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Data Visualization: A Practical Introduction

Kieran Healy
Publisher: 
Princeton University Press
Publication Date: 
2019
Number of Pages: 
272
Format: 
Paperback
Price: 
40.00
ISBN: 
9780691181622
Category: 
General
BLL Rating: 

The Basic Library List Committee suggests that undergraduate mathematics libraries consider this book for acquisition.

[Reviewed by
Bill Satzer
, on
03/8/2019
]

The era of big data has brought renewed interest to perennial questions about the best ways to visualize data. Classic works such as Tufte’s The Visual Display of Quantitative Information and Cleveland’s The Elements of Graphing Data provide examples of good and bad presentations of data and offer suggestions for effective display techniques. Books like Chang’s R Graphics Cookbook provide code recipes for many varieties of plots. Munzner’s Visualization Analysis and Design illustrates best practices but focuses more on the cognitive aspects of successful and unsuccessful displays. Yet little has previously been available that provides detailed guidance showing how to produce images from data and explains why certain methods work better than others.

The current book aims to fill this gap. The author has several related goals: he wants students to understand the basics behind effective visualization of data, to have a practical sense of why some graphs and figures work while others fail or actively mislead, to learn how to create plots of many different kinds using code that they produce themselves, and to know how to create effective presentations. The book uses the R language environment, tidyverse R packages that share a common data format, and the accompanying graphics package ggplot. It is very much a how-to book with a hands-on approach, showing the principles and practices of examining and presenting data using R and associated software. No previous knowledge of R is expected.

After an insightful introduction to the concept of looking at data, the author carefully introduces the reader to the R language environment and associated support software. The student learns through worked examples. These begin with scatterplots and summaries of single variables and move on to more complex data and data handling techniques. Topics include plotting continuous and categorical variables, layering information on graphics and transforming data to produce visual summaries. The author shows how to create the usual kind of plot elements such as linear fits, error ranges, and boxplots. He also describes how to highlight features of data, label special items of interest and annotate plots. One chapter explores handling data arising from statistical modeling, but this is not developed fully.

The author is a sociologist and most of his data and examples come from the social sciences. The issues he treats and the range of visualization types he presents are broad, so much of the book would be of value to those more interested in scientific and engineering data.

This is not a cookbook for creating graphics but it does lead students along a clearly prescribed path to create graphics using the R tools. It is not intended to teach data analysis or any aspect of statistics. It assumes very little mathematical background and uses very little mathematics. The book describes techniques very effectively but often leaves open the question of what the data really mean.

The author wants to help students produce visualizations that present data honestly and with attention to aesthetics. He doesn’t want students to fool themselves or others because of badly presented data.

This book is a strong introduction to techniques of presenting data in a visual format. It could be used for an undergraduate data visualization course or a supplement to courses in statistics or data analysis. It would support self-study as well, particularly if the reader worked through all the examples. It could also serve as a survey for a reader interested in how to think about visualization.


Bill Satzer (bsatzer@gmail.com) was a senior intellectual property scientist at 3M Company. His training is in dynamical systems and particularly celestial mechanics; his current interests are broadly in applied mathematics and the teaching of mathematics.


 

 


 

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