

RStudio Connect is a publishing platform that helps to operationalize the data science work you’re doing. We’ll review the current state of RStudio including its ability to host Shiny applications and Plumber APIs, schedule and render R Markdown documents, and manage access. Then we’ll unveil some exciting new features that we’ve been working on, and give you a sneak peek at what’s coming up next. Materials: http://rstd.io/rsc170

plumber rstudio Shiny Jeff Allen RStudio Data Science Machine Learning Python Stats Tidyverse Data Visualization Data Viz Ggplot Technology Coding Connect Server Pro Shiny Rmarkdown Package Manager CRAN Interoperability Serious Data Science Dplyr Forcats Ggplot2 Tibble Readr Stringr Tidyr Purrr Github Data Wrangling Tidy Data Odbc Rayshader Plumber Blogdown Gt Lazy Evaluation Tidymodels Statistics Debugging Programming Education Rstats Open Source Oss Reticulate

Shiny in Production: Data Products at Scale Workshop - rstudio::conf(2019L)

What is the 2-day Shiny in Production Workshop? That’s a great question, I’m glad you asked. Shiny in Production | Data Products at Scale Workshop - 2 Days This two-day workshop will teach you the best practices that make production-quality Shiny application performance possible. Does 30,000 simultaneous users for a Shiny application sound challenging? Shiny is used all over the world to deliver interactive, visual data products from data science teams to internal and external audiences at scale. The course will not spend time on writing applications, but will focus on the open source and professional ecosystem around shiny that includes performance optimization, testing, and production deployment. This workshop relies on RStudio professional products for deployment. You should take this workshop if you are comfortable writing Shiny apps but are afraid for what will happen when people start looking at them. The workshop is taught by Sean Lopp, RStudio Solutions Engineer. Sean works with RStudio product teams and helps enterprise customers to realize the value of R and Shiny. Speakers: Sean Lopp and a who’s-who of RStudio Engineers, R-Admins, and Shiny Developers

Advanced R Markdown 2-day Workshop - rstudio::conf(2019L)

What is the 2-day Advanced R Markdown Workshop? That’s a great question, I’m glad you asked. This is a two-day hands-on workshop based on the book “R Markdown: The Definitive Guide”. This workshop is designed for those who want to take their R Markdown skills to the next level. We’ll talk about many low-level details in the rmarkdown package and the whole R Markdown ecosystem. The two goals of this workshop are: 1) learn how to fully customize R Markdown output (HTML, LaTeX/PDF, Word, and PowerPoint); and 2) learn more about existing R Markdown extensions in the ecosystem, such as flexdashboard, bookdown, blogdown, pkgdown, xaringan, rticles, and learnr. We will also talk about how to use or develop new language engines (languages that are not R), how to develop HTML widgets, and integrate Shiny with R Markdown. You should take this workshop if you have experience programming in R and want to learn how to take advantage of the amazing breadth and depth of R Markdown. You’ll get the most from it if you enjoy learning how R Markdown works under the hood (which will involve reading some source code), and are seriously interested in hacking (playing) with HTML, JavaScript, CSS, LaTeX, and command-line tools. We will give minimal tutorials on these languages and tools in the workshop, but it may be easier for you to keep pace with the instructor if you already know them before. This workshop is led by Yihui Xie, Software Engineer and Data Scientist at RStudio. Yihui is the main author of the open-source knitr package for reproducible research and dynamic report generation. He has also created and co-authored several other R packages, including rmarkdown, bookdown, blogdown, xaringan, tinytex, DT, shiny, and leaflet. He has authored and co-authored four books: Dynamic Documents with R and knitr, bookdown: Authoring Books and Technical Documents with R Markdown, blogdown: Creating Websites with R Markdown, and R Markdown: The Definitive Guide. Speakers: Yihui Xie and Alison Hill with guest lecturers Jiena Gu and Hao Zhu

