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E-grāmata: Graphics for Statistics and Data Analysis with R

4.50/5 (4 ratings by Goodreads)
(University of Northern British Columbia, Prince George, Canada)
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Graphics for Statistics and Data Analysis with RPalielināt
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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 StatisticianTM by the Statistical Society of Canada and the American Statistical Association.

Recenzijas

"A leading expert wrote the book. The book is an exposition of statistical methodology that focuses on ideas and concepts and makes extensive use of graphical presentation, but readers should have some prior experience of statistical methodology. The chapters also contain many exercises with solutions and hints presented in the Appendix. The R codes are available for download on the website. The book presents data and Programmes to replicate the models developed, offers new methods that are ready to use, and explores graphical statistics in its entirety from the fundamentals of modern methods. The book is also a complete reference manual and should be considered a must-have companion for the interested advanced audience." ~International Society for Clinical Biostatistics

". . . this is a book I can recommend for consideration in a course or as a course supplement. It is generally clear and well-written, and the statistical aspects of some of these methods are explained in sufficient detail to put these in context." ~Michael Friendly, Journal of Agricultural, Biological, and Environmental Statistics "A leading expert wrote the book. The book is an exposition of statistical methodology that focuses on ideas and concepts and makes extensive use of graphical presentation, but readers should have some prior experience of statistical methodology. The chapters also contain many exercises with solutions and hints presented in the Appendix. The R codes are available for download on the website. The book presents data and Programmes to replicate the models developed, offers new methods that are ready to use, and explores graphical statistics in its entirety from the fundamentals of modern methods. The book is also a complete reference manual and should be considered a must-have companion for the interested advanced audience." ~International Society for Clinical Biostatistics

". . . this is a book I can recommend for consideration in a course or as a course supplement. It is generally clear and well-written, and the statistical aspects of some of these methods are explained in sufficient detail to put these in context." ~Michael Friendly, Journal of Agricultural, Biological, and Environmental Statistics

