book

Graphical Data Analysis with R

by Antony Unwin

September 2018

Beginner to intermediate

310 pages

7h 56m

English

Chapman and Hall/CRC

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Acknowledgements
1.1 Graphics in action1.2 Introduction1.3 What is Graphical Data Analysis (GDA)?The Iris datasetStudent Admissions at UC Berkeley datasetPima Indians diabetes datasetGDA in context1.4 Using this book, the R code in it, and the book’s webpageMain pointsExercisesFigure 1.1Figure 1.2Figure 1.3Figure 1.4Figure 1.5Figure 1.6Figure 1.7Figure 1.8Figure 1.9
Summary2.1 Literature review2.2 Interactive graphics2.3 Other graphics software2.4 Websites2.5 Datasets2.6 Statistical texts
Summary3.1 Introduction3.2 What features might continuous variables have?3.3 Looking for featuresGalton's heightsSome more heights—PearsonScottish hill races (best times)How are the variables in the Boston dataset distributed?Hidalgo stamps thicknessHow long is a movie?3.4 Comparing distributions by subgroups3.5 What plots are there for individual continuous variables?3.6 Plot options3.7 Modelling and testing for continuous variablesMain pointsExercisesFigure 3.1Figure 3.2Figure 3.3Figure 3.4Figure 3.5Figure 3.6Figure 3.7Figure 3.8Figure 3.9Figure 3.10Figure 3.11Figure 3.12Figure 3.13Figure 3.14
Summary4.1 Introduction4.2 What features might categorical variables have?4.3 Nominal data—no fixed category orderMeta analyses—how big was each study?AnorexiaWho sailed on the Titanic?Opinion polls4.4 Ordinal data—fixed category orderSurveysAnd more surveys4.5 Discrete data—counts and integers Deaths by horsekicksGoals in soccerBenford’s Law4.6 Formats, factors, estimates, and barchartsShape of the datasetCoding of variablesEstimates shown as bars4.7 Modelling and testing for categorical variablesMain pointsExercisesFigure 4.1Figure 4.2Figure 4.3Figure 4.4Figure 4.5Figure 4.6Figure 4.7Figure 4.8Figure 4.9Figure 4.10Figure 4.11Figure 4.12
Summary5.1 Introduction5.2 What features might be visible in scatterplots?5.3 Looking at pairs of continuous variablesThe evils of drink?Old FaithfulMovie ratings5.4 Adding models: lines and smoothsCars and mpgPearson heights5.5 Comparing groups within scatterplots5.6 Scatterplot matrices for looking at many pairs of variablesCrime in the U.S.Swiss banknotesFunctions for drawing sploms5.7 Scatterplot options5.8 Modelling and testing for relationships between variablesMain pointsExercisesFigure 5.1Figure 5.2Figure 5.3Figure 5.4Figure 5.5Figure 5.6Figure 5.7Figure 5.8Figure 5.9Figure 5.10Figure 5.11Figure 5.12Figure 5.13Table 5.1
Summary6.1 Introduction6.2 What is a parallel coordinate plot (pcp)?Functions for drawing pcp's6.3 Features you can see with parallel coordinate plots6.4 Interpreting clustering results6.5 Parallel coordinate plots and time series6.6 Parallel coordinate plots for indices6.7 Options for parallel coordinate plotsAlignmentScalingOutliersVariable orderFormatting6.8 Modelling and testing for multivariate continuous data6.9 Parallel coordinate plots and comparing model resultsMain pointsExercisesFigure 6.1Figure 6.2Figure 6.3Figure 6.4Figure 6.5Figure 6.6Figure 6.7Figure 6.8Figure 6.9Figure 6.10Figure 6.11Figure 6.12Figure 6.13Figure 6.14Figure 6.15Figure 6.16Figure 6.17Figure 6.18Figure 6.19Table 6.1
Summary7.1 Introduction7.2 Data on the sinking of the Titanic7.3 What is a mosaicplot?7.4 Different mosaicplots for different questions of interest7.5 Which mosaicplot is the right one?7.6 Additional options7.7 Modelling and testing for multivariate categorical dataMain pointsExercisesFigure 7.1Figure 7.2Figure 7.3Figure 7.4Figure 7.5Figure 7.6Figure 7.7Figure 7.8Figure 7.9Figure 7.10Figure 7.11Figure 7.12
Summary8.1 Introduction8.2 Many individual displays8.3 Multivariate overviewsScatterplot matricesParallel coordinate plotsHeatmapsGlyphs8.4 Multivariate overviews for categorical variables8.5 Graphics by groupTrellis graphicsGroup plots8.6 Modelling and testing for overviewsMain pointsExercisesFigure 8.1Figure 8.2Figure 8.3Figure 8.4Figure 8.5Figure 8.6Figure 8.7Figure 8.8Figure 8.9Figure 8.10Figure 8.11Figure 8.12Figure 8.13

