Reproducible Research with R and R Studio

The R Series

by Christopher Gandrud

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.

Genres: Programming, Research Methods, Nonfiction

323 pages, ebook

First published June 12, 2015

Reviews

[Jordan Munn · Rating 3 out of 5]

This book overpromises a bit. Gandrud claims to want to talk about sustainable ways to conduct reproducible research, which is a very laudable goal. He sketches out a project workflow to do this, but then he gets seduced by the weeds and starts just giving a tutorial about various actual coding commands in R, knitr, R Markdown, etc. He ends up giving relatively very short shrift to discussing and analyzing the actual workflow itself, its mechanics, pros and cons, ramifications, etc. Beyond that, there are good tidbits scattered throughout, as there usually are in these types of walkthrough books. I appreciate Gandrud's perspectives throughout. The main points are fine, I just wish he had dwelt on them more.

[Sefa]

One of the best among many books on data science and reproducible research using R or Python. This book focuses on the entire flow of the reproducible research using R. It also points some interesting issues such as citing reproducible research, licensing it, etc. at the end of the book. Also it has a great cover summarizing the book as good as its title.

A not reproducible book on reproducible research would be awkward. The book provides all the source code and the text that can be run to generate the book itself at https://github.com/christophergandrud....

[John Ayers · Rating 4 out of 5]

I learned a lot from this book. The book files are available in GitHub, but some files are missing, so that some of the files don't run without errors. This is disappointing given that the aim of the book is to make work reproducible and code future-proof. Would like to see a discussion of new "container tools" like Docker, which would be more useful and closer to the aims of the book then sections like "12.3 Slideshows".

[Eric · Rating 5 out of 5]

A very good book about reproducible research with R and Rstudio. Highly recommended.

[Mike Martos · Rating 4 out of 5]

Very easy to read, and follow, clear examples, basic tool for a data scientist.

[CoChen · Rating 3 out of 5]

Good books for RStudio users, but I am using ESS with Emacs.