That is precisely the reason for the existence of the ave function.
Using Wickham's example:
> x1 <- rep(c("A", "B", "C"), 3)
> x2 <- c(rep(1, 3), rep(2, 3), 1, 2, 1)
> x3 <- c(1, 2, 3, 4, 5, 6, 2, 6, 4)
> df <- data.frame(x1, x2, x3)
> df$grpx3 <- ave(df$x3, list(x1,x2))
> df
x1 x2 x3 grpx3
1 A 1 1 1.5
2 B 1 2 2.0
3 C 1 3 3.5
4 A 2 4 4.0
5 B 2 5 5.5
6 C 2 6 6.0
7 A 1 2 1.5
8 B 2 6 5.5
9 C 1 4 3.5
Note that the default function is mean() but other functions could be
specified.
--
David Winsemius
On Mar 31, 2009, at 12:09 PM, Alan Cohen wrote:
Hi all,
I'm trying to improve my R skills and make my programming more
efficient and succinct. I can solve the following question, but
wonder if there's a better way to do it:
I'm trying to calculate mean by several variables and then put this
back into the original data set as a new variable. For example, if
I were measuring weight, I might want to have each individual's
weight, and also the group mean by, say, race, sex, and geographic
region. The following code works:
x1<-rep(c("A","B","C"),3)
x2<-c(rep(1,3),rep(2,3),1,2,1)
x3<-c(1,2,3,4,5,6,2,6,4)
x<-as.data.frame(cbind(x1,x2,x3))
x3.mean<-rep(0,nrow(x))
for (i in 1:nrow(x)){
+ x3.mean[i]<-mean(as.numeric(x[,3][x[,1]==x[,1][i]&x[,2]==x[,2]
[i]]))
+ }
cbind(x,x3.mean)
x1 x2 x3 x3.mean
1 A 1 1 1.5
2 B 1 2 2.0
3 C 1 3 3.5
4 A 2 4 4.0
5 B 2 5 5.5
6 C 2 6 6.0
7 A 1 2 1.5
8 B 2 6 5.5
9 C 1 4 3.5
However, I'd love to be able to do this with "apply" rather than a
for-loop. Or is there a built-in function? Any suggestions?
Also, any way to avoid the hassles with having to convert to a data
frame and then again to numeric when one variable is character?
Cheers,
Alan Cohen
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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