An IDEMS CommonTech Education Project
Browse 32 openly hosted digital textbooks. Filter by language, software and subject, or search across titles, descriptions, keywords and authors.
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A practical extension of introductory psychology statistics that reframes common analyses as special cases of the general linear model (GLM) using R. Each analysis is covered in a consistent five-step format including hypotheses, R code, statistical decisions, APA reporting, and visualisation.
A gentle introduction to data science for both technical and non-technical readers, using R for hands-on examples across data manipulation, visualisation, statistics, and data mining. Suitable as a standalone introductory course text or supplement to an advanced analytics course, and freely adaptable for non-commercial use.
An introductory statistics textbook for undergraduate psychology students, covering core methods from descriptive statistics and correlation through t-tests, ANOVA, factorial designs, and simulation. Part of a larger open course package including a lab manual and course website.
An applied intermediate statistics textbook extending multiple linear regression to generalised linear models (GLMs) and multilevel models using R, with real case studies throughout. Covers Poisson and logistic regression, likelihood theory, correlated data, longitudinal analysis, and multilevel generalised linear models. Designed for undergraduates who have completed a first regression course.
A concise guide to the bookdown R package for authoring books, technical documents, theses, and course notes using R Markdown. Covers Markdown syntax, figures, tables, cross-references, theorems, output formats (HTML, PDF, EPUB), customisation, and publishing to platforms like GitHub and Netlify. Suitable for anyone wanting to produce professional multi-page documents from R Markdown source files.
A practical introduction to crime mapping and spatial data analysis using R. Developed from teaching materials for upper-level undergraduate and graduate courses, and equally useful for crime analysts and law enforcement practitioners.
A comprehensive first introduction to data science using R and the Tidyverse, covering data wrangling, visualisation, classification, regression, clustering, and statistical inference. Also includes practical chapters on reproducible workflows with Jupyter, version control with Git and GitHub, and setting up a computing environment.
The Python version of Data Science: A First Introduction, covering the same comprehensive curriculum - data wrangling, visualisation, classification, regression, clustering, and statistical inference - using Python and Pandas. Also includes chapters on reproducible workflows with Jupyter and version control with Git and GitHub.
An accessible hands-on guide to conducting meta-analyses in R, covering effect sizes, pooling, heterogeneity, forest plots, subgroup analyses, meta-regression, and publication bias. Also includes advanced methods such as multilevel, network, Bayesian, and SEM meta-analysis, alongside practical tools for power analysis, risk of bias plots, and reporting. Requires no expert-level programming or statistical background.
A practical guide to data visualisation built around real-world case studies spanning sport, science, politics, health, and society. Covers not just how to create graphics but how to understand, evaluate, and improve them, with guidance on colour, ordering, wrangling, and interpretation. Suitable for anyone working with data who wants to communicate findings effectively through graphics.
The first book of the second edition of Introduction to Data Science, covering R programming, data wrangling, visualization, and productivity tools through real-world case studies. No prior R experience required - ideal as a first course in data science.
The second book of the Introduction to Data Science series, covering probability, statistical inference, regression, and machine learning through real-world case studies in R. Teaches students to think statistically — reasoning about uncertainty, model assumptions, and prediction — rather than just applying computational tools. Suitable for an advanced one-semester data science course.
A modern introduction to statistics emphasising exploratory data analysis, multivariate relationships, and simulation-based inference. Covers regression modelling, bootstrapping, randomization, and classical inference through the Central Limit Theorem, with updated datasets and exercises throughout.
An introductory statistics textbook for college students with little mathematical background, integrating R through a deliberately limited set of commands to support rather than distract from statistical learning. Covers core topics from descriptive statistics and probability through inference, regression, and logistic modelling, with a focus on understanding over computation.
A rigorous yet accessible introduction to applied statistics for undergraduate level and beyond, emphasising practical applications and real data. Designed to build statistical thinking without requiring advanced mathematics.
An adaptation of Learning Statistics with R for undergraduate psychology students, replacing R with CogStat — a free software tool designed around automatic statistical analysis. Covers descriptive statistics, probability, hypothesis testing, t-tests, ANOVA, linear regression, categorical data analysis, and Bayesian statistics, with CogStat-specific guidance throughout.
