Hi,
May be I misunderstood ur question.
You could do this:
res<-aggregate(.~group,data=data1,mean)
res$gender<-data1$gender[match(res$gender,as.numeric(data1$gender))]
res
# group x gender
#1 1 -1.074343 m
#2 2 1.750686 f
A.K.
----- Original Message -----
From: Martin Batholdy <batho...@googlemail.com>
To: "r-help@r-project.org" <r-help@r-project.org>
Cc:
Sent: Friday, January 11, 2013 10:07 AM
Subject: [R] aggregate data.frame based on column class
Hi,
When using the aggregate function to aggregate a data.frame by one or more
grouping variables I often have the problem, that I want the mean for some
numeric variables but the unique value for factor variables.
So for example in this data-frame:
data <- data.frame(x = rnorm(10,1,2), group = c(rep(1,5), rep(2,5)), gender
=c(rep('m',5), rep('f',5)))
aggregate(data, by=list(data$group), FUN=mean)
I would like to have 'm' and 'f' in the third column, not NA.
I see the problem, that it could happen that there is no unique factor level in
a group –
but is there an alternative function who at least tries what I am aiming at?
That is;
"aggregate the data.frame by a list of grouping variables,
for numeric variables compute the mean,
for factor variables return the unique factor value"
Thanks!
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and provide commented, minimal, self-contained, reproducible code.
