I’ve looked through a number of posts with similar titles but none seem to help me out with this code.

I have a very large array (1200 rows, 48 columns, 6 sheets). I’d like to flatten it into a matrix.

So a smaller version to work with:

nchanges <- 4
ncat <- 3
npatches <- 6

A <- array(c(1, -1, 0, 0, 0, 1, -1, 0, 0, 0, 1, -1), 
           dim=c(nchanges, ncat, npatches))
colnames(A) <- c("S", "I", "R")

and it comes out looking like this (although six slices of it)

, , 1

      S  I  R
[1,]  1  0  0
[2,] -1  1  0
[3,]  0 -1  1
[4,]  0  0 -1

I would like to condense it into one slice to end up with a matrix which looks similar to this:

     S1  S2 S3 S4 S5 S6 I1 I2 I3 I4 I5 I6 R1 R2 R3 R4 R5 R6
[1,]  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
[2,]  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
[3,]  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
[4,]  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0
[5,]  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0
[6,]  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0
[7,]  -1 0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0
[8,]  0  -1 0  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0
[9,]  0  0  -1 0  0  0  0  0  1  0  0  0  0  0  0  0  0  0
[10,] 0  0  0  -1 0  0  0  0  0  1  0  0  0  0  0  0  0  0
[11,] 0  0  0  0  -1 0  0  0  0  0  1  0  0  0  0  0  0  0
[12,] 0  0  0  0  0  -1 0  0  0  0  0  1  0  0  0  0  0  0
...
nrow = npatches*nchanges

I can figure out how to do it with for loops, but I’m sure there is a significantly more computationally smart and efficient way to do this.