

Programming in R makes serious statistics serious fun  - vladimir
http://greg.weebly.com/1/post/2008/10/programming-in-r-makes-serious-statistics-serious-fun.html

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timr
R is becoming huge in the bioinformatics world (I used it a lot for my own
research), but I wouldn't go so far as to compare it to a general-purpose
language like ECMAScript (and yes, ECMAScript is general-purpose when compared
to R/SPlus). R is a good domain-specific language -- it has a great library of
statistical methods for analysis and modeling -- but it's dog slow, a huge
memory hog (i.e. forget large datasets), and has I/O facilities that are only
a bit more advanced than what you get in FORTRAN 77.

My general research workflow was to do all of my custom logic in a faster,
smaller language (i.e. Perl, C++), then to dump tab-delimited files that I
could load into R for summary analysis and graphing. I would also sometimes
prototype my code in R (if it involved tricky matrix math or statistical
models), then re-write everything in a more capable language when it came time
to work with real data.

That said, R has its strengths -- the graphics language is _really_ powerful,
once you take the time to learn it. Kind of like a functional interface to
postscript...

