Contents
List of Tables xv
List of Figures xvii
Preface xxiii
I Introduction to Data Science 1
1 Prologue: Why data science? 3
1.1 What is data science? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Case study: The evolution of sabermetrics . . . . . . . . . . . . . . . . . . 6
1.3 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4 Further resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2 Data visualization 9
2.1 The 2012 federal election cycle . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.1 Are these two groups different? .......................................................................10
2.1.2 Graphing variation ....................................................................................... 11
2.1.3 Examining relationships among variables ................................................... 12
2.1.4 Networks .............................................................................................................13
2.2 Composing data graphics ........................................................................................ 14
2.2.1 A taxonomy for data graphics ................................................................ 14
2.2.2 Color ............................................................................................................. 19
2.2.3 Dissecting data graphics .............................................................................. 20
2.3 Importance of data graphics: Challenger .............................................................. 23
2.4 Creating effective presentations............................................................................. 27
2.5 The wider world of data visualization .................................................................... 28
2.6 Further resources ........................................................................................................... 30
2.7 Exercises .................................................................................................................. 30
3 A grammar for graphics 33
3.1 A grammar for data graphics ............................................................................ 33
3.1.1 Aesthetics ..................................................................................................... 34
3.1.2 Scale ............................................................................................................. 37
3.1.3 Guides .......................................................................................................... 38
3.1.4 Facets ........................................................................................................... 38
3.1.5 Layers ........................................................................................................... 38
3.2 Canonical data graphics in R .................................................................................. 39
3.2.1 Univariate displays ....................................................................................... 39