Michael -
It sounds like there something different in the
parts of the data you're not including. If another
poster's suggestion of specifying analysis variables
instead of id variables doesn't work, and you can send
me the data set, I'd be happy to take a look.
- Phil
On Sat, 24 Oct 2009, Michael Jones wrote:
If I make the data from smaller:
featureDataHead = head(featureData)
featureDataHead = featureDataHead[ , 1:4]
melt(featureDataHead,id.var='feature',variable_name='cell.line')
It works fine
On Sat, Oct 24, 2009 at 1:54 PM, Michael Jones <mdjone...@gmail.com> wrote:
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 <spec...@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
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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______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
