Big Book of R

#rlang #r
https://www.bigbookofr.com/

Running RStudio in a rootless container on Linux.

This approach avoids dealing with compiling basic packages or adding extra repositories to your main OS, and it's targeted for running the container on your computer and not somewhere else.

1. Get Podman (make sure it works rootless)
2. Run the following command in a terminal (see "man podman-run" for details on what it does)

podman run --rm -it -p 8787:8787 -e PASSWORD=baa -v $HOME:/root/home -v $HOME/.config/rstudio:/root/.config/rstudio docker.io/rocker/verse

there are two programming languages i love

#rlang because you can modify anything, anywhere, anytime

#rust because you can't

off-late, I've been feeling like i need to start learning a programming language with practical applications. something I can use to write stuff on my Linux system for my use or something which i can put up on my Sourcehut. and something a little challenging too, because where's the fun in straightforward stuff?

R has been great but, of course, that's just for data analysis. python seemed interesting but more recently Rust is what has been drawing me in.

how does one get started with Rust? what are some good resources for learning and practicing the language? i can figure out the Emacs side of things myself.

Looking at some of my old R code this morning... As #rlang goes, it's good code, but I wish now I'd done it with #CommonLisp or #julialang
#TechnicalDebt

I'm explaining Hamiltonian Monte Carlo in my grad-level stats class tomorrow, so I put together this animation illustrating HMC in one dimension. I find it very soothing.

#Rlang question for #bioinformatics & #datascience folks.

I'm used to python/julia way of running everything in a contained environment. What's the best way to do it for R these days? Renv or just stick with conda?

What does #rlang need to help folks easily migrate to #codeberg ?

Some ideas:
codeberg equivalent to r-lib/actions
migration guide
usethis::use_codeberg() - create new repo or provide instructions for migrating if already on GitHub, similar to use_github
usethis::use_codeberg_ci() - equivalent to use_github_action

anything else that would help people make the jump?

post: Geocaching meets R language

This post is about solving a task with R language for one letterbox hybrid cache, which happen to be my favorite.

https://www.sarahgebauer.com/post/geocaching-meets-r-language

`R:Pak` is pretty good! Really impressed with the installation messaging, especially having not used R for a while!

https://pak.r-lib.org/

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

Every once in a while I say to myself: "It seems like using LLMs to code is widespread. Every time I've tried this in the past it has provided objectively wrong code at least half the time, but surely things must have improved if everyone is still using them!"

Today was one of those days (giving qwen2.5-coder some very simple R problems) and, nope, still just absolute garbage output. The majority of the code it produced was sometimes obviously, but sometimes subtly wrong.

#Seagl2024 presents: Culling Seagull Records: Using R to Curate Community Science Checklists
By: Richard Littauer
Saturday, 11/09 13:30PST
Category: Everything Else
Link: https://pretalx.seagl.org/2024/talk/7QPK9S/
#FOSS #FLOSS #RLang #science

When writing strings, #Rlang
does not have the option of using triple quotes like in Python. However, since #RStats

4.0 (April 2020) makes it possible to use `r"(...)"`, similar to C++, so you don't have to worry about single or double quotes. https://stat.ethz.ch/pipermail/r-announce/2020/000653.html

Blog post: Cutting down the size of eBird datasets using R to help speed of dev work.

https://www.burntfen.com/2024-10-24/cutting-down-the-size-of-ebird-datasets-for-r-work

Migrated my first #rlang package repo from GitHub to #Codeberg

https://codeberg.org/dgkf/options

In the process:

set up mirroring back to GH
added a notice of migration with rationale
adapted my most-used r-lib/actions (R CMD check, test, coverage, pkgdown)
set up pages publishing

Next up, publishing a release from the new project home.

#Pixi pack: una opción para resolver el problema de distribución de paquetes multiplataforma y de lenguaje agnóstico.
Pixi es un proyecto de @prefix que moderniza la gestión de dependencias basado en paquetes #conda, que por defecto uso el canal de #condaforge (luego, no es susceptible a los cambios de licencia de #anaconda).

#python #rlang #data #fortran

@prefix https://hachyderm.io/@prefix/113151718824543158