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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

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?”

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/