{"product_id":"data-visualization-in-r-and-python-isbn-9781394289486","title":"Data Visualization in R and Python","description":"\u003cp\u003e\u003cb\u003eCommunicate the data that is powering our changing world with this essential text\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eData Visualization in R and Python\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eData Visualization in R and Python\u003c\/i\u003e readers will also find: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eCoverage suitable for anyone with a foundational knowledge of R and Python\u003c\/li\u003e\n\u003cli\u003eDetailed treatment of tools including the Ggplot2, Seaborn, and Altair libraries, Plotly\/Dash, Shiny, and others\u003c\/li\u003e\n\u003cli\u003eCase 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\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eData Visualization in R and Python\u003c\/i\u003e is ideal for any student or professional looking to understand the working principles of this key field. \u003c\/p\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003eIntroduction xv\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Static Graphics with ggplot (R) and Seaborn (Python) 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Scatterplots and Line Plots 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 R: ggplot 4\u003c\/p\u003e \u003cp\u003e1.1.1 Scatterplot 4\u003c\/p\u003e \u003cp\u003e1.1.2 Repulsive Textual Annotations: Package ggrepel 13\u003c\/p\u003e \u003cp\u003e1.1.3 Scatterplots with High Number of Data Points 15\u003c\/p\u003e \u003cp\u003e1.1.4 Line Plot 17\u003c\/p\u003e \u003cp\u003e1.2 Python: Seaborn 19\u003c\/p\u003e \u003cp\u003e1.2.1 Scatterplot 21\u003c\/p\u003e \u003cp\u003e1.2.2 Line Plot 25\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Bar Plots 29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 R: ggplot 29\u003c\/p\u003e \u003cp\u003e2.1.1 Bar Plot and Continuous Variables: Ranges of Values 33\u003c\/p\u003e \u003cp\u003e2.2 Python: Seaborn 34\u003c\/p\u003e \u003cp\u003e2.2.1 Bar Plot with Three Variables 35\u003c\/p\u003e \u003cp\u003e2.2.2 Ranges of Values from a Continuous Variable 37\u003c\/p\u003e \u003cp\u003e2.2.3 Visualizing Subplots 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Facets 43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 R: ggplot 44\u003c\/p\u003e \u003cp\u003e3.1.1 Case 1: Temperature 44\u003c\/p\u003e \u003cp\u003e3.1.2 Case 2: Air Quality 45\u003c\/p\u003e \u003cp\u003e3.2 Python: Seaborn 49\u003c\/p\u003e \u003cp\u003e3.2.1 Facet for Scatterplots and Line Plot 50\u003c\/p\u003e \u003cp\u003e3.2.2 Line Plot 50\u003c\/p\u003e \u003cp\u003e3.2.3 Facet and Graphics for Categorical Variables 51\u003c\/p\u003e \u003cp\u003e3.2.4 Facet and Bar Plots 51\u003c\/p\u003e \u003cp\u003e3.2.5 Facets: General Method 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Histograms and Kernel Density Plots 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 R: ggplot 59\u003c\/p\u003e \u003cp\u003e4.1.1 Univariate Analysis 60\u003c\/p\u003e \u003cp\u003e4.1.2 Bivariate Analysis 63\u003c\/p\u003e \u003cp\u003e4.1.3 Kernel Density Plots 67\u003c\/p\u003e \u003cp\u003e4.2 Python: Seaborn 71\u003c\/p\u003e \u003cp\u003e4.2.1 Univariate Analysis 71\u003c\/p\u003e \u003cp\u003e4.2.2 Bivariate Analysis 73\u003c\/p\u003e \u003cp\u003e4.2.3 Logarithmic Scale 75\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Diverging Bar Plots and Lollipop Plots 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 