Resources

This page lists resources that can help you learn about R effectively. Use the following links to quickly navigate to the various sections:

Official R Resources

  • www.r-project.org: The R Project home page. You will find official documentation, links to download all versions of R, information about the R contributors and foundation, and much more.
  • The R Journal: The refereed journal with research articles introducing new R packages, updates on news and events, and textbook reviews.
  • www.bioconductor.org: The home site for the Bioconductor suite of packages aimed at bioinformatics.

Forums and Mailing Lists

  • RStudio Community Portal: A new community forum created by RStudio to discuss all things related to R and RStudio!
  • The official R mailing lists:
  • Stack Overflow: An innovative Q&A style forum for programming questions. You can find questions related to R by putting the tag [r] at the end of a search query.
  • Cross Validated: Another Q&A style forum much like Stack Overflow for statisticians, data analysts, data miners, and data visualization experts. Many questions about R and other statistical packages appear here.

Community Resources

  • Rseek: The customized search engine created by Sasha Goodman designed specifically for R related queries. There is also funcionality for adding Rseek to the available search engines inside Firefox and other browsers.
  • Quick-R: Robert Kabacoff’s excellent overview of how to accomplish tasks like importing and managing data, performing basic and some advanced statistical routines, and how to create basic plots within R. I find myself accessing this site anytime I need a quick refresher of a concept.
  • R Cookbook: A wiki-style tutorial created by Winston Chang with some excellent examples for manipulating data, statistical analyses, and producing graphics. Content from this site has been published as the R Graphics Cookbook.
  • R Programming: A wikibook of many R topics organized by tasks. Best of all, anyone can contribute their knowledge of R to this wikibook.
  • r4stats.com: Robert Muenchen’s companion reference site to his excellent R for SAS and SPSS Users textbook. This site contains example programs for data management, graphics, statistics, and basic R programming among others. Also contains a nice table showing which SAS, SPSS, and R packages can be used to accomplish a variety of statistical analyses.
  • R for Data Science by Hadley Wickham and Garrett Grolemund. An excellent overview of common tasks within a data science workflow, illustrating the innovative use of the tidyverse set of packages throughout.
  • R Cookbook by Paul Teetor: A well-written example (recipe) based guide through a wide selection of topics that belongs on any R user’s bookshelf.
  • The Art of R Programming by Norman Matloff: An excellent survey of R that is easily accessible to those with more of a computer science background, and not just statistics. If you are looking to improve the way you develop R scripts, this is a must-have in my opinion!
  • R for SAS and SPSS Users by Robert Muenchen: This is arguably the best textbook for new users of R who have experience using SAS or SPSS. If you want a preview of the text, Robert has a free PDF version from his earlier notes on using R.
  • ggplot2 - Elegant Graphics for Data Analysis by Hadley Wickham: If you want to truly master creating graphics using the ggplot2 package, this reference from the package’s author tells you how to master the unique language and the many configurations available for ggplot2 graphics.

Online Training & Courses

  • R for the rest of us: A new platform created by David Keyes with R courses that are approachable to a wide variety of audiences. Quote from the home site: “You don’t need a PhD in statistics or years of coding experience to learn R. Anyone can learn the most powerful tool for data analysis and visualization.”
  • Chromebook Data Science: Chromebook Data Science (CBDS) is a free, massive open online educational program offered through Leanpub to help anyone who can read, write, and use a computer to move into data science, the number one rated job.
  • Dataquest: Whether you’re new to the field or looking to take a step up in your career, Dataquest can teach you the data skills you’ll need. Learn Python, R, SQL, data visualization, data analysis, and machine learning.
  • Swirl: The swirl R package teaches you R programming and data science interactively, at your own pace, and right in the R console!
  • Woz U Data Science: Part of the Woz U education platform, the Data Science curriculum focuses on the fundamentals of computer science, statistics, and applied mathematics, while incorporating real-world examples. Instructor videos are available for each course and students can receive certifications upon course completion.