📈 R language is making a comeback – Tiobe

“Programming language R is known for fitting statisticians and data scientists like a glove,” said Paul Jansen, CEO of software quality services vendor Tiobe, in a bulletin accompanying the December index. “As statistics and large-scale data visualization become increasingly important, R has regained popularity.”

https://www.infoworld.com/article/4102696/r-language-is-making-a-comeback-tiobe.html

#r #rlang #science #stats

R language is making a comeback – Tiobe

Tiobe index of programming language popularity index has the R language for statistical computin back in its top 10.

InfoWorld
Want to use {pak} as backend for {renv} actions? Set the variable RENV_CONFIG_PAK_ENABLED = TRUE https://github.com/rstudio/renv/issues/1210 #renv #pak #reproducibility #RLang
[pak] Use pak for package installation · Issue #1210 · rstudio/renv

Related issues: #1268 #1331 #1330 #1334

GitHub

Hello again, R . . .

R for sure has deficits (but so does python), but for exploratory data analysis, particularly ones heavy in statistics, R can sometimes shine. I still abhore passing variables into functions, but I get why for simple EDA one might want non-standard evaluation.

R is making a comeback:
https://www.infoworld.com/article/4102696/r-language-is-making-a-comeback-tiobe.html

#rlang

R language is making a comeback – Tiobe

Tiobe index of programming language popularity index has the R language for statistical computin back in its top 10.

InfoWorld
Dark times call for desperate measures, and with my career being a little hosed, I'm writing a book! It's about teaching R users to write code like software engineers do, and the hope is that I can use it partially as content marketing for my coaching offerings. Here's the first chapter: #R #R-lang

R the Software Engineering Way...
R the Software Engineering Way: Introduction and Chapter Zero | deadSimpleTech

It is worth noting from the very beginning that a software engineer's work doesn't start with writing code, but with setting up the development environment and the tools that they need to write code effectively. Good tooling can make the difference between you writing clean, tight, maintainable code on the one hand and creating an unmaintainable abomination on the other. This entire first chapter, then, is dedicated to setting up a development environment that lets you build things in R in a consistent, reproducible and easy to fix or revert way. We'll start with basic command line skills, move on to version control and then finally discuss containerisation and the setting up of a development container for your project.

deadSimpleTech

Heya R devs - did you know you can run all your favorite GitHub actions on Codeberg?

Codeberg is rolling out Forgejo actions - an (almost) drop-in replacement for GitHub actions, which means we can (almost) use `r-lib/actions` directly on a free and open source platform!

Just a couple tweaks are needed, and for your convenience I'm automatically mirroring r-lib/actions and applying those changes so they're ready to use.

https://codeberg.org/r-codeberg/r-lib-actions

#rlang #rstats #codeberg

r-lib-actions

A read-only mirror of github.com/r-lib/actions with minimal changes in order to support use as Forgejo Actions

Codeberg.org
@etiennebacher Jarl is working as an R linter in #HelixEditor now, thank you! https://jarl.etiennebacher.com/editors#helix #RLang
Editor support – jarl

Update at haikuports, TEXstudio updated to 4.9.0, R updated to 4.5.2

In the screenshot Cantor 25.08.3 with R.

Too bad we can't update RKWard yet due to it's dependency of qtwebengine (Qt6)

#HaikuOS #haikuports #Rlang #TEXstudio #opensource #software

Update at haikuports, TEXstudio updated to 4.9.0, R updated to 4.5.2

In the screenshot Cantor 25.08.3 with R.

Too bad we can't update RKWard yet due to it's dependency of qtwebengine (Qt6)

#HaikuOS #haikuports #Rlang #TEXstudio #opensource #software

@tommytang.bsky.social On #ComputerLanguages for #Bioinformatics There is an era of #Bioinformaticians who learned #R #RLang back when #Python #PythonLang wasn't very good at statistics/plotting. More recently, the trend has shifted towards the latter.

Doing a little geo math in R the other day, and for some reason my computer was really struggling to calculate ~3 million distances between pairs of locations. I figured my intuition was just off, and set it aside.

Today I took a second look - did it on a small subset and...figured out that the "m" in "distm" is not *meters*, but rather, *matrix*, and I was asking it to calculate the distance between every unique combination of my three million locations, and thus calculating something like 10 *trillion* distances. Oops!

Let this be a reminder of the value of running things on a subset first to do a little reasonableness check  #rlang #statistics