A review of R for Data Science: key elements and a critical analysis

A detailed review of a recent data science book by Hadley Wickham and Garrett Grolemund is developed herein. Technical book reviews should provide a guide to the readers, a sense of the appropriate audience, the specifics of the software/language, and identify critical thinking questions that emerge through reading the specifics of these books. This is a pre-print, extended version of a review of 'R for Data Science', and it provides a relatively comprehensive framing of this particular book. The context and background of the authors is introduced, key elements of this book - primarily the workflow proposed and the value of the tidyverse - are summarized, and a critical analysis of the book was done. The following critical questions were addressed in the review.

(1) Does this book (or any data science book for that matter) effectively communicate basic versus advanced data science concepts to the reader?

(2) Does this book extend or improve upon previous resources particularly for the individual in- terested in using and learning data science to do statistics in R?

(3) Can this book be read as a general data science book and by extension how much is this an R versus RStudio book?

The importance of reading a book associated with tools one uses in computer science such as R versus rapid, online solution-based reading is very effectively established in 'R for Data Science'. Time spent with a technical book providing the big picture for the tools one uses to solve problems in R is important for deeper learning and insights.