Online resources for handling big data and parallel computing in R

[This article was first published on RDataMining, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

by Yanchang Zhao, RDataMining.com

Compared with many other programming languages, such as C/C++ and Java, R is less efficient and consumes much more memory. Fortunately, there are some packages that enables parallel computing in R and also packages for processing big data in R without loading all data into RAM. I have collected some links to online documents and slides on handling big data and parallel computing in R, which are listed below. Many online resources on other topics related to data mining with R can be found at http://www.rdatamining.com/resources/onlinedocs.

  • State of the Art in Parallel Computing with R
    It provides an excellent overview and comparison of R packages for parallel computing, including packages for computer cluster, packages for grid computing, and packages for multi-core systems.

    http://www.jstatsoft.org/v31/i01/paper

 


To leave a comment for the author, please follow the link and comment on their blog: RDataMining.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)