Data Visualization in R and Python

Communicate the data that is powering our changing world with this essential text The advent of machine learning and neural networks in recent years, along with other technologies under the broader umbrella of ‘artificial intelligence,’ has produced an explosion in Data Science research and applications. Data Visualization, which combines the technical knowledge of how to work with data and the visual and communication skills required to present it, is an integral part of this subject. The expansion of Data Science is already leading to greater demand for new approaches to Data Visualization, a process that promises only to grow. Data Visualization in R and Python offers a thorough overview of the key dimensions of this subject. Beginning with the fundamentals of data visualization with Python and R, two key environments for data science, the book proceeds to lay out a range of tools for data visualization and their applications in web dashboards, data science environments, graphics, maps, and more. With an eye towards remarkable recent progress in open-source systems and tools, this book offers a cutting-edge introduction to this rapidly growing area of research and technological development. Data Visualization in R and Python readers will also find: Coverage suitable for anyone with a foundational knowledge of R and Python Detailed treatment of tools including the Ggplot2, Seaborn, and Altair libraries, Plotly/Dash, Shiny, and others Case studies accompanying each chapter, with full explanations for data operations and logic for each, based on Open Data from many different sources and of different formats Data Visualization in R and Python is ideal for any student or professional looking to understand the working principles of this key field.

Table of Contents:
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 Tooltips 205 14.1.3.2 Interactive Legend 207 14.1.3.3 Dynamic Zoom 208 14.1.3.4 Mouse Hovering and Contextual Change of Color 210 14.1.3.5 Drop-Down Menu and Radio Buttons 212 14.1.3.6 Selection with Brush 214 14.1.3.7 Graphics as Legends 220 14.2 Line Plots 225 14.2.1 Static Graphics 225 14.2.2 Interactive Graphics 228 14.2.2.1 Highlighted Lines with Mouse Hover 228 14.2.2.2 Aligned Tooltips 231 14.3 Bar Plots 235 14.3.1 Static Graphics 235 14.3.1.1 Diverging Bar Plot 239 14.3.1.2 Plots with Double Scale 240 14.3.1.3 Stacked Bar Plots 244 14.3.1.4 Sorted Bars 246 14.3.2 Interactive Graphics 247 14.3.2.1 Synchronized Bar Plots 247 14.3.2.2 Bar Plot with Slider 251 14.4 Bubble Plots 257 14.4.1 Interactive Graphics 257 14.4.1.1 Bubble Plot with Slider 257 14.5 Heatmaps and Histograms 260 14.5.1 Interactive Graphics 260 14.5.1.1 Heatmaps 260 14.5.1.2 Histograms 262 Part III Web Dashboards 267 15 Shiny Dashboards 271 15.1 General Organization 271 15.2 Second Version: Graphics and Style Options 280 15.3 Third Version: Tabs, Widgets, and Advanced Themes 286 15.4 Observe and Reactive 289 16 Advanced Shiny Dashboards 295 16.1 First Version: Sidebar, Widgets, Customized Themes, and Reactive/Observe 295 16.1.1 Button Widget: Observe Context 297 16.1.2 Button Widget: Mode of Operation 298 16.1.3 HTML Data Table 301 16.2 Second Version: Tabs, Shinydashboard, and Web Scraping 303 16.2.1 Shiny Dashboard 303 16.2.2 Web Scraping of HTML Tables 308 16.2.3 Shiny Dashboards and Altair Graphics Integration 