@noamross
A simple bar chart is far better than this.

#dataviz #datavis #rstats

#RStats challenge: How to make a pie-beeswarm like this one from the Minnesota Star Tribune showing the relative sizes of ICE/CBP and local police forces?

https://www.startribune.com/how-ice-numbers-compare-to-twin-cities-largest-police-forces/601562617

(Challenge 2: Try not to argue about pie charts in the replies 😉)

[blog] Introducing distionary for Building and Probing Distributions 🐟 🔔

Vincenzo Coia presents distionary, a package for quickly and easily making probability distributions from a couple of inputs or from the built-in library.
Explore the package's main features with some nice examples and usecases.

https://ropensci.org/blog/2026/01/20/introducing-distionary/

🙏 Shoutouts to reviewers Katrina Brock and Christophe Dutang

#RStats
@rstats@a.gup.pe

#RStats challenge: How to make a pie-beeswarm like this one from the Minnesota Star Tribune showing the relative sizes of ICE/CBP and local police forces?

https://www.startribune.com/how-ice-numbers-compare-to-twin-cities-largest-police-forces/601562617

(Challenge 2: Try not to argue about pie charts in the replies 😉)

[blog] Introducing distionary for Building and Probing Distributions 🐟 🔔

Vincenzo Coia presents distionary, a package for quickly and easily making probability distributions from a couple of inputs or from the built-in library.
Explore the package's main features with some nice examples and usecases.

https://ropensci.org/blog/2026/01/20/introducing-distionary/

🙏 Shoutouts to reviewers Katrina Brock and Christophe Dutang

#RStats
@rstats@a.gup.pe

#rstats I've given a workshop a few days ago on handling large data (think tens to hundreds of millions of rows) with Polars in Python and in R.

Here are my introductory slides: https://brussels-large-data-r-python.etiennebacher.com

And the associated repo: https://github.com/etiennebacher/brussels_teaching_january_2026

#rstats I've given a workshop a few days ago on handling large data (think tens to hundreds of millions of rows) with Polars in Python and in R.

Here are my introductory slides: https://brussels-large-data-r-python.etiennebacher.com

And the associated repo: https://github.com/etiennebacher/brussels_teaching_january_2026

No codecov.io support? No problem. Use {covr} to generate the stats and generate your own badge dynamically.

https://codeberg.org/adamhsparks/epicrop/src/commit/86b79211bfaf4cd51646f23e0ae9a361f23c34e2/README.Rmd#L12

You can also show users more specific coverage stats as well.
https://codeberg.org/adamhsparks/epicrop/src/commit/d5a54f5f1d4d2208a3c8353db7cf468971d6c3fe/README.Rmd#L103

It looks like this when rendered: https://codeberg.org/adamhsparks/epicrop/src/branch/main/README.md

#RStats

No codecov.io support? No problem. Use {covr} to generate the stats and generate your own badge dynamically.

https://codeberg.org/adamhsparks/epicrop/src/commit/86b79211bfaf4cd51646f23e0ae9a361f23c34e2/README.Rmd#L12

You can also show users more specific coverage stats as well.
https://codeberg.org/adamhsparks/epicrop/src/commit/d5a54f5f1d4d2208a3c8353db7cf468971d6c3fe/README.Rmd#L103

It looks like this when rendered: https://codeberg.org/adamhsparks/epicrop/src/branch/main/README.md

#RStats

#RStats📦rix 0.17.4 is out!

Major #Python QoL improvements: automatic LD_LIBRARY_PATH setup for easy integration with uv, new py_src_dir for editable-style Python dev in #Nix shells, and smarter cachix detection (no more noise on #NixOS).

Plus docs cleanup & cache setup updates

"Géomatique et cartographie avec R" has been updated! 🗺️

https://rgeocarto.github.io/

- Données vectorielles avec {sf}
(vector data processing)
- Données raster avec {terra}
(raster data processing)
- Cartographie avec {mapsf}
(thematic mapping)
- OpenstreetMap

This manual is in French. A lighter, slightly outdated English version is available here: https://rcarto.github.io/cartography_with_r/

#gis #gisChat #rstats #rspatial

{read.abares} Looks good dressed in @rOpenSci doc formatting!

https://docs.ropensci.org/read.abares/

#RStats

I have released version 0.6.2 of @rOpenSci 's {{charlatan}}. An #RStats package to generate realistic looking data.

This point release came from a change in CRAN or the devtools package that showed me that I duplicated something in the code.

"Géomatique et cartographie avec R" has been updated! 🗺️

https://rgeocarto.github.io/

- Données vectorielles avec {sf}
(vector data processing)
- Données raster avec {terra}
(raster data processing)
- Cartographie avec {mapsf}
(thematic mapping)
- OpenstreetMap

This manual is in French. A lighter, slightly outdated English version is available here: https://rcarto.github.io/cartography_with_r/

#gis #gisChat #rstats #rspatial

#RStats📦rix 0.17.4 is out!

Major #Python QoL improvements: automatic LD_LIBRARY_PATH setup for easy integration with uv, new py_src_dir for editable-style Python dev in #Nix shells, and smarter cachix detection (no more noise on #NixOS).

Plus docs cleanup & cache setup updates

{read.abares} Looks good dressed in @rOpenSci doc formatting!

https://docs.ropensci.org/read.abares/

#RStats

#rstats #dataviz #psy6136
Lecture 2 for my course in Categorical Data Analysis, on discrete distributions

Slides: https://friendly.github.io/psy6136/lectures/02-Discrete.pdf

I have released version 0.6.2 of @rOpenSci 's {{charlatan}}. An #RStats package to generate realistic looking data.

This point release came from a change in CRAN or the devtools package that showed me that I duplicated something in the code.

#rstats #dataviz #psy6136
Lecture 2 for my course in Categorical Data Analysis, on discrete distributions

Slides: https://friendly.github.io/psy6136/lectures/02-Discrete.pdf