The R Series Statistics G r a Graphical Data Analysis with R shows you what information you p can gain from graphical displays. The book focuses on why you draw h Graphical Data graphics to display data and which graphics to draw (and uses R to i c do so). All the datasets are available in R or one of its packages and a the R code is available at rosuda.org/GDA. l Analysis with R Graphical data analysis is useful for data cleaning, exploring data D structure, detecting outliers and unusual groups, identifying trends a and clusters, spotting local patterns, evaluating modelling output, t a and presenting results. This book guides you in choosing graphics and understanding what information you can glean from them. It A can be used as a primary text in a graphical data analysis course n or as a supplement in a statistics course. Colour graphics are used a throughout. l y Features s • Concentrates on why graphics are drawn and what they reveal i s • Emphasises the value of drawing a variety of different graphics • Uses real datasets in R to show how graphical data analysis w works in practice i • Supplies R code for all the graphics on the author’s website t h • Includes a set of exercises in each chapter to facilitate hands-on learning R 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. His research focuses on data visualisation, especially in interactive graphics. U n w Antony Unwin i n K25332 www.crcpress.com K25332_cover.indd 1 3/4/15 3:38 PM Graphical Data Analysis with R K25332_FM.indd 1 3/4/15 4:04 AM Chapman & Hall/CRC The R Series Series Editors John M. Chambers Torsten Hothorn Department of Statistics Division of Biostatistics Stanford University University of Zurich Stanford, California, USA Switzerland Duncan Temple Lang Hadley Wickham Department of Statistics RStudio University of California, Davis Boston, Massachusetts, USA Davis, California, USA Aims and Scope This book series reflects the recent rapid growth in the development and application of R, the programming language and software environment for statistical computing and graphics. R is now widely used in academic research, education, and industry. It is constantly growing, with new versions of the core software released regularly and more than 6,000 packages available. It is difficult for the documentation to keep pace with the expansion of the software, and this vital book series provides a forum for the publication of books covering many aspects of the development and application of R. The scope of the series is wide, covering three main threads: • Applications of R to specific disciplines such as biology, epidemiology, genetics, engineering, finance, and the social sciences. • Using R for the study of topics of statistical methodology, such as linear and mixed modeling, time series, Bayesian methods, and missing data. • The development of R, including programming, building packages, and graphics. The books will appeal to programmers and developers of R software, as well as applied statisticians and data analysts in many fields. The books will feature detailed worked examples and R code fully integrated into the text, ensuring their usefulness to researchers, practitioners and students. K25332_FM.indd 2 3/4/15 4:04 AM Published Titles Stated Preference Methods Using R, Hideo Aizaki, Tomoaki Nakatani, and Kazuo Sato Using R for Numerical Analysis in Science and Engineering, Victor A. Bloomfield Event History Analysis with R, Göran Broström Computational Actuarial Science with R, Arthur Charpentier Statistical Computing in C++ and R, Randall L. Eubank and Ana Kupresanin Reproducible Research with R and RStudio, Christopher Gandrud Introduction to Scientific Programming and Simulation Using R, Second Edition, Owen Jones, Robert Maillardet, and Andrew Robinson Nonparametric Statistical Methods Using R, John Kloke and Joseph McKean Displaying Time Series, Spatial, and Space-Time Data with R, Oscar Perpiñán Lamigueiro Programming Graphical User Interfaces with