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. forming a correlation matrix, rounding the values, removing similar ones and use the indexing to get my "reduced" data again.

cor(mydata)
mydata <- round(mydata,2)
mydata <- mydata[,!duplicated (mydata)]
## then do the indexing...

I would like to know if this could be done in short command, or some advanced function. I'm learning how to make use of the powerful tools in the R language, which avoids such long unnecessary commands

I was thinking of something like

mydata <- mydata[, which(apply(mydata, 2, function(x) !duplicated(round(cor(x),2))))]

Sorry I know the above command doesn't work, but I hope I would be able to do this.

a play-data that applies to the question:

mydata <- structure(list(V1 = c(1L, 2L, 5L, 4L, 366L, 65L, 43L, 456L, 876L, 
78L, 687L, 378L, 378L, 34L, 53L, 43L), V2 = c(2L, 2L, 5L, 4L, 
366L, 65L, 43L, 456L, 876L, 78L, 687L, 378L, 378L, 34L, 53L, 
41L), V3 = c(10L, 20L, 10L, 20L, 10L, 20L, 1L, 0L, 1L, 2010L, 
20L, 10L, 10L, 10L, 10L, 10L), V4 = c(2L, 10L, 31L, 2L, 2L, 5L, 
2L, 5L, 1L, 52L, 1L, 2L, 52L, 6L, 2L, 1L), V5 = c(4L, 10L, 31L, 
2L, 2L, 5L, 2L, 5L, 1L, 52L, 1L, 2L, 52L, 6L, 2L, 3L)), .Names = c("V1", 
"V2", "V3", "V4", "V5"), class = "data.frame", row.names = c(NA, 
-16L))

Many thanks

Answer 1

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