Hi:
Here's another approach using the reshape2 package. I called your data
frame dat in the code below.
library('reshape2')
mdat <- melt(dat, measure = c('y', 'f'))
acast(mdat, e1 ~ variable ~ e2, fun = sum, margins = 'e1')
, , con
y f
can21 108
france 21 114
italy 21 126
usa
Hi eric,
Try
lapply(with(x, split(x, e2)), function(l){
r <- with(l, aggregate(list(y, f), list(e1), sum))
colnames(r) <- c('e1', 'y', 'f')
r
})
HTH,
Jorge
On Sun, Aug 14, 2011 at 1:20 PM, eric <> wrote:
> I have a data frame called test shown below that i would like to summarize
> in
> a
I hope this will help you get going
b <- sapply(unique(test$e2), function(x) {
out <- aggregate(cbind(y,f)~e1, subset(test, e2==x),"sum")
out <- rbind(out, data.frame(e1="total", y=sum(out$y), f=sum(out$f)))
out <- list(out)
names(out) <- x
out
})
> b
$std
e1 y f
1
I have a data frame called test shown below that i would like to summarize in
a particular way :
I want to show the column sums (columns y ,f) grouped by country (column
e1). However, I'm looking for the data to be split according to column e2.
In other words, two tables of sum by country. One tab
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