Splitting up a correlation matrix in R - r

Question

I have a dataset with 400 variables that I am using to produce a correlation matrix (i.e. comparing each variable against one another). The resulting matrix has the following structure:
var_1
var_2
var_3
var_4
var_5
var_6
etc (to 400 vars)
var_1
1
0.1
0.2
0.2
0.4
0.8
var_2
0.1
1
0.15
0.3
0.11
0.6
var_3
0.2
0.15
1
0.47
0.05
0.72
var_4
0.2
0.3
0.47
1
0.25
0.54
var_5
0.4
0.11
0.05
0.25
1
0.84
var_6
0.8
0.6
0.72
0.54
0.84
1
etc (to 400 vars)
I am then generating a figure of the correlation matrix with the corrplot package using the following command:
library(corrplot)
corrplot(df, order = "hclust",
tl.col = "black", tl.srt = 45)
Unsurprisingly, this results in a very large figure that is interpretable. I was hoping to split up my matrix and then create separate correlation matrix figures (realistically 20 pairwise comparisons of variables at a time). I am struggling to find code that will help me split up my matrix and plug it back into corrplot. Any help would be hugely appreciated!
Thank you.

Answer 1

One approach would be to split your correlation matrix up and then plot all submatrices. The challenge with that is that you are setting order=, which you (apparently) do not know a priori. Assuming you want to allow corrplot to determine the order, then here's a method: plot the whole thing first capturing the function's return value (which contains order information), then split the matrix and plot the components.
Helpful: while most plotting functions operate by side-effect (creating a plot, not necessarily returning values), some return information useful for working with or around its plot components. corrplot is no different; from ?corrplot:
Value:
(Invisibly) returns a 'list(corr, corrTrans, arg)'. 'corr' is a
reordered correlation matrix for plotting. 'corrPos' is a data
frame with 'xName, yName, x, y, corr' and 'p.value'(if p.mat is
not NULL) column, which x and y are the position on the
correlation matrix plot. 'arg' is a list of some corrplot() input
parameters' value. Now 'type' is in.
With this, let's get started. I'll be using mtcars
Plot the whole thing. If this takes a long time or you don't want it to try to plot in R's graphics pane, then uncomment the png and dev.off, intended just to dump the plot itself to "nothing". ("NUL" is a windows thing ... I suspect "/dev/null" should work on most other OSes, untested.)
# png("NUL")
CP <- corrplot::corrplot(M, order="hclust", tl.col="black", tl.srt=45)
# dev.off()
str(CP)
# List of 3
# $ corr : num [1:6, 1:6] 1 0.4 0.8 0.1 0.2 0.2 0.4 1 0.84 0.11 ...
# ..- attr(*, "dimnames")=List of 2
# .. ..$ : chr [1:6] "var_1" "var_5" "var_6" "var_2" ...
# .. ..$ : chr [1:6] "var_1" "var_5" "var_6" "var_2" ...
# $ corrPos:'data.frame': 36 obs. of 5 variables:
# ..$ xName: chr [1:36] "var_1" "var_1" "var_1" "var_1" ...
# ..$ yName: chr [1:36] "var_1" "var_5" "var_6" "var_2" ...
# ..$ x : num [1:36] 1 1 1 1 1 1 2 2 2 2 ...
# ..$ y : num [1:36] 6 5 4 3 2 1 6 5 4 3 ...
# ..$ corr : num [1:36] 1 0.4 0.8 0.1 0.2 0.2 0.4 1 0.84 0.11 ...
# $ arg :List of 1
# ..$ type: chr "full"
rownames(CP$corr) (which equals colnames(CP$corr)) provides the column/row order, so we can use those as our ordering (resulting from order="hclust".
M_reord <- M[rownames(CP$corr), colnames(CP$corr)]
M_reord
# var_1 var_5 var_6 var_2 var_3 var_4
# var_1 1.0 0.40 0.80 0.10 0.20 0.20
# var_5 0.4 1.00 0.84 0.11 0.05 0.25
# var_6 0.8 0.84 1.00 0.60 0.72 0.54
# var_2 0.1 0.11 0.60 1.00 0.15 0.30
# var_3 0.2 0.05 0.72 0.15 1.00 0.47
# var_4 0.2 0.25 0.54 0.30 0.47 1.00
Now we split the matrix up. For this example, I'll assume your "20" is really "3". Some helper objects:
grps <- (seq_len(nrow(M)) - 1) %/% 3
grps
# [1] 0 0 0 1 1 1
eg <- expand.grid(row = unique(grps), col = unique(grps))
eg
# row col
# 1 0 0
# 2 1 0
# 3 0 1
# 4 1 1
where the row of eg counts columns-first (top-bottom then left-right), like so:
Given a specific submatrix number (row of eg), plot it. Let's try "2":
subplt <- 2
rows <- which(grps == eg[subplt, "row"])
cols <- which(grps == eg[subplt, "col"])
corrplot::corrplot(M[rows, cols], tl.col="black", tl.srt=45)
If you want to automate plotting these (e.g., in an rmarkdown document, in shiny), then you can loop over them with:
for (subplt in seq_len(nrow(eg))) {
rows <- which(grps == eg[subplt, "row"])
cols <- which(grps == eg[subplt, "col"])
corrplot::corrplot(M[rows, cols], tl.col="black", tl.srt=45)
}