A wealth of online resources are available for R. These include search engines, websites, blogs, and online communities. Some of the most useful and informative online resources for learning about R will be discussed here.
A few valuable R websites are highlighted here.
The official R website is the definitive source for R updates and documentation. It offers the most recent versions of R for each operating system. Also included are valuable documents, such as the R FAQ, CRAN mirror listing, and contributed packages glossary. The R Project for Statistical Computing can be found at:
Quick-R is an excellent resource for efficiently retrieving information on R topics, along with examples of how related techniques can be executed. It covers a wealth of subjects in R, including a wide range of statistical methods and graphics types. Its organized and aesthetic format makes it easy to locate and decipher the desired information. Quick-R can be found at:
The R Programming wikibook shares information on general R topics, as well as references to statistical methods. All of its content is presented in wiki format with minimal description, making it a resource for quickly locating and indulging in code samples. The R Programming wikibook can be found at:
The R Graph Gallery presents a collection of some of the most advanced and unique graphics that have been generated using R. Images of each visualization are accompanied by the source code used to create them and references to any required packages. The R Graph Gallery can be found at:
Blogs are another informative online resource for learning about R.
R bloggers is an extensive collection of over 100 blogs (and counting) that are dedicated to sharing knowledge related to R. It is a prime resource for gaining insights on complex and cutting edge data analysis techniques. The combined authorship of R bloggers represents perhaps the most active and timely sharers of R content on the internet. R bloggers can be found at:
The R Tutorial Series provides user-friendly guides for people who are learning about R. It primarily focuses on providing brief statistics tutorials with detailed execution examples. This blog was created by the author of this book and follows a similar style. The R Tutorial Series is available at:
Online communities are places to connect with other R users and seek and share information.
The R-help mailing list is both an ongoing resource for answers to R questions and an archive of past conversations dating back to 1997. The odds are that any difficulty that you encounter in R has already been queried to this list at one time or another. If not, you can always join the list and contribute to the community by submitting your own questions. Therefore, the R-help mailing list is one of the first places you should look when you are having trouble with a particular facet of R. You can join the R-help mailing list at:
https://stat.ethz.ch/mailman/listinfo/r-help
A searchable archive of the R-help mailing list is available at:
A number of additional mailing lists are available for users seeking information about R. The most prominent ones, which cover the major announcements, contributed packages, and development of R can be found using the Mailing Lists link on the official website at:
Endless amounts of information about R can also be found by searching the internet.
R Seek is a Google-based search engine that helps users find what they are looking for by automatically optimizing their queries to yield relevant results. Users can search for a topic as they normally would, but are more likely to be presented with output that is related to R than if they had entered the same terms into a standard search engine. The R Seek search engine is available at:
Google is another valuable resource for searching R, although it does take some practice to acquire meaningful results through this engine. It can be difficult to find relevant webpages via Google, because R's one letter name tends to be ignored or inappropriately parsed by search engine algorithms. Thus, it is often useful to use quotations along with additional terms when searching Google. For example, to find information on conducting multiple regression in R, it is better to use include additional quoted keywords, such as R Project or R statistics than just the letter R alone. The Google search engine is available at: