[R] Best way to compute the difference between two levels of a factor ?

[Eik Vettorazzi]

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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Eik Vettorazzi

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