A couple of days after scheduling the “Data Science Wars: Python vs. R” post, I’ve found this article by Seth Brown:
I’ve been thinking about the future of data analysis lately and which
statistical language du jour will rise to prominence. On one side, there
are languages built for doing statistics, which have some rudimentary
programming capabilities, and, on the other side, there are languages built
for programming, which have rudimentary statistical capabilities. This
schism requires statisticians and scientists to be fluent in multiple
languages, impairs the development of better tools, leads to feature
duplication across languages, and generates needless technical debt.
After a much, much longer post than my comment, the authors’s conclusion is exactly the same: a general purpose programming language is more productive even if it lacks initially some of the advance algorithms and functions available in the highly specialized tool or language.
Original title and link: On The Future Of Statistical Languages