Write HTML, PDF, ePub, and Kindle books with R Markdown
The bookdown package is an open-source R package that facilitates writing books and long-form articles/reports with R Markdown. Features include:
- Generate printer-ready books and ebooks from R Markdown documents.
- A markup language easier to learn than LaTeX, and to write elements such as section headers, lists, quotes, figures, tables, and citations.
- Multiple choices of output formats: PDF, LaTeX, HTML, EPUB, and Word.
- Possibility of including dynamic graphics and interactive applications (HTML widgets and Shiny apps).
- Support a wide range of languages: R, C/C++, Python, Fortran, Julia, Shell scripts, and SQL, etc.
- LaTeX equations, theorems, and proofs work for all output formats.
- Can be published to GitHub, bookdown.org, and any web servers.
- Integrated with the RStudio IDE.
- One-click publishing to https://bookdown.org.
Below is a list of featured books. For a full list, please see the archive page. For the full documentation of the bookdown package, please see the free online book bookdown: Authoring Books and Technical Documents with R Markdown.
by Robin Lovelace, Jakub Nowosad, Jannes Muenchow
Welcome | Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. Read more →
by Winston Chang
This cookbook contains more than 150 recipes to help scientists, engineers, programmers, and data analysts generate high-quality graphs quickly—without having to comb through all the details of R’s graphing systems. Each recipe tackles a specific problem with a solution you can apply to your own project and includes a discussion of how and why the recipe works. […] Welcome to the R Graphics Cookbook, a practical guide that provides more than 150 recipes to help you generate high-quality graphs quickly, without having to comb through all the details of R’s graphing systems. Each recipe … Read more →
by Yihui Xie, Amber Thomas, Alison Presmanes Hill
A guide to creating websites with R Markdown and the R package blogdown. […] A note from the authors: Some of the information and instructions in this book are now out of date because of changes to Hugo and the blogdown package. If you have suggestions for improving this book, please file an issue in our GitHub repository. Thanks for your patience while we work to update the book, and please stay tuned for the revised version! In the meantime, you can find an introduction to the changes and new features in the v1.0 release blog post and this “Up & running with blogdown in 2021” blog post. … Read more →
by Chester Ismay and Albert Y. Kim
An open-source and fully-reproducible electronic textbook for teaching statistical inference using tidyverse data science tools. […] This is the website for Statistical Inference via Data Science: A ModernDive into R and the Tidyverse! Visit the GitHub repository for this site and find the book on Amazon. You can also purchase it at CRC Press using promo code ADC21 for a discounted price. This work by Chester Ismay and Albert Y. Kim is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International … Read more →
by Yihui Xie
A guide to authoring books with R Markdown, including how to generate figures and tables, and insert cross-references, citations, HTML widgets, and Shiny apps in R Markdown. The book can be exported to HTML, PDF, and e-books (e.g. EPUB). The book style is customizable. You can easily write and preview the book in RStudio IDE or other editors, and host the book wherever you want (e.g. bookdown.org). Read more →
by Julia Silge and David Robinson
A guide to text analysis within the tidy data framework, using the tidytext package and other tidy tools […] This is the website for Text Mining with R! Visit the GitHub repository for this site, find the book at O’Reilly, or buy it on Amazon. This work by Julia Silge and David Robinson is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 United States … Read more →
by Yihui Xie, Christophe Dervieux, Emily Riederer
This book showcases short, practical examples of lesser-known tips and tricks to helps users get the most out of these tools. After reading this book, you will understand how R Markdown documents are transformed from plain text and how you may customize nearly every step of this processing. For example, you will learn how to dynamically create content from R code, reference code in other documents or chunks, control the formatting with customer templates, fine-tune how your code is processed, and incorporate multiple languages into your analysis. Read more →
by Colin Fay, Sébastien Rochette, Vincent Guyader, Cervan Girard
A book about engineering shiny application that will later be sent to production. This book cover project management, structuring your project, building a solid testing suite, and optimizing your codebase. We describe in this book a specific workflow: design, prototype, build, strengthen and deploy. […] This book will soon be available in print, published in the R Series by Chapman & Hall. The online version of this book is free to read here (thanks to Chapman & Hall/CRC), and licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. This book will … Read more →
by Rafael A. Irizarry
This book introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression and machine learning and helps you develop skills such as R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux shell, version control with GitHub, and reproducible document preparation with R markdown. Read more →
by Yihui Xie, J. J. Allaire, Garrett Grolemund
The first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem. With R Markdown, you can easily create reproducible data analysis reports, presentations, dashboards, interactive applications, books, dissertations, websites, and journal articles, while enjoying the simplicity of Markdown and the great power of R and other languages. Read more →
by Colin Gillespie, Robin Lovelace
