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
spec...@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.
