[peatmoss]

Eh, it’s not as bad as people like to whinge that it is. There are indeed warts, but they’re pretty overblown. If you are comfortable with functional idioms R mostly does what you want without a great deal of fuss. If you’re predisposed to procedural idioms, then you’re going to be fighting the language.

I started learning R about the time I started reading How to Design Computer Programs, and I found it pretty easy to transfer that model of thinking to R. And I find Clojure, Racket, and Scheme to also be somewhat comfortable after a short reacclimation period.

Some of the convenience bits have to do with most functions working on vectors without needing to explicitly iterate most of the time. Also libraries. If you want to estimate a linear regression, or make some exploratory plots, or try some rando statistical method that your graduate advisor suggests, you don’t have to worry about whether it’s already been implemented for you in R.

You can do a lot of heavy lifting by cribbing off of example code because most code is short. You just get heaps of leverage by using R.

Look, I like to do things the hard way a lot. My whole life is pretty much a string of highest friction path choices. For data science R is easy because all the work has been done for you. It's the difference between writing GUI apps against Cocoa APIs vs, I dunno, XLib or Motif.