Useful packages

[1] rbenchmark

benchmark is a simple wrapper around system.time.
benchmark evaluates each of the expressions in the specified environment, replicating the evaluation as many times as specified, and returning the results conveniently wrapped into a data frame.

It simplifies the iterations of tests.

Tips

[1] Benchmark the performance

proc.time determines how much real and CPU time (in seconds) the currently running R process has already taken.

system.time for timing an R expression.

gc.time for how much of the time was spent in garbage collection.

(https://stat.ethz.ch/R-manual/R-devel/library/base/html/system.time.html)

ptm <- proc.time()

for (i in 1:50) mad(stats::runif(500))

proc.time() - ptm

Rprof()

summaryRprof()

[2] Parallel Processing

I am currently using MALDIquant, analyzing Mass Spectrometry data. This package has a few multi-threaded methods, but only supported on Unix systems.
(https://cran.r-project.org/web/packages/MALDIquant/MALDIquant.pdf)

 MALDIquant offers multi-core support using mclapply and mcmapply.

## load package

library("MALDIquant")

## load example data

data("fiedler2009subset", package="MALDIquant")

## run single-core baseline correction

print(system.time(

b1 <- removeBaseline(fiedler2009subset, method="SNIP")

))

if(.Platform$OS.type == "unix") 

{

## run multi-core baseline correction

print(system.time(

b2 <- removeBaseline(fiedler2009subset, method="SNIP", mc.cores=2)

))

print(all.equal(b1, b2))

}

I use detectPeaks method as an example in MSDA toolbox.

[3] Some observations

Noted that, using  apply() function, such as lapply() , mapply(), doesn't mean your code will run faster.