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

[Peter Ehlers]

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
>>