Research Methods in R
2019 Edition.
Research Methods in R is a set of guides on how to use R as your central research methods tool. The target audience is psychology undergraduate students. Research Methods in R is Creative Commons, so you are free to reuse these materials and adapt them as you wish, as long as you attribute them to their authors, and as long as your modifications have a Creative Commons licence.
The advantages of R over other software packages are discussed here. Many psychology degree programmes have switched to R over the last few years, here is a partial list.
List of guides

Going further with R (work in progress)
1. Absolute Beginners’ Guide to R
A series of worksheets on using R for data analysis in psychology. No previous knowledge of R, or of psychology, is assumed.

Introduction to RStudio. How to get RStudio, and a basic introduction to the software.

Exploring data. Means, medians, and histograms.

Group differences. Means and standard deviations, by group. Filtering data. Effect size.

Evidence. Introduction to p values. Traditional betweensubjects ttest. Bayesian betweensubjects ttest.

Using RStudio projects. Creating a new project. Using an R script. Analysing your own data.

Entering data by hand. Entering data into a spreadsheet. Saving data into your RStudio project.

Calculating your module mark. How to calculate a final module mark from your component marks, using R.

Interrater reliability. Percentage agreement. Cohen’s kappa.

Relationships. Frequency and contingency tables. Mosaic plots. Traditional chisquare test. Bayesian test.

Relationships, part 2. Density plots. Scatter plots. Correlation coefficient. Bayesian and traditional tests.

Cheat sheet. Everything we’ve covered so far, condensed.

Other resources. A list of other Creative Commons resources about using R.
2. Putting R to work
These are mainly further practice in the skills learned in Absolute Beginners’. Where the exercises contain completely new skills, these are shown in bold. Where the excercises extend a skill you’ve already been taught, these are shown in italics. The exercises become somewhat more difficult as you go down the list.
If you are a current undergraduate student at Plymouth University, you should complete the accompanying Psych:EL (Psychology: Experiential Learning) activity first, in order to generate your own set of data. If you’re not, you can download sample data files here.

Autobiographical memory. Entering data by hand, histograms.

Face recognition. Means, filtering data, and a bar graph.

Spatial navigation. More on bar graphs.

Response compatibility. Means, filtering data, standard deviations, and density plots.

Visual illusions. Filtering data, means, violin plot, Bayesian ttest.

Facial attractiveness. Means, standard deivations, interquartile range, and density plots.

Police lineup. Contingency table, mosaic plot, Bayesian contingency test, means, density plot, Bayesian ttest

Risk taking. Means, combining data frames, filtering data, and density plots.

Animal Welfare. Percentage agreement, Cohen’s kappa, contingency tables, bar charts.

Creativity and the environment. Preprocessing, means, density plots, effect size, Bayesian ttest.

Political psychology. Means, filtering data, summarising data, density plots, effect size, Bayesian ttest, traditional ttest.
3. A Very Brief Guide to R
The Absolute Beginners’ Guide to R and Putting R to Work provide, between them, about 20 hours of introductory material. For those in a hurry, the Very Brief Guide to R covers the most critical material from those two courses in about four hours.

Using RStudio: Brief introduction to the software

Exploring data: Loading data, calculating means

Group differences: Grouping, density plots, filtering.

Evidence, part 1: Bayesian and traditional ttests

Evidence, part 2: Bayes and traditional correlation, scatterplot
4. Research Methods in Practice (Quantitative section)
These are intermediatelevel materials, designed to follow on from An Absolute Beginners’ Guide to R and Putting R to work (or from A Very Brief Guide to R, if you’re in a hurry). They are maintained by Ben Whalley on a separate site, but have been designed to fit in here in this sequence of materials. Only the quantitative section of Ben’s site contains information concerning the usage of R.
 Research Methods in Practice: Data handling, multiple regression.
5. Intermediate Guide to R
These are intermediatelevel materials, designed to follow on from An Absolute Beginners’ Guide to R and Putting R to work (or from A Very Brief Guide to R, if you’re in a hurry). They provide analysis methods for conducting realistic, highquality studies in psychology. They are aimed at a secondyear undergraduate audience.

Revision: A quick recap of key information covered in earlier courses.

Statistical power: How to calculate the statistical power of experiments.

Data preprocessing: Getting data from labbased (OpenSesame) experiments into a format closer to something you can actually analyse, in five steps: loading, selecting, filtering, summarising, and combining.

Withinsubject differences: Data preprocessing (pivoting and mutating). Onefactor withinsubject Bayesian ANOVA.

Understanding interactions: Learn what an interaction is, and learn how to do line plots at the same time.

Factorial differences: Twofactor Bayesian ANOVA (one within, one between)
6. Going further with R
These are slightly more advanced materials, aimed at a finalyear undergraduate psychology audience.

Traditional ANOVA: Just in case someone insists you use the older, pvalue based, approach to ANOVA … and you don’t feel like saying ‘no’.

Factorial differences, part 2: More on twofactor Bayesian ANOVA.

Bettter graphs: Producing publicationquality graphs in R.
The following materials are currently being developed, so on clicking these links, you may find just notes, or incomplete worksheets.
 Other resources: Links to external resources.
Source code
These teaching materials were generated using a combination of Markdown and RMarkdown. The full source code is available on github.
Licence
This material is distributed under a Creative Commons licence. CCBYSA 4.0.
Parts of this material have been adpated from these other Creative Commons materials:
 May, J. (2018). Getting Results with R.
 Whalley, B. (2018). Just Enough R.
 Wills, A. (2015). R for Experimental Psychologists.
Acknowledgements
Thanks to the following people for their feedback and advice on these materials:
Jackie Andrade, Eleanor Andrade May, Martyn Atkins, Patric Bach, Dale Barr, Chris Berry, Laura Charlton, Lisa DeBruine, Charlotte Edmunds, Emily Filewood, Giorgio Ganis, Phil Gee, Michaela Gummerum, Yaniv Hanoch, Cathryn Harries, Courtney Hooton, Angus Inkster, Jasmin Jones, Peter Jones, Laith Kahn, Chris Longmore, Jon May, Anthony Mee, Chris Mitchell, Millie Monks, Karol Nedza, Alyson Norman, Charlie Reynolds, Matt Roser, Paul Sharpe, Alastair Smith, Julian Stander, Sylvia Terbeck, Michael Verde, Clare Walsh, Ben Whalley.