With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

No credit card required

Book Description

See How Graphics Reveal Information

Graphical Data Analysis with R shows you what information you can gain from graphical displays. The book focuses on why you draw graphics to display data and which graphics to draw (and uses R to do so). All the datasets are available in R or one of its packages and the R code is available at rosuda.org/GDA.

Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. This book guides you in choosing graphics and understanding what information you can glean from them. It can be used as a primary text in a graphical data analysis course or as a supplement in a statistics course. Colour graphics are used throughout.

1. Preliminaries
2. Preface
3. Chapter 1 Setting the Scene
1. 1.1 Graphics in action
2. 1.2 Introduction
3. 1.3 What is Graphical Data Analysis (GDA)?
4. 1.4 Using this book, the R code in it, and the book’s webpage
5. Main points
6. Exercises
4. Chapter 2 Brief Review of the Literature and Background Materials
5. Chapter 3 Examining Continuous Variables
1. Summary
2. 3.1 Introduction
3. 3.2 What features might continuous variables have?
4. 3.3 Looking for features
5. 3.4 Comparing distributions by subgroups
6. 3.5 What plots are there for individual continuous variables?
7. 3.6 Plot options
8. 3.7 Modelling and testing for continuous variables
9. Main points
10. Exercises
6. Chapter 4 Displaying Categorical Data
1. Summary
2. 4.1 Introduction
3. 4.2 What features might categorical variables have?
4. 4.3 Nominal data—no fixed category order
5. 4.4 Ordinal data—fixed category order
6. 4.5 Discrete data—counts and integers Deaths by horsekicks
7. 4.6 Formats, factors, estimates, and barcharts
8. 4.7 Modelling and testing for categorical variables
9. Main points
10. Exercises
7. Chapter 5 Looking for Structure: Dependency Relationships and Associations
1. Summary
2. 5.1 Introduction
3. 5.2 What features might be visible in scatterplots?
4. 5.3 Looking at pairs of continuous variables
5. 5.4 Adding models: lines and smooths
6. 5.5 Comparing groups within scatterplots
7. 5.6 Scatterplot matrices for looking at many pairs of variables
8. 5.7 Scatterplot options
9. 5.8 Modelling and testing for relationships between variables
10. Main points
11. Exercises
8. Chapter 6 Investigating Multivariate Continuous Data
1. Summary
2. 6.1 Introduction
3. 6.2 What is a parallel coordinate plot (pcp)?
4. 6.3 Features you can see with parallel coordinate plots
5. 6.4 Interpreting clustering results
6. 6.5 Parallel coordinate plots and time series
7. 6.6 Parallel coordinate plots for indices
8. 6.7 Options for parallel coordinate plots
9. 6.8 Modelling and testing for multivariate continuous data
10. 6.9 Parallel coordinate plots and comparing model results
11. Main points
12. Exercises
9. Chapter 7 Studying Multivariate Categorical Data
1. Summary
2. 7.1 Introduction
3. 7.2 Data on the sinking of the Titanic
4. 7.3 What is a mosaicplot?
5. 7.4 Different mosaicplots for different questions of interest
6. 7.5 Which mosaicplot is the right one?
8. 7.7 Modelling and testing for multivariate categorical data
9. Main points
10. Exercises
10. Chapter 8 Getting an Overview
1. Summary
2. 8.1 Introduction
3. 8.2 Many individual displays
4. 8.3 Multivariate overviews
5. 8.4 Multivariate overviews for categorical variables
6. 8.5 Graphics by group
7. 8.6 Modelling and testing for overviews
8. Main points
9. Exercises
11. Chapter 9 Graphics and Data Quality: How Good Are the Data?
1. Summary
2. 9.1 Introduction
3. 9.2 Missing values
4. 9.3 Outliers
5. 9.4 Modelling and testing for data quality
6. Main points
7. Exercises
12. Chapter 10 Comparisons, Comparisons, Comparisons
1. Summary
2. 10.1 Introduction
3. 10.2 Making comparisons
4. 10.3 Making visual comparisons
5. 10.4 Comparing group effects graphically
6. 10.5 Comparing rates visually
7. 10.6 Graphics for comparing many subsets
8. 10.7 Graphics principles for comparisons
9. 10.8 Modelling and testing for comparisons
10. Main points
11. Exercises
13. Chapter 11 Graphics for Time Series
1. Summary
2. 11.1 Introduction
3. 11.2 Graphics for a single time series
4. 11.3 Multiple series
5. 11.4 Special features of time series
6. 11.5 Alternative graphics for time series
7. 11.6 R classes and packages for time series
8. 11.7 Modelling and testing time series
9. Main points
10. Exercises
14. Chapter 12 Ensemble Graphics and Case Studies
1. Summary
2. 12.1 Introduction
3. 12.2 What is an ensemble of graphics?
4. 12.3 Combining different views—a case study example
5. 12.4 Case studies
15. Chapter 13 Some Notes on Graphics with R
1. Summary
2. 13.1 Graphics systems in R