sapply() won't be notably faster.
What you might try (no guarantee) is wrapping things up as if you were
going to use sapply() but instead use mclapply() from the parallel
package -- that will parallelize the results and should be faster by
roughly as many cores as you use. The "no guarantee" disclaimer comes
from me not being able to guarantee that I/O is nicely parallelized
having never tried it myself
(it probably is though, those R folks are pretty good at what they do)
Michael
On Mon, Jun 18, 2012 at 4:38 PM, Chang, Emily@OEHHA
<emily.ch...@oehha.ca.gov> wrote:
> Dear all,
>
> I have many csv files whose contents I want to change a bit en masse. So far,
> I've written code that can change them in a for loop, like so:
>
> # Subset of files in the folder I want to change
> subset = "somestring"
> # Retrieve list of files to change
> filelist=list.files()
> filelist = filelist[grep(subset, filelist)]
>
> for(i in 1:length(filelist)){
> setwd(readdir)
> temp = read.csv(filelist[i],as.is = T, strip.white = T)
> >whatever I want to do to temp
>
> setwd(writedir)
> write.table(temp, file = filelist[i], sep = ",", col.names=NA)
> }
>
>
> It's a little slow though, so I would like to get rid of the for loop but
> preserve its function. Would it be possible to use sapply() or something
> similar? Any insight would be appreciated!
>
> Best regards,
> Emily
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