Here's the plyr way I should have thought of earlier:
require(plyr) ddply(data, "ID", numcolwise(diff)) Still requires your data to be ordered. Peter Ehlers On 2012-03-21 04:51, Eik Vettorazzi wrote:
Hi Sylvain, assuming your data frame is ordered by ID and TIME, how about this aggregate(cbind(X,Y)~ID,data, function(x)(x[2]-x[1])) #or doing this for all but the first 2 columns of data: aggregate(data[,-(1:2)],by=list(data$ID), function(x)(x[2]-x[1])) cheers. Am 21.03.2012 09:48, schrieb wphantomfr:Dear R-help Members, I am wondering if anyone think of the optimal way of computing for several numeric variable the difference between 2 levels of a factor. To be clear let's generate a simple data frame with 2 numeric variables collected for different subjects (ID) and 2 levels of a TIME factor (time of evaluation) data=data.frame(ID=c("AA","AA","BB","BB","CC","CC"),TIME=c("T1","T2","T1","T2","T1","T2"),X=rnorm(6,10,2.3),Y=rnorm(6,12,1.9)) ID TIME X Y 1 AA T1 9.959540 11.140529 2 AA T2 12.949522 9.896559 3 BB T1 9.039486 13.469104 4 BB T2 10.056392 14.632169 5 CC T1 8.706590 14.939197 6 CC T2 10.799296 10.747609 I want to compute for each subject and each variable (X, Y, ...) the difference between T2 and T1. Until today I do it by reshaping my dataframe to the wide format (the columns are then ID, X.T1, X.T2, Y.T1,Y.T2) and then compute the difference between successive columns one by one : data$Xdiff=data$X.T2-data$X.T1 data$Ydiff=data$Y.T2-data$Y.T1 ... but this way is probably not optimal if the difference has to be computed for a large number of variables. How will you handle it ? Thanks in advance Sylvain Clément
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