Sum correlated variables - r

Question

I have a list of 200 variables and I want to sum those that are highly correlated.
Assuming this is my data
mydata <- structure(list(APPLE= c(1L, 2L, 5L, 4L, 366L, 65L, 43L, 456L, 876L, 78L, 687L, 378L, 378L, 34L, 53L, 43L),
PEAR= c(2L, 2L, 5L, 4L, 366L, 65L, 43L, 456L, 876L, 78L, 687L, 378L, 378L, 34L, 53L, 41L),
PLUM = c(10L, 20L, 10L, 20L, 10L, 20L, 1L, 0L, 1L, 2010L,20L, 10L, 10L, 10L, 10L, 10L),
BANANA= c(2L, 10L, 31L, 2L, 2L, 5L, 2L, 5L, 1L, 52L, 1L, 2L, 52L, 6L, 2L, 1L),
LEMON = c(4L, 10L, 31L, 2L, 2L, 5L, 2L, 5L, 1L, 52L, 1L, 2L, 52L, 6L, 2L, 3L)),
.Names = c("APPLE", "PEAR", "PLUM", "BANANA", "LEMON"),
class = "data.frame", row.names = c(NA,-16L))
I have found this code which I am not sure how to tweak in order to leverage it for my purpose
https://stackoverflow.com/a/39484353/4797853
var.corelation <- cor(as.matrix(mydata), method="pearson")
library(igraph)
# prevent duplicated pairs
var.corelation <- var.corelation*lower.tri(var.corelation)
check.corelation <- which(var.corelation>0.62, arr.ind=TRUE)
graph.cor <- graph.data.frame(check.corelation, directed = FALSE)
groups.cor <- split(unique(as.vector(check.corelation)), clusters(graph.cor)$membership)
lapply(groups.cor,FUN=function(list.cor){rownames(var.corelation)[list.cor]})
The output that I am looking for is 2 data frames as follow:
DF1
GROUP1 GROUP2
3 16
4 40
ETC..
The values are the sum of the values within a group
DF2
ORIGINAL_VAR GROUP
APPLE 1
PEAR 1
PLUM 2
BANANA 2
LEMON 2

Answer 1

Try this (assuming that you have only clustered into 2 groups):
DF1 <- cbind.data.frame(GROUP1=rowSums(mydata[,groups.cor[[1]]]),
GROUP2=rowSums(mydata[,groups.cor[[2]]]))
DF1
GROUP1 GROUP2
1 3 16
2 4 40
3 10 72
4 8 24
5 732 14
6 130 30
7 86 5
8 912 10
9 1752 3
10 156 2114
11 1374 22
12 756 14
13 756 114
14 68 22
15 106 14
16 84 14
DF2 <- NULL
for (i in 1:2) {
DF2 <- rbind(DF2,
cbind.data.frame(ORIGINAL_VAR=rownames(var.corelation)[groups.cor[[i]]],
GROUP=i))
}
DF2
ORIGINAL_VAR GROUP
1 PEAR 1
2 APPLE 1
3 BANANA 2
4 LEMON 2
5 PLUM 2