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
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 Yihui Xie, Amber Thomas, Alison Presmanes Hill
A guide to creating websites with R Markdown and the R package blogdown. […] In the summer of 2012, I did my internship at AT&T Labs Research,1 where I attended a talk given by Carlos Scheidegger (https://cscheid.net), and Carlos said something along the lines of “if you don’t have a website nowadays, you don’t exist.” Later I paraphrased it as: “I web, therefore I am a spiderman.” Carlos’s words resonated very well with me, although they were a little exaggerated. A well-designed and maintained website can be extremely helpful for other people to know you, and you do not need to wait for … 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, 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 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 Gaston Sanchez
This book aims to provide a panoramic perspective of the wide array of string manipulations that you can perform with R. If you are new to R, or lack experience working with character data, this book will help you get started with the basics of handling strings. Likewise, if you are already familiar with R, you will find material that shows you how to do more advanced string and text processing operations. 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 Chester Ismay and Albert Y. Kim
An open-source and fully-reproducible electronic textbook bridging the gap between traditional introductory statistics and data science courses. […] Help! I’m new to R and RStudio and I need to learn about them! However, I’m completely new to coding! What do I do? If you’re asking yourself this question, then you’ve come to the right place! Start with our Introduction for Students. This is version 0.4.0 of ModernDive published on July 21, 2018. For previous versions of ModernDive, see Section 1.5. This book assumes no prerequisites: no algebra, no calculus, and no prior programming/coding … 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 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. Colin Gillespie is Senior lecturer (Associate professor) at Newcastle University, UK. His research interests are high performance statistical computing and Bayesian statistics. He is regularly employed as a consultant by Jumping Rivers and has been teaching R since 2005 at a variety of levels, ranging … Read more →
by Hadley Wickham
This is the website for work-in-progress 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 it explains some of R’s quirks and shows how some parts that seem horrible do have a positive side. This edition is a work in progress. If you’re looking for the electronic version of the 1st edition, you can find it online at http://adv-r.had.co.nz/. This work, as a whole, is licensed … 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
2nd edition […] 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 written for three audiences: (1) people finding themselves doing forecasting in business when they may not have had any formal training in the area; … Read more →
by Claus O. Wilke
A guide to making visualizations that accurately reflect the data, tell a story, and look professional. […] This is an online preview of the book “Fundamentals of Data Visualization” to be published with O’Reilly Media, Inc. The book is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional. It has grown out of my experience of working with students and postdocs in my laboratory on thousands of data visualizations. Over the years, I have noticed that the same issues arise over and over. I have attempted to collect my accumulated … 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 Garrett Grolemund, Hadley Wickham
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 the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data. Read more →