PsyTeachR
The psyTeachR team at the University of Glasgow School of Psychology and Neuroscience has successfully made the transition to teaching reproducible research using R across all undergraduate and postgraduate levels. Our curriculum now emphasizes essential ‘data science’ graduate skills that have been overlooked in traditional approaches to teaching, including programming skills, data visualisation, data wrangling and reproducible reports. Students learn about probability and inference through data simulation as well as by working with real datasets.
This website contains our open materials for teaching reproducible research. See our GitHub for open data and code.
Course Books
Undergraduate Series
Level 1: Data Skills
Our first-year undergraduate course covers current state of psychological science and what Open Science is as well as its importance. It also aims to make students confident and competent at using RStudio as a tool to achieve good data management skills.
Authors: Emily
Nordmann, Heather
Cleland-Woods
Contact: Emily
Nordmann
Contributors: Jack Taylor,
Shannon McNee
Level 2: Analysis
Our second-year undergraduate course covers data skills such as R Markdown, data wrangling with tidyverse, and data visualisation with ggplot2. It also introduces statistical concepts such as permutation tests, NHST, alpha, power, effect size, and sample size. Semester 2 focusses on correlations and the general linear model.
This book is currently being updated and many chapters have not yet been posted. Access the older version.
Authors: Phil
McAleer, Carolina
Kuepper-Tetzel, Helena
Paterson
Contact: Carolina
Kuepper-Tetzel
Level 3: Statistical Models
This third-year undergraduate course teaches students how to specify, estimate, and interpret statistical models corresponding to various study designs, using a General Linear Models approach.
Author: Dale Barr
Postgraduate Books
Fundamentals of Quantitative Analysis
This book contains materials for students on the MSc Conversion in Psychological Studies/Science, a one-year postgraduate degree for students with a non-psychology undergraduate degree. This research methods course covers core data skills that allow you to manipulate and analyse quantitative data.
Author: Emily
Nordmann
Contact: Phil
McAleer
Data Skills for Reproducible Research
This book provides an overview of skills needed for reproducible research and open science using the statistical programming language R and tidyverse packages. It covers reproducible workflows, data visualisation, data tidying and wrangling, archiving, iteration and functions, probability and data simulations. It assumes at least an undergraduate level of methods training, but no prior experience in R.
Authors: Lisa
DeBruine, Dale
Barr
Contact: Lisa
DeBruine
Contributors: Rebecca
Lai
Statistics and Research Design
Coming soon! The second term statistics course for our MSc in Research Methods.
Authors: James
Bartlett, Christoph
Scheepers, Guillaume
Rousselet
Contact: James
Bartlett
Other Books
Glossary of Terms
PsyTeachR books (and external websites) can link to the glossary to define common terms. Anyone can contribute to the glossary through the github project.
Contact: Lisa DeBruine
Applied Data Skills
This book provides an overview of the basic skills needed to turn raw data into informative summaries and visualisations presented in professional reports and presentations.
Authors: Emily Nordmann, Lisa DeBruine
A Handy Workbook for Research Methods & Statistics
A Handy Workbook to help students understand Research Methods and Statistics through worked examples and self-tests.
Author: Phil McAleer
Data Visualisation Using R, For Researchers Who Don’t Use R
In this tutorial, we provide a practical introduction to data visualisation using R, specifically aimed at researchers who have little to no prior experience of using R.
Authors: Emily Nordmann, Phil McAleer, Wilhelmiina Toivo, Helena Paterson, Lisa DeBruine
Contact: Emily Nordmann
Building Web Apps with R Shiny
Learn the basics of Shiny app programming, including skills that will form the basis of almost any app you want to build.
Contact: Lisa DeBruine
Coding Club: Creating an R Package
Making an R package develops generic coding skills and gives you valuable insight to how R works. Covers setting up a package project, creating functions, documenting them with roxygen, creating vignettes, unit testing, package testing, version control with git, and distribution with github.
Contact: Lisa DeBruine
PsyTeachR Book Template
Fork the GitHub project to get started making a book with the psyTeachR styles and conventions. This includes custom code to make the subheader menu accessible on mobile or smaller screens, and integrated support for webexercises. The template has a CC-BY-SA license, so feel free to make any modifications.
Contact: Lisa DeBruine
R Packages
{webexercises}
The R package {webexercises} helps instructors easily
create interactive web pages that students can use in self-guided
learning.
(formerly webex
)
Authors: Dale Barr, Lisa DeBruine
{markr}
The R package {markr} helps instructors create
individual feedback documents and marking summaries from flexibly
organised spreadsheets and other types of input.
Authors: Lisa DeBruine, Helena Paterson, Phil McAleer
{faux}
The R package {faux} package makes it easier to simulate
data with a specified structure.
Author: Lisa DeBruine
Web Apps
simulate
Shiny app exploring a few basic distributions that you can sample in R. Generating random samples is a good way to get better intuitions about what data look like. By Lisa DeBruine, source code on github, License: CC-BY-SA
normal-distributions
Shiny app to help students understand how to use the distribution and
quantile functions pnorm() and rnorm() for the
normal distribution. By Jack Taylor, source code on github,
License: CC-BY-SA
peek
Shiny app that asks: Just how bad is it to peek at your data every few observations and stop collecting data once you have a significant result? By Lisa DeBruine, source code on github, License: CC-BY-SA
covariance-quiz
Shiny app quiz on covariance matrices. By Dale Barr, source code on github, License: CC-BY-SA
plotdemo
Shiny app to visualize simulated data from a 2x2 factorial design with 6 different plots. By Lisa DeBruine, source code on github, License: CC-BY-SA
faux-app
Shiny app to set up a factorial design, simulate data, visualise and download it. By Lisa DeBruine, source code on github, License: CC-BY-SA
lmem_sim
Shiny app demonstrating simulation of data with crossed random effects of subjects and items, to accompany tutorial paper by Debruine and Barr (2021). By Lisa DeBruine, source code on github, License: CC-BY-SA