Visualizing Data is about visualization

tools that provide deep insight into the

structure of data. There are graphical

tools such as coplots, multiway dot plots,

and the equal count algorithm. There are

fitting tools such as loess and bisquare

that fit equations, nonparametric curves,

and nonparametric surfaces to data.

But the book is much more than just a

compendium of useful tools. It conveys a

strategy for data analysis that stresses

the use of visualization to thoroughly

study the structure of data and to check

the validity of statistical models fitted

to data. The result of the tools and the

strategy is a vast increase in what you can

learn from your data. The book demonstrates

this by reanalyzing many data sets from the

scientific literature, revealing missed

effects and inappropriate models fitted

to data.

## Visualizing Data

• 6 years ago

A Valuable ToolThis book was recommended highly to me by a former university professor (and now consultant). It exceeds my expectations. The figures and acompanying explanations are very clear, as is the language throughout. Visualizing Data discusses several tools with which I was not familiar, and clarifies tools that I thought I understood (including box plots). I have taken several university statistics classes, but I believe this book would help anyone involved in displaying or interpreting data. A picture may be worth a thousand words, but when your business depends on it, a well-defined plot or graph can be worth much more. Visualizing Data enables you to produce well-defined plots and graphs with confidence.Wonderful for its intended audienceFirst and foremost, this book has a definite audience: people who need to produce graphs for somewhat sophisticated audiences. This is not a book about producing graphs for mass marketing or other flashy arenas. While this point is implicit throughout the book, it is not often stated explicitly.The biggest strength of this book, and what makes it worth the purchase, is Cleveland’s discussion about the relationship between graphing and visual processing. We’ve all seen a thousand pie charts, for example, but it turns out that people are not good at visually processing pie charts. The way we process visually has implications for everything from line graph construction to color choices to deciding how to code data on XY scatter plots. Although this information does exist in other places, Cleveland brings it together concisely here. Some of the discussion can get a bit technical, however, so be warned.This is a great first book to read to learn more about how to construct graphs, and it has enough references to point you to other sources if you feel you need more. I myself have purchased several other books about the visual representation of data (including Cleveland’s other book “”), but this is where I started, and the information in this book has enriched my understanding of those other books immeasurably.Behaviour Elucidation par Excellence! U didn’t know this B4Behaviour elucidation is done amazingly well. This book is even more powerful than Cleveland’s “Elements of Graphing Data”. Key words for what you achieve: incisive, powerful, salient behaviour eludidation. The principles of graphical perception from “Elements” are great (and themselves powerful) but this book invents and emphasizes yet more incisive visualizations. These new visualizations involve considerable computation IN SUPPORT OF CONSTRUCTING the graphs. But the GRAPHS — and the behaviours they make manifest/salient — are the point. As in “Elements”, Cleveland is not just about the techniques as if they were rote procedure; he helps you build perspective too. This book, in a very real sense, (even explicitly so stated by Cleveland himself) is an alternative paradigm to the pervasive statistical inference paradigm. No wonder, then, that another reviewer (a Statistics student) learned so much he had never even seen before. Boy was “Visualizing” useful for a project I had on univariate data in multiple categorical groups (folding durability; 6 groups of data); Chapter 2 of “Visualizing” TRULY had me seeing things I NEVER would’ve otherwise. The book also guides you in the computations you need to get to the visualizations.