Reproducible Research with R and R Studio: (Chapman & Hall/CRC: The R Series)

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 knitr 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 Conclusion 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? Bibliography Index

About the Author :
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.

Review :
"… a very practical book that teaches good practice in organizing reproducible data analysis and comes with a series of examples. … an extremely valuable overview of the current capabilities of R, RStudio, and related software tools for reproducible research. I recommend this book to anyone who wants to learn more about these fascinating tools." —Biometrical Journal, 2014 "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 "this book addresses all types of researchers in the field of life sciences: from biology to genetics and the social sciences, from epidemiology to medicine, and beyond. What is interesting about it is that it addresses newcomers in the field of research, upper level undergraduates, but also more experienced researchers. Furthermore, it can be used reading from the first page to the last one, step-by-step, like a textbook, or it can be addressed by experienced researchers only in specific parts. It is self-sufficient, containing everything for researchers interested in performing reproducible research . . . As a conclusion, I recommend the use of this book for beginners, but also for more advanced users, since the information presented by this book is detailed, useful and interesting." —Costela Serban, International Society for Clinical Biostatistics