microbenchmark (version 1.4.10)

microbenchmark: Sub-millisecond accurate timing of expression evaluation.


microbenchmark serves as a more accurate replacement of the often seen system.time(replicate(1000, expr)) expression. It tries hard to accurately measure only the time it takes to evaluate expr. To achieved this, the sub-millisecond (supposedly nanosecond) accurate timing functions most modern operating systems provide are used. Additionally all evaluations of the expressions are done in C code to minimize any overhead.


  list = NULL,
  times = 100L,
  unit = NULL,
  check = NULL,
  control = list(),
  setup = NULL


Object of class ‘microbenchmark’, a data frame with columns expr and time. expr contains the deparsed expression as passed to microbenchmark or the name of the argument if the expression was passed as a named argument. time is the measured execution time of the expression in nanoseconds. The order of the observations in the data frame is the order in which they were executed.



Expressions to benchmark.


List of unevaluated expressions to benchmark.


Number of times to evaluate each expression.


Default unit used in summary and print.


A function to check if the expressions are equal. By default NULL which omits the check. In addition to a function, a string can be supplied. The string ‘equal’ will compare all values using all.equal, ‘equivalent’ will compare all values using all.equal and check.attributes = FALSE, and ‘identical’ will compare all values using identical.


List of control arguments. See Details.


An unevaluated expression to be run (untimed) before each benchmark expression.


Olaf Mersmann


This function is only meant for micro-benchmarking small pieces of source code and to compare their relative performance characteristics. You should generally avoid benchmarking larger chunks of your code using this function. Instead, try using the R profiler to detect hot spots and consider rewriting them in C/C++ or FORTRAN.

The control list can contain the following entries:


the order in which the expressions are evaluated. “random” (the default) randomizes the execution order, “inorder” executes each expression in order and “block” executes all repetitions of each expression as one block.


the number of warm-up iterations performed before the actual benchmark. These are used to estimate the timing overhead as well as spinning up the processor from any sleep or idle states it might be in. The default value is 2.

See Also

print.microbenchmark to display and boxplot.microbenchmark or autoplot.microbenchmark to plot the results.


Run this code
## Measure the time it takes to dispatch a simple function call
## compared to simply evaluating the constant \code{NULL}
f <- function() NULL
res <- microbenchmark(NULL, f(), times=100L)

## Print results:


## Plot results:

## Pretty plot:
if (requireNamespace("ggplot2")) {

## Example check usage
my_check <- function(values) {
  all(sapply(values[-1], function(x) identical(values[[1]], x)))

f <- function(a, b)
  2 + 2

a <- 2
## Check passes
microbenchmark(2 + 2, 2 + a, f(2, a), f(2, 2), check=my_check)
if (FALSE) {
a <- 3
## Check fails
microbenchmark(2 + 2, 2 + a, f(2, a), f(2, 2), check=my_check)
## Example setup usage
x <- rnorm(10)
microbenchmark(x, rnorm(10), check=my_check, setup=set.seed(21))
## Will fail without setup
if (FALSE) {
microbenchmark(x, rnorm(10), check=my_check)
## using check
a <- 2
microbenchmark(2 + 2, 2 + a, sum(2, a), sum(2, 2), check='identical')
microbenchmark(2 + 2, 2 + a, sum(2, a), sum(2, 2), check='equal')
attr(a, 'abc') <- 123
microbenchmark(2 + 2, 2 + a, sum(2, a), sum(2, 2), check='equivalent')
## check='equal' will fail due to difference in attribute
if (FALSE) {
microbenchmark(2 + 2, 2 + a, sum(2, a), sum(2, 2), check='equal')

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