[R] Best way to compute the difference between two levels of a factor ?
wphantomfr
wphantomfr at gmail.com
Wed Mar 21 12:19:13 CET 2012
> Okay, try this:
>
> result <- with(data,
> aggregate(data[,-(1:2)], by=list(ID), FUN=diff))
That's it !! I didn't knew the "diff" function. Your solution works
perfectly.
Thanks Peter for this !
Sylvain
Le 21/03/12 12:01, Peter Ehlers a écrit :
> 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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