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Graphical Data Analysis with R
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Graphical Data Analysis with R
by Antony Unwin
Graphical Data Analysis with R
Preliminaries
Preface
Acknowledgements
Chapter 1 Setting the Scene
1.1 Graphics in action
1.2 Introduction
1.3 What is Graphical Data Analysis (GDA)?
The Iris dataset
Student Admissions at UC Berkeley dataset
Pima Indians diabetes dataset
GDA in context
1.4 Using this book, the R code in it, and the book’s webpage
Main points
Exercises
Figure 1.1
Figure 1.1
Figure 1.2
Figure 1.3
Figure 1.4
Figure 1.5
Figure 1.6
Figure 1.7
Figure 1.8
Figure 1.9
Chapter 2 Brief Review of the Literature and Background Materials
Summary
2.1 Literature review
2.2 Interactive graphics
2.3 Other graphics software
2.4 Websites
2.5 Datasets
2.6 Statistical texts
Chapter 3 Examining Continuous Variables
Summary
3.1 Introduction
3.2 What features might continuous variables have?
3.3 Looking for features
Galton's heights
Some more heights—Pearson
Scottish hill races (best times)
How are the variables in the Boston dataset distributed?
Hidalgo stamps thickness
How long is a movie?
3.4 Comparing distributions by subgroups
3.5 What plots are there for individual continuous variables?
3.6 Plot options
3.7 Modelling and testing for continuous variables
Main points
Exercises
Figure 3.1
Figure 3.1
Figure 3.2
Figure 3.3
Figure 3.4
Figure 3.5
Figure 3.6
Figure 3.7
Figure 3.8
Figure 3.9
Figure 3.10
Figure 3.11
Figure 3.12
Figure 3.13
Figure 3.14
Chapter 4 Displaying Categorical Data
Summary
4.1 Introduction
4.2 What features might categorical variables have?
4.3 Nominal data—no fixed category order
Meta analyses—how big was each study?
Anorexia
Who sailed on the Titanic?
Opinion polls
4.4 Ordinal data—fixed category order
Surveys
And more surveys
4.5 Discrete data—counts and integers Deaths by horsekicks
Goals in soccer
Benford’s Law
4.6 Formats, factors, estimates, and barcharts
Shape of the dataset
Coding of variables
Estimates shown as bars
4.7 Modelling and testing for categorical variables
Main points
Exercises
Figure 4.1
Figure 4.1
Figure 4.2
Figure 4.3
Figure 4.4
Figure 4.5
Figure 4.6
Figure 4.7
Figure 4.8
Figure 4.9
Figure 4.10
Figure 4.11
Figure 4.12
Chapter 5 Looking for Structure: Dependency Relationships and Associations
Summary
5.1 Introduction
5.2 What features might be visible in scatterplots?
5.3 Looking at pairs of continuous variables
The evils of drink?
Old Faithful
Movie ratings
5.4 Adding models: lines and smooths
Cars and mpg
Pearson heights
5.5 Comparing groups within scatterplots
5.6 Scatterplot matrices for looking at many pairs of variables
Crime in the U.S.
