Reproducible Research with R and RStudio

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ISBN 9781466572843
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  • Presents straightforward examples to illustrate a reproducible research workflow, from data gathering to the presentation of results
  • Uses the integrated developer environment RStudio
  • Shows how to automatically generate tables and figures using R
  • Discusses the R package knitr for literate programming
  • Provides instructions on formatting a presentation document via markup languages
  • Describes cloud storage and versioning services such as Dropbox and Git/Github
  • Explains how to use Unix-like shell programs for working with large research projects
  • Offers examples and a reproducible research project available for download on the author’s website


Bringing together computational research tools in one accessible source, Reproducible Research with R and RStudio guides you in creating dynamic and highly reproducible research. Suitable for researchers in any quantitative empirical discipline, it presents practical tools for data collection, data analysis, and the presentation of results.

With straightforward examples, the book takes you through a reproducible research workflow, showing you how to use:

  • R for dynamic data gathering and automated results presentation
  • knitr for combining statistical analysis and results into one document
  • LaTeX for creating PDF articles and slide shows, and Markdown and HTML for presenting results on the web
  • Cloud storage and versioning services that can store data, code, and presentation files; save previous versions of the files; and make the information widely available
  • Unix-like shell programs for compiling large projects and converting documents from one markup language to another
  • RStudio to tightly integrate reproducible research tools in one place

Whether you’re an advanced user or just getting started with tools such as R and LaTeX, this book saves you time searching for information and helps you successfully carry out computational research. It provides a practical reproducible research workflow that you can use to gather and analyze data as well as dynamically present results in print and on the web. Supplementary files used for the examples and a reproducible research project are available on the author’s website.

Table of Contents

Getting Started
Introducing Reproducible Research
What Is Reproducible Research?
Why Should Research Be Reproducible?
Who Should Read This Book?
The Tools of Reproducible Research
Why Use R, knitr, and RStudio for Reproducible Research?
Book Overview

Getting Started with Reproducible Research
The Big Picture: A Workflow for Reproducible Research
Practical Tips for Reproducible Research

Getting Started with R, RStudio, and knitr
Using R: The Basics
Using RStudio
Using knitr: The Basics

Getting Started with File Management
File Paths and Naming Conventions
Organizing Your Research Project
Setting Directories as RStudio Projects
R File Manipulation Commands
Unix-like Shell Commands for File Management
File Navigation in RStudio

Data Gathering and Storage
Storing, Collaborating, Accessing Files, and Versioning
Saving Data in Reproducible Formats
Storing Your Files in the Cloud: Dropbox
Storing Your Files in the Cloud: GitHub
RStudio and GitHub

Gathering Data with R
Organize Your Data Gathering: Makefiles
Importing Locally Stored Data Sets
Importing Data Sets from the Internet
Advanced Automatic Data Gathering: Web Scraping

Preparing Data for Analysis
Cleaning Data for Merging
Merging Data Sets

Analysis and Results
Statistical Modeling and
Incorporating Analyses into the Markup
Dynamically Including Modular Analysis Files
Reproducibly Random: set.seed
Computationally Intensive Analyses

Showing Results with Tables
Basic knitr Syntax for Tables
Table Basics
Creating Tables from R Objects

Showing Results with Figures
Including Non-knitted Graphics
Basic knitr Figure Options
Knitting R’s Default Graphics
Including ggplot2 Graphics
JavaScript graphs with googleVis

Presentation Documents
Presenting with LaTeX
The Basics
Bibliographies with BibTeX
Presentations with LaTeX Beamer

Large LaTeX Documents: Theses, Books, and Batch Reports
Planning Large Documents
Large Documents with Traditional LaTeX
knitr and Large Documents
Child Documents in a Different Markup Language
Creating Batch Reports

Presenting on the Web with Markdown
The Basics
Markdown with Pandoc and Custom CSS
Slideshows with Markdown, knitr, and HTML
Publishing Markdown Documents

Citing Reproducible Research
Licensing Your Reproducible Research
Sharing Your Code in Packages
Project Development: Public or Private?
Is it Possible to Completely Future Proof Your Research?



Author Bio(s)

Christopher Gandrud is a research associate at the Hertie School of Governance. He was previously a lecturer of international relations at Yonsei University and a fellow in government at the London School of Economics (LSE). He has published articles on political economy and quantitative methods in the Review of International Political Economy and the International Political Science Review. He earned a PhD in political science from the LSE.

Editorial Reviews

"Gandrud has written a great outline of how a fully reproducible research project should look from start to finish, with brief explanations of each tool that he uses along the way. … the readers who will get the most use from this book are those are already working in R and just need a way to organize their work. That being said, advanced undergraduate students in mathematics, statistics, and similar fields as well as students just beginning their graduate studies would benefit the most from reading this book. Many more experienced R users or second-year graduate students might find themselves thinking, ‘I wish I’d read this book at the start of my studies, when I was first learning R!’ … a good text for beginning graduate students or advanced undergraduate students who are just starting to do technical research. … This book could be used as the main text for a class on reproducible research …"
The American Statistician, November 2014

"Three recent books have significantly influenced how I use R in reproducible work: Dynamic Documents with R and knitr by Yihui Xie, Reproducible Research with R and RStudio by Christopher Gandrud, and Implementing Reproducible Research edited by Victoria Stodden, Friedrich Leisch, and Roger D. Peng … I recommend all three books to R users at any level. There really is something here for everyone."
—Richard Layton, PhD, PE, Rose-Hulman Institute of Technology, Terre Haute, Indiana, USA