Lush: My favorite small programming language

R and its ecosystem have some unbeatable features, but, generally speaking, the "old", base R is too arcane to be widely useful. Also, being "made by statisticians for statisticians" should be a big warning sign.

Despite being made by statisticians, I ironically find that munging R packages together for certain classes of analysis such a slog that it prevents me from doing the actual statistical thinking. Sometimes the plots fall behind commercial packages, sometimes the diagnostics, and sometimes you have to combine multiple incompatible packages to get what a commercial package can do.

(Survival analysis and multilevel modeling comes to mind.)

On the contrary, I find base R less arcane than the current de jour python libraries which copied it

This is so far from my experience. For me, R codes do tend to skimp on polish so it takes longer to get to the initial figure, but that is made up for by enabling me to see the data from a much richer perspective (to some extent because I had to think harder about what the output meant) such that I can find all the bugs in the data and in the underlying experimental plan: the stuff which makes it clear all the commercial reports are mostly useless anyway because Garbage in -> Garbage out