R has done more to bring non-coders into professional programming than anything else in my opinion and I love that they are sticking to their roots with self-paced courses.
What are their roots? I really didn't enjoy my experiences with R, but I also was forced to take it and have little interest in learning how to be a statistician.
Statisticians. Sorry who I meant was the whole of academia who before had an excuse to pass there stats work on to a statistician but now are happy programmers.
In all seriousness I've taught postdocs who were willing to learn everything from Python to C++/MPI to assembler. The key is just to be intellectually curious and enjoy solving problems. R is very often the thin end of the wedge -- its learning curve is more like a wall, but once you've survived that, most programming language quirks seem like NBD. And lord knows the real physical world is far quirkier still.
R is ugly as shit (ok not as bad as Perl, and it is lispy enough not to be infuriating) but it gives you an enormous arsenal of tools. It's not like R is unique in this way, any decent programming language enables this, it's just that R and CRAN seem to have a working implementation of most anything an analyst is likely to need; if you need speed you call out to C++ or FORTRAN, it's not beautiful but it gets things done. So more of your time can be spent in the pursuit of understanding, to poke and prod, visualize your results, solve problems and (sometimes) discover things no one else has seen.
If someone DOESN'T like those last two things, what the hell are they doing in academia anyways? Choose another line of work and make a lot more money :-)