Reproducible Examples with the reprex package

Reproducible Examples and the reprex package. https://speakerdeck.com/jennybc/reprex-reproducible-examples-with-r Jump to: 0:08 Intro 0:40 Basic usage of reprex 3:35 Motivation, why use reprex? “Help me help you” 4:08 Define reprex? Three commons ways to use the term. noun, a reproducible example the reprex package. a tool to build R reprexs reprex::reprex(), a function in reprex to make a reprex. 5:26 When should you use a reprex? 6:14 reprex installation and setup. How do you actually get repex on your machine? 7:59 Advanced setup and discussion. 9:45 Please use advanced features responsibly. 11:02 Why does the reprex package exist? Anyone who has helped teach R or dealt with github issues, twitter, stack overflow & RStudio community questions knows that helping people diagnose their coding problems can be hard. This tool comes from hard-won experience. It’s aim to is help people ask well formed questions and increase the chances of getting well formed answers quickly. 12:52 philosophy behind reprex code that I can run code that I don’t need to run code that I can easily run 13:52 code that I can run. 17:25 Tips on writing good reprexs. Dos and don’ts. 18:52 How do I get my data into my reprex? Getting small data and CSV type data into your reprex is easy. “I have a big hairy data object and I can only show their problem by using it”, but that’s not always the case. 21:02 code that I don’t need to run reprex gives your reader the code and reveals the output being produced by that code. For experienced coders, that might be enough to help you. 22:44 code that I can easily run Don’t copy and paste from the R console. This is usually annoying for your reader. Worse than console copy-pasta is the screenshot. (Many people think screenshots of code are downright offensive.) 25:03 reprex_clean If you copy someone else’s reprex into your consolve, it may include their output, making your new reprex a untidy. Here are tips for taking someone else’s reprex code and output, and create a clean reprex reply. 25:54 shock and awe More interesting features of the reprex package. 26:29 What about figures and plots in your reprex? So happy you asked about that. reprex will automatically upload your images to imgur.com. 28:23 Create a reprex by explicitly providing your code in the reprex call. 29:00 when you need your reprex to work in the current working directory. 30:45 Differently flavored markdown. Optimize your reprex markdown output for github, stack overflow, or the RStudio community. 30:31 Make your reprex create an R script, with your reprex outputs as comments. This is handy for pasting into an email or slack-type-app. 32:25 Rich text format, rtf output. (currently experimental feature as of this video) 33:06 supress the reprex add at the bottom of your reprex 33:19 Include session info. 33:54 Auto styling of your code. Good if you’re dealing with poorly formatting code. 34:25 Change your comments string. 34:32 Silence Tidyverse startup messages. 35:00 Capture a reprex that sends messages to standard output and standard input (e.g. package installation compilation messages). 36:13 Set up personal defaults for your reprex usage. 36:54 reprex RStudio addins; render reprex and reprex selection. These accelerate your use of reprex. 39:01 The human side of reproducible examples. How to ask questions in ways that are most likely to get answered. Sorry for the tough love, but this is important. Why are you always asked to give a reprex? Experts try to use reproducible examples to ensure their advice works. Making a good reprex is hard. But, you are asking them to solve a problem for you, so meet them halfway. Creating reprexes is good coding practice. Making a good reprex is often a good way to debug your issue in the embarrassment-free privacy of your own home. reprexes lead to discussions more likely to help people in the future. 44:34 Behind the scenes of reprex 44:44 Thanks for those that helped make reprex possible. Questions and Answers 46:05 can reprex capture variables and objects in the current environment? (not yet, maybe in development) 47:25 does reprex actually check that the code is self contained? (self contained) 48:08 does readr::read_csv support the text argument? (yep, just read the help manual for readr)

Tidyverse Train-the-Trainer Certification Workshop - rstudio::conf(2019L)

