new blog post:

Of course, someone has to write imperative code to build reproducible data science pipelines. It doesn’t have to be you.

brodrigues.co/posts/2025-1...

Looks like a good guide - the general data cleaning part is a lean intro to some very common issues in all sorts of data. Would be great if every phd who touches raw data was offered a short course in these basics (in R or Python or whatever HipsterScript) cleaning-data-r.ala.org.au/2_general-cl...

Was asked about collinearity again, so here's Vahove's 2019 post on why it isn't a problem that needs a solution. Design the model(s) to answer a formal question and free your mind janhove.github.io/posts/2019-0...

I'm excited to speak this afternoon at #useR2025 on outgrowing your laptop with #Positron for #rstats users!

You can check out my slides at juliasilge.github.io/useR-2025/

Really nice new paper by Jingyu Zhang, Oliver Lüdtke, and Alexander Robitzsch on the performance of doubly robust estimators of the ATE. A great example of clear writing and reporting, useful visualization through tables, and a review of modern literature. osf.io/5uj2f_v2

I've written a simple guide to the new Positron IDE for #rstats and #python programming. I think that RStudio is still the most thoughtfully designed IDE for R programming but Positron is very useful if you also code a lot in Python.

github.com/erikaduan/r_...