R: ggplot 83\u003c\/p\u003e \u003cp\u003e5.1.1 Diverging Bar Plot 83\u003c\/p\u003e \u003cp\u003e5.1.2 Lollipop Plot 89\u003c\/p\u003e \u003cp\u003e5.2 Python: Seaborn 91\u003c\/p\u003e \u003cp\u003e5.2.1 Diverging Bar Plot 91\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Boxplots 99\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 R: ggplot 100\u003c\/p\u003e \u003cp\u003e6.2 Python: Seaborn 105\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Violin Plots 109\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 R: ggplot 110\u003c\/p\u003e \u003cp\u003e7.1.1 Violin Plot and Scatterplot 113\u003c\/p\u003e \u003cp\u003e7.1.2 Violin Plot and Boxplot 114\u003c\/p\u003e \u003cp\u003e7.2 Python: Seaborn 117\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Overplotting, Jitter, and Sina Plots 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Overplotting 121\u003c\/p\u003e \u003cp\u003e8.2 R: ggplot 122\u003c\/p\u003e \u003cp\u003e8.2.1 Categorical Scatterplot 122\u003c\/p\u003e \u003cp\u003e8.2.2 Violin Plot and Scatterplot with Jitter 123\u003c\/p\u003e \u003cp\u003e8.2.3 Sina Plot 126\u003c\/p\u003e \u003cp\u003e8.2.4 Beeswarm Plot 129\u003c\/p\u003e \u003cp\u003e8.2.5 Comparison Between Jittering, Sina plot, and Beeswarm plot 131\u003c\/p\u003e \u003cp\u003e8.3 Python: Seaborn 131\u003c\/p\u003e \u003cp\u003e8.3.1 Strip Plot and Swarm Plot 131\u003c\/p\u003e \u003cp\u003e8.3.2 Sina Plot 134\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Half-Violin Plots 137\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 R: ggplot 138\u003c\/p\u003e \u003cp\u003e9.1.1 Custom Function 138\u003c\/p\u003e \u003cp\u003e9.1.2 Raincloud Plot 141\u003c\/p\u003e \u003cp\u003e9.2 Python: Seaborn 144\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Ridgeline Plots 147\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 History of the Ridgeline 147\u003c\/p\u003e \u003cp\u003e10.2 R: ggplot 148\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Heatmaps 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 R: ggplot 157\u003c\/p\u003e \u003cp\u003e11.2 Python: Seaborn 160\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Marginals and Plots Alignment 165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 R: ggplot 165\u003c\/p\u003e \u003cp\u003e12.1.1 Marginal 165\u003c\/p\u003e \u003cp\u003e12.1.2 Plots Alignment 166\u003c\/p\u003e \u003cp\u003e12.1.3 Rug Plot 168\u003c\/p\u003e \u003cp\u003e12.2 Python: Seaborn 170\u003c\/p\u003e \u003cp\u003e12.2.1 Subplots 170\u003c\/p\u003e \u003cp\u003e12.2.2 Marginals: Joint Plot 173\u003c\/p\u003e \u003cp\u003e12.2.3 Marginals: Joint Grid 173\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Correlation Graphics and Cluster Maps 177\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 R: ggplot 178\u003c\/p\u003e \u003cp\u003e13.1.1 Cluster Map 178\u003c\/p\u003e \u003cp\u003e13.2 Python: Seaborn 182\u003c\/p\u003e \u003cp\u003e13.2.1 Cluster Map 182\u003c\/p\u003e \u003cp\u003e13.3 R: ggplot 184\u003c\/p\u003e \u003cp\u003e13.3.1 Correlation Matrix 184\u003c\/p\u003e \u003cp\u003e13.4 Python: Seaborn 184\u003c\/p\u003e \u003cp\u003e13.4.1 Correlation Matrix 184\u003c\/p\u003e \u003cp\u003e13.4.2 Diagonal Correlation Heatmap 186\u003c\/p\u003e \u003cp\u003e13.4.3 Scatterplot Heatmap 188\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Interactive Graphics with Altair 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Altair Interactive Plots 195\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Scatterplots 