Large loops hang in R?

Question

Suppose I want perform a simulation using the following function:

fn1 <- function(N) {
  res <- c()
  for (i in 1:N) {
    x <- rnorm(2)
    res <- c(res, x[2]-x[1])
  }
  res
}

For very large N, computation appears to hang. Are there better ways of doing this?

(Inspired by: https://stat.ethz.ch/pipermail/r-help/2008-February/155591.html)

Answer 1

The efficiency of loops can be increased tremendously in R through the use of the apply functions which essentially process whole vectors of data at once rather than looping through them. For the loop shown above, there are two basic operations happening during each iteration:

# A vector of two random numbers is generated
x <- rnorm( 2 )

# The difference between those numbers is calculated
x[2] - x[1]

In this case the appropriate function would be sapply(). sapply() operates on a list of objects, such as the vector generated by the loop statement 1:N and returns a vector of results:

sapply( 1:N, function( i ){ x <- rnorm(2); return( x[2] - x[1] ) } )

Note that the index value i is available during the function call and successively takes on the values between 1 and N, however it is not needed in this case.

Getting into the habit of recognizing where apply can be used over for is a very valuable skill- many R libraries for parallel computation provide plug-and-play parallelization through apply functions. Using apply can often allow access to significant performance increases on multicore systems with zero refactoring of code.