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. Cover Title Page Copyright Contents Preface Introduction About the Companion Website Part I Static Graphics with ggplot (R) and Seaborn (Python) Chapter 1 Scatterplots and Line Plots 1.1 R: ggplot 1.1.1 Scatterplot 1.1.2 Repulsive Textual Annotations: Package ggrepel 1.1.3 Scatterplots with High Number of Data Points 1.1.4 Line Plot 1.2 Python: Seaborn 1.2.1 Scatterplot 1.2.2 Line Plot Chapter 2 Bar Plots 2.1 R: ggplot 2.1.1 Bar Plot and Continuous Variables: Ranges of Values 2.2 Python: Seaborn 2.2.1 Bar Plot with Three Variables 2.2.2 Ranges of Values from a Continuous Variable 2.2.3 Visualizing Subplots Chapter 3 Facets 3.1 R: ggplot 3.1.1 Case 1: Temperature 3.1.2 Case 2: Air Quality 3.2 Python: Seaborn 3.2.1 Facet for Scatterplots and Line Plot 3.2.2 Line Plot 3.2.3 Facet and Graphics for Categorical Variables 3.2.4 Facet and Bar Plots 3.2.5 Facets: General Method Chapter 4 Histograms and Kernel Density Plots 4.1 R: ggplot 4.1.1 Univariate Analysis 4.1.2 Bivariate Analysis 4.1.3 Kernel Density Plots 4.2 Python: Seaborn 4.2.1 Univariate Analysis 4.2.2 Bivariate Analysis 4.2.3 Logarithmic Scale Chapter 5 Diverging Bar Plots and Lollipop Plots 5.1 R: ggplot 5.1.1 Diverging Bar Plot 5.1.2 Lollipop Plot 5.2 Python: Seaborn 5.2.1 Diverging Bar Plot Chapter 6 Boxplots 6.1 R: ggplot 6.2 Python: Seaborn Chapter 7 Violin Plots 7.1 R: ggplot 7.1.1 Violin Plot and Scatterplot 7.1.2 Violin Plot and Boxplot 7.2 Python: Seaborn Chapter 8 Overplotting, Jitter, and Sina Plots 8.1 Overplotting 8.2 R: ggplot 8.2.1 Categorical Scatterplot 8.2.2 Violin Plot and Scatterplot with Jitter 8.2.3 Sina Plot 8.2.4 Beeswarm Plot 8.2.5 Comparison Between Jittering, Sina plot, and Beeswarm plot 8.3 Python: Seaborn 8.3.1 Strip Plot and Swarm Plot 8.3.2 Sina Plot Chapter 9 Half‐Violin Plots 9.1 R: ggplot 9.1.1 Custom Function 9.1.2 Raincloud Plot 9.2 Python: Seaborn Chapter 10 Ridgeline Plots 10.1 History of the Ridgeline 10.2 R: ggplot Chapter 11 Heatmaps 11.1 R: ggplot 11.2 Python: Seaborn Chapter 12 Marginals and Plots Alignment 12.1 R: ggplot 12.1.1 Marginal 12.1.2 Plots Alignment 12.1.3 Rug Plot 12.2 Python: Seaborn 12.2.1 Subplots 12.2.2 Marginals: Joint Plot 12.2.3 Marginals: Joint Grid Chapter 13 Correlation Graphics and Cluster Maps 13.1 R: ggplot 13.1.1 Cluster Map 13.2 Python: Seaborn 13.2.1 Cluster Map 13.3 R: ggplot 13.3.1 Correlation Matrix 13.4 Python: Seaborn 13.4.1 Correlation Matrix 13.4.2 Diagonal Correlation Heatmap 13.4.3 Scatterplot Heatmap Part II Interactive Graphics with Altair Chapter 14 Altair Interactive Plots 14.1 Scatterplots 14.1.1 Static Graphics 14.1.1.1 JSON Format: Data Organization 14.1.1.2 Plot Alignment and Variable Types 14.1.2 Facets 14.1.3 Interactive Graphics 14.1.3.1 Dynamic Tooltips 14.1.3.2 Interactive Legend 14.1.3.3 Dynamic Zoom 14.1.3.4 Mouse Hovering and Contextual Change of Color 14.1.3.5 Drop‐Down Menu and Radio Buttons 14.1.3.6 Selection with Brush 14.1.3.7 Graphics as Legends 14.2 Line Plots 14.2.1 Static Graphics 14.2.2 Interactive Graphics 14.2.2.1 Highlighted Lines with Mouse Hover 14.2.2.2 Aligned Tooltips 14.3 Bar Plots 14.3.1 Static Graphics 14.3.1.1 Diverging Bar Plot 14.3.1.2 Plots with Double Scale 14.3.1.3 Stacked Bar Plots 14.3.1.4 Sorted Bars 14.3.2 Interactive Graphics 14.3.2.1 Synchronized Bar Plots 14.3.2.2 Bar Plot with Slider 14.4 Bubble Plots 14.4.1 Interactive Graphics 14.4.1.1 Bubble Plot with Slider 14.5 Heatmaps and Histograms 14.5.1 Interactive Graphics 14.5.1.1 Heatmaps 14.5.1.2 Histograms Part III Web Dashboards Chapter 15 Shiny Dashboards 15.1 General Organization 