196\u003c\/p\u003e \u003cp\u003e14.1.1 Static Graphics 197\u003c\/p\u003e \u003cp\u003e14.1.1.1 JSON Format: Data Organization 200\u003c\/p\u003e \u003cp\u003e14.1.1.2 Plot Alignment and Variable Types 201\u003c\/p\u003e \u003cp\u003e14.1.2 Facets 202\u003c\/p\u003e \u003cp\u003e14.1.3 Interactive Graphics 205\u003c\/p\u003e \u003cp\u003e14.1.3.1 Dynamic Tooltips 205\u003c\/p\u003e \u003cp\u003e14.1.3.2 Interactive Legend 207\u003c\/p\u003e \u003cp\u003e14.1.3.3 Dynamic Zoom 208\u003c\/p\u003e \u003cp\u003e14.1.3.4 Mouse Hovering and Contextual Change of Color 210\u003c\/p\u003e \u003cp\u003e14.1.3.5 Drop-Down Menu and Radio Buttons 212\u003c\/p\u003e \u003cp\u003e14.1.3.6 Selection with Brush 214\u003c\/p\u003e \u003cp\u003e14.1.3.7 Graphics as Legends 220\u003c\/p\u003e \u003cp\u003e14.2 Line Plots 225\u003c\/p\u003e \u003cp\u003e14.2.1 Static Graphics 225\u003c\/p\u003e \u003cp\u003e14.2.2 Interactive Graphics 228\u003c\/p\u003e \u003cp\u003e14.2.2.1 Highlighted Lines with Mouse Hover 228\u003c\/p\u003e \u003cp\u003e14.2.2.2 Aligned Tooltips 231\u003c\/p\u003e \u003cp\u003e14.3 Bar Plots 235\u003c\/p\u003e \u003cp\u003e14.3.1 Static Graphics 235\u003c\/p\u003e \u003cp\u003e14.3.1.1 Diverging Bar Plot 239\u003c\/p\u003e \u003cp\u003e14.3.1.2 Plots with Double Scale 240\u003c\/p\u003e \u003cp\u003e14.3.1.3 Stacked Bar Plots 244\u003c\/p\u003e \u003cp\u003e14.3.1.4 Sorted Bars 246\u003c\/p\u003e \u003cp\u003e14.3.2 Interactive Graphics 247\u003c\/p\u003e \u003cp\u003e14.3.2.1 Synchronized Bar Plots 247\u003c\/p\u003e \u003cp\u003e14.3.2.2 Bar Plot with Slider 251\u003c\/p\u003e \u003cp\u003e14.4 Bubble Plots 257\u003c\/p\u003e \u003cp\u003e14.4.1 Interactive Graphics 257\u003c\/p\u003e \u003cp\u003e14.4.1.1 Bubble Plot with Slider 257\u003c\/p\u003e \u003cp\u003e14.5 Heatmaps and Histograms 260\u003c\/p\u003e \u003cp\u003e14.5.1 Interactive Graphics 260\u003c\/p\u003e \u003cp\u003e14.5.1.1 Heatmaps 260\u003c\/p\u003e \u003cp\u003e14.5.1.2 Histograms 262\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Web Dashboards 267\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Shiny Dashboards 271\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 General Organization 271\u003c\/p\u003e \u003cp\u003e15.2 Second Version: Graphics and Style Options 280\u003c\/p\u003e \u003cp\u003e15.3 Third Version: Tabs, Widgets, and Advanced Themes 286\u003c\/p\u003e \u003cp\u003e15.4 Observe and Reactive 289\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Advanced Shiny Dashboards 295\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 First Version: Sidebar, Widgets, Customized Themes, and Reactive\/Observe 295\u003c\/p\u003e \u003cp\u003e16.1.1 Button Widget: Observe Context 297\u003c\/p\u003e \u003cp\u003e16.1.2 Button Widget: Mode of Operation 298\u003c\/p\u003e \u003cp\u003e16.1.3 HTML Data Table 301\u003c\/p\u003e \u003cp\u003e16.2 Second Version: Tabs, Shinydashboard, and Web Scraping 303\u003c\/p\u003e \u003cp\u003e16.2.1 Shiny Dashboard 303\u003c\/p\u003e \u003cp\u003e16.2.2 Web Scraping of HTML Tables 308\u003c\/p\u003e \u003cp\u003e16.2.3 Shiny Dashboards and Altair Graphics Integration 315\u003c\/p\u003e \u003cp\u003e16.2.4 Altair and Reticulate: Installation and Configuration 319\u003c\/p\u003e \u003cp\u003e16.2.5 Simple Dashboard for Testing Shiny-Altair Integration 320\u003c\/p\u003e \u003cp\u003e16.3 Third Version: Altair Graphics 321\u003c\/p\u003e \u003cp\u003e16.3.1 Cleveland Plot and Other Graphics 325\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Plotly