R Learning Resources
A curated collection of open-source R learning resources for longitudinal data analysis and research workflows.
All resources marked with Open Source are freely available.
Tip: Start with a learning path below, or filter by category, level, or topic. Items marked ★ are particularly good starting points.
Start Here
Choose a learning path based on your experience level and goals.
Start Here: R Fundamentals
Best for: No prior R experience
Learn core R syntax, data structures, and visualization before tackling statistical models.
Start Here: Longitudinal Analysis
Best for: Comfortable with R basics and regression
Move from cross-sectional analysis to mixed models and growth curves for repeated-measures data.
Advanced: SEM & Bayesian Methods
Best for: Experienced with mixed models, ready for advanced techniques
Explore structural equation modeling, Bayesian approaches, and specialized longitudinal techniques.
Showing all 47 resources
Books
From R fundamentals to advanced mixed models and SEM—these texts cover the methods essential for longitudinal research.
R for Data Science (2e)
Hadley Wickham, Mine Cetinkaya-Rundel & Garrett Grolemund
The essential introduction to modern R and the tidyverse. Covers the complete data science workflow from import to communication.
Hands-On Programming with R
Garrett Grolemund
Learn R by building real projects. A practical, hands-on introduction focusing on programming fundamentals.
R Programming for Data Science
Roger D. Peng
A solid introduction to R programming for data science from Johns Hopkins. Covers fundamental concepts and practical techniques.
The Pirate's Guide to R (YaRrr!)
Nathaniel D. Phillips
A fun, accessible introduction to R with a pirate theme. Great for absolute beginners who want an engaging learning experience.
R Cookbook (2e)
James (JD) Long & Paul Teetor
Practical recipes for common R tasks. Each recipe solves a specific problem with working code examples.
ggplot2: Elegant Graphics for Data Analysis (3e)
Hadley Wickham, Danielle Navarro & Thomas Lin Pedersen
The definitive guide to ggplot2 and the grammar of graphics. Essential for creating publication-quality visualizations.
Efficient R Programming
Colin Gillespie & Robin Lovelace
Write faster, more efficient R code. Covers profiling, optimization, and best practices for production workflows.
The Art of R Programming
Norman Matloff
A deeper dive into R as a programming language. Covers data structures, debugging, and software development practices.
Advanced R (2e)
Hadley Wickham
Master R's internals: environments, functional programming, metaprogramming, and performance optimization.
Deep R Programming
Marek Gagolewski
An in-depth exploration of R programming, covering advanced topics from a computer science perspective.
R Inferno
Patrick Burns
A guide to R's circles of hell - common pitfalls, gotchas, and how to avoid them. Essential reading for serious R users.
Software for Data Analysis: Programming with R
John M. Chambers
From the creator of S and R. Deep insights into R's design philosophy and programming paradigms.
Videos
Video courses and tutorials covering R programming, statistical modeling, and data analysis workflows.
Tutorials
Hands-on interactive tutorials for learning R syntax, data wrangling, and statistical concepts at your own pace.
Introduction to R
Comprehensive R course from Johns Hopkins Bloomberg School of Public Health. Excellent for health researchers.
{swirl} - Learn R, in R
Interactive R tutorials that run directly in your R console. Learn by doing at your own pace.
CS50's Introduction to Programming with R
Harvard's official R programming course materials. Includes problem sets and lecture notes.
fasteR - Fast Lane to Learning R
Get productive in R as quickly as possible. A no-nonsense, efficient approach to learning R basics.
Cheatsheets
Quick reference guides for R syntax, tidyverse verbs, and common statistical functions—keep these handy.
Base R Cheatsheet
Essential R functions and syntax. The foundation for everything else.
Data Transformation with dplyr
Filter, arrange, select, mutate, and summarize data with dplyr verbs.
Data Visualization with ggplot2
Build any visualization with ggplot2's grammar of graphics.
R Markdown Cheatsheet
Create reproducible reports combining code, output, and narrative.
Shiny Cheatsheet
Build interactive web applications with Shiny's reactive framework.
String Manipulation with stringr
Pattern matching, extraction, and text manipulation with stringr.
R Packages
Core packages for mixed models, growth curves, SEM, Bayesian methods, and missing data handling used throughout longitudinal.dev.
Collection of R packages for data science: ggplot2, dplyr, tidyr, readr, and more. The modern R workflow.
Create elegant, publication-quality graphics using the grammar of graphics. The gold standard for R visualization.
A grammar of data manipulation. Filter, select, mutate, summarize, and join data with intuitive verbs.
The workhorse for linear and generalized linear mixed-effects models. Essential for longitudinal analysis.
Linear and nonlinear mixed-effects models. More flexible correlation structures than lme4.
Adds p-values and hypothesis tests to lme4 models using Satterthwaite and Kenward-Roger approximations.
Generalized linear mixed models with flexible distributions: zero-inflation, beta, negative binomial, and more.
Latent Variable Analysis. Fit structural equation models, growth curves, and confirmatory factor analysis.
Extensions for lavaan: measurement invariance, reliability, missing data, and model comparison utilities.
Advanced SEM and matrix algebra. Maximum flexibility for complex models including twin studies.
Bayesian SEM using lavaan syntax. Integrates with Stan and JAGS for MCMC estimation.
Bayesian regression models using Stan. Multilevel models, GAMs, and more with intuitive R formula syntax.
Bayesian applied regression modeling via Stan. Pre-compiled models for faster iteration.
Generalized Estimating Equations for population-averaged models with correlated data.
Generalized Additive Models with smooth terms. Flexible modeling of nonlinear relationships.
Multivariate Imputation by Chained Equations. The standard for multiple imputation in R.
Multiple imputation for cross-sectional and time-series data using bootstrapping and EM algorithm.
Combine multiple ggplot2 plots into a single figure with intuitive operators.
Create animated visualizations showing change over time. Perfect for longitudinal data.