@datascience@genomic.social2025-12-04 11:00:01
@datascience@genomic.social2025-12-04 11:00:01
@datascience@genomic.social2025-11-05 11:00:01
@datascience@genomic.social2025-10-05 10:00:01
{nplyr} has helper functions to work on nested dataframes: #rstats #datascience
@datascience@genomic.social2025-10-04 10:00:01
Im using case_when() quite a lot, case_match() is new to me: #rstats
@sascha_wolfer@fediscience.org2025-10-31 08:25:52
#Rstats problems: Did you ever think that the dots for dotted lines are a bit too far apart per default? TIL that it's super easy to change this.
In #ggplot2, simply try something like:
scale_linetype_manual(
values = c(a = "dotted", b = "11")
)
The '11' means: 1 point for a dot, 1 point for a gap. Find out more here: https://stackoverflow.com/questions/25788945/how-to-define-more-line-types-for-graphs-in-r-custom-linetype
@datascience@genomic.social2025-09-05 10:00:01
I have a habbit of making (too) many (small) packages for functionality that might be reused in different context. {box} might be an alternative by making scripts into modlues that can be loaded: #RStats
@datascience@genomic.social2025-11-04 11:00:01
Polars is a lightning fast DataFrame library/in-memory query engine with parallel execution and cache efficiency. And now you can use is with the tidyverse syntax: #rstats
@erc_bk@fosstodon.org2025-10-28 14:58:20
Hmm — an htmlwidget serializer. I'd like to see that out in the wild. #RStats
@datascience@genomic.social2025-11-03 11:00:01
@datascience@genomic.social2025-10-03 10:00:01
@EgorKotov@datasci.social2025-09-19 15:59:51
#rstats #spanishoddata will be presented next week at the MNO-MINDS ESSnet Project Final Conference https://cros.ec.eur…
![spanishoddata RS ~ TC
7 AS [1 5 Jan] “%
RO vie
R package to access national Mg <7 \
high-quality and open-access < LANNY
datasets on movement Ei [> 9
patterns derived from mobile pre. W272
telephone datasets in Spain _— 7
oT 5 github.com/rOpenSpain/spanishoddata
=
Final
Conference
| 23-25 September 2025 p
OECD - Auditorium La Muette, - J;
2 rue André Pascal, 75016 - \
Paris, France oe Deore
He MNO-MINDS
he Exmaparns Union = Fam)
Be N ou &
the European Union J pr](https://cdn.hosted.spacebear.ee/sb-7813c8e7630eb77777dba4eacfbd818e/media_attachments/files/115/231/778/273/716/854/original/e5ac0dec33a60a4b.png)
@teoten@social.linux.pizza2025-09-16 03:35:13
@datascience@genomic.social2025-10-02 10:00:01
There are many situations were you need access to different R versions: rig is a way to manage them #rstats
@sascha_wolfer@fediscience.org2025-11-21 08:27:08
On my way to the U Cologne & the SFB „Prominence in Language“ - thanks to @… 😊 See you soon! #rstats #udpipe #linguistics
@datascience@genomic.social2025-11-02 11:00:01
{piggyback} makes it easier to attach large files (e.g. input data) to code in github repos: #rstats
@datascience@genomic.social2025-12-02 11:00:01
If you use Quarto to make presentations for a professional setting, it is important to choose the right theme, e.g. #rstats
@erc_bk@fosstodon.org2025-10-30 20:52:02
Just as I've started to get a pretty comfortable with all the coordinate representations, projections, etc., I see this. If I would've came across an older dataset with this before now, I'm not sure what I would've done. Tried an internet search out of curiosity, but didn't come up with much. sp::char2dms converts it to numerical coordinates, but I didn't see any other functions/pkgs out there that does that conversion.
@datascience@genomic.social2025-10-26 11:00:01
What They Forgot to Teach You About R: #rstats
@stefan@social.linux.pizza2025-09-10 06:40:41
Hey #rstats folks! I'm curious to know if you use {renv} when developing packages? Or are there any downsides to it?
