All the Tools for Gathering and Analyzing Data and Presenting Results Reproducible Research with R and RStudio, Second 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. New to the Second Edition * The rmarkdown package that allows you to create reproducible research documents in PDF, HTML, and Microsoft Word formats using the simple and intuitive Markdown syntax * Improvements to RStudio's interface and capabilities, such as its new tools for handling R Markdown documents * Expanded knitr R code chunk capabilities * The kable function in the knitr package and the texreg package for dynamically creating tables to present your data and statistical results * An improved discussion of file organization, enabling you to take full advantage of relative file paths so that your documents are more easily reproducible across computers and systems * The dplyr, magrittr, and tidyr packages for fast data manipulation * Numerous modifications to R syntax in user-created packages * Changes to GitHub's and Dropbox's interfaces Create Dynamic and Highly Reproducible Research This updated book provides all the tools to combine your research with the presentation of your findings. It saves you time searching for information so that you can spend more time actually addressing your research questions. Supplementary files used for the examples and a reproducible research project are available on the author's website.
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
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