outer() trades off space for speed. It *does* vectorize calculations (= perform the loops in the underlying C code).
The apply() family of functions (eapply,mapply and rapply are other base R versions that you missed; there are others in packages) are basically just efficiently written looping functions. They may or may not offer much speedup over explicit loops. As you said, their greatest advantage is elegance and code readability (as functional programming, rather than procedural programming, constructs). As you also said, vectorizing calculations is a central theme in R that takes some getting used to. I know of no general prescriptions for how to do it; I, too, am still learning. Finally, please heed Roland's (and r-help's) advice: provide a small, reproducible example if you want specific help. -- Bert Gunter -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Roland Rau Sent: Thursday, August 07, 2008 2:22 PM To: rcoder Cc: r-help@r-project.org Subject: Re: [R] long run time for loop operation & matrix fill Hi rcoder, rcoder wrote: > Hi everyone, > > I'm running some code containing an outer and inner loop, to fill cells in a > 2500x1500 results matrix. I left my program running overnight, and it was > still running when I checked 17 hours later. I have tested the operation on > a smaller matrix and it executes fine, so I believe there is nothing wrong > with the code. I was just wondering if this is normal program execution > speed for such an operation on a P4 with 2GB RAM? > loops are not one of the strengths in R, I would say (At least not explicit ones). This is why many books and manuals on R devote considerable space on "the whole object view", vectorizing calculations, and general strategies how to avoid loops in R. I (we) don't know what your actual program is doing. Probably applying a rather complicated function to each cell of your matrix? I did this code: mymatrix <- matrix(rep(0.1, 2500*1500), ncol=1500) system.time( for (i in 1:(nrow(mymatrix))) { for (j in 1:(ncol(mymatrix))) { mymatrix[i,j] <- i+j } if ((i %% 100)==0) cat(i,"\n") } ) (cat output omitted) and it took user system elapsed 139.09 55.56 199.42 seconds. The best strategy is usually to avoid such loops. For example, obtaining the same results could have been achieved by: > system.time( + roland <- outer(X=1:2500, Y=1:1500, FUN=function(a,b) a+b) + ) user system elapsed 0.25 0.09 0.34 Quite a speed-up, I would say, no? Generally using 'outer' and the apply family (apply, tapply, lapply, sapply -- did I forget one?) can perform miracles in terms of speed. And it allows also to express ideas in very elegant ways, in my opinion. I have to admit, though, that it takes a while to grasp the various concepts (and I am also still learning). Maybe you could supply a small, working code example as the posting guide suggests? This might give you more help for your specific needs. Hope this helps, Roland ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
