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Praise for the First Edition

*"The main strength of this book is that it provides a unified framework of graphical tools for data analysis, especially for univariate and low-dimensional multivariate data. In addition, it is clearly written in plain language and the inclusion of R code is particularly useful to assist readers’ understanding of the graphical techniques discussed in the book. … It not only summarises graphical techniques, but it also serves as a practical reference for researchers and graduate students with an interest in data display."* -Han Lin Shang, *Journal of Applied Statistics*

* Graphics for Statistics and Data Analysis with R, Second Edition*, presents the basic principles of graphical design and applies these principles to engaging examples using the graphics and lattice packages in R. It offers a wide array of modern graphical displays for data visualization and representation. Added in the second edition are coverage of the ggplot2 graphics package, material on human visualization and color rendering in R, on screen, and in print.

Features

- Emphasizes the fundamentals of statistical graphics and best practice guidelines for producing and choosing among graphical displays in R
- Presents technical details on topics such as: the estimation of quantiles, nonparametric and parametric density estimation; diagnostic plots for the simple linear regression model; polynomial regression, splines, and locally weighted polynomial regression for producing a smooth curve; Trellis graphics for multivariate data
- Provides downloadable R code and data for figures at www.graphicsforstatistics.com

**Kevin J. Keen **is a Professor of Mathematics and Statistics at the University of Northern British Columbia (Prince George, Canada) and an Accredited Professional Statistician^{TM} by the Statistical Society of Canada and the American Statistical Association.

