Plot correlation matrix in R

Question

I have a csv file containings a matrix:

version getSize() length() ... power
0         23000    23421        0.8
0           ..      ..           ..
1           ..      ..           ..
1           ..      ..           ..

I want to aggregate by similar versions applying the mean function to the columns. The columns are too many to write them. I also want to calculate the correlation matrix and binding the power column at the sides of the plot. My code is this:

matrix <- read.csv("/home/francesco/University/UoA/matrix.csv", header=TRUE, sep=",", fileEncoding="windows-1252")
power <- matrix[,"power"]
binded <- cbind(matrix,power)
aggregated <- aggregate(. ~ version, data = binded, mean)
corMatrix <- cor(aggregated, method="spearman")
library(lattice)
levelplot(corMatrix)

The plot is pretty confused and I get this warning:

Warning message:
In cor(aggregated, method = "spearman") : standard deviation is zero

A short extract of matrix.csv is:

version,native_drawBitmap,nPrepareDirty,nDrawDisplayList,startGC,power
00083,8,88,308,12,0.8967960131052847
00083,0,176,404,1,0.867644513259528
00084,8,88,307,10,0.8980234065469381
00084,0,181,408,1,0.871799879659241

Someone knows what I'm doing wrong?

Thanks in advance

Answer 1

Well, with your sample data, the native_drawBitmap column becomes all 4's. Since this has no variance, you can't calculate a pair-wise correlation with any other variables and you get the error. If you leave out this column, it will work. Here is an example.

#sample data in friendly copy/paste-able format
mm<-data.frame(
    version = c(83, 83, 84, 84), 
    native_drawBitmap = c(8, 0, 8, 0),
    nPrepareDirty = c(88, 176, 88, 181), 
    nDrawDisplayList = c(308, 404, 307, 408), 
    startGC = c(12, 1, 10, 1), 
    power = c(0.896796013105285, 0.867644513259528, 
        0.898023406546938, 0.871799879659241)
)

# these are not needed and don't make sence. Why are you
#trying to re-add the column from mm back onto mm?
# power <- mm[,"power"]
# binded <- cbind(mm,power)
aggregated <- aggregate(. ~ version, data = mm, mean)

#error
corMatrix <- cor(aggregated, method="spearman")
#no error
corMatrix <- cor(aggregated[,-2], method="spearman")

You may have other columns in your data that have no variability after aggregation. Be sure to find these and remove them.