[datastoat]

> This seems very much like an outsider's critique

Spot on! This same sentiment was expressed very well in a paper evaluating the R language [1]:

"This rather unlikely linguistic cocktail would probably never have been prepared by computer scientists, yet the language has become surprisingly popular."

It would be nice to see a post about R that analyses _why_ it's so hugely popular with data scientists. It's easy to write "R doesn't do what we computer scientists think languages should do, so it's no good". It's harder to analyse what R gets right (for its domain) that other languages get wrong. Personally, I think it's not just that R has the best data-handling libraries (ggplot2, plyr, data.table), it's its "unlikely linguistic cocktail" that is perfectly suited for data exploration.

I think that maybe we hear the views of software engineers who get handed a messy R script and are asked to make it run in production, or make it run on big datasets, and so they only ever see the downsides of R. R wasn't designed to make life easy for production! It's designed to make it easy to explore datasets, which often means one-off code, 99% of which you run and then delete because your hypothesis about the data was wrong.

[1] https://www.researchgate.net/publication/240040602_Evaluatin...