This book will teach you how to use R to solve your statistical, data science and machine learning problems. Importing data, computing descriptive statistics, running regressions (or more complex machine learning models) and generating reports are some of the topics covered. No previous experience with R is needed.
Mastering Software… by Roger D. Peng et al. [PDF/iPad/Kindle]
This book covers R software development for building data science tools. This book provides rigorous training in the R language and covers modern software development practices for building tools that are highly reusable, modular, and suitable for use in a team-based environment or a community of developers. (Printed copies coming soon!)
Efficient R Programming is about increasing the amount of work you can do with R in a given amount of time. It’s about both computational and programmer efficiency.
rOpenSci Packages: Development, Maintenance, and Peer Review
Extended version of the rOpenSci packaging guide. This book is a guide for authors, maintainers, reviewers and editors of rOpenSci. The first section of the book contains our guidelines for creating and testing R packages. The second section is dedicated to rOpenSci’s software peer review process: what it is, our policies, and specific guides for authors, editors and reviewers throughout the process. The third and last section features our best practice for nurturing your package once it has been onboarded: how to collaborate with other developers, how to document releases, how to promote your package and how to leverage GitHub as a development platform. The third section also features a chapter for anyone wishing to start contributing to rOpenSci packages.
Simple mechanisms for defining and interpreting package options. Provides
helpers for interpreting environment variables, global options, defining
default values and more.
Prototype implementation of an extension to S3 that provides explicit class definitions and a form of multiple dispatch. Represents the output of the Object-oriented Programming Working Group, sponsored by the R Consortium.
Ever heard the phrase “Read the source, Luke”? It’s a play on “Use the force, Luke” from Star Wars, with no definite source 😉 that we could find^[We erroneously first linked to a rather recent blog post but Robert Link corrected us in a comment that we reproduce here in case the post gets separated from its comments: ““Use the Source, Luke” goes way back before 2012, and probably even before blogs were a thing.