Graphics for Statistics and Data Analysis with R

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 StatisticianTM 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

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