I very much agree with this. I use python for (different types of) data analysis too, and in python in particular it feels like the "boilerplate" to "science" ratio is rather high in the direction of "boilerplate". R manages to abstract this away very effectively, as the article highlights.
The beauty of R is that you can write one line of code and use some hot-off-the-PhD-thesis cutting-edge-just-published-in-J.-Stat.-Soft-chunk of statistical analysis in your totally different, completely whacky problem, and it's fast, and (by and large) works.
Of course, that's its biggest problem as well. Scientifically, it will quite happily give you a 150 mm howitzer to aim at your foot, assuming you know best.
> hot-off-the-PhD-thesis cutting-edge-just-published-in-J.-Stat.-Soft-chunk of statistical analysis
I think you mean "poorly-documented-cobbled-together-under-deadlines-never-to-be-maintained by someone who has no idea of software principles". Very few labs have a dedicated software engineer to actually turn this software into a usable/hackable tool let alone maintain it.
thats an unnecessary negative stance. not every algorithm needs to be scalable and over optimized to be useful in most cases. and if something becomes really useful in R it ends up being reimplemented in more effe five ways down the road.
The beauty of R is that you can write one line of code and use some hot-off-the-PhD-thesis cutting-edge-just-published-in-J.-Stat.-Soft-chunk of statistical analysis in your totally different, completely whacky problem, and it's fast, and (by and large) works.
Of course, that's its biggest problem as well. Scientifically, it will quite happily give you a 150 mm howitzer to aim at your foot, assuming you know best.