It’s widely understood that, in R programming, one should avoid for loops and always try to use apply-type functions.

But this isn’t entirely true. It may have been true for Splus, back in the day: As I recall, that had to do with the entire environment from each iteration being retained in memory.

Here’s a simple example:

> x <- matrix(rnorm(4000*40000), ncol=4000)

> system.time({
+     mx <- rep(NA, nrow(x))
+     for(i in 1:nrow(x)) mx[i] <- max(x[i,])
+  })
   user  system elapsed 
  3.719   0.446   4.164

> system.time(mx2 <- apply(x, 1, max))
   user  system elapsed 
  5.548   1.783   7.333

There’s a great commentary on this point by Uwe Ligges and John Fox in the May, 2008, issue of R News (see the “R help desk”, starting on page 46, and note that R News is now the R Journal).

Also see the related discussion at stackoverflow.

They say that apply can be more readable. It can certainly be more compact, but I usually find a for loop to be more readable, perhaps because I’m a C programmer first and an R programmer second.

A key point, from Ligges and Fox: “Initialize new objects to full length before the loop, rather than increasing their size within the loop.”