Among data scientists and analysts, R has long held its own against Python in the battle to determine the “best” analytical programming language. Statisticians and numerically-savvy academics of all stripes have typically preferred R to Python, so lots of R&D—both inside the ivory tower and in various industries— gets done in R.
Part of R's popularity comes from its dizzying array of packages, contributed by a lively community of developers. As of January 2017, there were 10,000 R packages available for download, a milestone that testifies to its wide-spread adoption across lots of different industries.
Still, for all its flexibility, R remains true to its roots as a statistical programming language. Stack Overflow's 2017 survey found that while R is growing in every industry surveyed, it's growing fastest in sectors that lean heavily on statistics: academia, healthcare, and government.
Whether you're new to the R for data science community, or you're already an active package-creator or analyst, listening in on conversations among influencers is great way to stay up to speed and find the most relevant news about R for data science.
On the heels of releasing support for R in Mode's integrated Notebooks, we've compiled a list of the people we follow most closely in the R data science community. If you want to stay in the know, here are 7 people you should follow on Twitter.
- Hadley Wickham. Author of R for Data Science (with Garrett Grolemund), chief scientist at RStudio, and author of the tidyverse packages, including ggplot2, plyr, dplyr, and reshape2.
If you see a #tidyverse issue that you'd love us to work on, please 👍 it. We're listening! #rstats https://t.co/Utf34Gb9Eg— Hadley Wickham (@hadleywickham) April 13, 2018
Follow Hadley: @hadleywickham
- Mara Averick. Tidyverse developer advocate at RStudio. Open source contributor and civic tech advocate.
😻 Jaw-droppingly cool, w/ 🌟 explanations: "Lego color themes as topic models" by @nateaff https://t.co/NtmZmxXTxu #rstats #dataviz #tidytext pic.twitter.com/P3TWJg3UZf— Mara Averick (@dataandme) April 15, 2018
Follow Mara: @dataandme
- Roger Peng. Professor at Johns Hopkins Department of Biostatistics. Author of R Programming for Data Science. Co-editor of Simply Statistics. Co-host of the podcast The Effort Report (with Elizabeth Matsui).
This was a fantastic episode of @storywithdata’s podcast. It very accurately captured the complexity of doing good data analysis. https://t.co/gNGXcieMXr— Roger D. Peng (@rdpeng) March 13, 2018
Follow Roger: @rdpeng
- Hilary Parker. Data scientist at Stitch Fix. Co-host of the podcast Not So Standard Deviations.
My slides from #WiAC2018. This talk is special to me -- pulls together many of the ideas cultivated with @rdpeng on @NSSDeviations. https://t.co/qevFSykfk3— Hilary Parker (@hspter) April 13, 2018
Follow Hilary: @hspter
- Gabriela de Queiroz. Founder of RLadies (@RLadiesGlobal). Data scientist, mentor, advisor.
The slides for my talk "Statistics for Data Science: what you should know and why" at #ddtx18: https://t.co/NFfSv0EwwA— Gabriela de Queiroz (@gdequeiroz) January 28, 2018
Follow Gabriela: @gdequeiroz
- David Robinson. Chief data scientist at DataCamp. Co-author of tidytext and Text Mining with R. Author of the broom, gganimate, and fuzzyjoin packages, and Introduction to Empirical Bayes.
New blog post, about how we structure data science within @DataCamp and what a Chief Data Scientist does https://t.co/52ibJTqCBT #rstats pic.twitter.com/Y2jXsqyUOF— David Robinson (@drob) April 10, 2018
Follow David: @drob
- Julia Silge. Data science and visualization at StackOverflow, co-author of tidytext and Text Mining with R.
Follow Julia: [**@juliasilge**](https://twitter.com/juliasilge) *** Are there R data science influencers you think we should add to this list? We'd love to hear who you follow! Tweet at us [@ModeAnalytics](https://twitter.com/ModeAnalytics). Or, reach out on the [Mode forum](https://forum.modeanalytics.com/), where you can talk to other analysts and data scientists and compare notes.
Check out my new @DataCamp course on practical supervised machine learning with #rstats! In four case studies, 💪 exercise your skills from 📊 exploratory data analysis to ⚖️ model evaluation.https://t.co/dvcpSqAZBy pic.twitter.com/8FB5a0NCmF— Julia Silge (@juliasilge) April 23, 2018