Graphics for Statistics and Data Analysis with R / Edition 1

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Graphics for Statistics and Data Analysis with R presents the basic principles of sound graphical design and applies these principles to engaging examples using the graphical functions available in R. It offers a wide array of graphical displays for the presentation of data, including modern tools for data visualization and representation.

The book considers graphical displays of a single discrete variable, a single continuous variable, and then two or more of each of these. It includes displays and the R code for producing the displays for the dot chart, bar chart, stemplot, boxplot, and variations on the quantile-quantile plot. The author discusses nonparametric and parametric density estimation, diagnostic plots for the simple linear regression model, polynomial regression, and locally weighted polynomial regression for producing a smooth curve through data on a scatterplot. The last chapter illustrates visualizing multivariate data with examples using Trellis graphics.

Showing how to use graphics to display or summarize data, this text provides best practice guidelines for producing and choosing among graphical displays. It also covers the most effective graphing functions in R.


Emphasizes the fundamentals of statistical graphics

Describes the strengths and weaknesses of each graphical display in R

Presents technical theoretical details on topics such as the estimation of quantiles, kernel density estimation, locally weighted polynomial regression, and splines

Includes engaging examples of real-world data, end-of-chapter exercises, and many illustrations, with some in color

Provides downloadable R code and data for the figures in the text on

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Editorial Reviews

From the Publisher
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. … I enjoyed reading this 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. … it is a recommended purchase for any statistical reference library.
—Han Lin Shang, Journal of Applied Statistics, April 2012

This textbook is intended for graduate students in statistics seeking to learn the basic principles of graphical design for the presentation of data. Experienced statisticians can also find this book very useful as it has a comprehensive discussion of graphical displays as well as R commands listed for many of the textbook’s examples. … This textbook is also a handy reference for graphical analysis and contains engaging examples of real-world data and exercises for those seeking reference on statistical graphics at the end of each chapter.
—Vanessa Narayanassamy, Significance, December 2011

The chapter exercises are the highlight of this book, and a valuable resource for teachers. The problems are well conceived, methodical, and reinforce objectives discussed in the chapter. … Aspects of the text may be a valuable resource for an introductory statistics course which uses R. The extensive chapter exercises should give students ample practice. … For the experienced R user, the book is a handy reference to create sophisticated graphical summaries for one- and two-variable visualization problems.
—Samuel J. Frame, The American Statistician, August 2011

The author states that it can be used as a textbook for a dedicated course on graphical data analysis or as a supplementary text in courses in statistics and data analysis. My general reaction is that it succeeds in these goals. … The book is impressive for the sheer number of graphs … these graphs are quite well done …
Biometrics, 67, September 2011

The emphasis in the book is on graphs for single discrete and continuous variables and bivariate relationships thereof, and it is very thorough in that regard. … The author also gives a lot of attention to detail … quite suitable as a course textbook, either as a supplementary text for a regular statistics course or as the main text for a specialized course on graphical methods. … The book certainly achieves its goal, increasing graphicacy, that is, the ability to display and exchange information with graphics. I thoroughly enjoyed reading the book and would recommend it to anyone interested in learning more about graphical displays of quantitative information.
Statistics in Medicine, 2011, 30

This is a textbook for graduate students in statistics and a helpful resource for practitioners. It provides a comprehensive discussion about methods for data representation and graphical display, and a guideline on when, which and how they can be applied.… I found the examples particularly useful. The author thoroughly evaluates competing graphical methods in terms of effective display when applied to the same data. There is a series of exercises at the end of each chapter, which complements its worked examples. … a valuable resource for practitioners in seeking a reference on statistical graphics.
Journal of the Royal Statistical Society, Series A, April 2011

The book is methodical and complete … Reading this book will give you the ability to recognize and create the majority of the named graphics of statistics … . I would recommend this book if you were interested in a detailed survey of 1D and 2D graphics … .
Journal of Statistical Software, September 2010, Volume 36

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Product Details

Meet the Author

Kevin J. Keen is a statistician and an associate professor of mathematics at the University of Northern British Columbia in Prince George, Canada.

