apply mean function to a subset of data

Here are several ways to get there, but your original loop is fine once it is corrected:
for (i in 1:2)  smean[i] <- mean(toy$diam[toy$group==i][1:nsel[i]])
smean
[1] 0.271489 1.117015

Using sapply() to hide the loop:
smean <- sapply(1:2, function(x) mean((toy$diam[toy$group==x])[1:nsel[x]]))
smean
[1] 0.271489 1.117015

Or use head()
smean <- sapply(1:2, function(x) mean(head(toy$diam[toy$group==x], nsel[x])))
smean
[1] 0.271489 1.117015

Or mapply() instead of sapply
smean <- mapply(function(x, y) mean(head(x, y)) , x=split(toy$diam, toy$group), y=nsel)
smean
1        2 
0.271489 1.117015

------------------------------
David L. Carlson
Department of Anthropology
Texas A&M University

-----Original Message-----
From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Jim Lemon
Sent: Saturday, April 2, 2016 6:14 PM
To: Pedro Mardones <mardones.p at gmail.com>
Cc: r-help mailing list <r-help at r-project.org>
Subject: Re: [R] apply mean function to a subset of data

Hi Pedro,
This may not be much of an improvement, but it was a challenge.

selvec<-as.vector(matrix(c(nsel,unlist(by(toy$diam,toy$group,length))-nsel),
 ncol=2,byrow=TRUE))
TFvec<-rep(c(TRUE,FALSE),length.out=length(selvec))
toynsel<-rep(TFvec,selvec)
by(toy[toynsel,]$diam,toy[toynsel,]$group,mean)

Jim
Dear all;

This must have a rather simple answer but haven't been able to figure it
out: I have a data frame with say 2 groups (group 1 & 2). I want to select
from group 1 say "n" rows and calculate the mean; then select "m" rows from
group 2 and calculate the mean as well. So far I've been using a for loop
for doing it but when it comes to a large data set is rather inefficient.
Any hint to vectorize this would be appreciated.

toy = data.frame(group = c(rep(1,10),rep(2,8)), diam =
c(rnorm(10),rnorm(8)))
nsel = c(6,4)
smean <- c(0,0)
for (i in 1:2)  smean[i] <- mean(toy$diam[1:nsel[i]])

Thanks

Pedro

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apply mean function to a subset of data

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