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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