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Table of Contents

List of figures

List of tables



I Introduction 1

1 The Graphical Display of Information 3

1.1 Introduction 3

1.2 Know the Intended Audience 7

1.3 Principles of Effective Statistical Graphs 8

1.4 Graphicacy 12

1.5 Graphical Statistics 17

1.6 Conclusion 19

1.7 Exercises 20

II A Single Discrete Variable 23

2 Basic Charts for the Distribution of a Single Discrete Variable 25

2.1 Introduction 25

2.2 An Example from the United Nations 26

2.3 The Dot Chart 28

2.4 The Bar Chart 31

2.5 The Pie Chart 41

2.6 Conclusion 47

2.7 Exercises 49

3 Advanced Charts for the Distribution of a Single Discrete Variable 55

3.1 Introduction 55

3.2 The Stacked Bar Chart 56

3.3 The Pictograph 59

3.4 Variations on the Dot and Bar Charts 69

3.5 Frames, Grid Lines, and Order 75

3.6 Conclusion 78

3.7 Exercises 79

III A Single Continuous Variable 83

4 Exploratory Plots for the Distribution of a Single Continuous Variable 85

4.1 Introduction 85

4.2 The Dotplot 85

4.3 The Stemplot 87

4.4 The Boxplot 95

4.5 The EDF Plot 104

4.6 Conclusion 112

4.7 Exercises 113

5 Diagnostic Plots for the Distribution of a Continuous Variable 117

5.1 Introduction 117

5.2 The Quantile-Quantile Plot 117

5.3 The Probability Plot 123

5.4 Estimation of Quartiles and Percentiles 124

5.5 Conclusion 138

5.6 Exercises 138

6 Nonparametric Density Estimation for a Single Continuous Variable 143

6.1 Introduction 143

6.2 The Histogram 143

6.3 Kernel Density Estimation 160

6.4 Spline Density Estimation 183

6.5 Choosing a Plot for a Continuous Variable 183

6.6 Conclusion 189

6.7 Exercises 194

7 Parametric Density Estimation for a Single Continuous Variable 199

7.1 Introduction 199

7.2 Normal Density Estimation 200

7.3 Transformations to Normality 204

7.4 Pearson's Curves 209

7.5 Gram-Charlier Series Expansion 218

7.6 Conclusion 220

7.7 Exercises 223

IV Two Variables 229

8 Depicting the Distribution of Two Discrete Variables 231

8.1 Introduction 231

8.2 The Grouped Dot Chart 231

8.3 The Grouped Dot-Whisker Chart 238

8.4 The Two-Way Dot Chart 240

8.5 The Multi-Valued Dot Chart 243

8.6 The Side-by-Side Bar Chart 244

8.7 The Side-by-Side Bar-Whisker Chart 246

8.8 The Side-by-Side Stacked Bar Chart 247

8.9 The Side-by-Side Pie Chart 250

8.10 The Mosaic Chart 254

8.11 Conclusion 255

8.12 Exercises 257

9 Depicting the Distribution of One Continuous Variable and One Discrete Variable 263

9.1 Introduction 263

9.2 The Side-by-Side Dotplot 263

9.3 The Side-by-Side Boxplot 266

9.4 The Notched Boxplot 269

9.5 The Variable-Width Boxplot 271

9.6 The Back-to-Back Stemplot 275

9.7 The Side-by-Side Stemplot 276

9.8 The Side-by-Side Dot-Whisker Plot 276

9.9 The Trellis Kernel Density Estimate 281

9.10 Conclusion 284

9.11 Exercises 285

10 Depicting the Distribution of Two Continuous Variables 289

10.1 Introduction 289

10.2 The Scatterplot 289

10.3 The Sunflower Plot 291

10.4 The Bagplot 295

10.5 The Two-Dimensional Histogram 299

10.6 Two-Dimensional Kernel Density Estimation 307

10.7 Conclusion 312

10.8 Exercises 314

V Statistical Models for Two or More Variables 319

11 Graphical Displays for Simple Linear Regression 321

11.1 Introduction 321

11.2 The Simple Linear Regression Model 325

11.3 Residual Analysis 338

11.4 Influence Analysis 345

11.5 Conclusion 360

11.6 Exercises 363

12 Graphical Displays for Polynomial Regression 367

12.1 Introduction 367

12.2 The Polynomial Regression Model 368

12.3 Splines 373

12.4 Locally Weighted Polynomial Regression 378

12.5 Conclusion 384

12.6 Exercises 386

13 Visualizing Multivariate Data 391

13.1 Introduction 391

13.2 Depicting Distributions of Three or More Discrete Variables 391

13.3 Depicting Distributions of One Discrete Variable and Two or More Continuous Variables 399

13.4 Observations of Multiple Variables 411

13.5 The Multiple Linear Regression Model 420

13.6 Conclusion 433

13.7 Exercises 434

References 437

Index 443

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