Dear Erik,
Thank you very much. Indeed ave did the same job amazingly fast! I did not
know the function before.
Many thanks to all R experts who answer to this mailing list, it's amazing
how much help you offer to the newbies :)
Kind regards,
Stella
On Tue, Jun 1, 2010 at 6:11 PM, Erik Iverson
Stella Pachidi wrote:
Dear Erik and R experts,
Thank you for the fast response!
I include an example with the ChickWeight dataset:
ap.dat <- ChickWeight
matchMeanEx <- function(ind,dataTable,aggrTable)
{
index <- which((aggrTable[,1]==dataTable[["Diet"]][ind]) &
(aggrTable[,2]==dataTabl
Dear Erik and R experts,
Thank you for the fast response!
I include an example with the ChickWeight dataset:
ap.dat <- ChickWeight
matchMeanEx <- function(ind,dataTable,aggrTable)
{
index <- which((aggrTable[,1]==dataTable[["Diet"]][ind]) &
(aggrTable[,2]==dataTable[["Chick"]][ind]))
as.n
Take a look at
?split (and unsplit)
eg:
Dur <- rnorm(100)
Attr1=rep(c("A","B"),each=50)
Attr2=rep(c("A","B"),times=50)
ap.dat <-data.frame(Attr1,Attr2,Dur)
split.fact <- paste(ap.dat$Attr1,ap.dat$Attr2)
ap.list <-split(ap.dat,split.fact)
ap.mean <-lapply(ap.list,function(x){
x$meanDur=re
It's easiest for us to help if you give us a reproducible example. We
don't have your datasets (ap.dat), so we can't run your code below.
It's easy to create sample data with the random number generators in R,
or use ?dput to give us a sample of your actual data.frame.
I would guess your prob
Dear R experts,
I would really appreciate if you had an idea on how to use more
efficiently the aggregate method:
More specifically, I would like to calculate the mean of certain
values on a data frame, grouped by various attributes, and then
create a new column in the data frame that will have
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