What is the 2-day Tidyverse Train-the-Trainer Workshop? That’s a great question, I’m glad you asked. Register at https://rstd.io/conf Learn more at https://rstd.io/conf-agenda Tidyverse Train-the-Trainer Certification Workshop - 2 Days Day 1 of the course will be co-taught by Mine Cetinkaya-Rundel and Garrett Grolemund, RStudio Data Scientists and Professional Educators. On Day 2, Mine will teach the Shiny track and Garrett will teach the Tidyverse track. This two-day workshop will equip you to teach R effectively. We will draw on RStudio’s experience teaching R to recommend tips for designing, teaching, and supporting short R courses. On Day 1 of the course, you will learn practical activities that you can use immediately to improve your presentation style, learning outcomes, and student engagement. You will leave the class with a cognitive model of learning that you can use to develop your own effective workshops or courses within your organization. The course will also cover how to use RStudio Cloud and its curriculum of tutorials to jump-start your own lessons. On Day 2 of the course, participants will have the option to choose one of two tracks: Teaching the Tidyverse or Teaching Shiny. Teaching the Tidyverse: Classroom examples will focus on how to teach students to do data analysis with the Tidyverse. We will use Master the Tidyverse, which is an award-winning two-day workshop developed by RStudio, as an example. Participants will receive the course materials for teaching Master the Tidyverse. You should take this workshop if you work for a training partner and want to qualify as an RStudio Certified Tidyverse Instructor or if you are an advocate for R in your organization. You should be proficient in the Tidyverse already and be prepared to submit examples of your work. Prior teaching experience is helpful, but not required. Please bring a laptop and a device that has video recording capabilities (such as a laptop or cell phone). Instructors: Garrett Grolemund, Mine Çetinkaya-Rundel

Mine Çetinkaya-Rundel

Shiny Train-the-Trainer Workshop - rstudio::conf(2019L)

What is the 2-day Shiny Train-the-Trainer Workshop? That’s a great question, I’m glad you asked. Register at https://rstd.io/conf Learn more at https://rstd.io/conf-agenda Shiny Train-the-Trainer Certification Workshop - 2 Day Day 1 of the course will be co-taught by Mine Cetinkaya-Rundel and Garrett Grolemund, RStudio Data Scientists and Professional Educators. On Day 2, Mine will teach the Shiny track and Garrett will teach the Tidyverse track. This two-day workshop will equip you to teach R effectively. We will draw on RStudio’s experience teaching R to recommend tips for designing, teaching, and supporting short R courses. On Day 1 of the course, you will learn practical activities that you can use immediately to improve your presentation style, learning outcomes, and student engagement. You will leave the class with a cognitive model of learning that you can use to develop your own effective workshops or courses within your organization. The course will also cover how to use RStudio Cloud and its curriculum of tutorials to jump-start your own lessons. On Day 2 of the course, participants will have the option to choose one of two tracks: Teaching the Tidyverse or Teaching Shiny. Teaching Shiny: Classroom examples will focus on teaching Shiny at the beginner and intermediate levels. The course materials will build on RStudio’s Mastering Shiny workshop as well as the upcoming book from the author of the Shiny package, Joe Cheng, and they will cover the entire lifecycle of a Shiny app: build ️ improving ️ share. Participants will receive the course materials for teaching Mastering Shiny. You should take this workshop if you work as a training partner and want to qualify as an RStudio Certified Shiny Instructor or if you are an advocate for R in your organization. You should be proficient in Shiny already and be prepared to submit examples of your work. Prior teaching experience is helpful, but not required. Please bring a laptop and a device that has video recording capabilities (such as a laptop or cell phone). Instructors: Garrett Grolemund, Mine Çetinkaya-Rundel

Joe Cheng, Mine Çetinkaya-Rundel

Working with Two Datasets: Binds, Set Operations, and Joins – Pt 4 Intro to Data Manipulation

Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. Keep your R code clean and clear and reduce the cognitive load required for common but often complex data science tasks. dplyr docs: dplyr.tidyverse.org/reference/ http://dplyr.tidyverse.org/reference/setops.html http://dplyr.tidyverse.org/reference/join.html Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw /01:44 Intro and what’s covered Ground Rules: /02:40 What’s a tibble /04:50 Use View /05:25 The Pipe operator: /07:20 What do I mean by data wrangling? Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM /00:48 Goal 1 Making your data suitable for R /01:40 tidyr “Tidy” Data introduced and motivated /08:10 tidyr::gather /12:30 tidyr::spread /15:23 tidyr::unite /15:23 tidyr::separate Pt. 3: Data manipulation tools: dplyr https://youtu.be/Zc_ufg4uW4U /00.40 setup /02:00 dplyr::select /03:40 dplyr::filter /05:05 dplyr::mutate /07:05 dplyr::summarise /08:30 dplyr::arrange /09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation) /11:45 dplyr::group_by Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together 00.42 dplyr::bind_cols 01:27 dplyr::bind_rows 01:42 Set operations dplyr::union, dplyr::intersect, dplyr::set_diff 02:15 joining data - dplyr::left_join, dplyr::inner_join, - dplyr::right_join, dplyr::full_join, Cheatsheets: https://www.rstudio.com/resources/cheatsheets/ Documentation: tidyr docs: tidyr.tidyverse.org/reference/ tidyr vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html dplyr docs: http://dplyr.tidyverse.org/reference/ dplyr one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html dplyr two-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html