@datascience@genomic.social2025-11-01 11:00:01
Primer to get you started with Optimization and Mathematical Programming in R #rstats
@datascience@genomic.social2025-12-01 11:00:01
TidyX: screencasts explaining different aspects of the R language and the coding process. #rstats
@datascience@genomic.social2025-10-01 10:00:01
Not sure any longer which libraries your script actually needs? #rstats
@EgorKotov@datasci.social2025-09-08 12:30:27
@stefan@social.linux.pizza2025-09-10 06:40:41
Hey #rstats folks! I'm curious to know if you use {renv} when developing packages? Or are there any downsides to it?
@datascience@genomic.social2025-09-30 10:00:01
@erc_bk@fosstodon.org2025-10-14 20:25:24
Didn't know that {skimr::skim} worked an arrow object. It's pretty cool, but it hits your RAM pretty hard. That's only a 70MB directory of parquet files, and my RAM usage went up by ~1.3GB. #RStats Code: https://ray.so/fCoXwso
@datascience@genomic.social2025-09-29 10:00:01
@datascience@genomic.social2025-10-29 11:00:01
{spiralize} can be used to highlight cyclic data, e.g. multi year time series. #rstats
@datascience@genomic.social2025-11-28 11:00:01
Are you making slides with Quarto or R Markdown and need a timer e.g. for breaks or group work? There is the {countdown} package for you: #rstats
@datascience@genomic.social2025-09-28 10:00:01
A pictures says more than 1000 words. How much more can an audio representation of your data tell you? #rstats
@datascience@genomic.social2025-09-27 10:00:01
The {esquisse} package makes it easy to plot your data in different ways with a drag and drop interface: #rstats
@datascience@genomic.social2025-10-28 11:00:01
@datascience@genomic.social2025-10-27 11:00:01
Getting started with Shiny to make interactive web-apps with R: #rstats
@datascience@genomic.social2025-11-26 11:00:01
Keynote from rstudio::conf 2022: The past and future of shiny. #rstats
@datascience@genomic.social2025-09-25 10:00:01
@datascience@genomic.social2025-10-31 11:00:01
Follow along when @… walks you through how she tackles a new dataset: https://www.youtube.com/c/JuliaSilge
@datascience@genomic.social2025-10-25 10:00:01
@datascience@genomic.social2025-11-23 11:00:00
{slider} helps with aggregation over (sliding) windows, both index and time period based: #rstats
@datascience@genomic.social2025-11-29 11:00:01
GitHub Actions for the R language: Makes automatic testing of your R package much easier and making sure your package works on different OS and R versions is a matter of just a few lines of yaml: #rstats
@datascience@genomic.social2025-09-23 10:00:01
Its good to have many tests in your R package, but it can be a pain to debug some failing tests when it happens. {lazytest} for the rescue: only rerun the failing tests, until they pass: #RStats
@datascience@genomic.social2025-11-22 11:00:00
@datascience@genomic.social2025-11-21 11:00:01
@datascience@genomic.social2025-09-21 10:00:01
Use multi level models with {parsnip}: http://multilevelmod.tidymodels.org/ #rstats #ML
@datascience@genomic.social2025-11-20 11:00:01
A curated list of awesome tools to assist 📦 development in R programming language. #rstats #📦
@datascience@genomic.social2025-09-20 10:00:01
The inner working of parquette/arrow data in R: #rstats
@datascience@genomic.social2025-10-20 10:00:01
@datascience@genomic.social2025-09-19 10:00:01
{dtrack} makes documentation of data wrangling part of the analysis and creates pretty flow charts: #rstats
@datascience@genomic.social2025-11-19 11:00:01
The functions in the {withr} package allow to change your environment temporarily. E.g. create a temp file for a {testthat} test and clean it up afterwards. #rstats
@datascience@genomic.social2025-09-18 10:00:01
@datascience@genomic.social2025-11-18 11:00:01
The {purrr} package is a powerfull way to replace loops. The {furrr} package takes this approach one step further by parallel execution: #rstats
@datascience@genomic.social2025-11-16 11:00:01
Make sure your code follows a consitent style using the {lintr} package. #rstats
@datascience@genomic.social2025-09-16 10:00:01
@datascience@genomic.social2025-10-17 10:00:01
