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.
Table of contents
 Preliminaries
 Preface
 Chapter 1 Setting the Scene
 Chapter 2 Brief Review of the Literature and Background Materials
 Chapter 3 Examining Continuous Variables

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
 4.4 Ordinal data—fixed category order
 4.5 Discrete data—counts and integers Deaths by horsekicks
 4.6 Formats, factors, estimates, and barcharts
 4.7 Modelling and testing for categorical variables
 Main points
 Exercises

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
 5.4 Adding models: lines and smooths
 5.5 Comparing groups within scatterplots
 5.6 Scatterplot matrices for looking at many pairs of variables
 5.7 Scatterplot options
 5.8 Modelling and testing for relationships between variables
 Main points
 Exercises

Chapter 6 Investigating Multivariate Continuous Data
 Summary
 6.1 Introduction
 6.2 What is a parallel coordinate plot (pcp)?
 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
 6.8 Modelling and testing for multivariate continuous data
 6.9 Parallel coordinate plots and comparing model results
 Main points
 Exercises
 Chapter 7 Studying Multivariate Categorical Data
 Chapter 8 Getting an Overview
 Chapter 9 Graphics and Data Quality: How Good Are the Data?
 Chapter 10 Comparisons, Comparisons, Comparisons
 Chapter 11 Graphics for Time Series
 Chapter 12 Ensemble Graphics and Case Studies

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

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
 Chapter 14 Summary
 References
Product information
 Title: Graphical Data Analysis with R
 Author(s):
 Release date: September 2018
 Publisher(s): Chapman and Hall/CRC
 ISBN: 9781315360041
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