I am trying to go from a matrix that has columns that "belong together" to one where the row-sums of the relevant sub-matrices have been formed. I.e. going from
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [,16]
[1,] 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61
[2,] 2 6 10 14 18 22 26 30 34 38 42 46 50 54 58 62
[3,] 3 7 11 15 19 23 27 31 35 39 43 47 51 55 59 63
[4,] 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64
to
[,1] [,2] [,3] [,4] [,5]
[1,] 15 30 46 185 220
[2,] 18 32 48 190 224
[3,] 21 34 50 195 228
[4,] 24 36 52 200 232
I assume there must be some much more elegant and faster way to do this than by looping over the indices as I do below (particularly, my real matrix would be more like 4000 by many thousands).
example <- matrix(1:64, nrow=4)
myindex <- c(1,1,1,2,2,3,3,4,4,4,4,4,5,5,5,5)
summed <- matrix( rep(unique(myindex), each=dim(example)[1]), nrow=dim(example)[1])
for (i in 1:length(unique(myindex))){
summed[,i] <- apply(X=example[,(myindex==i)], MARGIN=1, FUN=sum)
}
It is probably my lack of experience with apply and tapply that prevents me from figuring this out. A fast dplyr approach would of course also be welcome.
We can use a one liner with sapply:
sapply(unique(myindex), function(x) rowSums(example[, which(myindex == x), drop = FALSE]))
[,1] [,2] [,3] [,4] [,5]
[1,] 15 30 46 185 220
[2,] 18 32 48 190 224
[3,] 21 34 50 195 228
[4,] 24 36 52 200 232
We let sapply loop over all unique values of myindex, and use which to define the columns which should be included into the rowSums.
Edit: Included drop = FALSE to prevent single indexes from simplifying to vector. Thanks @mt1022 for pointing out the bug!