cheatsheets dplyr rstudio tibble tidyr tidyverse tidyverse.org Grammar of Data Manipulation Data Science Data Wrangling Applied Statistics Statistics RStudio Data Manipulation Joins Merge Rstats Two Tables Two Tibbles Two Datasets Two Data.frames Two Dataframes Two Tables at the Same Time Intersect Union Setdiff Left_join Right_join Full_join Anti_join

What is data wrangling? Intro, Motivation, Outline, Setup – Pt. 1 Data Wrangling Introduction

Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. These videos introduce you to these tools. Keep your R code clean and clear and reduce the cognitive load required for common but often complex data science tasks. Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw 01:44 Intro and what’s covered Ground Rules 02:40 What’s a tibble 04:50 Use View 05:25 The Pipe operator: 07:20 What do I mean by data wrangling? Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM /00:48 Goal 1 Making your data suitable for R /01:40 tidyr “Tidy” Data introduced and motivated /08:15 tidyr::gather /12:38 tidyr::spread /15:30 tidyr::unite /15:30 tidyr::separate Pt. 3: Data manipulation tools: dplyr https://youtu.be/Zc_ufg4uW4U 00.40 setup /02:00 dplyr::select /03:40 dplyr::filter /05:05 dplyr::mutate /07:05 dplyr::summarise /08:30 dplyr::arrange /09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation) /11:45 dplyr::group_by /15:00 dplyr::group_by Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together /00.42 dplyr::bind_cols /01:27 dplyr::bind_rows /01:42 Set operations dplyr::union, dplyr::intersect, dplyr::set_diff /02:15 joining data dplyr::left_join, dplyr::inner_join, dplyr::right_join, dplyr::full_join, Cheatsheets: https://www.rstudio.com/resources/cheatsheets/ Documentation: tidyr docs: tidyr.tidyverse.org/reference/ tidyr vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html dplyr docs: http://dplyr.tidyverse.org/reference/ dplyr one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html dplyr two-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html New York Times “For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights”, By STEVE LOHRAUG. 17, 2014 https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html

Tidy Data and tidyr – Pt 2 Intro to Data Wrangling with R and the Tidyverse

Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. Keep your code clean and clear and reduce the cognitive load required for common but often complex data science tasks. http://tidyr.tidyverse.org/reference/ http://tidyr.tidyverse.org/reference/gather http://tidyr.tidyverse.org/reference/spread http://tidyr.tidyverse.org/reference/unite http://tidyr.tidyverse.org/reference/separate Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw /01:44 Intro and what’s covered Ground Rules /02:40 What’s a tibble /04:50 Use View /05:25 The Pipe operator: /07:20 What do I mean by data wrangling? Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM 00:48 Goal 1 Making your data suitable for R 01:40 tidyr “Tidy” Data introduced and motivated 08:10 tidyr::gather 12:30 tidyr::spread 15:23 tidyr::unite 15:23 tidyr::separate Pt. 3: Data manipulation tools: dplyr https://youtu.be/Zc_ufg4uW4U 00.40 setup /02:00 dplyr::select /03:40 dplyr::filter /05:05 dplyr::mutate /07:05 dplyr::summarise /08:30 dplyr::arrange /09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation) /11:45 dplyr::group_by /15:00 dplyr::group_by Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together /00.42 dplyr::bind_cols /01:27 dplyr::bind_rows /01:42 Set operations dplyr::union, dplyr::intersect, dplyr::set_diff /02:15 joining data dplyr::left_join, dplyr::inner_join, dplyr::right_join, dplyr::full_join, Cheatsheets: https://www.rstudio.com/resources/cheatsheets/ Documentation: tidyr docs: tidyr.tidyverse.org/reference/ tidyr vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html dplyr docs: http://dplyr.tidyverse.org/reference/ dplyr one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html dplyr two-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html

