Reviews of Books and Teaching Materials

There are two interwoven stories in Graham J. Williams’ The Essentials of Data Science: Knowledge Discovery Using R. The first provides a hands-on introduction to the use and strengths of R through the integrated development environment RStudio. The second concerns the lens and promise of data science. Williams paints a compelling picture in his introduction of R through RStudio by detailing how one can create technical data sciencemodels, construct stunning data visualizations, and develop reproducible documents in RStudio. The author does not attempt to provide a complete treatment of the broad field of data science. Rather, he focuses on two essential elements of the discipline: data visualization and the application of classification trees and random forests. Williams provides an excellent self-contained introduction to R and RStudio, making it a natural fit for the R Series within CRC Press. In Chapters 2 through 4, he effortlessly weaves data science terminology with R code, much of which is from the rattle package, also written by Williams. Following a common outline for introducing the programming aspects of data science, Williams begins with the basics of R and RStudio, transitions to data wrangling and cleaning, and finally creates preliminary data visualizations. Throughout these three chapters, Williams develops a careful and effective approach for working with new datasets, illustrating this approach using a dataset about weather in Australia. Williams later models and reinforces this template in his presentation of two case studies in Chapters 5 and 6. Williams demonstrates additional functionality of RStudio’s integrated development environment, from creating functions to crafting LATEX documents, in Chapters 9 through 11. Outlining an argument that data science is more than just formulae and charts, Williams invites readers to spend time and energy documenting their procedures both through elegant code and through prose suitable for a general audience. His inclusion of instructions for knitting R with LATEX as well as his style recommendations for naming R files not only signals the importance of documentationwithin data science but provides readers with practical methods for doing so. Data science is clearly the second theme of the book. However, despite being in the title of the book, data science does not read as the central story. The first chapter contextualizes the rapidly expanding field of data science, drawing lines between various aspects of data science. Data science does not play a leading role again until Chapters 7 and 8, whereWilliams introduces decision trees and random forests. It is refreshing to see these topics in an introductory text as they are often relegated to advanced courses. Further, Williams presents a common approach to implementing and evaluating both classification techniques. This approach emphasizes the iterative nature of data science, the importance of evaluation, and the reality that there is no single way to appraise data science models. The consistency in this approach across trees and forests reinforces the fundamentals of and commonalities between data science methods. Williams’ writing style will likely appeal to digital humanists and social scientists interested in expanding their research portfolios by immersing themselves in data science. Requiring little previous conceptual background, this book alternates between longer self-contained explanations and blocks of code. Williams does a nice job of introducing the concepts that a beginner needs to know as these concepts naturally arise, and at times he reminds his reader of ideas presented in earlier chapters. In addition to the Australia weather dataset that serves as the central example throughout the book, Williams chooses case studies that arise from various disciplines, further broadening the book’s potential audience and impact. In Chapter 5, he explores Australian sea ports, while in Chapter 6, he offers a study of browser data from web logs. Both datasets were published by the Australian government. By including them, Williams reminds his readers of the easily overlooked wealth of publicly available data. As the title suggests, The Essentials of Data Science: Knowledge Discovery Using R relies heavily on R code to illuminate the main data science concepts discussed in the book’s prose. For each introduced data science concept or task, the author offers a supporting procedure in R, making it possible for a reader to engage with the concept in a hands-on fashion. While this is an effective pedagogical technique, the success of this book hinges on the reader’s ability to install the rattle package. Installing this package is nontrivial for Apple computers and can require a sophisticated knowledge of one’s operating system. Due to the self-contained introduction to many of the features of R and RStudio, Graham J. Williams’ The Essentials of Data Science: Knowledge Discovery Using R would make an excellent recommended or supplementary text for a course that plans to use the rattle package. This book would also serve as a great resource for those with an interest in data science who would like a hands-on approach to learning R and getting a flavor for a handful of topics within data science.