Remove highly correlated variables

Question

I have a huge dataframe 5600 X 6592 and I want to remove any variables that are correlated to each other more than 0.99 I do know how to do this the long way, step by step i.e.

Answer 1

@David A small change in your code make it more robust to negative correlation , by providing

abs(x) > 0.99 

instead of only

x > 0.99

data.new <- data[,!apply(tmp,2,function(x) any(abs(x) > 0.99))]

cheers..!!!

Answer 2

I'm sure there are many ways to do this and certainly some better than this, but this should work. I basically just set the upper triangle to be zero and then remove any rows that have values over 0.99.

> tmp <- cor(data)
> tmp[upper.tri(tmp)] <- 0
> diag(tmp) <- 0
# Above two commands can be replaced with 
# tmp[!lower.tri(tmp)] <- 0
#
> 
> data.new <- data[,!apply(tmp,2,function(x) any(x > 0.99))]
> head(data.new)
   V2 V3 V5
1   2 10  4
2   2 20 10
3   5 10 31
4   4 20  2
5 366 10  2
6  65 20  5

Answer 3

This is my R code this would be helpfull for you

library('caret')

df1 = read.csv("stack.csv")

print (df1)

     GA     PN     PC   MBP    GR    AP
1 0.033  6.652  6.681 0.194 0.874 3.177
2 0.034  9.039  6.224 0.194 1.137 3.400
3 0.035 10.936 10.304 1.015 0.911 4.900
4 0.022 10.110  9.603 1.374 0.848 4.566
5 0.035  2.963 17.156 0.599 0.823 9.406
6 0.033 10.872 10.244 1.015 0.574 4.871
7 0.035 21.694 22.389 1.015 0.859 9.259
8 0.035 10.936 10.304 1.015 0.911 4.500


df2 = cor(df1)
hc = findCorrelation(df2, cutoff=0.3) # putt any value as a "cutoff" 
hc = sort(hc)
reduced_Data = df1[,-c(hc)]
print (reduced_Data)

     GA     PN    GR    AP
1 0.033  6.652 0.874 3.177
2 0.034  9.039 1.137 3.400
3 0.035 10.936 0.911 4.900
4 0.022 10.110 0.848 4.566
5 0.035  2.963 0.823 9.406
6 0.033 10.872 0.574 4.871
7 0.035 21.694 0.859 9.259
8 0.035 10.936 0.911 4.500

and to write down a reduced data into new csv just use:

write.csv(reduced_Data, file = "outfile.csv", row.names = FALSE)