Building reproducible analytical pipelines with R
Bookdowns
The {targets} R package user manual
The Data Validation Cookbook
How I Use R
R Shiny Applications Book
Welcome | Mastering Shiny
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JavaScript 4 Shiny - Field Notes
Oui.
Introduction | Engineering Production-Grade Shiny Apps
A book about engineering shiny application that will later be sent to production. This book cover project management, structuring your project, building a solid testing suite, and optimizing your codebase. We describe in this book a specific workflow: design, prototype, build, strengthen and deploy.
YaRrr! The Pirate’s Guide to R
An introductory book to R written by, and for, R pirates
Writing R extensions
Writing R Extensions covers how to create your own packages, write R help files, and the foreign language (C, C++, Fortran, …) interfaces.
R Manuals - The R Manuals
The R Language
R_inferno.pdf
R for Excel Users
This is a workshop for RStudio::conf(2020) in San Francisco, California
R Cookbook, 2nd Edition
Second edition of R Cookbook
Field Guide to the R Ecosystem
This guide aims to introduce the reader to the main elements of the R ecosystem.
Efficient R programming
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.
Cookbook for R
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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.
RAP Companion
R for Data Engineers
Exploring Enterprise Databases with R: A Tidyverse Approach
An introduction to Docker and PostgreSQL for R users to simulate use cases behind corporate walls.
DevOps for Data Science
34 Workflow | Big Book of R
300+ Free R programming books
qinwf/awesome-R: A curated list of awesome R packages, frameworks and software.
A curated list of awesome R packages, frameworks and software.
resouRces - More Databases with R Resources
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Big Book of R
300+ Free R programming books
Style Guide for Loss Data Analytics
This is a style guide to help authors write chapters for Loss Data Analytics
R for Data Science
Efficient R programming
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.