Data Manipulation Tools: dplyr – Pt 3 Intro to the Grammar of Data Manipulation with R

Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. Keep your code clean and clear and reduce the cognitive load required for common but often complex data science tasks. dplyr docs: dplyr.tidyverse.org/reference/ http://dplyr.tidyverse.org/reference/union.html http://dplyr.tidyverse.org/reference/intersect.html http://dplyr.tidyverse.org/reference/set_diff.htm Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw /01:44 Intro and what’s covered Ground Rules /02:40 What’s a tibble /04:50 Use View /05:25 The Pipe operator: /07:20 What do I mean by data wrangling? Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM /00:48 Goal 1 Making your data suitable for R /01:40 tidyr “Tidy” Data introduced and motivated /08:10 tidyr::gather /12:30 tidyr::spread /15:23 tidyr::unite /15:23 tidyr::separate Pt. 3: Data manipulation tools: dplyr https://youtu.be/Zc_ufg4uW4U 00.40 setup 02:00 dplyr::select 03:40 dplyr::filter 05:05 dplyr::mutate 07:05 dplyr::summarise 08:30 dplyr::arrange 09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation) 11:45 dplyr::group_by Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together /00.42 dplyr::bind_cols /01:27 dplyr::bind_rows /01:42 Set operations dplyr::union, dplyr::intersect, dplyr::set_diff /02:15 joining data dplyr::left_join, dplyr::inner_join, dplyr::right_join, dplyr::full_join, Cheatsheets: https://www.rstudio.com/resources/cheatsheets/ Documentation: tidyr docs: tidyr.tidyverse.org/reference/ tidyr vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html dplyr docs: http://dplyr.tidyverse.org/reference/ dplyr one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html dplyr two-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html

Interacting with Databases by Bárbara Borges from Shiny from useR! Brussels 2017

Connecting to an external database from R can be challenging. This is made worse when you need to interact with a database from a live Shiny application. To demystify this process, I’ll do two things. First, I’ll talk about best practices when connecting to a database from Shiny. There are three important packages that help you with this and I’ll weave them into this part of the talk. The DBI package does a great job of standardizing how to establish a connection, execute safe queries using SQL (goodbye SQL injections!) and close the connection. The dplyr package builds on top of this to make even easier to connect to databases and extract data, since it allows users to query the database using regular dplyr syntax in R (no SQL knowledge necessary). Yet a third package, pool, exists to help you when using databases in Shiny applications, by taking care of connection management, and often resulting in better performance. Second, I’ll demo these concepts in practice by showing how we can connect to a database from Shiny to create a CRUD application. I will show the application running and point out specific parts of the code (which will be publicly available)

Videos

YOU can contribute to base R! | Ella Kaye | Data Science Lab

Same model 🤖 different magic 🪄🎩

Working with Larger than Memory Data in R

Building Accessible, On-Brand Documents with Quarto

Compliance Without Friction: Mastering the Persistent Analysis Lifecycle

The Three-Click Rule for Executive Dashboards | Jason Frederick | Data Science Hangout

New and updated open source packages from last month

Demonstrating Value as the First Data Scientist | Hansel Palencia | Data Science Hangout

Wake up babe, new IDE for Jupyter notebooks just dropped

How to use the native pipe in R

Building Reproducible LLM Workflows | Leslie Emery | Data Science Hangout

LLM agent skills for coding and development

Claude Code without typing anything 🤷🏻‍♂️ | Joey Marshall | Data Science Lab

Building healthy data communities | Rachael Dempsey & Libby Heeren | Data Science Hangout

Introducing Posit AI for Positron and RStudio

dbplyr: dplyr for databases

Free and open-source tools for Python users

What are the lifecycle badges on tidyverse packages?

Building Posit Assistant: An AI Agent for Data Science and Analysis

Plotnine: Grammar of Graphics in Python

What is literate programming?

Ethical concerns, LLMs, and sensitive data in Legal | Tim Golden | Data Science Hangout

Easiest way to get started on a Python project?!