Summary9.1 Introduction9.2 Missing valuesVisualising patterns of missing valuesMissings dependent on values of other variables (MAR)Reasons for missings and dealing with missings9.3 OutliersWhat is an outlier?Examples of outliersUnivariate outliersMultivariate outliersCategorical outliersDealing with outliersA possible strategy for outliers9.4 Modelling and testing for data qualityMain pointsExercisesFigure 9.1Figure 9.2Figure 9.3Figure 9.4Figure 9.5Figure 9.6Figure 9.7Figure 9.8Figure 9.9Figure 9.10Figure 9.11Figure 9.12
Summary10.1 Introduction10.2 Making comparisonsTypes of comparisonComparing like with like10.3 Making visual comparisonsComparing to a standardComparing new data with old dataComparing subgroupsComparing time series (Playfair's import/export data)10.4 Comparing group effects graphically10.5 Comparing rates visually10.6 Graphics for comparing many subsets10.7 Graphics principles for comparisons10.8 Modelling and testing for comparisonsMain pointsExercisesFigure 10.1Figure 10.2Figure 10.3Figure 10.4Figure 10.5Figure 10.6Figure 10.7Figure 10.8Figure 10.9Figure 10.10Figure 10.11Figure 10.12Figure 10.13Figure 10.14
Summary11.1 Introduction11.2 Graphics for a single time series11.3 Multiple seriesRelated series for the same populationSame series for different subgroupsSeries with different scalesOne plot versus many11.4 Special features of time seriesData definitionsLength of time seriesRegular and irregular time seriesTime series of different kinds of variablesForecastingSeeing patterns11.5 Alternative graphics for time series11.6 R classes and packages for time series11.7 Modelling and testing time seriesMain pointsExercisesFigure 11.1Figure 11.2Figure 11.3Figure 11.4Figure 11.5Figure 11.6Figure 11.7Figure 11.8Figure 11.9Figure 11.10
Summary12.1 Introduction12.2 What is an ensemble of graphics?12.3 Combining different views—a case study example12.4 Case studiesMoral statistics of FranceAirbags and car accidentsAthletes’ blood measurementsMarijuana arrestsCrohn’s diseaseFootballers in the four major European leaguesDecathlonIntermissionFigure 12.1Figure 12.2Figure 12.3Figure 12.4Figure 12.5
Summary13.1 Graphics systems in R13.2 Loading datasets and packages for graphical analysis13.3 Graphics conventions in statistics13.4 What is a graphic anyway?13.5 Options for all graphicsWindow size and shapeScalesTextColour and appearance13.6 Some R graphics advice and coding tipsTo get a new graphics windowResizing windowsDefault plotsPoints in scatterplots or in other point plotsPrinting graphicsMultiple windowsDrawing several independent plots in one windowNaming objectsReordering categories for a barchart (and ordering in general)Reshaping datasets and graphicsMissing valuesUsing the code and finding out about function options13.7 Other graphics13.8 Large datasets13.9 Perfecting graphicsFigure 13.1Figure 13.2
14.1 Data analysis and graphics14.2 Key features of GDA14.3 Strengths and weaknesses of GDA14.4 Recommendations for GDA

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References

[Aggarwal, 2013] Aggarwal, C. (2013). Outlier Analysis. Springer, New York.

[Agresti, 2007] Agresti, A. (2007). Categorical Data Analysis. Wiley, New York, 2nd edition.

[Albert, 1994] Albert, J. (1994). Exploring baseball hitting data: What about those breakdown statistics? Journal of the American Statistical Association, 89(427):1066–1074.

[ASA, 2014] ASA (2014). Publications of the American Statistical Association. http://amstat.tandfonline.com. (accessed 18.10.2014).

[Bache and Lichman, 2013] Bache, K. and Lichman, M. (2013). UCI machine learning repository. http://archive.ics.uci.edu/ml. (accessed 12.11.2014).

[Barnett, 1994] Barnett, A. (1994). How numbers can trick you. Technology Review, 97(7):38–45.

[Barnett and Lewis, 1994] ...

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Publisher Resources

ISBN: 9781498786775