@datascience@genomic.social2026-01-22 11:00:01
@datascience@genomic.social2026-01-22 11:00:01
@datascience@genomic.social2026-01-23 11:00:01
@datascience@genomic.social2025-11-23 11:00:00
{slider} helps with aggregation over (sliding) windows, both index and time period based: #rstats
@datascience@genomic.social2025-12-22 11:00:01
@sascha_wolfer@fediscience.org2026-01-22 08:35:08
What‘s your go-to #python or #rstats tool(chain) for splitting #German compounds? I‘ve tried a few but was not really satisfied. Maybe I missed something. #NLP #linguistics
@datascience@genomic.social2026-02-18 11:00:01
What They Forgot to Teach You About R: #rstats
@datascience@genomic.social2026-02-21 11:00:01
{spiralize} can be used to highlight cyclic data, e.g. multi year time series. #rstats
@EgorKotov@datasci.social2026-01-10 21:44:57
@datascience@genomic.social2025-12-21 11:00:01
Do you need inspiration how to present a dataset in a clear figure and what package to use? Check out #rstats
@datascience@genomic.social2026-02-23 11:00:01
Follow along when @… walks you through how she tackles a new dataset: https://www.youtube.com/c/JuliaSilge
@datascience@genomic.social2026-01-21 11:00:00
A pictures says more than 1000 words. How much more can an audio representation of your data tell you? #rstats
@datascience@genomic.social2026-02-22 11:00:01
{ggblanket}, a wrapper around #ggplot for quick, explorative plots with sensible defaults and less code. https://davidhodge931.github.io/ggblanket/
@EgorKotov@datasci.social2025-12-10 17:23:40
@sascha_wolfer@fediscience.org2025-12-07 15:48:57
@datascience@genomic.social2026-01-20 11:00:00
The {esquisse} package makes it easy to plot your data in different ways with a drag and drop interface: #rstats
@erc_bk@fosstodon.org2026-01-25 17:20:08
![time_slices <- 300 # 300 days
block_length <- 14 # 2 weeks
n_samples <- 200 # 200 resamples
block_idxs <- make_block_indices(
n_time = dim(r5to10)[2],
time_slices = time_slices,
block_length = block_length,
n_samples = n_samples
)
plan(future.mirai](https://cdn.fosstodon.org/media_attachments/files/115/956/878/735/908/539/original/68da6a09c87a2466.png)
@datascience@genomic.social2026-02-20 11:00:00
@EgorKotov@datasci.social2026-02-06 21:55:01
#rstats osrm.backend 0.2 is out.
I fixed a bug that might have caused lockup if you tried to calculate too large origin-destination matrices. Should work just fine now! (though there is a limit of course, as it works over REST API, but parallel requests are your friend).
osrm.backend::osrm_start("data_folder")
- router is installed automagically on all OS
- graph p…
@datascience@genomic.social2026-02-19 11:00:01
Getting started with Shiny to make interactive web-apps with R: #rstats
@datascience@genomic.social2026-01-18 11:00:01
@sascha_wolfer@fediscience.org2025-12-16 14:56:49
When #teaching #Rstats / #statistics courses, I (and several colleagues of mine) made the experience that it is indeed pretty hard for a lot of students to cope with the file system on their computer. They have questions like: How do I know the "path" of a file? How do I control in which directory something is saved? WHY DO I NEED THIS?!?
I don't want to make fun of these students because I know that this could be because operating systems are increasingly obscuring file/directory systems from their users.
But if I want to teach students to use a scripting/ #programming language independently, that's a real problem!
So my questions to you are: Do you have the same impression when teaching? And if so: How do you deal with this from a teaching perspective? To be honest, I don't want to use precious course time to teach the absolute basics of computers' file systems in the first session(s).