Graphics 329\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Plotly Graphics 329\u003c\/p\u003e \u003cp\u003e17.1.1 Scatterplot 331\u003c\/p\u003e \u003cp\u003e17.1.2 Line Plot 334\u003c\/p\u003e \u003cp\u003e17.1.3 Marginals 334\u003c\/p\u003e \u003cp\u003e17.1.4 Facets 334\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Dash Dashboards 339\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Preliminary Operations: Import and Data Wrangling 340\u003c\/p\u003e \u003cp\u003e18.1.1 Import of Modules and Submodules 340\u003c\/p\u003e \u003cp\u003e18.1.2 Data Import and Data-Wrangling Operations 341\u003c\/p\u003e \u003cp\u003e18.2 First Dash Dashboard: Base Elements and Layout Organization 341\u003c\/p\u003e \u003cp\u003e18.2.1 Plotly Graphic 341\u003c\/p\u003e \u003cp\u003e18.2.2 Themes and Widgets 342\u003c\/p\u003e \u003cp\u003e18.2.3 Reactive Events and Callbacks 344\u003c\/p\u003e \u003cp\u003e18.2.4 Data Table 345\u003c\/p\u003e \u003cp\u003e18.2.5 Color Palette Selector and Data Table Layout Organization 348\u003c\/p\u003e \u003cp\u003e18.3 Second Dash Dashboard: Sidebar, Widgets, Themes, and Style Options 355\u003c\/p\u003e \u003cp\u003e18.3.1 Sidebar, Multiple Selection, and Checkbox 355\u003c\/p\u003e \u003cp\u003e18.3.2 Dark Themes 360\u003c\/p\u003e \u003cp\u003e18.3.3 Radio Buttons 361\u003c\/p\u003e \u003cp\u003e18.3.4 Bar Plot 363\u003c\/p\u003e \u003cp\u003e18.3.5 Container 364\u003c\/p\u003e \u003cp\u003e18.4 Third Dash Dashboard: Tabs and Web Scraping of HTML Tables 365\u003c\/p\u003e \u003cp\u003e18.4.1 Multi-page Organization: Tabs 365\u003c\/p\u003e \u003cp\u003e18.4.2 Web Scraping of HTML Tables 370\u003c\/p\u003e \u003cp\u003e18.4.3 Second Tab’s Layout 371\u003c\/p\u003e \u003cp\u003e18.4.4 Second Tab’s Reactive Events 372\u003c\/p\u003e \u003cp\u003e18.5 Fourth Dash Dashboard: Light Theme, Custom CSS Style Sheet, and Interactive Altair Graphics 377\u003c\/p\u003e \u003cp\u003e18.5.1 Light Theme and External CSS Style Sheet 377\u003c\/p\u003e \u003cp\u003e18.5.2 Altair Graphics 379\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart IV Spatial Data and Geographic Maps 389\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Geographic Maps with R 391\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 Spatial Data 392\u003c\/p\u003e \u003cp\u003e19.2 Choropleth Maps 397\u003c\/p\u003e \u003cp\u003e19.2.1 Eurostat – GISCO: giscoR 400\u003c\/p\u003e \u003cp\u003e19.3 Multiple and Annotated Maps 404\u003c\/p\u003e \u003cp\u003e19.3.1 From ggplot to Plotly Graphics 408\u003c\/p\u003e \u003cp\u003e19.4 Spatial Data (sp) and Simple Features (sf) 408\u003c\/p\u003e \u003cp\u003e19.4.1 Natural Earth 408\u003c\/p\u003e \u003cp\u003e19.4.2 Format sp and sf: Centroid and Polygons 410\u003c\/p\u003e \u003cp\u003e19.4.3 Differences Between Format sp and Format sf 411\u003c\/p\u003e \u003cp\u003e19.5 Overlaid Graphical Layers 413\u003c\/p\u003e \u003cp\u003e19.6 Shape Files and GeoJSON Datasets 419\u003c\/p\u003e \u003cp\u003e19.7 Venice: Open Data Cartography and Other Maps 420\u003c\/p\u003e \u003cp\u003e19.7.1 Tiled Web Maps 430\u003c\/p\u003e \u003cp\u003e19.7.1.1 Package ggmap 430\u003c\/p\u003e \u003cp\u003e19.7.1.2 Package Leaflet 431\u003c\/p\u003e \u003cp\u003e19.7.2 Tiled Web Maps and Layers of sf Objects 433\u003c\/p\u003e \u003cp\u003e19.7.2.1 Tiled Web Maps with ggmap 435\u003c\/p\u003e \u003cp\u003e19.7.2.2 Tiled Web Map with Leaflet 440\u003c\/p\u003e \u003cp\u003e19.7.3 Maps with Markers and Annotations 445\u003c\/p\u003e \u003cp\u003e19.8 