Reordering rows and columns in R - r

Question

I know this has been answered before, but,
given a correlation matrix which looks like this:
V A B C D
A 1 0.3 0.1 0.4
B 0.2 1 0.4 0.3
C 0.1 0 1 0.9
D 0.3 0.3 0.1 1
which can be loaded in R as follows:
corr.matrix <- read.table("path/to/file", sep = '\t', header = T)
rownames(corr.matrix) <- corr.matrix$V
corr.matrix <- corr.matrix[, 2:ncol(corr.matrix)]
Based on 2 other files that dictate which of the rows and columns to be plotted (Because some are of no interest to me), I want to rearrange the rows and columns in to how the 2 separate files dictate.
For example:
cols_order.txt
C
D
E
B
A
...
rows.txt
D
E
Z
B
T
A
...
I read those other 2 files like this:
rows.order <- ("rows_order.txt", sep = '\n', header=F)
colnames(rows.order) <- "Variant"
cols.order <- ("cols_order.txt", sep = '\n', header=F)
colnames(cols.order) <- "Variant"
And after this step I do this:
corr.matrix <- corr.matrix[rows.order$Variant, cols.order$Variant]
The values that I don't want to be plotted are successfully removed, but the order gets scrambled. How can I fix this?
The .order datasets are read correctly (I checked 3 times).

Answer 1

Here is a potential solution to your question. I tried to re-create a small-sized data.frame based on your question. The key here is the match function as well as some basic subsetting/filtering techniques in R:
## Re-create your example:
V <- data.frame(
A = c(1 , 0.3, 0.1 , 0.4),
B = c(0.2, 1 , 0.4 , 0.3),
C = c(0.1, 0 , 1 , 0.9),
D = c(0.3, 0.3, 0.1 , 1)
) #matrix() also ok
rownames(V) <- LETTERS[1:4]
## Reorder using `match` function
## Needs to be in data.frame form
## So use as.data.frame() if needed
## Here, I don't have the text file
## So if you want to load in txt files specifying rows columns
## Use `read.csv` or `read.table to load
## And then store the relevant info into a vector as you did
col_order <- c("C","D","E","B","A")
col_order_filtered <- col_order[which(col_order %in% colnames(V))]
rows <- c("D","E","Z","B","T","A")
## Filter rows IDs, since not all are present in your data
row_filtered <- rows[rows %in% rownames(V)]
V1 <- V[match(rownames(V), row_filtered), match(colnames(V), col_order_filtered)]
V1 <- V1[-which(rownames(V1)=="NA"), ]
V1
## D C A B
## C 0.1 1.0 0.1 0.4
## B 0.3 0.0 0.3 1.0
## A 0.3 0.1 1.0 0.2
Alternatively, if you are comfortable with dplyr package and the syntax, you can use it and often it is handy:
## Continued from previous code
library(dplyr)
V2 <- V %>%
select(C, D, B, A, everything()) %>%
slice(match(rownames(V), row_filtered))
rownames(V2) <- row_filtered
V2
## C D B A
## D 1.0 0.1 0.4 0.1
## B 0.0 0.3 1.0 0.3
## A 0.1 0.3 0.2 1.0
Hope that helps.