Conclusion

18 Feb 20162 minutes to read

Next steps

This section has been of necessity, a short one that is designed primarily to give you a flavor what R can do for you and how you can use it in your own work. The depth and breadth of R especially when coupled with its contributed packages make it impossible for anyone book to do it justice. But hopefully this serves as a jumping off point for some of your own explorations with R.

There is a short list of possible next steps for learning about statistical programming and data analysis. These resources include books and websites, associated software, and local and global events for users of R.

Books

The growing popularity of R is mirrored in the increasing collection of books that are available. Some excellent resources include:

• An Introduction to R by The R Core Team, available here.
• The R Book by Michael J. Crawley
• Statistics: An Introduction Using R by Michael J. Crawley
• R in a Nutshell: A Desktop Quick Reference (2e) by Joseph Adler
• R Cookbook by Paul Teetor
• R Graphics Cookbook by Winston Chang
• ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham
• Lattice: Multivariate Data Visualization with R by Deepayan Sarkar
• Learning RStudio for R Statistical Computing by Mark van der Loo and Edwin de Jonge
• Getting Started with RStudio by John Verzani
• Many more are listed here.

Websites

In addition to published books, several online resources are of great value. Surprisingly, a conventional Google search with the letter R and the topic of interest always leads to sites related to this software. Here are a few resources:

• The R Project website
• The R Journal is the open access, refereed journal of the R project for statistical computing.
• R-bloggers, news and tutorials about R, contributed by over 400 bloggers. There are 200-300 new posts each month.
• Rseek.org by Sasha Goodman
• Stack Overflow has great discussions on R.
• Wikibooks: R Programming Wikibook
• Quick-R
• ggplot2 website by Hadley Wickham

Software

For developers who are comfortable working with C++, Dirk Eddelbuettel and Romain Francois have developed Rcpp, an integration of R and C++ that can provide dramatic improvements in processing speed.

1. Rcpp by Dirk Eddelbuettel and Romain Francois

2. Tutorial by Hadley Wickham

Events

International and local groups meet to present research using R and new methods that can be applied in your own research:

• useR! is an international conference that takes place in June or July of each year

  1. The 2013 conference was at the University of Castilla-La Mancha in Albacete, Spain.

  2. The 2014 conference was at UCLA in Los Angeles, California.

• Local R User Groups

1. See the full list at Revolution Analytics.

Conclusion

Hopefully this guidelines have left you excited about the possibilities of using R, and motivated to learn more and even return the favor by contributing to the R community.