Thematic Maps with tmap 448\u003c\/p\u003e \u003cp\u003e19.8.1 Static and Interactive Visualizations 451\u003c\/p\u003e \u003cp\u003e19.8.2 Cartographic Layers: Rome’s Archaeological Sites 457\u003c\/p\u003e \u003cp\u003e19.9 Rome’s Accommodations: Intersecting Geometries with Simple Features and tmap 460\u003c\/p\u003e \u003cp\u003e19.9.1 Centroids and Active Geometry 462\u003c\/p\u003e \u003cp\u003e19.9.2 Quantiles and Custom Legend 466\u003c\/p\u003e \u003cp\u003e19.9.3 Variants with Points and Popups 473\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Geographic Maps with Python 481\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 New York City: Plotly 481\u003c\/p\u003e \u003cp\u003e20.1.1 Choropleth Maps: plotly.express 484\u003c\/p\u003e \u003cp\u003e20.1.1.1 Dynamic Tooltips 485\u003c\/p\u003e \u003cp\u003e20.1.1.2 Mapbox 487\u003c\/p\u003e \u003cp\u003e20.1.2 Choropleth Maps: plotly.graph_objects (plotly go) 489\u003c\/p\u003e \u003cp\u003e20.1.3 GeoJSON Polygon, Multipolygon, and Missing id Element 490\u003c\/p\u003e \u003cp\u003e20.2 Overlaid Layers 491\u003c\/p\u003e \u003cp\u003e20.3 Geopandas: Base Map, Data Frame, and Overlaid Layers 495\u003c\/p\u003e \u003cp\u003e20.3.1 Extended Dynamic Tooltips 496\u003c\/p\u003e \u003cp\u003e20.3.2 Overlaid Layers: Dog Breeds, Dog Runs, and Parks Drinking Fountains 500\u003c\/p\u003e \u003cp\u003e20.4 Folium 507\u003c\/p\u003e \u003cp\u003e20.4.1 Base Maps, Markers, and Circles 508\u003c\/p\u003e \u003cp\u003e20.4.2 Advanced Tooltips and Popups 511\u003c\/p\u003e \u003cp\u003e20.4.3 Overlaid Layers and GeoJSON Datasets 514\u003c\/p\u003e \u003cp\u003e20.4.4 Choropleth Maps 515\u003c\/p\u003e \u003cp\u003e20.4.5 Geopandas 518\u003c\/p\u003e \u003cp\u003e20.4.6 Folium Heatmap 520\u003c\/p\u003e \u003cp\u003e20.5 Altair: Choropleth Map 522\u003c\/p\u003e \u003cp\u003e20.5.1 GeoJSON Maps 523\u003c\/p\u003e \u003cp\u003e20.5.2 Geopandas: NYC Subway Stations and Demographic Data 523\u003c\/p\u003e \u003cp\u003eIndex 529\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eMarco Cremonini, PhD,\u003c\/b\u003e 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, \u003ci\u003eData Science Fundamentals with R, Python,\u003c\/i\u003e and \u003ci\u003eOpen Data 1st Edition\u003c\/i\u003e.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eCommunicate the data that is powering our changing world with this essential text\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eData Visualization in R and Python\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eData Visualization in R and Python\u003c\/i\u003e readers will also find: \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eCoverage suitable for anyone with a foundational knowledge of R and Python\u003c\/li\u003e\n\u003cli\u003eDetailed treatment of tools including the Ggplot2, Seaborn, and Altair libraries, Plotly\/Dash, Shiny, and others\u003c\/li\u003e\n\u003cli\u003eCase 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\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eData Visualization in R and Python\u003c\/i\u003e is ideal for any student or professional looking to understand the working principles of this key field.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989026488549,"sku":"NP9781394289486","price":145.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394289486.jpg?v=1761782493","url":"https:\/\/k12savings.com\/es\/products\/data-visualization-in-r-and-python-isbn-9781394289486","provider":"K12savings","version":"1.0","type":"link"}