Preface to the First Edition xv
Preface to the Second Edition xvii
Acknowledgments xix
I Introduction 1(30)
1 The Graphical Display of Information
3(28)
1.1 Introduction
3(4)
1.2 Learning Outcomes
7(1)
1.3 Know the Intended Audience
7(2)
1.4 Principles of Effective Statistical Graphs
9(4)
1.4.1 The Layout of a Graphical Display
9(2)
1.4.2 The Design of Graphical Displays
11(2)
1.5 Graphicacy
13(5)
1.6 The Grammar of Graphics
18(6)
1.7 Graphical Statistics
24(2)
1.8 Conclusion
26(1)
1.9 Exercises
26(5)
II A Single Discrete Variable 31(78)
2 Basic Charts for the Distribution of a Single Discrete Variable
33(42)
2.1 Introduction
33(1)
2.2 Learning Outcomes
34(1)
2.3 An Example from the United Nations
35(1)
2.4 The Dot Chart
36(8)
2.5 The Bar Chart
44(14)
2.5.1 Definition
44(13)
2.5.2 Pseudo-Three-Dimensional Bar Chart
57(1)
2.6 The Pie Chart
58(8)
2.6.1 Definition
58(3)
2.6.2 Pseudo-Three-Dimensional Pie Chart
61(4)
2.6.3 Recommendations Concerning the Pie Chart
65(1)
2.7 Conclusion
66(2)
2.8 Exercises
68(7)
3 Advanced Charts for the Distribution of a Single Discrete Variable
75(34)
3.1 Introduction
75(1)
3.2 Learning Outcomes
76(1)
3.3 The Stacked Bar Chart
76(7)
3.3.1 Definition
76(5)
3.3.2 The Stacked Bar Plot versus the Bar Chart and the Pie Chart
81(2)
3.4 The Pictograph
83(8)
3.4.1 Definition
83(4)
3.4.2 The Pictograph versus the Dot Chart and the Bar Chart
87(4)
3.5 Variations on the Dot and Bar Charts
91(9)
3.5.1 The Bar-Whisker Chart
92(4)
3.5.2 Dot-Whisker Chart
96(4)
3.6 Frames, Grid Lines, and Order
100(4)
3.6.1 Frame
101(1)
3.6.2 Grid Lines
102(1)
3.6.3 Order
103(1)
3.7 Conclusion
104(1)
3.8 Exercises
105(4)
III A Single Continuous Variable 109(164)
4 Exploratory Plots for the Distribution of a Single Continuous Variable
111(36)
4.1 Introduction
111(1)
4.2 Learning Outcomes
111(1)
4.3 The Dotplot
112(4)
4.3.1 Definition
112(1)
4.3.2 Variations on the Dotplot
113(3)
4.4 The Stemplot
116(8)
4.4.1 Definition
116(8)
4.5 The Boxplot
124(9)
4.5.1 Definition
124(3)
4.5.2 Variations on the Boxplot
127(6)
4.6 The EDF Plot
133(9)
4.6.1 Definition
133(7)
4.6.2 The EDF Plot as a Diagnostic Tool
140(2)
4.7 Conclusion
142(1)
4.8 Exercises
142(5)
5 Diagnostic Plots for the Distribution of a Continuous Variable
147(30)
5.1 Introduction
147(1)
5.2 Learning Outcomes
147(1)
5.3 The Quantile-Quantile Plot
148(9)
5.4 The Probability Plot
157(1)
5.5 Estimation of Quartiles and Percentile?
158(15)
5.5.1 Estimation of Quartiles
159(6)
5.5.2 Estimation of Percentiles
165(8)
5.6 Conclusion
173(1)
5.7 Exercises
173(4)
6 Nonparametric Density Estimation for a Single Continuous Variable
177(62)
6.1 Introduction
177(1)
6.2 Learning Outcomes
177(1)
6.3 The Histogram
178(17)
6.3.1 Definition
178(15)
6.3.2 A Circular Variation on the Histogram: The Rose Diagram
193(2)
6.4 Kernel Density Estimation
195(29)
6.5 Spline Density Estimation
224(1)
6.6 Choosing a Plot for a Continuous Variable
224(7)
6.7 Conclusion
231(4)
6.8 Exercises
235(4)
7 Parametric Density Estimation for a Single Continuous Variable
239(34)
7.1 Introduction
239(1)
7.2 Learning Outcomes
240(1)
7.3 Normal Density Estimation
241(5)
7.4 Transformations to Normality
246(5)
7.5 Pearson's Curves
251(9)
7.6 Gram-Charlier Series Expansion
260(3)
7.7 Conclusion
263(3)
7.8 Exercises
266(7)
IV Two Variables 273(120)
8 Depicting the Distribution of Two Discrete Variables
275(42)
8.1 Introduction
275(1)
8.2 Learning Outcomes
275(1)
8.3 The Grouped Dot Chart
276(8)
8.4 The Grouped Dot-Whisker Chart
284(5)
8.5 The Two-Way Dot Chart
289(5)
8.6 The Multi-Valued Dot Chart
294(1)
8.7 The Side-by-Side Bar Chart
295(1)
8.8 The Side-by-Side Bar-Whisker Chart
295(2)
8.9 The Side-by-Side Stacked Bar Chart
297(3)
8.10 The Side-by-Side Pie Chart
300(3)
8.11 The Mosaic Chart
303(8)
8.12 Conclusion
311(2)
8.13 Exercises
313(4)
9 Depicting the Distribution of One Continuous Variable and One Discrete Variable
317(32)
9.1 Introduction
317(1)
9.2 Learning Outcomes
317(1)
9.3 The Side-by-Side Dotplot
318(3)
9.4 The Side-by-Side Boxplot
321(3)
9.5 The Notched Boxplot
324(3)
9.6 The Variable-Width Boxplot
327(5)
9.7 The Back-to-Back Stemplot
332(1)
9.8 The Side-by-Side Stemplot
333(1)
