R Resources for Psychologists

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Good resources for learning R as a Psychologist are hard to find. By that I mean that there are so many great sites and blogs around the internet to learn R. Thus, it may be hard to find learning resources that targets Psychology researchers.

Recently I wrote about four good R books targeted for Psychology students and researchers (i.e., R books for Psychologists). There are, however, of course other good resources for Psychological researchers to learn R programming.

Therefore, this post will list some of the best blogs and sites to learn R.  The post will be divided into two categories; general and Psychology focused R sites and blogs. For those who are not familiar with R I will start with a brief introduction on what R is (if you know R already; click here to skip to the links).

What is R?

If you are new to R you might wonder what R is? R is a free and open source programming language and environment. Data analysis in R is carried out by writing scripts and functions. Finally, R is a complete, interactive, and object-oriented language.

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In R statistical environment, you are able to carry out a variety of statistical and graphical techniques. For instance, linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, and many more can be carried using both frequentist and Bayesian paradigms.

If you are new to R, you may want to start with R commander. This will provide you with a menu making the process of  learning R a bit easier at the beginning. R can be downloaded here: The Comprehensive R Archive Network.

R Resources

One of the main things that I like with R is the broad and helpful community. This also means that there are many good resources for learning the language.

General R Resources

When starting off learning R any source may be useful. Here are some of the best R learning resources I’ve used:

  • Quick-R: A comprehensive source for information on how to carry out many common statistical analysis as well as descriptive statistics and graphs.
  • SimpleR notes for introducing the use of R for an introductory statistics course.
  • The R Guide
  • Cookbook for R provides solutions to many tasks and problems in data analysis.
  • Revolution Analytics – A blog for news and information about R. Publishes guides and articles about R.
  • R-bloggers A “blog aggregator” for R blogs. Here you can find, and follow, a lot of comprehensive, basic and advanced, guides and posts.
  • RStudio – my Integrated Development Environment (IDE) of choice when it comes to R.
  • How to Use Binder and R for Reproducible Research – Learn how to use Binder together with R, and RStudio to create a fully reproducible computational environment.
  • R Excel Tutorial: How to Read and Write xlsx files in R will give you a great overview on how to import Excel data into R.
  • Learn how to create scatter plots in R  will give you the skills to produce nice looking data visualizations using the tidyverse packages (e.g., ggplot2, dplyr, broom).

Psychology R Resources

If you are a psychology researcher aiming to learn R it can be helpful to learn from other psychologists more experienced with the R statistical language.  There are also some r-packages that are developed for psychology researchers.

  • Using R for Psychological Research – here you will find many tutorials for using R in psychological research.  These are very pedagogical and helpful. Here you also find the homepage of the great r-package psych.

If you are interested in learning how to reverse scores using R see the blog post “Reverse Scoring Using R Statistical Environment“.

Conclusion

Although, the learning curve for software such as R is steeper than using software with graphical interfaces (i.e., SPSS, Stata, and Statistica) it is not super hard to learn to carry out the most basic and classical statistical tests. If you aim for reproducibility learning R and/or LaTeX or RMarkdown is really the way to go.

Note, Python is another great, and more general, programming language that may prove useful for an experimental psychologist (e.g., programming experiments). See my newer posts on how to carry out data visualization, data analysis, data manipulation, and more using Python:


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