Hi:
Here are several equivalent ways to produce your desired output:
# Base package: transform()
df - transform(df, mean = ave(x, id, FUN = mean))
# plyr package
library('plyr')
ddply(df, .(id), transform, mean = mean(x))
# data.table package
library('data.table')
dt - data.table(df, key = 'id')
dt[, list(x, mean = mean(x)), by = 'id']
# doBy package
library('doBy')
transformBy(~ id, data = df, mean = mean(x))
HTH,
Dennis
On Fri, Aug 12, 2011 at 8:10 AM, Julia Moeller
julia.moel...@uni-erfurt.de wrote:
Hi,
as an R-beginner, I have a recoding problem and hope you can help me:
I am working on a SPSS dataset, which I loaded into R (load(C:/...)
I have 2 existing Variables: ID and X ,
and one variable to be computed: meanX.dependID (=mean of X for all rows in
which ID has the same value)
ID = subject ID. Since it is a longitudinal dataset, there are repeated
measurement points for each subject, each of which appears in a new row. So,
each ID value appears in many rows. (e.g. ID ==1 in row 1:5; ID ==2 in rows
6:8 etc).
Now: For all rows, in which ID has a certain value, meanX.dependID shall be
the mean of X in for these rows. How can I automatisize that, without having
to specify the number of the rows each time?
e.g.
ID X meanX.dependID
1 2 2.25
1 3 2.25
1 1 2.25
1 3 2.25
2 5 3.3
2 2 3.3
2 3 3.3
3 4 3
3 1 3
3 2 3
3 3 3
3 4 3
3 5 3
Thanks a lot! Hope this is the right place to post, if not, please tell me!
best,
Julia
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