Learning R for Researchers

R is a powerful open source environment for statistical computing. This post provides a selective list of resources for getting started with R including thoughts on books, online manuals, blogs, videos, user interfaces, and more. At the end of the post are some R resources specific to researchers in psychology. (UPDATED 4th May 2011)

Getting Started

• Download and Install R
  • R runs on Windows (see this FAQ for Windows), Mac, and Linux
  • It can be downloaded for free from the official R website
• Watch Videos on R:
  • I wrote a post with links to 100s of videos on R organised around topics of "What is R", "Introduction to R", and "Advanced R".
• Organise a user interface to R.
  • My general advice is:
  • Spend the first 10 hours using the basic R environment.
  • Then have a look at R Studio
  • Eventually, you may want to transition to a more sophisticated environment, such as:
    • Emacs with ESS: A popular and powerful interface, particularly if you already use Emacs or are considering adopting Emacs
    • If you have standardised on another editor, such as Notepad++ or Vim there are R interfaces available. It is well worth standardising on a powerful text editor, such as Emacs or Vim. I currently use Vim (see here for why I chose Vim).
    • StatET and Eclipse is another full featured environment (download and manual).
  • See this discussion on StackOverflow for more ideas
• Read some free online documentation
  • Introduction to R by Venables, Smith, and R Core
  • List of free online documentation on R
  • Quick-R: A fantastic resource to quickly check how to complete common analyses. This is particularly suited to researchers transitioning to R from tools such as SPSS or SAS.
• Memorise important R commands
  • Tom Short's R Reference Card: This concise list of commands is great; print it out and work through the commands.
  • R Abbreviations explained: R uses many short commands; this post explains their meaning in order to assist with recall
• Stay up to date on R News:
  • Subscribe to the RSS feed of R-Bloggers: It syndicates many, if not most, of the R related posts in the blogosphere.
  • If alternatively you want to follow fewer posts, David Smith tends to post important R news and links to noteworthy blog posts by others.
  • R Journal, formerly called "R News", publishes R related articles.
• Know how to find answers to your questions
  • Learn how to use built-in documentation (e.g., help, ?, apropos, help.search, etc.)
  • Google is still good (despite the many meanings of the letter "R"). I generally just use it
  • Rseek is an R specific search engine
• Know where to ask questions to the R community:
  • R Tag on StackOverflow: A good option for anything related to programming in R.
  • Cross Validated: This is part of the Stack Exchange network. This is a good option if the question concerns statistical elements of R.
  • R Help Mailing List: Another option for asking R related questions with many R gurus in attendance.
  • With all these forums a good question will typically be answered within a day.
• Learn about additional R packages
  • Base R often does most of what you want, but there are thousands of user contributed packages.
  • R Task Views organises the many R packages into various topics.
  • lattice, ggplot2, plyr, nlme are some general packages relevant to a lot of people.
• Get some good books (free or paid):
  • The question of good books on R was asked on Stack Overflow. In particular, this answer lists several good free online options.
  • The R website lists many of the increasing number books on R that are being released.
  • Some of the books on R that I have enjoyed reading include the following:
    • Software for Data Analysis (2008): John Chambers: This gives a sense of the philosophy and style of programming in R. It is an intermediate to advanced text.
    • Data Manipulation with R: Phil Spector: This book is short, concise, and very clear. The examples are well chosen.
    • Data Analysis and Graphics Using R - An Example-Based Approach: John Maindonald and John Braun: This provides a good introduction to R. It also covers many techniques useful in psychology introducing several interesting techniques that are not necessarily part of the standard psychology statistics curriculum.
    • Books in the ;The Springer UseR Series tend be quite good.
    • But there are many more.
• Engage with the R community
  • Join Twitter and check out the #rstats hashtag to find others interested in R
  • See the Reddit Statistics community for occasional R links.
  • See if there is an R Users Group near you (Directory of R User groups).
  • Consider attending the useR conference
  • Consider starting a blog and syndicating your content to R-Bloggers
  • Participate in one of the previously mentioned question and answer sites
  • Consider sharing your code on a site like github or eventually writing an R package and contributing it to CRAN.

Additional Resources

The following are some additional R web resources that I have liked over the while.

• Dolph Schluter's R Tips: Nice set of tips on R (particularly see "fit models" section)
• R in a few hours
• UCLA Short Courses on R: includes notes on moving from SPSS/Stata/SAS to R
• Michael Lavine: Introduction to Statistical Thought: Higher mathematical level; uses R to illustrate principles of probability, inference, modelling, etc.
• Delicious: Lists new R resources
• Cyclismo R Tutorial: Gives an introduction to basic statistics with R
• R Primer: A book introducing R and a little bit of statistics by Christopher Green
• Ecological Models and Data in R: Benjamin Bolker: An early draft of the book is available online. The book provides an introduction to model building for ecologists, but should also be relevant to researchers in psychology. It provides a fairly gentle introduction to statistical modelling.
• Shravan Vasishth and Michael Broe ;have an online book on simulations and statistics useful for social scientists.
• Dan Wright's Psych 101 course with R, SPSS, and Excel
• SimpleR: A free online earlier version of what later went on to become a book. ;It introduces R and basic statistics.
• Ashworth R Resources: A course on R with Lecture notes, datasets, and tutorials. I particularly liked the material on the general linear model.

source: http://jeromyanglim.blogspot.com/2009/06/learning-r-for-researchers-in.html