Biostatistics Software-Technology

R for Data Science: Visualize, Model, Transform, Tidy, and Import Data

R for Data Science
by Hadley Wickham and Garret Grolemund.
(c) 2017.

If the above quote is the mission of this book, consider the task accomplished. Where most books in computer science fall down in trying to be cute while communicating an educational message, this book addresses the task of education about R squarely, and it does so in a manner that engages the mind with interesting problems.

Usually, I skip the exercises sections of most computer books because, well, they offer challenges that are underwhelming. Recall is all that is required to answer them. Usually, I can figure them out in the confines of my mind so that I don’t have to waste my time looking up the answers or coding example code to check whether I’m right or where I err.

Not so for Hadley Wickham. Many of his questions were awakened my curiosity and had me applying me new knowledge in R Studio immediately. In fact, the only way I could answer my burning curiosity was to write code in order to test my hypotheses.

Rare is the computer book that is a page turner. This book qualifies as just that if one has the aptitude in statistics to embrace the challenges. R is an ideal language to handles these challenges in statistics, and Wickham and Grodemund fill the role of ideal apostles/evangelists to share this free fruit.

The fun part about R is that it is free, creative, and well-supplied with packages to solve interesting statistical problems. This book carries that message squarely to my lap (and then to my brain) in an engaging manner.