9.9 The Side-by-Side Dot-Whisker Plot
333(7)
9.10 The Trellis Kernel Density Estimate
340(4)
9.11 Conclusion
344(1)
9.12 Exercises
345(4)
10 Depicting the Distribution of Two Continuous Variables
349(44)
10.1 Introduction
349(1)
10.2 Learning Outcomes
349(1)
10.3 The Scatterplot
350(3)
10.4 The Sunflower Plot
353(4)
10.5 The Bagplot
357(5)
10.6 The Two-Dimensional Histogram
362(11)
10.6.1 Definition
362(3)
10.6.2 The Levelplot
365(5)
10.6.3 The Cloud Plot
370(3)
10.7 Two-Dimensional Kernel Density Estimation
373(11)
10.7.1 Definition
373(5)
10.7.2 The Contour Plot
378(3)
10.7.3 The Wireframe Plot
381(3)
10.8 Conclusion
384(2)
10.9 Exercises
386(7)
V Statistical Models for Two or More Variables 393(130)
11 Simple Linear Regression: Graphical Displays
395(46)
11.1 Introduction
395(4)
11.2 Learning Outcomes
399(1)
11.3 The Simple Linear Regression Model
400(14)
11.3.1 Definition
400(1)
11.3.2 The Scatterplot
400(3)
11.3.3 The Sunflower Plot
403(11)
11.4 Residual Analysis
414(6)
11.4.1 Definition
414(1)
11.4.2 Residual Scatterplots
414(5)
11.4.3 Depicting the Distribution of the Residuals
419(1)
11.4.4 Depicting the Distribution of the Semistandardized Residuals
420(1)
11.5 Influence Analysis
420(15)
11.5.1 Definition
420(2)
11.5.2 Matrix Notation for the Simple Linear Regression Model
422(1)
11.5.3 Depicting Standardized Residuals
423(1)
11.5.4 Depicting the Distribution of Studentized Residuals
424(3)
11.5.5 Depicting Leverage
427(1)
11.5.6 Depicting DFFITS
428(2)
11.5.7 Depicting DFBETAS
430(2)
11.5.8 Depicting Cook's Distance
432(1)
11.5.9 Influence Plots
433(2)
11.6 Conclusion
435(3)
11.7 Exercises
438(3)
12 Polynomial Regression and Data Smoothing: Graphical Displays
441(28)
12.1 Introduction
441(2)
12.2 Learning Outcomes
443(1)
12.3 The Polynomial Regression Model
443(4)
12.4 Splines
447(6)
12.5 Locally Weighted Polynomial Regression
453(11)
12.6 Conclusion
464(1)
12.7 Exercises
464(5)
13 Visualizing Multivariate Data
469(54)
13.1 Introduction
469(1)
13.2 Learning Outcomes
469(1)
13.3 Depicting Distributions of Three or More Discrete Variables
470(10)
13.3.1 The Sinking of the Titanic
470(2)
13.3.2 Thermometer Chart
472(3)
13.3.3 Three-Dimensional Bar Chart
475(1)
13.3.4 Trellis Three-Dimensional Bar Chart
476(4)
13.4 Depicting Distributions of One Discrete Variable and Two or More Continuous Variables
480(15)
13.4.1 Anderson's Iris Data
480(1)
13.4.2 The Superposed Scatterplot
481(2)
13.4.3 The Superposed Three-Dimensional Scatterplot
483(4)
13.4.4 The Scatterplot Matrix
487(3)
13.4.5 The Parallel Coordinates Plot
490(1)
13.4.6 The Trellis Plot
491(4)
13.5 Observations of Multiple Variables
495(10)
13.5.1 OECD Healthcare Service Data
495(2)
13.5.2 Chernoff's Faces
497(4)
13.5.3 The Star Plot
501(3)
13.5.4 The Rose Plot
504(1)
13.6 The Multiple Linear Regression Model
505(13)
13.6.1 Definition
505(2)
13.6.2 Modeling Perch Mass
507(1)
13.6.3 Residual Scatterplot Matrix
508(3)
13.6.4 Leverage Scatterplot Matrix
511(2)
13.6.5 Influence Plot
513(1)
13.6.6 Partial-Regression Scatterplot Matrix
513(2)
13.6.7 Partial-Residual Scatterplot Matrix
515(3)
13.6.8 Summary of the Model for Perch Mass
518(1)
13.7 Conclusion
518(1)
13.8 Exercises
519(4)
VI Appendices 523(52)
A Human Visualization
525(30)
A.1 Introduction
525(1)
A.2 Learning Outcomes
525(1)
A.3 Optics
526(3)
A.3.1 Introduction
526(1)
A.3.2 Geometrical Optics
527(2)
A.3.3 The Light Spectrum
529(1)
A.4 Anatomy of the Human Eye
529(5)
A.5 The Perception of Color
534(11)
A.6 Graphical Perception
545(6)
A.6.1 Weber's Law
545(3)
A.6.2 Stevens's Law
548(2)
A.6.3 The Gestalt Laws of Organization
550(1)
A.6.4 Kosslyn's Image Processing Model
551(1)
A.7 Conclusion
551(1)
A.8 Exercises
552(3)
B Color Rendering
555(20)
B.1 Introduction
555(1)
B.2 Learning Outcomes
555(1)
B.3 RGB and XYZ Color Spaces
556(5)
B.4 HSL and HSV Color Spaces
561(1)
B.5 CIELAB and CIELUV Color Spaces
562(1)
B.6 HCL Color Space
563(1)
B.7 CMYK Color Space
564(2)
B.8 Displaying Color in R
566(3)
B.9 Saving Color Documents from R
569(2)
B.10 Conclusion
571(1)
B.11 Exercises
572(3)
Bibliography 575(10)
Index 585
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.
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