You might also want to look at the plyr package,
http://had.co.nz/plyr. In particular, ddply + transform makes these
tasks very easy.
library(plyr)
ddply(mtcars, "cyl", transform, pos = seq_along(cyl), mpg_avg = mean(mpg))
Hadley
On Wed, Jun 24, 2009 at 11:48 AM, David
Hugh-Jones wrote:
> That
That seems to work. I should add that to make "ave" work like "by" one can
do:
mydata$newvar <- ave(1:nrow(mydata), mydata$some_factor, FUN= function (x) {
x <- ds[x,]
# ... etc...
})
Thanks!
David
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__
R-h
How about something like this:
> x
id data
1 1 0.7773207
2 3 0.9606180
3 2 0.4346595
4 3 0.7125147
5 2 0.344
6 2 0.3253522
7 2 0.7570871
8 3 0.2026923
9 3 0.7111212
10 2 0.1216919
> # compute running sum for each ID
> x$run <- ave(x$data, x$id, FUN=cumsum)
> x
id
Hello all
I have a big data frame and I regularly want to break it down into subsets,
calculate some new data, and add it back to the data frame.
At the moment my technique seems a bit ugly and embarrassing. Something
like:
result <- by(mydata, mydata$some_factor, function (x) {
# do something
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