An adaptation of Learning Statistics with R for undergraduate psychology students, replacing R with the beginner-friendly jamovi software. Covers descriptive statistics, probability, hypothesis testing, t-tests, ANOVA, regression, correlation, contingency tables, and factor analysis in an accessible, free open-source format.
An adaptation of Learning Statistics with R for undergraduate psychology students and beginners, using the free JASP software. Covers research design, descriptive statistics, probability, hypothesis testing, t-tests, ANOVA, correlation, regression, categorical data analysis, and Bayesian statistics in a conversational, accessible style.
A comprehensive introductory statistics textbook for undergraduate psychology students using R, covering research design, descriptive statistics, probability, hypothesis testing, regression, ANOVA, categorical data analysis, and Bayesian statistics. Originally developed as lecture notes at the University of Adelaide, it combines statistical theory with practical R programming throughout.
An introductory statistics textbook covering descriptive statistics, bivariate analysis, linear regression, probability, and basic inference. Written for a one-semester undergraduate course and first used at Colorado State University–Pueblo.
A gentle, computation-first introduction to statistical inference and data science using R and the Tidyverse, requiring no prior algebra, calculus, or programming experience. Covers data visualisation, wrangling, and importing with tidyverse, regression modelling with moderndive, and statistical inference via simulation-based methods using the infer package. Emphasises reproducibility and conceptual understanding over mathematical formulas throughout.
A practical recipe-based guide to R Markdown, covering installation, document elements, formatting, output formats (PDF, HTML, Word), tables, knitr chunk options, custom hooks, multilingual code chunks, project management, and workflow tips. Each short recipe demonstrates a single concept, making it easy to dip in and out as needed rather than reading cover to cover.
The definitive guide to R Markdown, covering the full ecosystem from basic document compilation through presentations, dashboards, books, websites, journal articles, interactive tutorials, and Shiny applications. Explains the underlying knitr and Pandoc architecture and covers advanced topics including parameterised reports, HTML widgets, custom templates, and new output formats
A practical, workflow-focused introduction to R for researchers and students who want to conduct and communicate reproducible research, treating reproducibility as a default practice rather than an advanced topic. Requires no prior programming experience.
A comprehensive introduction to statistical thinking that is grounded in mathematics and statistics but draws on computer science and psychology. The book treats statistics as a way of understanding a complex world - not just a set of computational procedures. Covers both classical and modern approaches including resampling, simulation, and the general linear model.
A concise, accessible guide to the most common statistical errors made by scientists, covering p-values, statistical power, pseudoreplication, multiple comparisons, base rate fallacies, researcher degrees of freedom, and publication bias. Requires no prior statistical knowledge and is equally relevant for beginners and experienced researchers.
An accessible introduction to statistics minimising mathematical formalism in favour of conceptual understanding and interpretation. Adapted largely from the open resource Online Statistics Education (Lane, Rice University).
A computational introduction to Bayesian statistics using Python, replacing mathematical notation with code and discrete approximations. Designed for readers with programming experience, it covers Bayes's theorem, estimation, prediction, hypothesis testing, hierarchical models, and simulation through a hands-on, code-first approach.
A practical introduction to probability and statistics for Python programmers, using real datasets and short programs to explore distributions, estimation, hypothesis testing, regression, time series, and survival analysis. Emphasises hands-on exploration over mathematical formalism.
A practical introduction to text mining in R using the tidytext package and tidy data principles, covering sentiment analysis, tf-idf, n-grams, topic modelling, and document-term matrices. Demonstrates how familiar data wrangling tools like dplyr and tidyr can be applied to natural language, with real-world case studies using Twitter, NASA datasets, and Usenet archives.
A practical guide to data science using the Tidyverse ecosystem in R, covering the full project lifecycle from data import and wrangling through visualisation and modelling using the Tidymodels framework. Aimed at R users looking to adopt a consistent, scalable tidy approach to data science.
A practical introduction to statistics for aspiring crime analysts using R, covering descriptive statistics, chi-squared tests, t-tests, ANOVA, correlation, and linear regression applied to crime data. Designed to be accessible beyond university by using free, open-source software rather than subscription-based tools like SPSS or Stata
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