**List of Figures**

**List of Tables**

**Preface to the First Edition **

**Preface to the Second Edition**

**Acknowledgments **

**I Introduction**

The Graphical Display of Information

Introduction

Learning Outcomes

Know the Intended Audience

Principles of Effective Statistical Graphs

The Layout of a Graphical Display

The Design of Graphical Displays

Graphicacy

The Grammar of Graphics

Graphical Statistics

Conclusion

Exercises

II A Single Discrete Variable

Basic Charts for the Distribution of a Single Discrete Variable

Introduction

Learning Outcomes

An Example from the United Nations

The Dot Chart

The Bar Chart

Definition

Pseudo Three-Dimensional Bar Chart

The Pie Chart

Definition

Pseudo Three-Dimensional Pie Chart

Recommendations Concerning the Pie Chart

Conclusion

Exercises

Advanced Charts for the Distribution of a Single Discrete Variable

Introduction

Learning Outcomes

The Stacked Bar Chart

Definition

The Stacked Bar Plot Versus the Bar Chart and the Pie Chart

The Pictograph

Definition

The Pictograph Versus the Dot Chart and the Bar Chart

Variations on the Dot and Bar Charts

The Bar-Whisker Chart

Dot-Whisker Chart

Frames, Grid Lines, and Order

Frame

Grid Lines

Order

Conclusion

Exercises

III A Single Continuous Variable

Exploratory Plots for the Distribution of a Single Continuous Variable

Introduction

Learning Outcomes

The Dotplot

Definition

Variations on the Dotplot

The Stemplot

Definition

The Boxplot

Definition

Variations on the Boxplot

The EDF Plot

Definition

The EDF Plot as a Diagnostic Tool

Conclusion

Exercises

Diagnostic Plots for the Distribution of a Continuous Variable

Introduction

Learning Outcomes

The Quantile-Quantile Plot

The Probability Plot

Estimation of Quartiles and Percentiles∗

Estimation of Quartiles

Estimation of Percentiles

Conclusion

Exercises

Nonparametric Density Estimation for a Single Continuous Variable

Introduction

Learning Outcomes

The Histogram

Definition

A Circular Variation on the Histogram: The Rose Diagram

Kernel Density Estimation∗

Spline Density Estimation∗

Choosing a Plot for a Continuous Variable∗

Conclusion

Exercises

Parametric Density Estimation for a Single Continuous Variable

Introduction

Learning Outcomes

Normal Density Estimation

Transformations to Normality

Pearson’s Curves∗

Gram-Charlier Series Expansion∗

Conclusion

Exercises

IV Two Variables

Depicting the Distribution of Two Discrete Variables

Introduction

Learning Outcomes

The Grouped Dot Chart

The Grouped Dot-Whisker Chart

The Two-Way Dot Chart

The Multi-Valued Dot Chart

The Side-by-Side Bar Chart

The Side-by-Side Bar-Whisker Chart

The Side-by-Side Stacked Bar Chart

The Side-by-Side Pie Chart

The Mosaic Chart

Conclusion

Exercises

Depicting the Distribution of One Continuous Variable and One Discrete Variable

Introduction

Learning Outcomes

The Side-by-Side Dotplot

The Side-by-Side Boxplot

The Notched Boxplot

The Variable-Width Boxplot

The Back-to-Back Stemplot

The Side-by-Side Stemplot

The Side-by-Side Dot-Whisker Plot

The Trellis Kernel Density Estimate∗

Conclusion

Exercises

Depicting the Distribution of Two Continuous Variables

Introduction

Learning Outcomes

The Scatterplot

The Sunflower Plot

The Bagplot

The Two-Dimensional Histogram

Definition

The Levelplot

The Cloud Plot

Two-Dimensional Kernel Density Estimation∗

Definition

The Contour Plot

The Wireframe plot

Conclusion

Exercises

V Statistical Models for Two or More Variables

Simple Linear Regression: Graphical Displays

Introduction

Learning Outcomes

The Simple Linear Regression Model

Definition

The Scatterplot

The Sunflower Plot

Residual Analysis

Definition

Residual Scatterplots

Depicting the Distribution of the Residuals

Depicting the Distribution of the Semistandardized Residuals

Influence Analysis

Definition

Matrix Notation for the Simple Linear Regression Model

Depicting Standardized Residuals

Depicting the Distribution of Studentized Residuals

Depicting Leverage

Depicting DFFITS

Depicting DFBETAS

Depicting Cook’s Distance

Influence Plots

Conclusion

Exercises

Polynomial Regression and Data Smoothing: Graphical Displays

Introduction

Learning Outcomes

The Polynomial Regression Model

Splines

Locally Weighted Polynomial Regression

Conclusion

Exercises

Visualizing Multivariate Data

Introduction

Learning Outcomes

Depicting Distributions of Three or More Discrete Variables

The Sinking of the Titanic

Thermometer Chart

Three-Dimensional Bar Chart

Trellis Three-Dimensional Bar Chart

Depicting Distributions of One Discrete Variable and Two or More Continuous Variables

Anderson’s Iris Data

The Superposed Scatterplot

The Superposed Three-Dimensional Scatterplot

The Scatterplot Matrix

The Parallel Coordinates Plot

The Trellis Plot

Observations of Multiple Variables

OECD Healthcare Service Data

Chernoff’s Faces

The Star Plot

The Rose Plot

The Multiple Linear Regression Model

Definition

Modeling Perch Mass

Residual Scatterplot Matrix

Leverage Scatterplot Matrix

Influence Plot

Partial-Regression Scatterplot Matrix

Partial-Residual Scatterplot Matrix

Summary of the Model for Perch Mass

Conclusion

Exercises

VI Appendices

Human Visualization

Introduction

Learning Outcomes

Optics

Introduction

Geometrical Optics

The Light Spectrum

Anatomy of the Human Eye

The Perception of Colour

Graphical Perception

Weber’s Law

Stevens’s Law

The Gestalt Laws of Organization

Kosslyn’s Image Processing Model

Conclusion

Exercises

Color Rendering

Introduction

Learning Outcomes

RGB and XYZ Color Spaces

HSL and HSV Color Spaces

CIELAB and CIELUV Color Spaces

HCL Color Space

CMYK Color Space

Displaying Color in R

Saving Color Documents from R

Conclusion

Exercises

Bibliography

Index

- MAT029000
- MATHEMATICS / Probability & Statistics / General