Loops inefficiency in R

Question

Good morning,

I have been developing for a few months in R and I have to make sure that the execution time of my code is not too long because I analyze big datasets.

Answer 1

Just a couple of comments. A for loop is roughly as fast as apply and its variants, and the real speed-ups come when you vectorise your function as much as possible (that is, using low-level loops, rather than apply, which just hides the for loop). I'm not sure if this is the best example, but consider the following:

> n <- 1e06
> sinI <- rep(NA,n)
> system.time(for(i in 1:n) sinI[i] <- sin(i))
   user  system elapsed 
  3.316   0.000   3.358 
> system.time(sinI <- sapply(1:n,sin))
   user  system elapsed 
  5.217   0.016   5.311 
> system.time(sinI <- unlist(lapply(1:n,sin),
+       recursive = FALSE, use.names = FALSE))
   user  system elapsed 
  1.284   0.012   1.303 
> system.time(sinI <- sin(1:n))
   user  system elapsed 
  0.056   0.000   0.057 

In one of the comments below, Marek points out that the time consuming part of the for loop above is actually the ]<- part:

> system.time(sinI <- unlist(lapply(1:n,sin),
+       recursive = FALSE, use.names = FALSE))
   user  system elapsed 
  1.284   0.012   1.303 

The bottlenecks which can't immediately be vectorised can be rewritten in C or Fortran, compiled with R CMD SHLIB, and then plugged in with .Call, .C or .Fortran.

Also, see these links for more info about loop optimisation in R. Also check out the article "How Can I Avoid This Loop or Make It Faster?" in R News.

Answer 2

vapply avoids the post-processing by requiring that you specify what the return value is. It turns out to be 3.4 times faster than the for-loop:

> system.time(for(i in 1:n) sinI[i] <- sin(i))
   user  system elapsed 
   2.41    0.00    2.39 

> system.time(sinI <- unlist(lapply(1:n,sin), recursive = FALSE, use.names = FALSE))
   user  system elapsed 
   1.46    0.00    1.45 

> system.time(sinI <- vapply(1:n,sin, numeric(1)))
   user  system elapsed 
   0.71    0.00    0.69