Running functions in R multiple times during benchmarking

I am trying to measure the computation time of a function in R using system.time(). I want to run the function a few hundred times to get an average bu开发者_高级运维t I don't want to copy and paste that many times. Is there an easier way to do that?

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The microbenchmark package takes a ,times= option and has the added bonus of being a bit more accurate.

> library(microbenchmark)
> m <- microbenchmark( seq(10)^2, (1:10)^2, times=10000)
> m
Unit: nanoseconds
       expr   min    lq median    uq     max
1  (1:10)^2  2567  3423   3423  4278   41918
2 seq(10)^2 44484 46195  46195 47051 1804147
> plot(m)

And using the not-yet-released autoplot() method for ggplot2:

autoplot(m)

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system.time(replicate (  ... stuff ..) )

Or: (hey, I'm not ashamed to have the same answer as Dirk.)

require(rbenchmark)
benchmark( stuff... )   # Nice for comparative work

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You want to use the rbenchmark package and its function benchmark() which does just about everything for you.

Here is the first example from its help page:

R> example(benchmark)

bnchmrR> # example 1
bnchmrR> # benchmark the allocation of one 10^6-element numeric vector, 
bnchmrR> # replicated 100 times
bnchmrR> benchmark(1:10^6)
    test replications elapsed relative user.self sys.self user.child sys.child
1 1:10^6          100   0.327        1      0.33        0          0         0

For truly expression-level benchmarking, there is also the microbenchmark package.