315 16.2.4 Altair and Reticulate: Installation and Configuration 319 16.2.5 Simple Dashboard for Testing Shiny-Altair Integration 320 16.3 Third Version: Altair Graphics 321 16.3.1 Cleveland Plot and Other Graphics 325 17 Plotly Graphics 329 17.1 Plotly Graphics 329 17.1.1 Scatterplot 331 17.1.2 Line Plot 334 17.1.3 Marginals 334 17.1.4 Facets 334 18 Dash Dashboards 339 18.1 Preliminary Operations: Import and Data Wrangling 340 18.1.1 Import of Modules and Submodules 340 18.1.2 Data Import and Data-Wrangling Operations 341 18.2 First Dash Dashboard: Base Elements and Layout Organization 341 18.2.1 Plotly Graphic 341 18.2.2 Themes and Widgets 342 18.2.3 Reactive Events and Callbacks 344 18.2.4 Data Table 345 18.2.5 Color Palette Selector and Data Table Layout Organization 348 18.3 Second Dash Dashboard: Sidebar, Widgets, Themes, and Style Options 355 18.3.1 Sidebar, Multiple Selection, and Checkbox 355 18.3.2 Dark Themes 360 18.3.3 Radio Buttons 361 18.3.4 Bar Plot 363 18.3.5 Container 364 18.4 Third Dash Dashboard: Tabs and Web Scraping of HTML Tables 365 18.4.1 Multi-page Organization: Tabs 365 18.4.2 Web Scraping of HTML Tables 370 18.4.3 Second Tab’s Layout 371 18.4.4 Second Tab’s Reactive Events 372 18.5 Fourth Dash Dashboard: Light Theme, Custom CSS Style Sheet, and Interactive Altair Graphics 377 18.5.1 Light Theme and External CSS Style Sheet 377 18.5.2 Altair Graphics 379 Part IV Spatial Data and Geographic Maps 389 19 Geographic Maps with R 391 19.1 Spatial Data 392 19.2 Choropleth Maps 397 19.2.1 Eurostat – GISCO: giscoR 400 19.3 Multiple and Annotated Maps 404 19.3.1 From ggplot to Plotly Graphics 408 19.4 Spatial Data (sp) and Simple Features (sf) 408 19.4.1 Natural Earth 408 19.4.2 Format sp and sf: Centroid and Polygons 410 19.4.3 Differences Between Format sp and Format sf 411 19.5 Overlaid Graphical Layers 413 19.6 Shape Files and GeoJSON Datasets 419 19.7 Venice: Open Data Cartography and Other Maps 420 19.7.1 Tiled Web Maps 430 19.7.1.1 Package ggmap 430 19.7.1.2 Package Leaflet 431 19.7.2 Tiled Web Maps and Layers of sf Objects 433 19.7.2.1 Tiled Web Maps with ggmap 435 19.7.2.2 Tiled Web Map with Leaflet 440 19.7.3 Maps with Markers and Annotations 445 19.8 Thematic Maps with tmap 448 19.8.1 Static and Interactive Visualizations 451 19.8.2 Cartographic Layers: Rome’s Archaeological Sites 457 19.9 Rome’s Accommodations: Intersecting Geometries with Simple Features and tmap 460 19.9.1 Centroids and Active Geometry 462 19.9.2 Quantiles and Custom Legend 466 19.9.3 Variants with Points and Popups 473 20 Geographic Maps with Python 481 20.1 New York City: Plotly 481 20.1.1 Choropleth Maps: plotly.express 484 20.1.1.1 Dynamic Tooltips 485 20.1.1.2 Mapbox 487 20.1.2 Choropleth Maps: plotly.graph_objects (plotly go) 489 20.1.3 GeoJSON Polygon, Multipolygon, and Missing id Element 490 20.2 Overlaid Layers 491 20.3 Geopandas: Base Map, Data Frame, and Overlaid Layers 495 20.3.1 Extended Dynamic Tooltips 496 20.3.2 Overlaid Layers: Dog Breeds, Dog Runs, and Parks Drinking Fountains 500 20.4 Folium 507 20.4.1 Base Maps, Markers, and Circles 508 20.4.2 Advanced Tooltips and Popups 511 20.4.3 Overlaid Layers and GeoJSON Datasets 514 20.4.4 Choropleth Maps 515 20.4.5 Geopandas 518 20.4.6 Folium Heatmap 520 20.5 Altair: Choropleth Map 522 20.5.1 GeoJSON Maps 523 20.5.2 Geopandas: NYC Subway Stations and Demographic Data 523 Index 529