Swiss banknotes
Functions for drawing sploms
5.7 Scatterplot options
5.8 Modelling and testing for relationships between variables
Main points
Exercises
Figure 5.1
Figure 5.1
Figure 5.2
Figure 5.3
Figure 5.4
Figure 5.5
Figure 5.6
Figure 5.7
Figure 5.8
Figure 5.9
Figure 5.10
Figure 5.11
Figure 5.12
Figure 5.13
Table 5.1
Table 5.1
Chapter 6 Investigating Multivariate Continuous Data
Summary
6.1 Introduction
6.2 What is a parallel coordinate plot (pcp)?
Functions for drawing pcp's
6.3 Features you can see with parallel coordinate plots
6.4 Interpreting clustering results
6.5 Parallel coordinate plots and time series
6.6 Parallel coordinate plots for indices
6.7 Options for parallel coordinate plots
Alignment
Scaling
Outliers
Variable order
Formatting
6.8 Modelling and testing for multivariate continuous data
6.9 Parallel coordinate plots and comparing model results
Main points
Exercises
Figure 6.1
Figure 6.1
Figure 6.2
Figure 6.3
Figure 6.4
Figure 6.5
Figure 6.6
Figure 6.7
Figure 6.8
Figure 6.9
Figure 6.10
Figure 6.11
Figure 6.12
Figure 6.13
Figure 6.14
Figure 6.15
Figure 6.16
Figure 6.17
Figure 6.18
Figure 6.19
Table 6.1
Table 6.1
Chapter 7 Studying Multivariate Categorical Data
Summary
7.1 Introduction
7.2 Data on the sinking of the Titanic
7.3 What is a mosaicplot?
7.4 Different mosaicplots for different questions of interest
7.5 Which mosaicplot is the right one?
7.6 Additional options
7.7 Modelling and testing for multivariate categorical data
Main points
Exercises
Figure 7.1
Figure 7.1
Figure 7.2
Figure 7.3
Figure 7.4
Figure 7.5
Figure 7.6
Figure 7.7
Figure 7.8
Figure 7.9
Figure 7.10
Figure 7.11
Figure 7.12
Chapter 8 Getting an Overview
Summary
8.1 Introduction
8.2 Many individual displays
8.3 Multivariate overviews
Scatterplot matrices
Parallel coordinate plots
Heatmaps
Glyphs
8.4 Multivariate overviews for categorical variables
8.5 Graphics by group
Trellis graphics
Group plots
8.6 Modelling and testing for overviews
Main points
Exercises
Figure 8.1
Figure 8.1
Figure 8.2
Figure 8.3
Figure 8.4
Figure 8.5
Figure 8.6
Figure 8.7
Figure 8.8
Figure 8.9
Figure 8.10
Figure 8.11
Figure 8.12
Figure 8.13
Chapter 9 Graphics and Data Quality: How Good Are the Data?
Summary
9.1 Introduction
9.2 Missing values
Visualising patterns of missing values
Missings dependent on values of other variables (MAR)
Reasons for missings and dealing with missings
9.3 Outliers
What is an outlier?
Examples of outliers
Univariate outliers
Multivariate outliers
Categorical outliers
Dealing with outliers
A possible strategy for outliers
9.4 Modelling and testing for data quality
Main points
Exercises
Figure 9.1
Figure 9.1
Figure 9.2
Figure 9.3
Figure 9.4
Figure 9.5
Figure 9.6
Figure 9.7
Figure 9.8
Figure 9.9
Figure 9.10
Figure 9.11
Figure 9.12
Chapter 10 Comparisons, Comparisons, Comparisons
Summary
10.1 Introduction
10.2 Making comparisons
Types of comparison
Comparing like with like
10.3 Making visual comparisons
Comparing to a standard
Comparing new data with old data
Comparing subgroups
Comparing time series (Playfair's import/export data)
10.4 Comparing group effects graphically
10.5 Comparing rates visually
10.6 Graphics for comparing many subsets
10.7 Graphics principles for comparisons
10.8 Modelling and testing for comparisons
Main points
Exercises
Figure 10.1
Figure 10.1
Figure 10.2
Figure 10.3
Figure 10.4
Figure 10.5
Figure 10.6
Figure 10.7
Figure 10.8
Figure 10.9
Figure 10.10
Figure 10.11
Figure 10.12
Figure 10.13
Figure 10.14
Chapter 11 Graphics for Time Series
Summary
11.1 Introduction
11.2 Graphics for a single time series
11.3 Multiple series
Related series for the same population
Same series for different subgroups
Series with different scales
One plot versus many
11.4 Special features of time series
Data definitions
Length of time series
Regular and irregular time series
Time series of different kinds of variables
Forecasting
Seeing patterns
11.5 Alternative graphics for time series
11.6 R classes and packages for time series
11.7 Modelling and testing time series
Main points
Exercises
Figure 11.1
Figure 11.1
Figure 11.2
Figure 11.3
Figure 11.4
Figure 11.5
Figure 11.6
Figure 11.7
Figure 11.8
Figure 11.9
Figure 11.10
Chapter 12 Ensemble Graphics and Case Studies
Summary
12.1 Introduction
12.2 What is an ensemble of graphics?
12.3 Combining different views—a case study example
12.4 Case studies
Moral statistics of France
Airbags and car accidents
Athletes’ blood measurements
Marijuana arrests
Crohn’s disease
Footballers in the four major European leagues
Decathlon
Intermission
Figure 12.1
Figure 12.1
Figure 12.2
Figure 12.3
Figure 12.4
Figure 12.5
Chapter 13 Some Notes on Graphics with R
Summary
13.1 Graphics systems in R
13.2 Loading datasets and packages for graphical analysis
13.3 Graphics conventions in statistics
13.4 What is a graphic anyway?
13.5 Options for all graphics
Window size and shape
Scales
Text
Colour and appearance
13.6 Some R graphics advice and coding tips
To get a new graphics window
Resizing windows
Default plots
Points in scatterplots or in other point plots
Printing graphics
Multiple windows
Drawing several independent plots in one window
Naming objects
Reordering categories for a barchart (and ordering in general)
Reshaping datasets and graphics
Missing values
Using the code and finding out about function options
13.7 Other graphics
13.8 Large datasets
13.9 Perfecting graphics
Figure 13.1
Figure 13.1
Figure 13.2
Chapter 14 Summary
14.1 Data analysis and graphics
14.2 Key features of GDA
14.3 Strengths and weaknesses of GDA
14.4 Recommendations for GDA
References
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