@datascience@genomic.social2025-09-12 10:00:01
@datascience@genomic.social2025-09-12 10:00:01
@datascience@genomic.social2025-10-12 10:00:01
{ivs} makes it easier to work with intervals: #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.social2025-09-11 10:00:01
@EgorKotov@datasci.social2025-09-08 12:30:27
@datascience@genomic.social2025-11-11 11:00:01
Function-oriented Make-like declarative workflows for 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.social2025-09-13 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.social2025-08-13 10:00:01
Sometimes you get data in less than optimal format, e.g. as a png of a figure 😭... In that case https://cran.r-project.org/web/packages/metaDigitise/vignettes/metaDigitise.html might be the rescue.
@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.social2025-09-10 10:00:01
Add some swag to your ggplots, with fontawesome symbols and colors: #rstats
@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…
@EgorKotov@datasci.social2025-09-03 11:14:38
#Accessibility modelers using #r5r #rstats, check this GUI for playing around with R5 network. If many people find it useful, I would get signal if I should invest any more free time into it.
@teoten@social.linux.pizza2025-09-16 03:35:13
@datascience@genomic.social2025-10-09 10:00:01
Tidy Modeling with R: #rstats #machinelearning
@datascience@genomic.social2025-09-09 10:00:01
@EgorKotov@datasci.social2025-08-31 14:45:06
New #rstats https://github.com/e-kotov/gridmaker Creates Eurostat GISCO compatible and INSPIRE-compliant grids with IDs that look like ‘CRS3035RES1000mN3497000E4448000’ or ‘1kmN3497E4447’. Input can be sf, or …
@datascience@genomic.social2025-10-08 10:00:01
Extract tables from pdfs with {tabulapdf} #rstats #datasciece
@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
@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
@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.social2025-11-10 11:00:00
A template for data analysis projects structured as R packages (or not) https://github.com/Pakillo/template by @…
@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…
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@datascience@genomic.social2025-09-03 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.social2025-09-06 10:00:01
The {conflicted} package makes sure that namespace conflicts are solved explicitly and prevents unpleasent surprises: #rstats
@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.social2025-09-05 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.social2025-11-09 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
@datascience@genomic.social2025-09-04 10:00:01
Linear programs help to find optimal solutions based on a set of constrains. I used {ompr} before, but the new package {tidyLP} looks promising and integrates with the tidyverse. #rstats #linearprograms #optimization
@datascience@genomic.social2025-10-04 10:00:01
Im using case_when() quite a lot, case_match() is new to me: #rstats
@datascience@genomic.social2025-11-03 11:00:01
@datascience@genomic.social2025-10-03 10:00:01
@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
@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.social2025-09-02 10:00:01
The fastverse is a suite of complementary high-performance packages for statistical computing and data manipulation in R. #rstats
@datascience@genomic.social2025-09-01 10:00:01
Customize what happens when you start R: #rstats #environment
@datascience@genomic.social2025-11-01 11:00:01
Primer to get you started with Optimization and Mathematical Programming in R #rstats
@datascience@genomic.social2025-10-01 10:00:01
Not sure any longer which libraries your script actually needs? #rstats
@datascience@genomic.social2025-09-30 10:00:01
@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.social2025-09-28 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.social2025-08-29 10:00:01
@datascience@genomic.social2025-08-28 10:00:01
@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.social2025-09-25 10:00:01
@datascience@genomic.social2025-10-25 10:00:01
@datascience@genomic.social2025-08-24 10:00:01
@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.social2025-08-23 10:00:01
@datascience@genomic.social2025-09-21 10:00:01
Use multi level models with {parsnip}: http://multilevelmod.tidymodels.org/ #rstats #ML
@datascience@genomic.social2025-09-20 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.social2025-09-18 10:00:01
@datascience@genomic.social2025-08-19 10:00:00
R doesnt need to be a hard and scientific tool 📈. You can use it to make art 🎨: #rstats
@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.social2025-08-18 10:00:02
Add highlighting to your quarto presentation using the RoughNotation library: #rstats
@datascience@genomic.social2025-09-16 10:00:01
@datascience@genomic.social2025-08-17 10:00:01
Are you interested in how dependency-heavy your (or another) package is and why? #rstats
@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.social2025-09-15 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.social2025-08-16 10:00:01
Using fonts in R graphics can be tricky at times. {showtext} aims to make it easier: #rstats
@datascience@genomic.social2025-08-14 10:00:00
Need some data to test a plot idea or algorithm? On #rstats #synthetic…
@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.social2025-09-24 10:00:01
Cute comics of R functions by @…: https://allisonhorst.com/r-packages-functions
@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.social2025-09-17 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 @…