How to contribute to TidyTuesday by curating a dataset | Jon Harmon | Data Science Lab

Clinical data science when your patients are kids | Nikolay Braykov | Data Science Hangout

Five data validation libraries for Python Polars

Comparing Posit Assistant and Claude Code

What’s new in dplyr 1.2.0

Deeply Unsexy: SQL’s Redemption Arc — with Tristan Handy

Not if, but how, AI will fail you

Your VP Is Doing a Rogue Analysis in Cursor Right Now — with Nell Thomas

Working with Jupyter Notebooks in Positron: From Exploration to Production

Behind the scenes of tidyverse development: new dplyr functions | Davis Vaughan | Data Science Lab

Analyzing a TidyTuesday dataset with Posit Assistant in RStudio | Hadley Wickham | Data Science Lab

Regression models still rule in P&C Insurance | Jim Weiss | Data Science Hangout

Python and AI

Astral (creators of uv) joins OpenAI

How to use Positron’s GitHub integration | Isabella Velásquez & Libby Heeren | Data Science Lab

Coding vs. thinking programmatically | Samia Baig | Data Science Hangout

pointblank, was I expecting this? |Hannah Frick

Claude Code for R | Hadley Wickham

Beyond drag bingo: Building community at work | Kristin Bott

Managing data platforms for 600 analysts | Tom Grace | Data Science Hangout

Data analysis with Posit AI-assistants | Sara Altman & Simon Couch | Data Science Lab

Motivating diverse data teams | Olivia Hebner | Data Science Hangout

The mall package: using LLMs with data frames in R & Python | Edgar Ruiz | Data Science Lab

Sleeping Rats and Sociopathic Agents — with Phillip Cloud

The Reluctant Admin: motorsports, data science, & IT | Brijesh Chejerla | Data Science Hangout

Charlie Gao: Advances in the Shiny Ecosystem

Julia Silge - Keynote PyCon Colombia 2025

Strategic Budget Optimization through Marketing Mix Modeling (MMM)

More productive but a lot less fun — with Charlie Marsh

Alenka Frim: What yoga teaches us about discipline and collaboration in data science

Driving Shiny adoption by removing friction | Jonathan Lin | Data Science Hangout

The one skill AI can’t replace | Alexander Schacht | Data Science Hangout

Using R package structure for data science projects | Kylie Ainslie | Data Science Lab

Positron workflows that make life easier | Andrew Heiss | Data Science Lab

Hackathons for education, not competition | Allissa Dillman | Data Science Hangout

Seriously, have you tried this AI stuff? I’ve never written code like this in my life.

What is it like submitting an abstract for posit::conf()?

Turbocharge your Shiny Apps with remote submission to HPC

Polars: The Blazing Fast Python Framework for Modern Clinical Trial Data Exploration

How to use {pointblank} to understand, validate, and document your data

Getting Started with LLM APIs in R

Creating Polished, Branded Documents with Quarto

The dessert-first approach to teaching data science | Mine Cetinkaya-Rundel | Data Science Hangout

People Analytics at different stages of company growth | Adrian Perez | Data Science Hangout

What even is dbt? An Analytics engineer explains | Laurie Merrell & Michael Chow | Data Science Lab

AI-Powered Data Engineering Workflows: Positron for Databricks Users

Rebecca Barter: Persistent learning, tool building, and ‘Will code even exist?’

Emily Riederer: Column selectors, data quality, and learning in public

Inspecting websites to find JSON data APIs | Marcos Huerta | Data Science Lab

querychat in R: Query Your Data with Natural Language | Shiny + LLMs | Veerle van Leemput

querychat in Python: Query Your Data with Natural Language | Shiny + LLMs | Veerle van Leemput

Teaching out loud and becoming StatQuest | Josh Starmer | Data Science Hangout

Open source development practices | Isabel Zimmerman & Davis Vaughan | Data Science Hangout

Integrating Shiny with Epic EHR | Matt Maloney | Data Science Hangout

How to deploy Shiny apps in 2026 | Alex Chisholm | Data Science Lab

Advent of Code for R users | Emil Hvitfeldt | Data Science Lab