[R] Speeding up "accumulation" code in large matrix calc?

[Petr Savicky]

On Fri, Feb 24, 2012 at 08:59:44AM -0800, robertfeldt wrote:
> Hi,
> 
> I have R code like so:
> 
> num.columns.back.since.last.occurence <- function(m, outcome) {
> 	nrows <- dim(m)[1];
> 	ncols <- dim(m)[2];
> 	res <- matrix(rep.int(0, nrows*ncols), nrow=nrows);
> 	for(row in 1:nrows) {
> 		for(col in 2:ncols) {
> 			res[row,col] <- if(m[row,col-1]==outcome) {0} else {1+res[row,col-1]}
> 		}
> 	}
> 	res;
> }
> 
> but on the very large matrices I apply this the execution times are a
> problem. I would appreciate any help to rewrite this with more
> "standard"/native R functions to speed things up.

Hi.

If the number of rows is large and the number of columns is not,
then try the following.

  # random matrix
  A <- matrix((runif(49) < 0.2) + 0, nrow=7)
  outcome <- 1

  # transformation
  B <- array(0, dim=dim(A))
  curr <- B[, 1]
  for (i in seq.int(from=2, length=ncol(A)-1)) {
      curr <- ifelse (A[, i-1] == outcome, 0, 1 + curr)
      B[, i] <- curr
  }

  # verify
  all(num.columns.back.since.last.occurence(A, 1) == B)

  [1] TRUE

Hope this helps.

Petr Savicky.