Efficient R Programming is about increasing the amount of work you can do with R in a given amount of time. It’s about both computational and programmer efficiency. […] This is the online version of the O’Reilly book: Efficient R programming. Pull requests and general comments are welcome. Get a hard copy from: Amazon (UK), Amazon (USA), O’Reilly Colin Gillespie is Senior Lecturer (Associate Professor) at Newcastle University, UK. He is an Executive Editor of the R Journal, with research interests including high performance statistical computing and Bayesian statistics. Colin founded the … Read more →
by Paul Roback and Julie Legler
An applied textbook on generalized linear models and multilevel models for advanced undergraduates, featuring many real, unique data sets. It is intended to be accessible to undergraduate students who have successfully completed a regression course. Even though there is no mathematical prerequisite, we still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. We believe strongly in case studies featuring real data and real research questions; thus, most of the data in the textbook arises from collaborative research conducted by the authors and their students, or from student projects. Our goal is that, after working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling. Read more →
by Jeroen Janssens
This is the website for Data Science at the Command Line, published by O’Reilly October 2014 First Edition. This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You’ll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data. To get you started—whether you’re on Windows, macOS, or Linux—author Jeroen Janssens has developed a Docker image packed with over 80 command-line tools. Discover why the command line is an agile, scalable, and extensible … Read more →
by Roger D. Peng
The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox. Read more →
by Hadley Wickham
This is the website for 2nd edition of “Advanced R”, a book in Chapman & Hall’s R Series. The book is designed primarily for R users who want to improve their programming skills and understanding of the language. It should also be useful for programmers coming to R from other languages, as help you to understand why R works the way it does. If you’re looking for the 1st edition, you can find it at http://adv-r.had.co.nz/. This work, as a whole, is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The code contained in this book is simultaneously … Read more →
by Pablo Casas
An intuitive and practical approach to data analysis, data preparation and machine learning, suitable for all ages! […] This book is now available at Amazon. Check it out! 📗 🚀. Link to the black & white version, also available on full-color. It can be shipped to over 100 countries. 🌎 The book will facilitate the understanding of common issues when data analysis and machine learning are done. Building a predictive model is as difficult as one line of R code: That’s it. But, data has its dirtiness in practice. We need to sculp it, just like an artist does, to expose its information in order … Read more →
by Kieran Healy
A practical introduction. […] Published by Princeton University Press. Incomplete draft. This version: 2018-04-25. You should look at your data. Graphs and charts let you explore and learn about the structure of the information you collect. Good data visualizations also make it easier to communicate your ideas and findings to other people. Beyond that, producing effective plots from your own data is the best way to develop a good eye for reading and understanding graphs—good and bad—made by others, whether presented in research articles, business slide decks, public policy advocacy, or … Read more →
by Rob J Hyndman and George Athanasopoulos
This is the second edition of Forecasting: Principles & Practice, which uses the forecast package in R. The third edition, which uses the fable package, is also available. Welcome to our online textbook on forecasting. This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly. We don’t attempt to give a thorough discussion of the theoretical details behind each method, although the references at the end of each chapter will fill in many of those details. The book is … Read more →
by Claus O. Wilke
A guide to making visualizations that accurately reflect the data, tell a story, and look professional. […] This is the website for the book “Fundamentals of Data Visualization,” published by O’Reilly Media, Inc. The website contains the complete author manuscript before final copy-editing and other quality control. If you would like to order an official hardcopy or ebook, you can do so at various resellers, including Amazon, Barnes and Noble, Google Play, or Powells. The book is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional. … Read more →
by Garrett Grolemund
This book will teach you how to program in R, with hands-on examples. I wrote it for non-programmers to provide a friendly introduction to the R language. You’ll learn how to load data, assemble and disassemble data objects, navigate R’s environment system, write your own functions, and use all of R’s programming tools. Throughout the book, you’ll use your newfound skills to solve practical data science problems. Read more →
by Hadley Wickham and Garrett Grolemund
This is the website for “R for Data Science”. This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use … Read more →
by Hadley Wickham, Jennifer Bryan
This book will teach you how to create a package, the fundamental unit of shareable, reusable, and reproducible R code. […] Welcome to R packages by Hadley Wickham and Jenny Bryan. Packages are the fundamental units of reproducible R code. They include reusable R functions, the documentation that describes how to use them, and sample data. In this book you’ll learn how to turn your code into packages that others can easily download and use. Writing a package can seem overwhelming at first. So start with the basics and improve it over time. It doesn’t matter if your first version isn’t … Read more →