(This article was first published on blog.RDataMining.com, and kindly contributed to R-bloggers)
by Yanchang Zhao, RDataMining.com
Below are simple examples of profiling R code, which help to find out which steps or functions are most time consuming. It is very useful for improving efficiency of R code.
# profiling of running time
Rprof(“myFunction.out”)
y <- myFunction(x) # this is the function to profile
Rprof(NULL)
summaryRprof(“myFunction.out”)
The example below profiles memory as well. Memory allocation can also be profiled with function Rprofmem().
# profiling of both time and memory
Rprof(“myFunction.out”, memory.profiling=T)
y <- myFunction(x)
Rprof(NULL)
summaryRprof(“myFunction.out”, memory=”both”)
A detailed example of profiling R code can be found at http://www.stat.berkeley.edu/~nolan/stat133/Fall05/lectures/profilingEx.html.
To leave a comment for the author, please follow the link and comment on his blog: blog.RDataMining.com.
R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series,ecdf, trading) and more...

Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).