Gergely Daróczi on reviving Budapest Users of R Network (BURN) + using R with Strava data for daily insights—his personal diabetes story.
Gergely Daróczi on reviving Budapest Users of R Network (BURN) + using R with Strava data for daily insights—his personal diabetes story.
R Consortium applauds the R Foundation & R Core on a major new investment in R’s future!
Nearly $650K (£499,981.21) over 24 months for “Enabling the Next Generation of Contributors to R,” modernizing infrastructure & governance, mentoring new contributors, and strengthening communication.
Partners include Posit, Google, A2-Ai and the R Consortium
Round 1 of the Research Software Maintenance Fund awarded just under £3 million to 13 projects selected for their potential to deliver high impact, value for money, feasibility, and quality. The funded projects span research areas supported by all seven UK research councils and involve software written in Python, R, C++, Fortran, JavaScript, and other languages. Listed below, the 13 successful projects are split into four large awards and nine small awards.
Join us on November 30 from 4:00 pm to 5:00 pm CET (GMT+1) for a talk on using #Generative #AI in #R with Sharon Machlis @smach — Tech Journalist and Data Professional.
In this session, Sharon will offer an accessible, high-level overview of what’s possible today — including how #large #language #models can help you write and improve your R code, as well as add #AI-driven features to your applications.
RSVP 🔗 https://www.meetup.com/rladies-paris/events/312083756/.
New Course Release! R Programming for Data Science by Roger D. Peng
This course brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. The skills taught in this course will lay the foundation for you to begin your journey learning data science.
Find it on Leanpub!
Doing some work on my #R package #RandomWalker and looks like we got a nice speedup coming for all of the generating functions. Over 2,000 replications saw a large speedup where the original function takes 2.8x as long as the new.
Today's blog post is a simple one, but still important to your data. Looking at dropping NA values.
Post: https://www.spsanderson.com/steveondata/posts/2025-10-20/

Master data cleaning in R! Learn two essential Base R methods, including complete.cases(), and the modern dplyr::drop_na() to quickly remove empty or incomplete rows. Plus, benchmark their performance!