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

[Peter Ehlers]

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
>>