R libraries implement performance-critical code in C++. But memory bugs in C++ code crash R, such as in this screenshot, even though R is designed to be memory safe.
fixest is an excellent library but a semi-frequent offender. And I've had this happen with other libraries too.
As someone who is using R because I am not prepared to debug C++, this can pretty much break a library for me. I hope that eventually Rust can take over C++'s role in #rstats
Hey #rstats people! I have a dataset where I’ve asked people to rank a set of 10 items from 1 to 10, and I want to compare those rankings between two groups. Sounds super simple, but I can’t work out what #statistic to use! I’ve found the nParLD package, but that’s not quite right as it’s not longitudinal data. Help?
A lightweight package that verifies your data meets your specified rules, and automatically selects the correct package (DBI, dplyr, data.table etc) to carry out the checks.
The {ggautomap} #rstats 📦 “provides #ggplot2 geometries that make use of cartographer, a framework for matching place names with map data. With ggautomap your input dataset doesn’t need to be spatially aware: The geometries will automatically attach the map data (providing it’s been registered with cartographer).” https://cidm-ph.github.io/ggautomap/index.html
By Carl Suster, on CRAN #RSpatial#GIS@rstats
I’m done cross-posting @elementary to Twitter. I really hoped it would get better, but with the latest news of Elon promoting Ron DeSantis’ presidential run, I’m done. It’s become a far-right social network no different from Parlor or Truth Social and I won’t be a part of it.
@danirabbit Big hugs. I left 20K followers in the #rstats world behind over there when I made the same call a while back. It’s scary. I hope folks are supportive. ❤️
When you struggle with theme()-arguments in {ggplot2}, you may like {ggeasy}, offering shortcuts to plot customization https://github.com/jonocarroll/ggeasy (worth visiting just bc of the hilarious cartoon)
I am skeptical when a package promises to make plotting easier because you have to remember the arguments you are looking for in either case. Here, however, I can see the added value for some arguments #rstats#dataviz@rstats
What does the network of people with common package contributions look like?
And much more!
There are also interactive {gt} tables so you can browse contribution and package statistics; and I’ve shared the data so you can explore your own questions. Enjoy!
The goal was to learn about applying splines to a circle for the polygon chapter, but splines are too hard for my brain right now, so this resulted instead. Forever searching for less computationally heavy ways to make space 🌃 #rtistry in #rstats w/ geom_polygon() & geom_point()
I've made a lightweight glossary #rstats package for quarto and R Markdown documents. You just tag words in your text like r glossary("term") and create a glossary table at the end of the section with glossary_table(). The definitions can be set in each glossary() function, or pulled from a YAML file.
I'm hoping to submit to CRAN soon, but would love if anyone had time for a quick test and feedback.
I wrote a thing. Again. I'm sorry. But also, the santoku #rstats package is a very nice solution to a problem that some of us have. Discretising a continuous variable is weirdly painful, and it's nice to have computational tools that get out of your way and let you think about what you want to do with your data
Helped someone debug some tidyverse data processing issues. It turns out "NA" was a legitimate code used in their data and readr by default interprets it as NA, not a string. Careful folks! #rstats
Edit: for anyone who doesn't know, read_csv() has an na parameter. The default is na = c("", "NA"). Setting it to na = "" fixed the issue.
@odr_k4tana@sharoz
in some ways the raise of the #tidyverse sect has funnelled as side effect a general less knowledge of #rstats When I see people call readr::read_rds instead of readRDS I just despair