It is usually said, that for– and while-loops should be avoided in R. I was curious about just how the different alternatives compare in terms of speed.

The first loop is perhaps the worst I can think of – the return vector is initialized without type and length so that the memory is constantly being allocated.

use_for_loop <- function(x){
  y <- c()
  
  for(i in x){
    y <- c(y, x[i] * 100)
  }
  return(y)
}

The second for loop is with preallocated size of the return vector.

use_for_loop_vector <- function(x){
  y <- vector(mode = "double", length = length(x))
  
  for(i in x){
    y[i] <- x[i] * 100
  }
  return(y)
}

I have noticed I use sapply() quite a lot, but I think not once have I used vapply() We will nonetheless look at both

use_sapply <- function(x){
  sapply(x, function(y){y * 100})
}

use_vapply <- function(x){
  vapply(x, function(y){y * 100}, double(1L))
}

And because I am a tidyverse-fanboy we also loop at map_dbl().

library(purrr)
use_map_dbl <- function(x){
  map_dbl(x, function(y){y * 100})
}

We test the functions using a vector of random doubles and evaluate the runtime with microbenchmark.

x <- c(rnorm(100))
mb_res <- microbenchmark::microbenchmark(
  `for_loop()` = use_for_loop(x),
  `for_loop_vector()` = use_for_loop_vector(x),
  `purrr::map_dbl()` = use_map_dbl(x),
  `sapply()` = use_sapply(x),
  `vapply()` = use_vapply(x),
  times = 500
)

The results are listed in table and figure below.

expr	min	lq	mean	median	uq	max	neval
for_loop()	8.440	9.7305	10.736446	10.2995	10.9840	26.976	500
for_loop_vector()	10.912	12.1355	13.468312	12.7620	13.8455	37.432	500
purrr::map_dbl()	22.558	24.3740	25.537080	25.0995	25.6850	71.550	500
sapply()	15.966	17.3490	18.483216	18.1820	18.8070	59.289	500
vapply()	6.793	8.1455	8.592576	8.5325	8.8300	26.653	500

  The clear winner is vapply() and for-loops are rather slow. However, if we have a very low number of iterations, even the worst for-loop isn’t too bad:

x <- c(rnorm(10))
mb_res <- microbenchmark::microbenchmark(
  `for_loop()` = use_for_loop(x),
  `for_loop_vector()` = use_for_loop_vector(x),
  `purrr::map_dbl()` = use_map_dbl(x),
  `sapply()` = use_sapply(x),
  `vapply()` = use_vapply(x),
  times = 500
)

expr	min	lq	mean	median	uq	max	neval
for_loop()	5.992	7.1185	9.670106	7.9015	9.3275	70.955	500
for_loop_vector()	5.743	7.0160	9.398098	7.9575	9.2470	40.899	500
purrr::map_dbl()	22.020	24.1540	30.565362	25.1865	27.5780	157.452	500
sapply()	15.456	17.4010	22.507534	18.3820	20.6400	203.635	500
vapply()	6.966	8.1610	10.127994	8.6125	9.7745	66.973	500