Re: c) I will be a dirty shill for VSCode and R lol, example here. I find it much better for R shiny development, projects with multiple people and projects with multiple languages. Notebook support is less good out of the box, you will have to get a jupyter kernel set up - but I use scripts more so than notebooks anyway.

Anyway, onto the question! Base R. Yeah, I said it! Whenever I have a weird enough situation where tidyverse functions won’t work due to poor quality data, then I shed a single solemn tear and quietly wish I had done the project in python as I start writing a for loop in what will no doubt be the most hacky solution ever.

Lack of gpu-enabled cross-platform functionality

utf Support under windows (which is not r’s fault) which might/will be (is already?) fixed

the rate at which packages change, sometimes breaking stuff (renv to the rescue)

how my place does not invest in proper integration but uses various scripts stored on a network drive with absolutely no documentation

That no one knows it. For many ML algorithms they rather get published first in R than in Python still, although I believe this is not true for deep learning, but rather stuff like tree based models.

Sometimes I can’t implement what I want to because I don’t have time, let alone skill to port a library from R into Python and make it sklearn compatible. But I also don’t want to be the only person in my team who understands the code. So Python it is.

Edit: just realized this is 7 months old, but I guess maybe we can boost activity by also answering to old posts?

With C, Jetbrain’s DataSpell supports R, though it is primarily Python, and the EAP version is free, though you will have to reinstall every month.

Everything. R is an absolute disaster on a lot of aspects.