{annotater}: Annotate package load calls, so we can have an idea of the overall purpose of the libraries we’re loading: #rstats
@datascience@genomic.social2025-11-15 11:00:01
@datascience@genomic.social2025-09-15 10:00:01
Lets be honest, we spend too much time cleaning data. {janitor} can help with that: #rstats #datasciece
@datascience@genomic.social2025-10-15 10:00:01
@datascience@genomic.social2025-11-14 11:00:01
@datascience@genomic.social2025-09-24 10:00:01
Cute comics of R functions by @…: https://allisonhorst.com/r-packages-functions
@datascience@genomic.social2025-10-14 10:00:01
{testthat} is great for automatic testing. Here are some tricks for the heavy user: #rstats
@datascience@genomic.social2025-11-24 11:00:00
Do you (sometimes) use print() or message() for debugging your code? Next time you can use {icecream} instead: #rstats
@datascience@genomic.social2025-09-12 10:00:01
@datascience@genomic.social2025-11-12 11:00:01
@datascience@genomic.social2025-09-11 10:00:01
@datascience@genomic.social2025-11-11 11:00:01
Function-oriented Make-like declarative workflows for R #rstats
@datascience@genomic.social2025-10-12 10:00:01
{ivs} makes it easier to work with intervals: #rstats
@datascience@genomic.social2025-10-24 10:00:01
I am more fluent in LaTeX than in plotmath expression. If you are the same, latex2exp will make your life easier. https://cran.r-project.org/web/packages/latex2exp/vignettes/using-latex2exp.html
@datascience@genomic.social2025-09-10 10:00:01
Add some swag to your ggplots, with fontawesome symbols and colors: #rstats
@datascience@genomic.social2025-10-09 10:00:01
Tidy Modeling with R: #rstats #machinelearning
@datascience@genomic.social2025-09-09 10:00:01
@datascience@genomic.social2025-10-10 10:00:01
Sometimes (often) one ends up needing to run older versions of R using older versions of packages. Evercran might be just the tool to help with that: #RStats…
@datascience@genomic.social2025-11-08 11:00:01
Beside the {report} package (yesterdays note) there are more tools in the easystats collection. #rstats
@datascience@genomic.social2025-09-08 10:00:01
{purrr} has some lesser known functions that make handling of failing function calls easier: safely, quietly, possibly: #rstats
@datascience@genomic.social2025-10-08 10:00:01
Extract tables from pdfs with {tabulapdf} #rstats #datasciece
@datascience@genomic.social2025-11-07 11:00:01
Automatically describe data and models as text using the {report} package. #rstats
@datascience@genomic.social2025-09-07 10:00:01
Interactive resizing of picture and table content in Rmd and Quarto: #rstats
@datascience@genomic.social2025-09-06 10:00:01
The {conflicted} package makes sure that namespace conflicts are solved explicitly and prevents unpleasent surprises: #rstats
@datascience@genomic.social2025-09-17 10:00:01
Friends Don't Let Friends Make Bad Graphs! Do you agree with the examples of bad graphs and the alternatives Chenxin Li (@chenxinli2.bsky.social) lists at #RStats
@datascience@genomic.social2025-10-18 10:00:01
It has happened to the best of us: You forgot the name of a function or the package that function was in but you are sure its there somewhere and does exactly what you need! Introduce {forgot}, it helps you find that function: https://github.com/parmsam/forgot
@datascience@genomic.social2025-09-14 10:00:01
Easier debugging of piped analyses in R: https://github.com/MilesMcBain/breakerofchains by @…
@datascience@genomic.social2025-09-13 10:00:01
If you set limits for a scale (e.g. x-axis) in ggplot, how would you like data outside of that range be handled? There is the oob parameter for that and a set of functions to use with it: https://scales.r-lib.org/reference/oob.html
@datascience@genomic.social2025-11-10 11:00:00
A template for data analysis projects structured as R packages (or not) https://github.com/Pakillo/template by @…
@datascience@genomic.social2025-11-09 11:00:01
There are frameworks like {golem} and {rhino} to make shiny development more robust, but I like the concept of {shinytest2} in providing a testing framework for pure shiny. https://rstudio.github.io/shinytest2/index.html