[R] data.frame tall skinny transformation

[Michael Jones]

Thanks Phil,

That worked great for the test case below but when I tried it on a
really big data.frame I get the error

$ melt(featureData,id.var='feature',variable_name='cell.line')
Error in data.frame(ids, x, data[, x]) :
 arguments imply differing number of rows: 1312, 1, 0

featureData has the same structure as x but just with more 'cell.line'
columns and features.



On Fri, Oct 23, 2009 at 7:52 PM, Phil Spector <spector at stat.berkeley.edu> wrote:
> Michael -
>   I think the easiest way is to use the melt function
> from the reshape package:
>
>> x
>
>   feature  5637 1321N1
> 1 feature1 -0.56  -0.93
> 2 feature2 -0.91  -0.94
> 3 feature3  0.44  -0.25
>>
>> library(reshape)
>> melt(x,id.var='feature',variable_name='cell.line')
>
>   feature cell.line value
> 1 feature1      5637 -0.56
> 2 feature2      5637 -0.91
> 3 feature3      5637  0.44
> 4 feature1    1321N1 -0.93
> 5 feature2    1321N1 -0.94
> 6 feature3    1321N1 -0.25
>
>
>                                        - Phil Spector
>                                         Statistical Computing Facility
>                                         Department of Statistics
>                                         UC Berkeley
>                                         spector at stat.berkeley.edu
>
>
> On Fri, 23 Oct 2009, Michael Jones wrote:
>
>> Hi,
>>
>> I have a data.frame that looks something like this.
>>
>>
>> feature   5637                1321N1
>> feature1 -0.568750616 -0.934748758
>> feature2 -0.913080902 -0.941455172
>> feature3 0.442477294 -0.257921866
>>
>> I want to change it to look like this.
>>
>> feature      cell.line     value
>> feature1    5637   -0.568750616
>> feature2    5637   -0.913080902
>> feature3    5637   0.442477294
>> feature1    1321N1  -0.934748758
>> feature2    1321N1 -0.941455172
>> feature3    1321N1  -0.257921866
>>
>>
>> I have tried to do it with for loops but it is very slow.
>>
>> # Make Feature data tall skinny
>> tsFeatures = c()
>> tsCellLines = c()
>> tsValues = c()
>>
>> for(aFeature in as.character(featureData$feature)){
>>     print(aFeature)
>>     for(cellLine in cellLines){
>>           tsCellLines = c(tsCellLines, as.character(cellLine))
>>           tsValues = c(tsValues, as.numeric(subset(featureData,
>> feature == aFeature, select = c(which(colnames(featureData) %in%
>> cellLine)))))
>>           tsFeatures = c(tsFeatures, aFeature)
>>     }
>> }
>> tsFeatureData = data.frame(features = tsFeatures, cell.line =
>> tsCellLines, value=tsValues)
>>
>> ______________________________________________
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>> and provide commented, minimal, self-contained, reproducible code.
>