Måns Thulin

Wow ... I'd never come across this use of with(), which allows combining the pipe and computing a correlation coefficient. Thanks to @mansthulin.bsky.social for explaining this in his fantastic book www.modernstatisticswithr.com!
#Rstats #code #neverknew

I'm extremely biased, but I think Modern Statistics with R is a great choice 😁
www.modernstatisticswithr.com

YouTube video by R Consortium Bootstrap inference made easy: p-values and confidence intervals in one line of code

I really enjoyed attending and speaking at R/Medicine this year! I learned a lot. Huge thanks to the organisers! My talk "Bootstrap inference made easy" is now available online: www.youtube.com/watch?v=EeAt...

#Rstats #Statsky

Very nice! I really like what this package has turned into. Now, if only it worked seamlessly with pipes... 😉

I prefer the term "overfitted regression models". Gives a different and perhaps more accurent ring to things:
- "Will overfitted regression models take our jobs?"
- "92 % of students use overfitted regression models in their studies."
- "Overfitted regression models in military decision making."

For the #rstats nerds: 405 is "Method not allowed". That is why R 4.0.5 was nicknamed "Shake and Throw", due to this piece of dubious pedagogy:

www.gocomics.com/peanuts/1978...

#rstats base ?`$`

@tiddlydump.bsky.social perhaps I have shared this before. It's an unfinished project but has some #rstats gems imho
cswr.nrhstat.org

Following this thread with great interest. I still haven't found an LLM that's even remotely useful for anything beyond the most trivial tasks (also when used with tools like {gander}). Too many hallucinated functions and ignored prompt instructions still.

I don't like var, as it's the name of a commonly used function. col is also a base R function. I'd go with x.

To be clear, I'm not saying that those are the actual priorities within the company. I'm just saying how things appear to users.

Agreed. But as a customer I see Posit pushing Python tools much more than R tools. Trying to break into the Python market I guess. Which makes sense, but it does give an impression that R isn't as important, and pushes R users towards Python rather than the other way around.

According to my system logs, the actually memory usage of the R session is 104 MB (plus 273 MB for RStudio). So this seems to be a bug in the memory usage report in RStudio.

Screenshot of RStudio, showing that the environment is empty but that 17.12 GiB of RAM is being used.

This seems like an awfully high RAM usage for a new #Rstats session in RStudio. 🤔 Anyone seen this before?

Posit

Looking at @posit.co from the outside, it definitely seems like their focus has shifted from #Rstats to Python. Looking at the 9 posts on the front page of the Posit blog (posit.co/blog/), 4 posts are language agnostic and 5 are about Python. None are about R.

Positron is built on top of VSCode, so it's pretty much the same as that in VSCode. But it also has some features from RStudio, like a variables pane and a data explorer.

RStudio offers both multiple panels for different scripts and parallel R sessions (through the terminal). But I agree that the git integration in VSCode is much better. Have you checked Positron out?

Light mode is still the default in most browser and on most sites, so that's what I usually go with. An added bonus is that the same plot can be used for print without any modifications, unlike dark mode plots.

The most common question I get when teaching ggplot2: "Can we change the background colour?"
Yes, we can - and much more. Here's how to create your own ggplot2 themes:
🔗 mansthulin.se/posts/ggplot...

#Rstats #datasky

And maybe with a description of what an RSS feed is? 😀

As long as it's done politely, absolutely! Preferably with a link to the part of the Quarto documentation that describes how to add an RSS feed (quarto.org/docs/website...)

"Pro tip: logistic regression should by default use ridge regularisation with the shrinkage parameter set to 1. That's why you always should use Python's sklearn.linear_model.LogisticRegression instead of #Rstats silly non-regularised glm."

The new release of RStudio prints errors, warnings and messages in different colours. That's a great improvement! I teach introductory #Rstats a lot, and students *always* think messages are errors. Hopefully this will help.

A deliberately chaotic and cluttered chart mocking bad data visualisation practices, featuring overlapping symbols, unreadable labels, clashing colours, and dual y-axes. The design satirises poor visual clarity and overcomplication.

For the prompt of "Extraterrestrial" on Day29 of the #30DayChartChallenge, I decided to make a chart designed in the style of an extraterrestrial who has never heard of good data visualisation principles! 📊

How many chart crimes can you spot? 🕵️‍♂️

#DataViz #RStats #ggplot2 #Day29

Chapter 10 of Modern Statistics with R cover those topics: www.modernstatisticswithr.com/sem.html

Still using caret. I strongly dislike some of the design choices of tidymodels (like how it misuses pipes and how you can't evaluate models using LOOCV).

Found in r/stopdoingscience
#RStats

And then hack Bluesky and delete this thread to hide the evidence

Seems like the kind of idea that's obvious, but only after you've had the idea. 🙂 I'm interested in how it performs compared to other methods. Will run some simulations of my own next week and let you know if I find anything interesting!

Interesting approach! Are there any papers studying this in more depth? The "via" link at the end of the Github readme is dead.