Graphical Data Analysis with R: 1st Edition (Hardback) book cover

Graphical Data Analysis with R

1st Edition

By Antony Unwin

Chapman and Hall/CRC

310 pages | 135 Color Illus.

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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.

Reviews

". . . the book follows a learning-by-doing approach.With numerous examples, the author shows how important qualitative aspects of data can be detected by means of simple plots, and how a few simple changes in a graph may uncover relevant information not visible before, setting aside the more technical aspects of plots in R. Still, for each graph, the respective R-code is provided in the book, and complete programme codes for the examples ae available on the book’s webpage. Thus, by copy and paste, one can easily rescale all graphs, change the aspect ratio and apply other modifications to the original plot. This blended-learning approach facilitates exploring the data graphically without requiring too much knowledge of R syntax. This book is therefore well suited for students and novice data analysts who want to learn from examples. It could also supplement theoretical statistics courses, and help statistics teachers in finding suitable graphical displays for various purposes."

—Jasmin Wachter, Universität Klagenfurt

"Overall, the book is a very good introduction to the practical side of graphical data analysis using R. The presentation of R code and graphics output is excellent, with colours used when required. The book appears to be free of typographical and other errors, and its index is useful. Also, the book is well written and neatly structured. I enjoyed reading the book and can recommend it to anyone who wants to learn more about their data through graphics using R. It will also be a valuable asset for a library and as part of an undergraduate course in applied statistics."

Journal of the Royal Statistical Society, Series A

"Throughout, the book follows a learning-by-doing approach. With numerous examples, the author shows how important qualitative aspects of data can be detected by means of simple plots, and how a few simple changes in a graph may uncover relevant information not visible before, setting aside the more technical aspects of plots in R. Still, for each graph, the respective R-code is provided in the book, and complete programme codes for the examples ae available on the book’s webpage. … This blended-learning approach facilitates exploring the data graphically without requiring too much knowledge of R syntax. This book is therefore well suited for students and novice data analysts who want to learn from examples. It could also supplement theoretical statistics courses, and help statistics teachers in finding suitable graphical displays for various purposes."

Statistical Papers, 2017

"… an attractive addition to the current statistical graphics texts as it demonstrates what can be learned through graphs."

Significance Magazine, February 2016

"… the strength of this book lies in the profound introduction to the topic of graphical data analysis. The comprehensive sectional introductions and overviews along with the ‘how-to’ might well be regarded as the modern update to Tukey’s 1977 landmark book."

Biometrical Journal, December 2015

"Antony Unwin’s very clever new book … is well written, clearly by a practitioner with wide experience, gives generally good (though sometimes opinionated) advice, and includes R code for nearly all examples, as well as nice collections of additional exercises for each chapter … Beyond the content, Unwin also does an admirable job of conveying enthusiasm for data graphics."

Journal of Educational and Behavioral Statistics, December 2015

"This text has the potential of bringing sophisticated visualization to a broad audience without resorting to mathematical formalizations or the skills of a graphics artist. It engages the reader with interesting graphics right from the start and overall is clear and unintimidating. Code for all examples is provided in the text and is available on a supporting website. What’s more, the code works as is, rather unusual and refreshing."

Journal of Statistical Software, November 2015

"For statisticians and experts in data analysis, the book is without doubt the new reference work on the subject."

—Thomas Rahlf, datendesign-r.de

…would also be an excellent suggested additional reading for a pragmatic graphical data analysis-oriented course.

—Reijo Sund, Centre for Research Methods, University of Helsinki

Table of Contents

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

About the Author

Antony Unwin is a professor of computer-oriented statistics and data analysis at the University of Augsburg. He is a fellow of the American Statistical Society, co-author of Graphics of Large Datasets, and co-editor of the Handbook of Data Visualization. His research focuses on data visualisation, especially in interactive graphics. His research group has developed several pieces of interactive graphics software and written packages for R.

About the Series

Chapman & Hall/CRC The R Series

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General
REF000000
REFERENCE / General