Data Visualization in R and Python - Marco Cremonini

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Résumé :

Preface xiii

Introduction xv

About the Companion Website xxiii

Part I Static Graphics with ggplot (R) and Seaborn (Python) 1

1 Scatterplots and Line Plots 3

1.1 R: ggplot 4

1.1.1 Scatterplot 4

1.1.2 Repulsive Textual Annotations: Package ggrepel 13

1.1.3 Scatterplots with High Number of Data Points 15

1.1.4 Line Plot 17

1.2 Python: Seaborn 19

1.2.1 Scatterplot 21

1.2.2 Line Plot 25

2 Bar Plots 29

2.1 R: ggplot 29

2.1.1 Bar Plot and Continuous Variables: Ranges of Values 33

2.2 Python: Seaborn 34

2.2.1 Bar Plot with Three Variables 35

2.2.2 Ranges of Values from a Continuous Variable 37

2.2.3 Visualizing Subplots 39

3 Facets 43

3.1 R: ggplot 44

3.1.1 Case 1: Temperature 44

3.1.2 Case 2: Air Quality 45

3.2 Python: Seaborn 49

3.2.1 Facet for Scatterplots and Line Plot 50

3.2.2 Line Plot 50

3.2.3 Facet and Graphics for Categorical Variables 51

3.2.4 Facet and Bar Plots 51

3.2.5 Facets: General Method 54

4 Histograms and Kernel Density Plots 59

4.1 R: ggplot 59

4.1.1 Univariate Analysis 60

4.1.2 Bivariate Analysis 63

4.1.3 Kernel Density Plots 67

4.2 Python: Seaborn 71

4.2.1 Univariate Analysis 71

4.2.2 Bivariate Analysis 73

4.2.3 Logarithmic Scale 75

5 Diverging Bar Plots and Lollipop Plots 83

5.1 R: ggplot 83

5.1.1 Diverging Bar Plot 83

5.1.2 Lollipop Plot 89

5.2 Python: Seaborn 91

5.2.1 Diverging Bar Plot 91

6 Boxplots 99

6.1 R: ggplot 100

6.2 Python: Seaborn 105

7 Violin Plots 109

7.1 R: ggplot 110

7.1.1 Violin Plot and Scatterplot 113

7.1.2 Violin Plot and Boxplot 114

7.2 Python: Seaborn 117

8 Overplotting, Jitter, and Sina Plots 121

8.1 Overplotting 121

8.2 R: ggplot 122

8.2.1 Categorical Scatterplot 122

8.2.2 Violin Plot and Scatterplot with Jitter 123

8.2.3 Sina Plot 126

8.2.4 Beeswarm Plot 129

8.2.5 Comparison Between Jittering, Sina plot, and Beeswarm plot 131

8.3 Python: Seaborn 131

8.3.1 Strip Plot and Swarm Plot 131

8.3.2 Sina Plot 134

9 Half-Violin Plots 137

9.1 R: ggplot 138

9.1.1 Custom Function 138

9.1.2 Raincloud Plot 141

9.2 Python: Seaborn 144

10 Ridgeline Plots 147

10.1 History of the Ridgeline 147

10.2 R: ggplot 148

11 Heatmaps 157

11.1 R: ggplot 157

11.2 Python: Seaborn 160

12 Marginals and Plots Alignment 165

12.1 R: ggplot 165

12.1.1 Marginal 165

12.1.2 Plots Alignment 166

12.1.3 Rug Plot 168

12.2 Python: Seaborn 170

12.2.1 Subplots 170

12.2.2 Marginals: Joint Plot 173

12.2.3 Marginals: Joint Grid 173

13 Correlation Graphics and Cluster Maps 177

13.1 R: ggplot 178

13.1.1 Cluster Map 178

13.2 Python: Seaborn 182

13.2.1 Cluster Map 182

13.3 R: ggplot 184

13.3.1 Correlation Matrix 184

13.4 Python: Seaborn 184

13.4.1 Correlation Matrix 184

13.4.2 Diagonal Correlation Heatmap 186

13.4.3 Scatterplot Heatmap 188

Part II Interactive Graphics with Altair 193

14 Altair Interactive Plots 195

14.1 Scatterplots 196

14.1.1 Static Graphics 197

14.1.1.1 JSON Format: Data Organization 200

14.1.1.2 Plot Alignment and Variable Types 201

14.1.2 Facets 202

14.1.3 Interactive Graphics 205

14.1.3.1 Dynamic Tooltip...

Biographie:
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Sommaire:

Marco Cremonini, PhD, is Assistant Professor in the Department of Social and Political Science at the University of Milan, Italy. He is a member of the BehaveLab, the Centre for Behavioral Sociology Research, at the University of Milan, and in addition to his participation in several European Union projects, he has also held researcher and visiting positions at elite US universities. Dr. Cremonini has authored two books with Wiley, Data Science Fundamentals with R, Python, and Open Data 1st Edition....