On 13/11/2014, at 3:21 am, Peter Simons
Hi Roman,
With Haskell you don't have to load the whole data set into memory, as Michael shows. With R, on the other hand, you do.
Can you please point me to a reference to back that claim up?
I'll offer [1] and [2] as a pretty good indications that you may not be entirely right about this.
It is *possible* to handle large data sets with R, but it is *usual* to deal with things in memory.
Besides, if you're not an R expert, and if the analysis you want to do is not readily available, it may be quite a pain to implement in R.
A heck of a lot of code in R has been developed by people who think of themselves as statisticians/financial analysts/whatever rather than programmers or “R experts”. There is much to dislike about R (C-like syntax, the ‘interactive if’ trap, the clash of naming styles) but it has to be said that R is a very good for for the data analysis problems S was designed for, and I personally would find it *far* easier to develop such a solution in R than Haskell. (For other problems, of course, it would be the other way around.) Not only does R already have a stupefying number of packages offering all sorts of analyses, so that it’s quite hard to find something that you *have* to implement, there is an extremely active mailing list with searchable archives and full of wizards keen to help. If you *did* have to implement something, you wouldn’t be on your own. The specific case of ‘zipwith f (tail vec) vec’ is easy: (1) vec[-1] is vec without its first element vec[-length(vec)] is vec without its last element (2) cbind(vec[-1], vec[-length(vec)]) is an array with 2 columns. (3) apply(cbind(vec[-1], vec[-length(vec)]), 1, f) applies f to the rows of that matrix. If f returns one number, the answer is a vector; if f returns a row, the answer is a matrix. Example:
vec <- c(1,2,3,4,5) mat <- cbind(vec[-1], vec[-length(vec)]) apply(mat, 1, sum) [1] 3 5 7 9 In this case, you could just do vec[-1] + vec[-length(vec)] and get the same answer.
Oddly enough, one of the tricks for success in R is, like Haskell, to learn your way around the higher-order functions in the library.