Reproducible Research with R and RStudio

Praise for previous editions:"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… 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!’…This book could be used as the main text for a class on reproducible research …" (The American Statistician) Reproducible Research with R and R Studio, Third Edition brings together the skills and tools needed for doing and presenting computational research. Using straightforward examples, the book takes you through an entire reproducible research workflow. This practical workflow enables you to gather and analyze data as well as dynamically present results in print and on the web. Supplementary materials and example are available on the author’s website. New to the Third Edition Updated package recommendations, examples, URLs, and removed technologies no longer in regular use. More advanced R Markdown (and less LaTeX) in discussions of markup languages and examples. Stronger focus on reproducible working directory tools. Updated discussion of cloud storage services and persistent reproducible material citation. Added discussion of Jupyter notebooks and reproducible practices in industry. Examples of data manipulation with Tidyverse tibbles (in addition to standard data frames) and pivot_longer() and pivot_wider() functions for pivoting data. Features Incorporates the most important advances that have been developed since the editions were published Describes a complete reproducible research workflow, from data gathering to the presentation of results Shows how to automatically generate tables and figures using R Includes instructions on formatting a presentation document via markup languages Discusses cloud storage and versioning services, particularly Github Explains how to use Unix-like shell programs for working with large research projects

I Getting Started 1 Introducing Reproducible Research What Is Reproducible Research? Why Should Research Be Reproducible? For science For you Who Should Read This Book? Academic researchers Students Instructors Editors Private sector researchers The Tools of Reproducible Research Why Use R, knitr/R Markdown, and RStudio for Reproducible Research? Installing the main software Installing markup languages GNU Make Other Tools Book Overview How to read this book Reproduce this book Contents overview 2 Getting Started with Reproducible Research The Big Picture: A Workflow for Reproducible Research Reproducible theory Practical Tips for Reproducible Research Document everything! Everything is a (text) file All files should be human readable Explicitly tie your files together Have a plan to organize, store, and make your files available 3 Getting Started with R, RStudio, and knitr/R Markdown Using R: The Basics Objects Functions The workspace & history R history Global R options Installing new packages and loading functions Using RStudio Using knitr and R Markdown: The basics What knitr does What rmarkdown does File extensions Code chunks Global chunk options knitr package options Hooks knitr, R Markdown, & RStudio knitr & R R Markdown and R 4 Getting Started with File Management File Paths & Naming Conventions Root directories Sub-directories & parent directories Working directories Absolute vs relative paths Spaces in directory & file names Organizing Your Research Project Organizing Research with RStudio Projects R File Manipulation Functions Unix-like Shell Commands for File Management File Navigation in RStudio II Data Gathering and Storage 5 Storing, Collaborating, Accessing Files, and Versioning Saving Data in Reproducible Formats Storing Your Files in the Cloud: Dropbox Storage Accessing data Contents v Collaboration Version control Storing Your Files in the Cloud: GitHub Setting up GitHub: Basic Version control with Git Remote storage on GitHub Accessing on GitHub Summing up the GitHub workflow RStudio & GitHub Setting up Git/GitHub with Projects Using Git in RStudio Projects 6 Gathering Data with R Organize Your Data Gathering: Makefiles R Make-like files GNU Make Importing Locally Stored Data Sets Importing Data Sets from the Internet Data from non-secure (http) URLs Data from secure (https) URLs Compressed data stored online Data APIs & feeds Advanced Automatic Data Gathering: Web Scraping 7 Preparing Data for Analysis Cleaning Data for Merging Get a handle on your data Reshaping data Renaming variables Ordering data Subsetting data Recoding string/numeric variables Creating new variables from old Changing variable types Merging Data Sets Binding Merging data frames Duplicate columns 8 Statistical Modeling and knitr/R Markdown Incorporating Analyses into the Markup Full code chunks Showing code & results inline Dynamically including non-R code in code chunks vi Contents Dynamically Including Modular Analysis Files Source from a local file Source from a URL Reproducibly Random: setseed() Computationally Intensive Analyses 9 Showing Results with Tables Basic knitr Syntax for Tables Table Basics Tables in LaTeX Tables in Markdown/HTML Creating Tables from Supported Class R Objects kable for Markdown and LaTeX xtable for LaTeX and HTML Fitting Large Tables in LaTeX xtable with non-supported class objects Creating variable description documents with xtable 10 Showing Results with Figures Including Non-knitted Graphics Including graphics in LaTeX Including graphics in Markdown/HTML Non-knitted graphics with knitr/rmarkdown Basic knitr/rmarkdown Figure Options Chunk options Global options Knitting R’s Default Graphics Including ggplot Graphics Showing regression results with caterpillar plots JavaScript Graphs with googleVis Basic googleVis figures Including googleVis in knitted documents JavaScript Graphs with htmlwidgets-based packages 11 Presenting with LaTeX The Basics Getting started with LaTeX editors Basic LaTeX command syntax The LaTeX preamble & body Headings Paragraphs & spacing Horizontal lines Text formatting Math Lists Footnotes Cross-references Bibliographies with BibTeX The bib file Including citations in LaTeX documents Generating a BibTeX file of R package citations Presentations with LaTeX Beamer Beamer basics knitr with LaTeX slideshows 12 Presenting in a Variety of Formats with R Markdown The Basics Getting started with Markdown editors Preamble and document structure Headings Horizontal lines Paragraphs and new lines Italics and bold Links Lists Math with MathJax Further Customizability with rmarkdown CSS style files and Markdown Slideshows with Markdown, R Markdown, and HTML HTML Slideshows with rmarkdown LaTeX Beamer Slideshows with rmarkdown Slideshows with Markdown and RStudio’s R Presentations Publishing HTML Documents Created with R Markdown Further information on R Markdown 13 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?

Christopher Gandrud is Head of Economics and Experimentation at Zalando SE where he leads teams of social data scientists and software engineers building large scale automated decision-making systems. He was previously a research fellow at the Institute for Quantitative Social Science, Harvard University developing statistical software for the social and physical sciences. 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.