Hello,
Try ?aggregate, it's probably faster.
With a made up data.frame, since you haven't provided us with a dataset,
simout.s1 <- data.frame(SID = rep(LETTERS[1:2], 10),
DOSENO = rep(letters[1:4], each = 5),
value = rnorm(20))
res2 <- aggregate(simout.s1$value, list(simout.s1$SID,
simout.s1$DOSENO), function(x)x[NROW(x)])
names(res2) <- names(simout.s1)
Use dput to post a data example. Something like the following.
dput(head(simout.s1, 50)) #paste the output of this in your next mail
Hope this helps,
Rui Barradas
Citando Jun Shen <jun.shen...@gmail.com>:
Dear list,
I have the following line of code to extract the last line of the split
data and put them back together.
do.call(rbind,lapply(split(simout.s1,simout.s1[c('SID','DOSENO')]),function(x)x[nrow(x),]))
the problem is when have a huge dataset, it takes too long to run.
(actually it's > 3 hours and it's still running).
The dataset is pretty big. I have 200,000 unique SID and 4 DOSENO, so
totally 800,000 split dataset. Is there anyway to speed it up? Thanks.
Jun
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