Benchmark comparison of for-loops versus apply()/sapply() in 3 different versions of R (2.5.0, 3.0.0, 4.2.0)

output.txt

> ############################################################################
> 
> # A comparison of for-loops versus apply() and sapply() for 1) computing the
> # row means in a matrix and 2) for computing the means of all elements in a
> # list. For task 1), we can also examine the performance of rowMeans() as a
> # specialized / vectorized function and for task 2), we can also compare
> # sapply() with vapply() (note: vapply() was added in version R-2.12.0). Also,
> # for the for-loop, we can examine what the impact is of pre-allocating the
> # vector in which to store the results versus 'growing' the vector in each
> # iteration.
> #
> # Notes: Not using a package like 'microbenchmark' for the timings since this
> # might not be available in older versions of R. The Sys.sleep() calls after
> # each timing allow the CPU to cool a bit to avoid that the performance is
> # impacted by thermal throttling.
> 
> ############################################################################

R version 2.5.0 (2007-04-23)
Copyright (C) 2007 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> ############################################################################
> 
> # for-loops versus apply() (versus rowMeans()) for computing row means
> 
> n <- 2000
> x <- matrix(runif(n*n), nrow=n, ncol=n)
> 
> f1 <- function(x) {
+    nr <- nrow(x)
+    m  <- rep(NA_real_, nr)
+    for (i in 1:nr) {
+       m[i] <- mean(x[i,])
+    }
+    return(m)
+ }
> f2 <- function(x) {
+    m <- c()
+    for (i in 1:nrow(x)) {
+       m <- c(m,mean(x[i,]))
+    }
+    return(m)
+ }
> 
> f3 <- function(x) apply(x, 1, mean)
> f4 <- function(x) rowMeans(x)
> 
> system.time(replicate(100, f1(x))); Sys.sleep(10)
   user  system elapsed 
  5.556   0.008   5.564 
> system.time(replicate(100, f2(x))); Sys.sleep(10)
   user  system elapsed 
  5.835   0.008   5.843 
> system.time(replicate(100, f3(x))); Sys.sleep(10)
   user  system elapsed 
  9.105   0.008   9.114 
> system.time(replicate(100, f4(x))); Sys.sleep(10)
   user  system elapsed 
  0.894   0.000   0.894 
> 
> ############################################################################
> 
> # for-loops versus sapply() (and vapply()) for computing list element means
> 
> x <- replicate(10000, runif(1000), simplify=FALSE)
> 
> f1 <- function(x) {
+    n <- length(x)
+    m  <- rep(NA_real_, n)
+    for (i in 1:n) {
+       m[i] <- mean(x[[i]])
+    }
+    return(m)
+ }
> f2 <- function(x) {
+    m <- c()
+    for (i in 1:length(x)) {
+       m <- c(m,mean(x[[i]]))
+    }
+    return(m)
+ }
> f3 <- function(x) sapply(x, mean)
> f4 <- function(x) vapply(x, mean, numeric(1))
> 
> system.time(replicate(100, f1(x))); Sys.sleep(10)
   user  system elapsed 
  7.955   0.004   7.959 
> system.time(replicate(100, f2(x))); Sys.sleep(10)
   user  system elapsed 
 10.905   0.003  10.909 
> system.time(replicate(100, f3(x))); Sys.sleep(10)
   user  system elapsed 
  8.345   0.013   8.358 
> system.time(replicate(100, f4(x))); Sys.sleep(10)
Error in f4(x) : could not find function "vapply"
Timing stopped at: 0 0 0 0 0 
> 
> ############################################################################

