The book “Advanced R, 2nd Edition” by Hadley Wickham (CRC Press) came out in 2019 with much fanfare. Wickham is an internationally recognized R guru, Chief Scientist at RStudio and a member of the R Foundation. He’s also responsible for bringing the Tidyverse suite of R packages to the global community of data scientists. Many of my colleagues have wholly adopted the “Tidyverse way” of coding when approaching data science projects. So when this book came out, it was an instant success.
Advanced R is available for purchase, or you can access it for free HERE. The book assists you in understanding how R works at a fundamental level. It’s designed for R coders who want to deepen their understanding of the language, and coders experienced in other languages who want to understand what makes R different and special. The book engages the reader with the foundations of R; three fundamental programming paradigms (functional, object-oriented, and metaprogramming); and powerful techniques for debugging and optimizing your code.
Wickham’s Advanced R book has many intriguing exercises that test your knowledge of deep operations of the R environment. The subject of this book review is a brand new title, “Advanced R Solutions” which is a well-crafted answer book containing all the solutions to each exercise appearing in Advanced R.
So many parts of the R language are highlighted in the exercises and solutions, including important topics like: names and values, vectors, subsetting, flow of control, functions, plus large topics like functional programming, object-oriented programming, metaprogramming. Another couple of chapters deal with measuring and improving performance.
Oddly, my favorite part of Advanced R solutions is Section I on Fundamentals. This might be because I can use a lot of the techniques discussed to dazzle my Intro to Data Science students with fun facts about the R language. For instance, from Q3 from Section 2.3 the diagram below illustrates memory usage when referencing a number of R objects – highly intuitive.
I really like this sort of book because it matches my data science style of constantly toying with coding and doing various data experiments. I typically carry around my R-enabled laptop in my backpack, and when I have a few moments, I’ll whip it out and start coding away. I’ve already started playing around with the many solutions provided in this book to strengthen my own knowledge of all the weird things R can do. It’s been a lot of fun so far. And I’m sure I can convince many of my students to do the same.
Advance R Solutions is a great educational resource that I can recommend to my students. I take the 1st four weeks of my class to quickly get my students up to speed with R. This rapid paced segment only allows for a cursory look at the language and many students ask me for resources to do a deeper dive. “Advanced R, 2nd Edition” and the new Advanced R Solutions constitute a dynamic duo for advancing my student’s knowledge in an orderly manner.
I would say that if you’re a data scientists who uses R, then Advanced R Solutions should be part of your professional library. Taking some time to explore the various solutions and expand on them with your own experiments, will only help you hone your skills and increase your knowledge. R is a great programming environment that I enjoy on a daily basis. Having yet another resource like this for deepening my knowledge can only lead to good things.
Contributed by Daniel D. Gutierrez, Editor-in-Chief and Resident Data Scientist for insideBIGDATA. In addition to being a tech journalist, Daniel also is a consultant in data scientist, author, educator and sits on a number of advisory boards for various start-up companies.
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