상세정보

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.

This book focuses on why one draws graphics to display data and which graphics to draw (and uses R to do so). 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. All the datasets are available in R or one of its packages and the R code is available online. Color graphics are used throughout the book.

정보제공 :

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

정보제공 :