15.2 Second Version: Graphics and Style Options 15.3 Third Version: Tabs, Widgets, and Advanced Themes 15.4 Observe and Reactive Chapter 16 Advanced Shiny Dashboards 16.1 First Version: Sidebar, Widgets, Customized Themes, and Reactive/Observe 16.1.1 Button Widget: Observe Context 16.1.2 Button Widget: Mode of Operation 16.1.3 HTML Data Table 16.2 Second Version: Tabs, Shinydashboard, and Web Scraping 16.2.1 Shiny Dashboard 16.2.2 Web Scraping of HTML Tables 16.2.3 Shiny Dashboards and Altair Graphics Integration 16.2.4 Altair and Reticulate: Installation and Configuration 16.2.5 Simple Dashboard for Testing Shiny‐Altair Integration 16.3 Third Version: Altair Graphics 16.3.1 Cleveland Plot and Other Graphics Chapter 17 Plotly Graphics 17.1 Plotly Graphics 17.1.1 Scatterplot 17.1.2 Line Plot 17.1.3 Marginals 17.1.4 Facets Chapter 18 Dash Dashboards 18.1 Preliminary Operations: Import and Data Wrangling 18.1.1 Import of Modules and Submodules 18.1.2 Data Import and Data‐Wrangling Operations 18.2 First Dash Dashboard: Base Elements and Layout Organization 18.2.1 Plotly Graphic 18.2.2 Themes and Widgets 18.2.3 Reactive Events and Callbacks 18.2.4 Data Table 18.2.5 Color Palette Selector and Data Table Layout Organization 18.3 Second Dash Dashboard: Sidebar, Widgets, Themes, and Style Options 18.3.1 Sidebar, Multiple Selection, and Checkbox 18.3.2 Dark Themes 18.3.3 Radio Buttons 18.3.4 Bar Plot 18.3.5 Container 18.4 Third Dash Dashboard: Tabs and Web Scraping of HTML Tables 18.4.1 Multi‐page Organization: Tabs 18.4.2 Web Scraping of HTML Tables 18.4.3 Second Tab's Layout 18.4.4 Second Tab's Reactive Events 18.5 Fourth Dash Dashboard: Light Theme, Custom CSS Style Sheet, and Interactive Altair Graphics 18.5.1 Light Theme and External CSS Style Sheet 18.5.2 Altair Graphics Part IV Spatial Data and Geographic Maps Chapter 19 Geographic Maps with R 19.1 Spatial Data 19.2 Choropleth Maps 19.2.1 Eurostat – GISCO: giscoR 19.3 Multiple and Annotated Maps 19.3.1 From ggplot to Plotly Graphics 19.4 Spatial Data (sp) and Simple Features (sf) 19.4.1 Natural Earth 19.4.2 Format sp and sf: Centroid and Polygons 19.4.3 Differences Between Format sp and Format sf 19.5 Overlaid Graphical Layers 19.6 Shape Files and GeoJSON Datasets 19.7 Venice: Open Data Cartography and Other Maps 19.7.1 Tiled Web Maps 19.7.1.1 Package ggmap 19.7.1.2 Package Leaflet 19.7.2 Tiled Web Maps and Layers of sf Objects 19.7.2.1 Tiled Web Maps with ggmap 19.7.2.2 Tiled Web Map with Leaflet 19.7.3 Maps with Markers and Annotations 19.8 Thematic Maps with tmap 19.8.1 Static and Interactive Visualizations 19.8.2 Cartographic Layers: Rome's Archaeological Sites 19.9 Rome's Accommodations: Intersecting Geometries with Simple Features and tmap 19.9.1 Centroids and Active Geometry 19.9.2 Quantiles and Custom Legend 19.9.3 Variants with Points and Popups Chapter 20 Geographic Maps with Python 20.1 New York City: Plotly 20.1.1 Choropleth Maps: plotly.express 20.1.1.1 Dynamic Tooltips 20.1.1.2 Mapbox 20.1.2 Choropleth Maps: plotly.graph&uscore;objects (plotly go) 20.1.3 GeoJSON Polygon, Multipolygon, and Missing id Element 20.2 Overlaid Layers 20.3 Geopandas: Base Map, Data Frame, and Overlaid Layers 20.3.1 Extended Dynamic Tooltips 20.3.2 Overlaid Layers: Dog Breeds, Dog Runs, and Parks Drinking Fountains 20.4 Folium 20.4.1 Base Maps, Markers, and Circles 20.4.2 Advanced Tooltips and Popups 20.4.3 Overlaid Layers and GeoJSON Datasets 20.4.4 Choropleth Maps 20.4.5 Geopandas 20.4.6 Folium Heatmap 20.5 Altair: Choropleth Map 20.5.1 GeoJSON Maps 20.5.2 Geopandas: NYC Subway Stations and Demographic Data Index EULA