Help yourself to these free books, tutorials, packages, cheat sheets, and many more materials for R programming. There’s a separate overview for handy R programming tricks. If you have additions, please comment below or contact me!
LAST UPDATED: 2021-09-24
Table of Contents (clickable)
Completely new to R? → Start learning here!
Introductory R
Introductory Books
- Introduction to R (R Core Team, 1999)
- R Language Definition (Manual) (R Core Team, 2000)
- Data Import/Export (R Core Team, 2000)
- SimpleR (Verzani, 2001-2)
- R for Beginners (Paradis, 2002)
- Introduction to R (Spector, 2004)
- Ecological Models and Data in R (Bolker, 2007)
- Software for Data Analysis: Programming with R (Chambers, 2008)
- Econometrics in R (Farnsworth, 2008)
- The Art of R Programming (Matloff, 2009)
- R in a Nutshell (Adler, 2010)
- R in Action: Data Analysis and Graphics with R (Kabacoff, 2011)
- R for Psychology Experiments and Questionnaires (Baron, 2011)
- The R Inferno (Burns, 2011)
- Cookbook for R (Chang, ???)
- The R Book (Crawley, 2013)
- Introduction to Data Technologies (Murrel, 2013)
- Introduction to Statistical Thought (Lavine, 2013)
- A (very) short introduction to R (Torfs & Bauer, 2014)***
- Advanced R (Wickham, 2014)
- Introduction to R (Vaidyanathan, 2014)
- Learning statistics with R (Navarro, 2014)
- Programming for Psychologists (Crump, 2014)
- IPSUR: Introduction to Probability and Statistics Using R (Kerns, 2014)
- Hands-On Programming with R (Grolemund, 2014)
- Getting used to R, RStudio, and R Markdown (2016)
- Introduction to R (Venables, Smith, & R Core Team, 2017)
- The R Language Definition (R Core Team, 2017)
- Functional Programming and Unit Testing for Data Munging with R (Rodrigues, 2017)
- YaRrr! The Pirate’s Guide to R (Phillips, 2017)***
- R for Data Science (Grolemund & Wickham, 2017)***
- An Introduction to Statistical and Data Sciences via R (Ismay & Kim, 2018) by ModernDive
- Answering questions with data (Crump, 2018)
- Statistical Thinking for the 21st Century (Poldrack, 2018)
- R Notes for Professionals book (Goalkicker, 2018)
- Learning Statistics with R (Navarro, 2019)
- R Graphics Cookbook – 2nd edition (Chang, 2019)
- Introduction to Open Data Science (The Ocean Health Index Team, 2019)
- Data Science with R: A Resource Compendium (Monkman, 2019)
- R in Action: Third Edition (Kabacoff, 2019)
- A Practical Extension of Introductory Statistics in Psychology using R (Pongpipat, Miranda, & Kmiecik, 2019)
- R for Marketing Students (Samuel Franssens, ????)
Online Courses
Style Guides
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Advanced R
Package Development
Non-standard Evaluation
Functional Programming
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Cheat Sheets
Many of the above cheat sheets are hosted in the official RStudio cheat sheet overview.
Data Manipulation
Data Visualization
Colors
Interactive / HTML / JavaScript widgets
ggplot2
ggplot2 extensions
- ggplot2 extensions overview***
ggthemes – plot style themeshrbrthemes – opinionated, typographic-centric themesggmap – maps with Google Maps, Open Street Maps, etc.ggiraph – interactive ggplotsgghighight – highlight lines or values, see vignetteggstance – horizontal versions of common plotsGGally – scatterplot matricesggalt – additional coordinate systems, geoms, etc.ggbeeswarm – column scatter plots or voilin scatter plotsggforce – additional geoms, see visual guideggrepel – prevent plot labels from overlappingggraph – graphs, networks, trees and moreggpmisc – photo-biology related extensionsgeomnet – network visualizationggExtra – marginal histograms for a plotgganimate – animations, see also the gganimate wiki pageggpage – pagestyled visualizations of text based dataggpmisc – useful additional geom_* and stat_* functionsggstatsplot – include details from statistical tests in plotsggspectra – tools for plotting light spectraggnetwork – geoms to plot networksggpoindensity – cross between a scatter plot and a 2D density plotggradar – radar chartsggsurvplot (survminer) – survival curvesggseas – seasonal adjustment toolsggthreed – (evil) 3D geomsggtech – style themes for plotsggtern – ternary diagramsggTimeSeries – time series visualizationsggtree – tree visualizationstreemapify – wilcox’s treemapsseewave – spectograms
Miscellaneous
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Shiny, Dashboards, & Apps
Markdown & Other Output Formats
- R Markdown cheat sheet by RStudio
- R Markdown reference guide by RStudio
- R Markdown Basics
- R Markdown tutorial by RStudio
- R Markdown gallery by RStudio
- The
knitr book (Xie, 2015) - Getting used to R, RStudio, and R Markdown (2016)
- R Markdown: The Definitive Guide (Xie, Allaire, & Grolemund, 2018)
- Introduction to R Markdown (Clark, 2018)
- R Markdown for Scientists (Tierney, 2019)
- R Markdown Tips and Tricks
- Pimp my RMD by Holtz Yan
- Pandoc syntax highlighting examples by Garrick Aden-Buie
- Creating slides with R Markdown (Video) by Brian Caffo
- Introduction to
xaringan by Yihui Xie - A quick demonstration of
xarigan - General Markdown cheat sheet
blogdown websites with R Markdown (Xie, Thomas, & Hill, 2018)blogdown tutorials- How to build a website with
blogdown in R, by Storybench - radix – online publication format designed for scientific and technical communication
- A template RStudio project with data analysis and manuscript writing by Thomas Julou
- Multiple reports from a single Markdown file (example 1) (example2)
