From reading this book, it is easy to tell that the authors made an effort to add as much value to the reader as possible by going above what they had to do.

This book was written as a part of HarvardX’s data science series on edx.org. Judging from the book, I am sure the course is well crafted, although the approximately $1000 price tag is a hard sell. Fortunately, you can read the book associated with the course for free!

This book does a good bit right, the authors start assuming essentially no knowledge of R and build from there. Most of everything you learn in this book comes from the perspective of the Tidyverse, starting with basic functionality through machine learning models.

Mastering Spark with R did a good job explaining to me as an R user why I would want to incorporate Spark into my data analysis workflow using the “sparlyr” package.

Writing an R package can be a daunting task, as you have to consider a lot more than just how to construct functions. This book walks you through the process to make the journey a little easier.

The title does a good bit of my job for me, this book is all about teaching you what you need to know to start doing data science.

This book provides an introduction to the R programming language through 3 “projects” that make learning R more fun, and give context for learning new programming concepts.

Shiny is an R package that allows R programmers to make web apps without the use of outside programming tools.

This book is aimed at intermediate R users that want to understand the R language at a foundational level.

This book takes you from knowing nothing about Bayesian statistics to an impressively high level.