Reproducible Research with R and R Studio

 
 
Chapman & Hall/CRC (Verlag)
  • 2. Auflage
  • |
  • erschienen am 3. September 2018
  • |
  • 323 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
978-1-315-36072-0 (ISBN)
 

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.

    2. Auflage
    • Englisch
    • London
    • |
    • Großbritannien
    Taylor & Francis Ltd
    • Für höhere Schule und Studium
    • Neue Ausgabe
    31 schwarz-weiße Abbildungen, 16 schwarz-weiße Tabellen
    978-1-315-36072-0 (9781315360720)

    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
    "The first edition of Reproducible Research with R and RStudio was an invaluable companion in the early stages of my journey, and I trust that the second edition will be equally useful to aspiring data analysts."
    -MAA Reviews, July 2015


    Praise for the First Edition:
    "... 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 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

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