[Rd] Speed of for loops

[Oleg Sklyar]

Tom,

*apply's generally speed up calculations dramatically. However, if and 
only if you do a repetitive operation on a vector, list matrix which 
does NOT require accessing other elements of that variable than the one 
currently in the *apply index. This means in your case any of *apply 
will not speed up your calculation (until you significantly rethink the 
code). At the same time, you can speed up your code by orders of 
magnitude using c-functions for "complex" vector indexing operations. If 
you need instructions, I can send you a very nice "Step-by-step guide 
for using C/C++ in R" which goes beyond "Writing R Extensions" document.

Otherwise, such questions should be posted to R-help, not Rd, please 
post correspondingly.

Best regards,
Oleg

Tom McCallum wrote:
> Hi Everyone,
> 
> I have a question about for loops.  If you have something like:
> 
> f <- function(x) {
> 	y <- rep(NA,10);
> 	for( i in 1:10 ) {
> 		if ( i > 3 ) {
> 			if ( is.na(y[i-3]) == FALSE ) {
> 				# some calculation F which depends on one or more of the previously  
> generated values in the series
> 				y[i] = y[i-1]+x[i];
> 			} else {
> 				y[i] <- x[i];
> 			}
> 		}
> 	}
> 	y
> }
> 
> e.g.
> 
>> f(c(1,2,3,4,5,6,7,8,9,10,11,12));
>   [1] NA NA NA  4  5  6 13 21 30 40
> 
> is there a faster way to process this than with a 'for' loop?  I have  
> looked at lapply as well but I have read that lapply is no faster than a  
> for loop and for my particular application it is easier to use a for loop.  
> Also I have seen 'rle' which I think may help me but am not sure as I have  
> only just come across it, any ideas?
> 
> Many thanks
> 
> Tom
> 
> 
> 

-- 
Dr Oleg Sklyar * EBI/EMBL, Cambridge CB10 1SD, England * +44-1223-494466