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
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(abs(x) > 0.99, na.rm = TRUE))]
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