R

This is a workshop for RStudio::conf(2020) in San Francisco, California

A guide to R development.

Second edition of R Cookbook

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.

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!)

This guide aims to introduce the reader to the main elements of the R ecosystem.

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.

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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.

This book showcases a basic example of how to create an R package based on S3 classes.

Best practice and tips for testing packages interfacing web resources.

An introduction to Docker and PostgreSQL for R users to simulate use cases behind corporate walls.

300+ Free R programming books

A curated list of awesome R packages, frameworks and software.

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300+ Free R programming books

Simple mechanisms for defining and interpreting package options. Provides helpers for interpreting environment variables, global options, defining default values and more.

A few tips on using bootstrap 5 popovers in Shiny

A few helpful design patterns for navigation in {shiny} applications using buttons in a {reactable} widget and

A simple R package development best-practices-example · GitHub

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.

Here is how to create a progress bar in R that is versatile when working with loops, for example, by using package purrr.