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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



Setting the Scene

Graphics in action

Introduction

What is graphical data analysis (GDA)?

Using this book, the R code in it, and the book's webpage

Brief Review of the Literature and Background Materials

Literature review

Interactive graphics

Other graphics software

Websites

Datasets

Statistical texts

Examining Continuous Variables

Introduction

What features might continuous variables have?

Looking for features

Comparing distributions by subgroups

What plots are there for individual continuous variables?

Plot options

Modelling and testing for continuous variables

Displaying Categorical Data

Introduction

What features might categorical variables have?

Nominal data-no fixed category order

Ordinal data-fixed category order

Discrete data-counts and integers

Formats, factors, estimates, and barcharts

Modelling and testing for categorical variables

Looking for Structure: Dependency Relationships and Associations

Introduction

What features might be visible in scatterplots?

Looking at pairs of continuous variables

Adding models: lines and smooths

Comparing groups within scatterplots

Scatterplot matrices for looking at many pairs of variables

Scatterplot options

Modelling and testing for relationships between variables

Investigating Multivariate Continuous Data

Introduction

What is a parallel coordinate plot (pcp)?

Features you can see with parallel coordinate plots

Interpreting clustering results

Parallel coordinate plots and time series

Parallel coordinate plots for indices

Options for parallel coordinate plots

Modelling and testing for multivariate continuous data

Parallel coordinate plots and comparing model results

Studying Multivariate Categorical Data

Introduction

Data on the sinking of the Titanic

What is a mosaicplot?

Different mosaicplots for different questions of interest

Which mosaicplot is the right one?

Additional options

Modelling and testing for multivariate categorical data

Getting an Overview

Introduction

Many individual displays

Multivariate overviews

Multivariate overviews for categorical variables

Graphics by group

Modelling and testing for overviews

Graphics and Data Quality: How Good Are the Data?

Introduction

Missing values

Outliers

Modelling and testing for data quality

Comparisons, Comparisons, Comparisons

Introduction

Making comparisons

Making visual comparisons

Comparing group effects graphically

Comparing rates visually

Graphics for comparing many subsets

Graphics principles for comparisons

Modelling and testing for comparisons

Graphics for Time Series

Introduction

Graphics for a single time series

Multiple series

Special features of time series

Alternative graphics for time series

R classes and packages for time series

Modelling and testing time series

Ensemble Graphics and Case Studies

Introduction

What is an ensemble of graphics?

Combining different views-a case study example

Case studies

Some Notes on Graphics with R

Graphics systems in R

Loading datasets and packages for graphical analysis

Graphics conventions in statistics

What is a graphic anyway?

Options for all graphics

Some R graphics advice and coding tips

Other graphics

Large datasets

Perfecting graphics

Summary

Data analysis and graphics

Key features of GDA

Strengths and weaknesses of GDA

Recommendations for GDA

References

General Index

Datasets Index