tidystats – automating updating of model statisticspapaja – preparing APA journal articlesblogdown – build websites with Markdown & Hugohuxtable – create Excel, html, & LaTeX tablesxaringan – make slideshows via remark.js and markdown summarytools – produces neat, quick data summary tables citr – RStudio Addin to Insert Markdown Citations
Cloud, Server, & Database
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Statistical Modeling & Machine Learning
Books
- Elements of Statistical Learning (Hastie, Tibshirani, & Friedman, 2001)
- Introduction to Statistical Learning (James, Witten, Hastie, & Tibshirani, 2013)
- Machine Learning with R (Lantz, 2013)
- Regression Models for Data Science in R (Caffo, 2015)
- R Programming for Data Science (Peng, 2016)
- Data Science Live Book (Casas, 2017)
- Statistical Foundations of Machine Learning (Bontempi & Taieb, 2017)
- R for Data Science (Grolemund & Wickham, 2017)
- Introduction to Data Science (Irizarry, 2018)
Courses
Cheat sheets
Time series
Survival analysis
Bayesian
Miscellaneous
corrr – easier correlation matrix management and exploration
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Natural Language Processing & Text Mining
- Text Mining Tutorial with
tm - Tidy Text Mining (Silges & Robinson, 2017) with
tidytext - Text Analysis with R for Students of Literature (Jockers, 2014)
- Tidytext tutorials by computational journalism
- 21 Recipes for Mining Twitter Data (Rudis, 2017) with
rtweet - Emil Hvitfeldt’s R-text-data GitHub repository
- Course: Introduction to Text Analytics with R @DataScienceDojo
- Course: Twitter Text Mining and Social Network Analysis (Zhoa, 2016) @RDataMining with
twitteR - Quantitative Analysis of Textual Data with
quanteda cheat sheet by Stefan Müller and Kenneth Benoit - List of resources for NLP & Text Mining by Stephen Thomas
- Packages — for an overview: CRAN Task View – Natural Language Processing:
tm – text mining.tidytext – text mining using tidyverse principlesquanteda – framework for quantitative text analysisgutenbergr – public domain works (free books to practice on)corpora – statistics and data sets for corpus frequency data.tau – Text Analysis UtilitiesSentiment140 – headache-free sentiment analysissentimentr – sentiment analysis using text polarityopenNLP – sentence detector, tokenizer, pos-tagger, shallow and full syntactic parser, named-entity detector, and maximum entropy models with OpenNLP.cleanNLP – natural language processing via tidy data modelsRSentiment – English lexicon-based sentiment analysis with negation and sarcasm detection functionalities.RWeka – data mining tasks with Wekawordnet – a large lexical database of English with WordNet .stringi – language processing wrapperstextcat – provides support for n-gram based text categorization.text2vec – text vectorization, topic modeling (LDA, LSA), word embeddings (GloVe), and similarities.lsa – Latent Semantic Analysistopicmodels -Latent Dirichlet Allocation (LDA) and Correlated Topics Models (CTM)lda -Latent Dirichlet Allocation and related models
Regular Expressions
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Geographic & Spatial mapping
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Integrated Development Environments (IDEs) &
Graphical User Inferfaces (GUIs)
Descriptions mostly taken from their own websites:
- RStudio*** – Open source and enterprise ready professional software
- Jupyter Notebook*** – open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text across dozens of programming languages.
- Microsoft R tools for Visual Studio – turn Visual Studio into a powerful R IDE
- R Plugins for Vim, Emax, and Atom editors
- Rattle*** – GUI for data mining
- equisse – RStudio add-in to interactively explore and visualize data
- R Analytic Flow – data flow diagram-based IDE
- RKWard – easy to use and easily extensible IDE and GUI
- Eclipse StatET – Eclipse-based IDE
- OpenAnalytics Architect – Eclipse-based IDE
- TinnR – open source GUI and IDE
- DisplayR – cloud-based GUI
- BlueSkyStatistics – GUI designed to look like SPSS and SAS
- ducer – GUI for everyone
- R commander (Rcmdr) – easy and intuitive GUI
- JGR – Java-based GUI for R
- jamovi &
jmv – free and open statistical software to bridge the gap between researcher and statistician - Exploratory.io – cloud-based data science focused GUI
- Stagraph – GUI for ggplot2 that allows you to visualize and connect to databases and/or basic file types
- ggraptr – GUI for visualization (Rapid And Pretty Things in R)
- ML Studio – interactive Shiny platform for data visualization, statistical modeling and machine learning
R & other software and languages
R & Excel
R & Python
R & SQL
sqldf – running SQL statements on R data frames
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R Help, Connect, & Inspiration
- RStudio Community
- R help mailing list
- R seek – search engine for R-related websites
- R site search – search engine for help files, manuals, and mailing lists
- Nabble – mailing list archive and forum
- R User Groups & Conferences
- R for Data Science Online Learning Community
- Stack Overflow – a FAQ for all your R struggles (programming)
- Cross Validated – a FAQ for all your R struggles (statistics)
- CRAN Task Views – discover new packages per topic
- The R Journal – open access, refereed journal of R
- Twitter: #rstats, RStudio, Hadley Wickham, Yihui Xie, Mara Averick, Julia Silge, Jenny Bryan, David Smith, Hilary Parker, R-bloggers
- Facebook: R Users Psychology
- Youtube: Ben Lambert, Roger Peng
- Reddit: rstats, rstudio, statistics, machinelearning, dataisbeautiful
R Blogs
R Conferences, Events, & Meetups
R Jobs
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