@datascience@genomic.social2025-12-17 11:00:00
@datascience@genomic.social2026-02-17 11:00:00
@datascience@genomic.social2025-12-16 11:00:00
@datascience@genomic.social2026-01-16 11:00:00
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.social2026-01-14 11:00:00
Use multi level models with {parsnip}: http://multilevelmod.tidymodels.org/ #rstats #ML
@datascience@genomic.social2026-01-13 11:00:00
The inner working of parquette/arrow data in R: #rstats
@datascience@genomic.social2026-02-12 11:00:01
@datascience@genomic.social2025-12-12 11:00:01
R doesnt need to be a hard and scientific tool 📈. You can use it to make art 🎨: #rstats
@datascience@genomic.social2026-01-12 11:00:00
{dtrack} makes documentation of data wrangling part of the analysis and creates pretty flow charts: #rstats
@datascience@genomic.social2026-01-11 11:00:00
@datascience@genomic.social2025-12-11 11:00:01
Add highlighting to your quarto presentation using the RoughNotation library: #rstats
@datascience@genomic.social2025-12-10 11:00:01
Are you interested in how dependency-heavy your (or another) package is and why? #rstats
@datascience@genomic.social2026-01-09 11:00:00
@datascience@genomic.social2026-02-09 11:00:00
{annotater}: Annotate package load calls, so we can have an idea of the overall purpose of the libraries we’re loading: #rstats
@datascience@genomic.social2025-12-09 11:00:01
Using fonts in R graphics can be tricky at times. {showtext} aims to make it easier: #rstats
@datascience@genomic.social2026-02-16 11:00:00
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.social2026-01-08 11:00:00
Lets be honest, we spend too much time cleaning data. {janitor} can help with that: #rstats #datasciece
@datascience@genomic.social2026-02-07 11:00:01
@datascience@genomic.social2025-12-07 11:00:00
Need some data to test a plot idea or algorithm? On #rstats #synthetic…
@datascience@genomic.social2025-12-27 11:00:00
@datascience@genomic.social2026-02-06 11:00:00
{testthat} is great for automatic testing. Here are some tricks for the heavy user: #rstats
@datascience@genomic.social2026-01-05 11:00:00
@datascience@genomic.social2026-01-04 11:00:00
@datascience@genomic.social2025-12-04 11:00:01
@datascience@genomic.social2026-02-04 11:00:00
{ivs} makes it easier to work with intervals: #rstats
@datascience@genomic.social2026-01-03 11:00:00
Add some swag to your ggplots, with fontawesome symbols and colors: #rstats
@datascience@genomic.social2026-02-01 11:00:00
Tidy Modeling with R: #rstats #machinelearning
@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
@datascience@genomic.social2026-02-02 11:00:01
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.social2026-01-02 11:00:00
@datascience@genomic.social2025-12-01 11:00:01
TidyX: screencasts explaining different aspects of the R language and the coding process. #rstats
@datascience@genomic.social2026-01-01 11:00:00
{purrr} has some lesser known functions that make handling of failing function calls easier: safely, quietly, possibly: #rstats
@datascience@genomic.social2026-01-31 11:00:01
Extract tables from pdfs with {tabulapdf} #rstats #datasciece
@datascience@genomic.social2025-12-31 11:00:00
Interactive resizing of picture and table content in Rmd and Quarto: #rstats
@datascience@genomic.social2025-12-30 11:00:00
The {conflicted} package makes sure that namespace conflicts are solved explicitly and prevents unpleasent surprises: #rstats
@datascience@genomic.social2025-12-28 11:00:00
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.social2025-12-29 11:00:00
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.social2025-11-28 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.social2026-01-28 11:00:00
{nplyr} has helper functions to work on nested dataframes: #rstats #datascience
@datascience@genomic.social2026-01-10 11:00:00
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.social2026-02-10 11:00:00
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.social2026-01-27 11:00:00
Im using case_when() quite a lot, case_match() is new to me: #rstats
@datascience@genomic.social2025-11-26 11:00:01
Keynote from rstudio::conf 2022: The past and future of shiny. #rstats
@datascience@genomic.social2026-01-26 11:00:00
@datascience@genomic.social2025-12-26 11:00:00
The fastverse is a suite of complementary high-performance packages for statistical computing and data manipulation in R. #rstats
@datascience@genomic.social2025-12-25 11:00:01
Customize what happens when you start R: #rstats #environment
@datascience@genomic.social2026-01-25 11:00:01
There are many situations were you need access to different R versions: rig is a way to manage them #rstats
@datascience@genomic.social2026-01-24 11:00:00
Not sure any longer which libraries your script actually needs? #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.social2025-11-24 11:00:00
Do you (sometimes) use print() or message() for debugging your code? Next time you can use {icecream} instead: #rstats