If you've wanted to get started with #python and #rstats in the browser (wasm style), don't miss out on Quarto-live, which makes this very easy: https://github.com/r-wasm/quarto-live
I'm using it myself on this (very much WIP) beginners course in R: beginr.org
We’re thrilled to announce the release of rsample 1.3.0!
rsample makes it easy to create resamples for assessing model performance. New updates include flexible grouping for bootstrap intervals and updates from tidyverse developer day.
Learn more: https://www.tidyverse.org/blog/2025/04/rsample-1-3-0/
idk. i’m very pessimistic that anything at all can get through to people there. i get the very strong impression that people there want me to share the pretty pictures, write the funny and self-deprecating #rstats blog posts, and then shut my fucking mouth about anything personal. danielle it’s so *unprofessional* for you to have messy reactions to being raped, or to have feelings about your human rights being wiped out in both the US and the UK in a matter of months. you aren’t here to have feelings you’re here to make nice things for us
Catching up on Day 16 of the #30DayChartChallenge with the prompt of "Negative"! 
Waterfall chart made in #RStats
Doesn't feel as intuitive when decreases are a positive outcome
Needed a few hacky approaches to get the right layout
Well this looks interesting!
https://cidree.github.io/duckspatial/
Thank you, past me. I have no recollection of writing this, but it's exactly what I need right now
https://gist.github.com/adamhsparks/5d50d77cfce1ba1d05407996bb29e089
It is now possible to easily pass data from and to Python and #RStats by serializing to json (but you can still convert more complex objects using rxp_py2r() and rxp_r2py() which uses {reticulate} under the hood for conversion)
Had a 
suggestion from @LeoRJorge to make it clearer the segments were countries:
Updated #RStats code @ https://ray.so/yNVidrt
I messed around with DBI and RSQLite and learned it’s actually pretty simple to use in R - just connect, write tables, and use SQL queries without all the complicated server stuff. Thanks to Alec Wong for suggesting this! #rstats
@defuneste @hrbrmstr @danwwilson yeah. I’m tired of checking multiple places. I’m mostly all in here. At least for workish / #RStats stuff
@hrbrmstr, I've definitely noted their lack of presence here after a reasonably good start. Once BS took off, gone...
It puzzles me given that there is a whole server here dedicated to #RStats, thanks @danwwilson, and the thought that Posit is a B Corp, I'd think this would be more in tune with the ethos. But I suspect that's just me looking at the whole situation through my own lens and that lens distorts BS due to my own personal dislike of that site even before this caving.
@joranelias Welcome to seeing the light!
Use code 'at @head', use packages that take stability and backward compatibility seriously, and when you must 'freeze' something build a Docker container (ideally off @head, easily and reliably and quickly with eg #r2u). #rstats
I “use” renv pretty regularly, but increasingly I wonder why I bother. More than half the time when I actually have to *use* it to restore a project that hasn't been touched in more than a year or two renv::restore() fails with tons of errors, I struggle for hours to figure it out and typically just give up and simply install current versions of all the packages and pray. #rstats
@josi yeap. I was very surprised to see this in real life because I thought the long vector issue was no longer an issue. It means that everything that has to interface with the data.frame type will be limited by this until it is addressed. We are shifting our workflows from data.table to duckdb (outside R) and then reintegrating back to #rstats when the datasets are smaller
Breaking #rstats. Yesterday I pointed out that data.table cannot handle large datasets (>2^31 rows). It seems data.frame in base R also has the same issue.
While a vector of that size will be generated without problems, putting it in a data.frame will fail (ALT-text for code)
![> r<-1:5
> r
[1] 1 2 3 4 5
> r<-1:2^31
> length(r)
[1] 2147483648
> d<-data.frame(r=r)
Error in if (mirn && nrows[i] > 0L) { :
missing value where TRUE/FALSE needed
In addition: Warning message:
In attributes(.Data) <- c(attributes(.Data), attrib) :
NAs introduced by coercion to integer range
>](https://mast.hpc.social/system/media_attachments/files/114/359/567/600/663/485/original/00f5ae3996b5e56f.png "> r<-1:5
> r
[1] 1 2 3 4 5
> r<-1:2^31
> length(r)
[1] 2147483648
> d<-data.frame(r=r)
Error in if (mirn && nrows[i] > 0L) { :
missing value where TRUE/FALSE needed
In addition: Warning message:
In attributes(.Data) <- c(attributes(.Data), attrib) :
NAs introduced by coercion to integer range
>")
La galería de proyectos del programa de Campeon(a|e)s es muy diversa y muestra la posibilidad de desarrollar ideas que colaboren a la ciencia abierta y reproducible.
Por ejemplo, el paquete {agroclimatico} por @paocorrales que incluye una serie de funciones para calcular índices y estadísticos climáticos e hidrológicos, algunos de ellos enfocados al sector agropecuario: https://docs.ropensci.org/agroclimatico/
Aplica aqui: https://champions-program.ropensci.org
Recent @DSLC club meetings:
R for Data Science: A field guide to base R https://youtu.be/XZoy3HWtkLI #RStats #R4DS
From the @DSLC chives:
"Advanced R: Vectors" https://youtu.be/mmcnkIjANps #RStats
:: "The Rust Programming Language: Getting Started & Introduction" https://youtu.be/DLG12TYJtkA #rustRustLangRustRustLang
Visit https://dslc.video for hours of new #DataScience videos every week!
I've been learning more D3 recently, and I figured the best (or at least most fun) way to learn it, was to make some generative art! 
This was one of the ways that helped me to understand #RStats better because using functions for things they weren't designed for helps you learn how they break
So here's my first attempt!
Making use of highlighting and annotations
Different layouts using facets
