On 2012-03-21 03:37, wphantomfr wrote:
Thanks peter for your fast answer.
your is really nice but if I have say 20 variables I have to write 20
statements like "DIF.X = X[TIME=="T2"] - X[TIME=="T1"]".
Does someone has a trick to avoid this ? It may not be easily possible.
Okay, try this:
result <- with(data,
aggregate(data[,-(1:2)], by=list(ID), FUN=diff))
This assumes that the dataframe is sorted as in your example. If
that's not the case, then use order to arrange it first:
data <- with(data, data[order(ID, TIME), ])
Peter Ehlers
Le 21/03/12 11:03, Peter Ehlers a écrit :
On 2012-03-21 01:48, wphantomfr wrote:
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 ?
One way is to use the plyr package:
library(plyr)
result<- ddply(data, "ID", summarize,
DIF.X = X[TIME=="T2"] - X[TIME=="T1"],
DIF.Y = Y[TIME=="T2"] - Y[TIME=="T1"])
Peter Ehlers
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