R, Michael F. Lawrence and John Verzani Analyzing Sensory Data with R, Sébastien Lê and Theirry Worch Parallel Computing for Data Science: With Examples in R, C++ and CUDA, Norman Matloff Analyzing Baseball Data with R, Max Marchi and Jim Albert Growth Curve Analysis and Visualization Using R, Daniel Mirman R Graphics, Second Edition, Paul Murrell Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving, Deborah Nolan and Duncan Temple Lang Multiple Factor Analysis by Example Using R, Jérôme Pagès Customer and Business Analytics: Applied Data Mining for Business Decision Making Using R, Daniel S. Putler and Robert E. Krider Implementing Reproducible Research, Victoria Stodden, Friedrich Leisch, and Roger D. Peng Graphical Data Analysis with R, Antony Unwin Using R for Introductory Statistics, Second Edition, John Verzani Advanced R, Hadley Wickham Dynamic Documents with R and knitr, Yihui Xie K25332_FM.indd 3 3/4/15 4:04 AM K25332_FM.indd 4 3/4/15 4:04 AM Graphical Data Analysis with R Antony Unwin University of Augsburg Germany K25332_FM.indd 5 3/4/15 4:04 AM CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487-2742 © 2015 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Version Date: 20150224 International Standard Book Number-13: 978-1-4987-1524-9 (eBook - PDF) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. 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Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com Contents Preface xi 1 SettingtheScene 1 1.1 Graphicsinaction . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 WhatisGraphicalDataAnalysis(GDA)? . . . . . . . . . . . . . . 5 1.4 Usingthisbook,theRcodeinit,andthebook’swebpage . . . . . 14 2 BriefReviewoftheLiteratureandBackgroundMaterials 19 2.1 Literaturereview . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2 Interactivegraphics . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3 Othergraphicssoftware . . . . . . . . . . . . . . . . . . . . . . . 21 2.4 Websites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.5 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.6 Statisticaltexts . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3 ExaminingContinuousVariables 27 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 Whatfeaturesmightcontinuousvariableshave? . . . . . . . . . . . 29 3.3 Lookingforfeatures . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4 Comparingdistributionsbysubgroups . . . . . . . . . . . . . . . . 44 3.5 Whatplotsarethereforindividualcontinuousvariables? . . . . . . 46 3.6 Plotoptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.7 Modellingandtestingforcontinuousvariables . . . . . . . . . . . 48 4 DisplayingCategoricalData 53 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4.2 Whatfeaturesmightcategoricalvariableshave? . . . . . . . . . . . 56 4.3 Nominaldata—nofixedcategoryorder . . . . . . . . . . . . . . . 57 4.4 Ordinaldata—fixedcategoryorder . . . . . . . . . . . . . . . . . 62 4.5 Discretedata—countsandintegers . . . . . . . . . . . . . . . . . 66 4.6 Formats,factors,estimates,andbarcharts . . . . . . . . . . . . . . 70 4.7 Modellingandtestingforcategoricalvariables . . . . . . . . . . . 71 vii viii 5 LookingforStructure:DependencyRelationshipsandAssociations 75 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 5.2 Whatfeaturesmightbevisibleinscatterplots? . . . . . . . . . . . 77 5.3 Lookingatpairsofcontinuousvariables . . . . . . . . . . . . . . . 78 5.4 Addingmodels:linesandsmooths . . . . . . . . . . . . . . . . . . 83 5.5 Comparinggroupswithinscatterplots . . . . . . . . . . . . . . . . 86 5.6 Scatterplotmatricesforlookingatmanypairsofvariables . . . . . 88 5.7 Scatterplotoptions . . . . . . . . . . . . . . . . . . . . . . . . . . 92 5.8 Modellingandtestingforrelationshipsbetweenvariables . . . . . 94 6 InvestigatingMultivariateContinuousData 99 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 6.2 Whatisaparallelcoordinateplot(pcp)? . . . . . . . . . . . . . . . 