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
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