In richelbilderbeek/ribir: ribir, basic phylogenetics page

This vignette aims to measure the alleged slowness of for loops.

Here we have a non-trivial function:

complex_function <- function(x) {
  out <- sqrt(x)
  out
}

Here we create two functions that work on a big vector, with and without a for loop:

f_for <- function() {
  values <- 1:100000
  n_values <- length(values)
  for (i in seq(1, n_values)) {
    values[i] <- complex_function(values[i])
  }
  values
}

f_no_for <- function() {
  values <- 1:100000
  values <- vapply(values, FUN = complex_function, FUN.VALUE = 0.0)
  values
}

They must give exactly the same results.

testit::assert(f_for() == f_no_for())

Here we run a benchmark, comparing their speeds:

tm <- microbenchmark::microbenchmark(f_for, f_no_for, times = 100000)

Plot as boxplot:

graphics::boxplot(tm)

View the raw results as table:

knitr::kable(summary(tm))

Conclusion: when not growing vectors, using a for-loop has little effect on run-time speed, if a non-trivial function is used. Already the sqrt function is non-trivial enough to show little effect.

richelbilderbeek/ribir documentation built on March 19, 2021, 3:55 a.m.