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Use R Lattice Multivariate Data Visualization with R (Sarkar)

 
 Author(s)  Deepayan Sarkar
 Title  Lattice Multivariate Data Visualization with R
 Edition  
 Year  2008
 Publisher  Springer
 ISBN  978-0-387-75968-5
 Website  www.springer.com
 book link
 




Table of Contents

Preface 

1 Introduction 

1.1 Multipanel conditioning

1.1.1 A histogram for every group

1.1.2 The Trellis call

1.1.3 Kernel density plots

1.2 Superposition 

1.3 The “trellis” object

1.3.1 The missing Trellis display

1.3.2 Arranging multiple Trellis plots

1.4 Looking ahead


Part I Basics

2 A Technical Overview of lattice 

2.1 Basic usage 

2.1.1 The Trellis formula

2.1.2 The data argument

2.1.3 Conditioning

2.1.4 Shingles

2.2 Dimension and physical layout

2.2.1 Aspect ratio

2.2.2 Layout 

2.2.3 Fine-tuning the layout: between and skip 

2.3 Grouped displays 

2.4 Annotation: Captions, labels, and legends

2.4.1 More on legends

2.5 Graphing the data 

2.5.1 Scales and axes

2.5.2 The panel function 

2.5.3 The panel function demystified

2.6 Return value 

3 Visualizing Univariate Distributions 

3.1 Density Plot 

3.2 Large datasets

3.3 Histograms

3.4 Normal Q–Q plots

3.4.1 Normality and the Box–Cox transformation

3.4.2 Other theoretical Q–Q plots

3.5 The empirical CDF

3.6 Two-sample Q–Q plots

3.7 Box-and-whisker plots

3.7.1 Violin plots

3.8 Strip plots 

3.9 Coercion rules

3.10 Discrete distributions 

3.11 A note on the formula interface

4 Displaying Multiway Tables 

4.1 Cleveland dot plot 

4.2 Bar chart

4.2.1 Manipulating order

4.2.2 Bar charts and discrete distributions

4.3 Visualizing categorical data

5 Scatter Plots and Extensions 

5.1 The standard scatter plot 

5.2 Advanced indexing using subscripts

5.3 Variants using the type argument 

5.3.1 Superposition and type

5.4 Scatter-plot variants for large data

5.5 Scatter-plot matrix 

5.5.1 Interacting with scatter-plot matrices 

5.6 Parallel coordinates plot

6 Trivariate Displays 

6.1 Three-dimensional scatter plots 

6.1.1 Dynamic manipulation versus stereo viewing 

6.1.2 Variants and panel functions 

6.2 Surfaces and two-way tables

6.2.1 Data preparation 

6.2.2 Visualizing surfaces

6.2.3 Visualizing discrete array data 

6.3 Theoretical surfaces 

6.3.1 Parameterized surfaces

6.4 Choosing a palette for false-color plots 


Part II Finer Control

7 Graphical Parameters and Other Settings

7.1 The parameter system

7.1.1 Themes 

7.1.2 Devices 

7.1.3 Initializing a graphics device 

7.1.4 Reading and modifying a theme 

7.1.5 Usage and alternative forms 

7.1.6 The par.settings argument

7.2 Available graphical parameters 

7.2.1 Nonstandard settings

7.3 Non-graphical options 

7.3.1 Argument defaults

7.4 Making customizations persistent

8 Plot Coordinates and Axis Annotation

8.1 Packets and the prepanel function 

8.2 The scales argument

8.2.1 Relation

8.2.2 Axis annotation: Ticks and labels 

8.2.3 Defaults 

8.2.4 Three-dimensional displays: cloud() and wireframe() 

8.3 Limits and aspect ratio 

8.3.1 The prepanel function revisited

8.3.2 Explicit specification of limits

8.3.3 Choosing aspect ratio by banking 

8.4 Scale components and the axis function

8.4.1 Components 

8.4.2 Axis

9 Labels and Legends

9.1 Labels

9.2 Legends

9.2.1 Legends as grid graphical objects 

9.2.2 The colorkey argument

9.2.3 The key argument

9.2.4 The problem with settings, and the auto.key argument 

9.2.5 Dropping unused levels from groups 

9.2.6 A more complicated example 

9.2.7 Further control: The legend argument

9.3 Page annotation

10 Data Manipulation and Related Topics

10.1 Nonstandard evaluation

10.2 The extended formula interface

10.3 Combining data sources with make.groups() 

10.4 Subsetting

10.4.1 Dropping of factor levels 

10.5 Shingles and related utilities

10.5.1 Coercion to factors and shingles 

10.5.2 Using shingles for axis breaks 

10.5.3 Cut-and-stack plots

10.6 Ordering levels of categorical variables 

10.7 Controlling the appearance of strips 

10.8 An Example Revisited 

11 Manipulating the “trellis” Object 

11.1 Methods for “trellis” objects

11.2 The plot(), print(), and summary() methods

11.3 The update() method and trellis.last.object()

11.4 Tukey mean–difference plot

11.5 Specialized manipulations

11.6 Manipulating the display

12 Interacting with Trellis Displays

12.1 The traditional graphics model 

12.1.1 Interaction

12.2 Viewports, trellis.vpname(), and trellis.focus() 

12.3 Interactive additions

12.4 Other uses


Part III Extending Trellis Displays

13 Advanced Panel Functions

13.1 Preliminaries

13.1.1 Building blocks for panel functions

13.1.2 Accessor functions

13.1.3 Arguments 

13.2 A toy example: Hypotrochoids and hypocycloids

13.3 Some more examples 

13.3.1 An alternative density estimate 

13.3.2 A modified box-and-whisker plot 

13.3.3 Corrgrams as customized level plots

13.4 Three-dimensional projections 

13.5 Maps

13.5.1 A simple projection scheme 

13.5.2 Maps with conditioning 

14 New Trellis Displays 

14.1 S3 methods 

14.2 S4 methods

14.3 New functions

14.3.1 A complete example: Multipanel pie charts

References

Index 




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