#rpackages — Public Fediverse posts

🚨🚨 BREAKING NEWS: #CRAN has too many R packages! 📦📦 Apparently, the R community is drowning in statistical knowledge, but who cares about quality when you can have QUANTITY? 🎉🤦‍♂️ Surely, sifting through this deluge of packages is everyone's idea of a fun Saturday night. 🙄
https://rworks.dev/posts/too-many-R-packages/ #Rpackages #Rcommunity #QualityVsQuantity #DataOverload #StatisticalKnowledge #HackerNews #ngated

Too many R packages: CRAN is inundated with submissions

https://rworks.dev/posts/too-many-R-packages/

#HackerNews #Rpackages #CRAN #dataanalysis #datascience #softwaredevelopment #open-source

✨ One of my favorite non-spatial R packages: beepr 🔔

Plays sounds when your R scripts finish running.

URL: https://cran.r-project.org/package=beepr

#RStats #RPackages

🎉 New in #typeR 0.2.0: typeRun()!

⚡ Types AND executes R code in real-time
🎮 Interactive pause/resume (ESC)
🧠 Smart output truncation
📄 Works with .R/. Rmd/.qmd

Perfect for live teaching & demos!

📦 https://github.com/Fgazzelloni/typeR
#RStats #RPackages

Check out our presentation about

🌟 bsvars.org design concept ✨

🌐 https://bsvars.org/2025-06-iifosf/

#bsvars #bsvarSIGNs #rstats #Rpackages

Check out https://r-multiverse.org - this a brilliant new approach to #rstats #RPackages in the whole development cycle from Beta to release to, and that is great, regular snapshots of the whole repo.

@feld good question!

i think we automatically package all #haskell packages from #hackage and some #stackage (18k) and all #Rpackages from #CRAN (27k)

the manually packaged python and perl libs are available for multiple versions. the supported ones are visible, older still work

check out the package sets on the left https://search.nixos.org/packages?channel=unstable&from=0&size=50&sort=relevance&type=packages&query=*

that blows up the number! we don't have ancient, unmaintained packages found in debian, but modern ones like in #AUR

#nixos #nix #packaging

@gregorni

1. write a function
2. write roxygen documentation
3. procrastinate for a hour
4. lunch
5. write unit tests

#rstats #RPackages

@askans

In my opinion, #R is very suitable for #MachineLearning. With R, machine learning can be easily integrated into usual #rstats data analysis workflows. #RPackages provide access to virtually all relevant machine learning algorithms like #NeuralNetworks, Support Vector machines (#SVM), #RandomForests, Extreme Gradient Boosting (#XGBoost), #WEKA algorithms, etc.

Does anyone of the @rstats group have further recommendations?

See reply for sources: 4 books on machine learning.