Reproducible Research with R and R Studio

Christopher Gandrud is a postdoctoral researcher in the Fiscal Governance Centre at the Hertie School of Governance. His research focuses on the international political economy of public financial and monetary institutions as well as applied social science statistics and software development. He has published many articles in peer-reviewed journals, including the Journal of Common Market Studies, Review of International Political Economy, Political Science Research and Methods, Journal of Statistical Software, and International Political Science Review. He earned a PhD in quantitative political science from the London School of Economics.

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/rmarkdown, 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/rmarkdown
Using R: the Basics
Using RStudio
Using knitr and rmarkdown: 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 Modelling 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 Supported Class R Objects





Showing Results with Figures
Including Non-Knitted Graphics
Basic knitr/rmarkdown Figure Options
Knitting R’s Default Graphics
Including ggplot2 Graphics
JavaScript Graphs with googleVis





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





Large knitr/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 and Other Formats with R Markdown
The Basics
Further Customizability with rmarkdown
Slideshows with Markdown, rmarkdown, and HTML
Publishing HTML Documents Created by R Markdown





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