R version 3.0.0 (2013-04-03) -- "Masked Marvel"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: x86_64-unknown-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> ############################################################################
> 
> # for-loops versus apply() (versus rowMeans()) for computing row means
> 
> n <- 2000
> x <- matrix(runif(n*n), nrow=n, ncol=n)
> 
> f1 <- function(x) {
+    nr <- nrow(x)
+    m  <- rep(NA_real_, nr)
+    for (i in 1:nr) {
+       m[i] <- mean(x[i,])
+    }
+    return(m)
+ }
> f2 <- function(x) {
+    m <- c()
+    for (i in 1:nrow(x)) {
+       m <- c(m,mean(x[i,]))
+    }
+    return(m)
+ }
> 
> f3 <- function(x) apply(x, 1, mean)
> f4 <- function(x) rowMeans(x)
> 
> system.time(replicate(100, f1(x))); Sys.sleep(10)
   user  system elapsed 
  5.110   0.011   5.122 
> system.time(replicate(100, f2(x))); Sys.sleep(10)
   user  system elapsed 
  5.433   0.000   5.433 
> system.time(replicate(100, f3(x))); Sys.sleep(10)
   user  system elapsed 
  7.546   0.012   7.558 
> system.time(replicate(100, f4(x))); Sys.sleep(10)
   user  system elapsed 
  0.808   0.000   0.809 
> 
> ############################################################################
> 
> # for-loops versus sapply() (and vapply()) for computing list element means
> 
> x <- replicate(10000, runif(1000), simplify=FALSE)
> 
> f1 <- function(x) {
+    n <- length(x)
+    m  <- rep(NA_real_, n)
+    for (i in 1:n) {
+       m[i] <- mean(x[[i]])
+    }
+    return(m)
+ }
> f2 <- function(x) {
+    m <- c()
+    for (i in 1:length(x)) {
+       m <- c(m,mean(x[[i]]))
+    }
+    return(m)
+ }
> f3 <- function(x) sapply(x, mean)
> f4 <- function(x) vapply(x, mean, numeric(1))
> 
> system.time(replicate(100, f1(x))); Sys.sleep(10)
   user  system elapsed 
  6.902   0.008   6.910 
> system.time(replicate(100, f2(x))); Sys.sleep(10)
   user  system elapsed 
 13.252   0.012  13.265 
> system.time(replicate(100, f3(x))); Sys.sleep(10)
   user  system elapsed 
  6.884   0.000   6.884 
> system.time(replicate(100, f4(x))); Sys.sleep(10)
   user  system elapsed 
  5.815   0.000   5.814 
> 
> ############################################################################

R version 4.2.0 (2022-04-22) -- "Vigorous Calisthenics"
Copyright (C) 2022 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> ############################################################################
> 
> # for-loops versus apply() (versus rowMeans()) for computing row means
> 
> n <- 2000
> x <- matrix(runif(n*n), nrow=n, ncol=n)
> 
> f1 <- function(x) {
+    nr <- nrow(x)
+    m  <- rep(NA_real_, nr)
+    for (i in 1:nr) {
+       m[i] <- mean(x[i,])
+    }
+    return(m)
+ }
> f2 <- function(x) {
+    m <- c()
+    for (i in 1:nrow(x)) {
+       m <- c(m,mean(x[i,]))
+    }
+    return(m)
+ }
> 
> f3 <- function(x) apply(x, 1, mean)
> f4 <- function(x) rowMeans(x)
> 
> system.time(replicate(100, f1(x))); Sys.sleep(10)
   user  system elapsed 
  4.980   0.016   4.996 
> system.time(replicate(100, f2(x))); Sys.sleep(10)
   user  system elapsed 
  5.383   0.000   5.384 
> system.time(replicate(100, f3(x))); Sys.sleep(10)
   user  system elapsed 
  5.814   0.203   6.019 
> system.time(replicate(100, f4(x))); Sys.sleep(10)
   user  system elapsed 
   0.76    0.00    0.76 
> 
> ############################################################################
> 
> # for-loops versus sapply() (and vapply()) for computing list element means
> 
> x <- replicate(10000, runif(1000), simplify=FALSE)
> 
> f1 <- function(x) {
+    n <- length(x)
+    m  <- rep(NA_real_, n)
+    for (i in 1:n) {
+       m[i] <- mean(x[[i]])
+    }
+    return(m)
+ }
> f2 <- function(x) {
+    m <- c()
+    for (i in 1:length(x)) {
+       m <- c(m,mean(x[[i]]))
+    }
+    return(m)
+ }
> f3 <- function(x) sapply(x, mean)
> f4 <- function(x) vapply(x, mean, numeric(1))
> 
> system.time(replicate(100, f1(x))); Sys.sleep(10)
   user  system elapsed 
  4.960   0.008   4.969 
> system.time(replicate(100, f2(x))); Sys.sleep(10)
   user  system elapsed 
 12.000   0.012  12.013 
> system.time(replicate(100, f3(x))); Sys.sleep(10)
   user  system elapsed 
  6.098   0.000   6.098 
> system.time(replicate(100, f4(x))); Sys.sleep(10)
   user  system elapsed 
  4.931   0.000   4.932 
> 
> ############################################################################