Reproducible Research with R and R Studio

The R Series

by Christopher Gandrud

Bringing together computational research tools in one accessible source, Reproducible Research with R and RStudio guides you in creating dynamic and highly reproducible research. Suitable for researchers in any quantitative empirical discipline, it presents practical tools for data collection, data analysis, and the presentation of results.With straightforward examples, the book takes you through a reproducible research workflow, showing you how to for dynamic data gathering and automated results presentation knitr for combining statistical analysis and results into one documentLaTeX for creating PDF articles and slide shows, and Markdown and HTML for presenting results on the web Cloud storage and versioning services that can store data, code, and presentation files; save previous versions of the files; and make the information widely available Unix-like shell programs for compiling large projects and converting documents from one markup language to anotherRStudio to tightly integrate reproducible research tools in one placeWhether you’re an advanced user or just getting started with tools such as R and LaTeX, this book saves you time searching for information and helps you successfully carry out computational research. It provides a practical reproducible research workflow that you can use to gather and analyze data as well as dynamically present results in print and on the web. Supplementary files used for the examples and a reproducible research project are available on the author’s website.

Genres: Programming, Research Methods, Nonfiction

294 pages, Kindle Edition

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.