Reproducible Research with R and RStudio

Description

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 Contents Preface Motivation Changes to the Third Edition Changes to the Second Edition Acknowledgments About the Author Stylistic Conventions Additional Resources Chapter Examples Short Example Project Updates Corrections Part I Getting Started 1 Introducing Reproducible Research 1.1 What Is Reproducible Research? 1.2 Why Should Research Be Reproducible? 1.3 Who Should Read This Book? 1.4 The Tools of Reproducible Research 1.5 Installing the main software 1.6 Book Overview Appendix: Additional R Setup 2 Getting Started with Reproducible Research 2.1 The Big Picture: A Workfow for Reproducible Research 2.2 Practical Tips for Reproducible Research 3 Getting Started with R, RStudio, and knitr/R Markdown 3.1 Using R: The Basics 3.2 Using RStudio 3.3 Using knitr and R Markdown: The Basics Appendix: Jupyter Interactive Notebooks Appendix: knitr and Lyx 4 Getting Started with File Management 4.1 File Paths and Naming Conventions 4.2 Organizing Your Research Project 4.3 Organizing Research with RStudio Projects 4.4 R File Manipulation Functions 4.5 Unix-like Shell Commands for File Management 4.6 File Navigation in RStudio Part II Data Gathering and Storage 5 Storing, Collaborating, Accessing Files, and Versioning 5.1 Saving Data in Reproducible Formats 5.2 Storing Your Files in the Cloud: Dropbox 5.3 Storing Your Files in the Cloud: GitHub 5.4 RStudio and GitHub 6 Gathering Data with R 6.1 Organize Your Data Gathering: Makefles 6.2 Importing Locally Stored Data Sets 6.3 Importing Data Sets from the Internet 6.4 Advanced Automatic Data Gathering: Web Scraping 7 Preparing Data for Analysis 7.1 Cleaning Data for Merging 7.2 Merging Data Sets Appendix Part III Analysis and Results 8 Statistical Modeling and knitr/R Markdown 8.1 Incorporating Analyses into the Markup 8.2 Dynamically Including Modular Analysis Files 8.3 Reproducibly Random: 8.4 Computationally Intensive Analyses 9 Showing Results with Tables 9.1 Basic Syntax for Tables 9.2 Table Basics 9.3 Creating Tables from Supported Class R Objects Appendix 10 Showing Results with Figures 10.1 Including Non-knitted Graphics 10.2 Basic Figure Options 10.3 Knitting R’s Default Graphics 10.4 Including Graphics 10.5 JavaScript Graphs with Part IV Presentation Documents 11 Presenting with LaTeX 11.1 The Basics 11.2 Bibliographies with BibTeX 11.3 Presentations with LaTeX Beamer 12 Presenting in a Variety of Formats with R Markdown 12.1 The Basics 12.2 Further Customizability with 12.3 Slideshows with Markdown, R Markdown, and HTML 12.4 Publishing HTML Documents Created with R Markdown 13 Conclusion 13.1 Citing Reproducible Research 13.2 Licensing Your Reproducible Research 13.3 Sharing Your Code in Packages 13.4 Project Development: Public or Private? 13.5 Is it Possible to Completely Future-Proof Your Research Bibliography Index