Robin Cura wrote on 12/12/2011 10:15:32 AM:
> Hello,
>
> I'm currently trying to convert a slow and ugly script I made, so that
it's
> faster and can be computed on a computer grid with the multicore
package.
> My problem is that I don't see how to turn some loops into an
"apply-able"
> function.
>
> Here's an example of my loops :
> I got a list of dataframes (or matrices like here), and I need to browse
> each cell of those many dataframes to compute a mean (or standard
deviation
> like here).
>
> Here's a example script :
>
> a <- b <- c <- d <- result <- matrix(nrow=3, ncol=3)
> a[] <- sample.int(n=100,size=9,replace=TRUE)
> b[] <- sample.int(n=100,size=9,replace=TRUE)
> c[] <- sample.int(n=100,size=9,replace=TRUE)
> d[] <- sample.int(n=100,size=9,replace=TRUE)
> result[] <- NA
> mylist <- list(a,b,c,d)
>
> for (row in 1:3)
> {
> for (col in 1:3)
> {
> tmpList <- log(mylist[[1]][row, col])
> for (listitem in 2:4)
> {
> tmpList <- c(tmpList, log(mylist[[listitem]][row, col]))
> }
> result[row, col] <- sd(tmpList)
> }
> }
>
> Considering I have to look at the same cell in each dataframe, I don't
> understand how I could turn this into a function, considering I need the
> row and column number to iterate.
>
> I succeeded improving my script duration a lot, but such loops are
really
> long to run, considering that my lists contains like 100 dataframes, who
> all contains thousands of values.
>
> Any help would be really appreciated
>
> Thanks in advance,
>
> Robin
Since your matrices are all the same dimension, your data would be easier
to handle if they were in an array.
# convert list of matrices to a single array
L <- length(mylist)
RC <- dim(mylist[[1]])
myarray <- array(unlist(mylist), dim=c(RC[1], RC[2], L))
# then your script can be whittled down to a single line
apply(log(myarray), 1:2, sd)
Jean
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