On Feb 25, 2011, at 10:14 AM, zem wrote:
Yeah, you are right
i want to post an short example what i want to do .. and in the
meantime i
solved the problem ...
but here is:
i have something like this dataframe:
c1<-c(1,2,3,2,2,3,1,2,2,2)
c2<-c(5,6,7,7,5,7,5,7,6,6)
c3<-rnorm(10)
x<-cbind(c1,c2,c3)
x
c1 c2 c3
[1,] 1 5 0.08279036
[2,] 2 6 0.59135988
[3,] 3 7 1.45520468
[4,] 2 7 -1.70094640
[5,] 2 5 0.13065228
[6,] 3 7 -1.12080980
[7,] 1 5 0.42779354
[8,] 2 7 -1.53111972
[9,] 2 6 0.29299987
[10,] 2 6 -0.01602095
#whith aggregate i receive this:
aggregate(x[,3],list(x[,1],x[,2]),mean)
Group.1 Group.2 x
1 1 5 0.2552920
2 2 5 0.1306523
3 2 6 0.2894463
4 2 7 -1.6160331
5 3 7 0.1671974
and the problem was that i was grouping by 2 columns, so i couldn't
copy the
result to x.
the solution was i made another column with paste(x[,1],x[,2],sep="_")
and then i used the solution from this link:
http://tolstoy.newcastle.edu.au/R/help/06/07/30184.html
so i solved my problem
Right. That works and has the virtue that it is reasonably clear what
is going on. Another approach, possibly even more clear and even more
R-ish, is to use the interaction() function.
> aggregate(x[,3], list(interaction(x[,1],x[,2]) ), mean)
Group.1 x
1 1.5 -0.658932424
2 2.5 0.824756795
3 2.6 0.640471421
4 2.7 -0.008519716
5 3.7 -0.053233855
Ivan, many thanks for your support and quik responses! :)
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David Winsemius, MD
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