100 6.3 Featuresyoucanseewithparallelcoordinateplots . . . . . . . . . 102 6.4 Interpretingclusteringresults . . . . . . . . . . . . . . . . . . . . 106 6.5 Parallelcoordinateplotsandtimeseries . . . . . . . . . . . . . . . 108 6.6 Parallelcoordinateplotsforindices . . . . . . . . . . . . . . . . . 112 6.7 Optionsforparallelcoordinateplots . . . . . . . . . . . . . . . . . 115 6.8 Modellingandtestingformultivariatecontinuousdata . . . . . . . 127 6.9 Parallelcoordinateplotsandcomparingmodelresults . . . . . . . 127 7 StudyingMultivariateCategoricalData 131 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 7.2 DataonthesinkingoftheTitanic . . . . . . . . . . . . . . . . . . 132 7.3 Whatisamosaicplot? . . . . . . . . . . . . . . . . . . . . . . . . 133 7.4 Differentmosaicplotsfordifferentquestionsofinterest . . . . . . . 140 7.5 Whichmosaicplotistherightone? . . . . . . . . . . . . . . . . . 148 7.6 Additionaloptions . . . . . . . . . . . . . . . . . . . . . . . . . . 149 7.7 Modellingandtestingformultivariatecategoricaldata . . . . . . . 151 8 GettinganOverview 155 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 8.2 Manyindividualdisplays . . . . . . . . . . . . . . . . . . . . . . . 159 8.3 Multivariateoverviews . . . . . . . . . . . . . . . . . . . . . . . . 162 8.4 Multivariateoverviewsforcategoricalvariables . . . . . . . . . . . 168 8.5 Graphicsbygroup . . . . . . . . . . . . . . . . . . . . . . . . . . 170 8.6 Modellingandtestingforoverviews . . . . . . . . . . . . . . . . . 174 ix 9 GraphicsandDataQuality:HowGoodAretheData? 177 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 9.2 Missingvalues . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 9.3 Outliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 9.4 Modellingandtestingfordataquality . . . . . . . . . . . . . . . . 193 10 Comparisons,Comparisons,Comparisons 197 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 10.2 Makingcomparisons . . . . . . . . . . . . . . . . . . . . . . . . . 199 10.3 Makingvisualcomparisons . . . . . . . . . . . . . . . . . . . . . 202 10.4 Comparinggroupeffectsgraphically . . . . . . . . . . . . . . . . 208 10.5 Comparingratesvisually . . . . . . . . . . . . . . . . . . . . . . . 212 10.6 Graphicsforcomparingmanysubsets . . . . . . . . . . . . . . . . 214 10.7 Graphicsprinciplesforcomparisons . . . . . . . . . . . . . . . . . 216 10.8 Modellingandtestingforcomparisons . . . . . . . . . . . . . . . 218 11 GraphicsforTimeSeries 223 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 11.2 Graphicsforasingletimeseries . . . . . . . . . . . . . . . . . . . 224 11.3 Multipleseries . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226 11.4 Specialfeaturesoftimeseries . . . . . . . . . . . . . . . . . . . . 233 11.5 Alternativegraphicsfortimeseries . . . . . . . . . . . . . . . . . 237 11.6 Rclassesandpackagesfortimeseries . . . . . . . . . . . . . . . . 238 11.7 Modellingandtestingtimeseries . . . . . . . . . . . . . . . . . . 238 12 EnsembleGraphicsandCaseStudies 243 12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243 12.2 Whatisanensembleofgraphics? . . . . . . . . . . . . . . . . . . 246 12.3 Combiningdifferentviews—acasestudyexample . . . . . . . . . 248 12.4 Casestudies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252 13 SomeNotesonGraphicswithR 257 13.1 GraphicssystemsinR . . . . . . . . . . . . . . . . . . . . . . . . 257 13.2 Loadingdatasetsandpackagesforgraphicalanalysis . . . . . . . . 258 13.3 Graphicsconventionsinstatistics . . . . . . . . . . . . . . . . . . 258 13.4 Whatisagraphicanyway? . . . . . . . . . . . . . . . . . . . . . . 259 13.5 Optionsforallgraphics . . . . . . . . . . . . . . . . . . . . . . . 261 13.6 SomeRgraphicsadviceandcodingtips . . . . . . . . . . . . . . . 264 13.7 Othergraphics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 13.8 Largedatasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 13.9 Perfectinggraphics . . . . . . . . . . . . . . . . . . . . . . . . . . 273