Data Visualization in R and Python (Marco Cremonini) (Z-Library)

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Data Visualization in R and Python

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Data Visualization in R and Python Marco Cremonini University of Milan, Italy

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Copyright © 2025 by John Wiley & Sons Inc. All rights reserved, including rights for text and data mining and training of artificial intelligence technologies or similar technologies Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permission. Trademarks: Wiley and the Wiley logo are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates in the United States and other countries and may not be used without written permission. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc. is not associated with any product or vendor mentioned in this book. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic formats. For more information about Wiley products, visit our web site at www.wiley.com. Library of Congress Cataloging-in-Publication Data is applied for: Hardback ISBN 9781394289486 Cover Design: Manuela Ruggeri Cover Images: Courtesy of Marco Cremonini, © ulimi/Getty Images Set in 9.5/12.5pt STIXTwoText by Straive, Chennai, India

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v 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

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vi Contents 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

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Contents vii 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

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viii Contents 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

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Contents ix 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

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x Contents 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

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Contents xi 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

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xiii Preface The idea of this handbook came to me when I noticed something that made me pause and reflect. What I saw was that when I mentioned data visualization to a person who know just a little about it, perhaps adding that it involves representing data and the results of data analysis with figures, sometimes even interactive one, the reaction was often of curiosity with a shade of perplexity, the name sounded nice, but what is it, exactly? After all, if we have a table with data and we want to produce a graph, isn’t it enough to search in a menu, choose the stylized figure of the graph you want to create and click? Is there so much to say to fill an entire book? When I also add that what I was talking about were completely different graphic tools from those of office automation and that, to tell the truth, it doesn’t even stop at the graphics, even if they are interactive, but there are also dashboards, i.e. the latest evolution of data visualization, when real dynamic web applications are created, then the expression of the interlocutor was generally crossed by a shadow of concern. At that moment, I typically threw the ace up the sleeve by saying that in data visualization there are also maps, geographical maps – why not? – those are data too, they are spatial data, geographical data, and the maps are produced with the zoom, the flags, colored areas, and also cartographic maps, you may work with maps of New York, Tokyo, Paris, Rome, New Delhi, you name it. At that point the interlocutors were usually looking puzzled, the references they had from the common experience were lost and doesn’t really know what this data visualization is about, only that there actually seems to be a lot to say, enough to fill an entire book. If anyone recognizes themselves in this interlocutor, be assured that you are in good company. Good in a literal not figurative sense, because data visualization is the Cinderella of data science that many admire but always from a certain distance, it arrives last and at the best moment it is forced to step back because there is no longer enough time to teach, study, or practice it. Yet, it frequently happens that those who, given the right opportunity to study and practice it, sense that it could be decidedly interesting, certainly prove useful and applicable

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xiv Preface in an infinite number of fields. This is due to a property that data visualization has and is instead absent in data analysis or code development: it stimulates visual creativity together with logic. Even statisticians and programmers use creativity, those who deny it have never been neither of them, but that is logical creativity. With data visualization, another dimension of otherwise neglected data science comes into play, the visual language combined with computational logic, meaning that data are represented with an expressive form that is no longer just logical and symbolic, but also perceptive, sensorial, shapes together with colors come into play, the once passive observer starts interacting, or projections of geographical areas suddenly become artifacts to use in a visual communication. Data visualiza- tion conveys different knowledge and logic for an expressive form that always has a double nature: computational for the data that feeds it, visual and sometimes interactive for the language it uses to communicate with the observer. There is enough to fill not a single book, in fact, what is contained in this book is a part of the discourse on data visualization, the one more practical and operative, other publications approach data visualization considering complementary aspects, such as the aesthetical composition of graphics, the storytelling behind a visual communication, and the syntax and semantic of a visual language together with the sensorial perception and psychology, and there is a lot to say for each one of these topics. All of them are essential for a complete understanding of the aim and extent of data visualization, but together they just don’t fit in one single handbook, unless presented in a truly superficial fashion, for this reason almost every book on data visualization focuses more explicitly on a few of those aspects. This book is dedicated to the more operational and computational issues, because you have to know the low-level logic behind modern data visualization artifacts and you have to know and practice with tools, they are not all alike, “just pick the easiest to use and you’re all set” is definitely not a good advice and, given the liveliness of the proprietary data visualization tools’ market, it is easy to forget about open-source ones, which instead rival and often surpass what proprietary tools are able to offer; may be with a little more of initial efforts, but not much. To conclude, data visualization probably deserves better consideration in educa- tional programs and a recognition as a coherent and evolving discipline. It could be a lot of fun to study and practice it, it could make also you pause and reflect about tools for communicating data science results with a visual language, and it includes many different aspects from diverse disciplines, both theoretical and practical, all converging and enmeshing in a coherent body of knowledge. These are all good characteristics for curious persons. The Cinderella role of data visu- alization can be overcome by recognizing its educational and professional value and, no less important, its creative stimulus. Marco Cremonini University of Milan October 8, 2024

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xv Introduction When you mention data visualization to a person who doesn’t know it, perhaps adding that it involves data and the results of data analysis with figures, sometimes even interactive ones, the reaction you observe is often that the person in front of you looks intrigued but doesn’t know exactly what it consists of. After all, if we have a table with data and we want to produce a graph, isn’t it enough to open the usual application, go to a certain drop-down menu, choose the stylized figure of the graph you want to create and click? Is there so much to say to fill an entire book? At that moment, when you perceive that the interlocutor is thinking of the well-known spreadsheet product, you may add that those described in the book are graphic tools completely different from those of office automation and, to tell the truth, we don’t even stop at the graphics, even if interactive, but there are also dashboards, namely the latest evolution of data visualization, when it is transformed into dynamic web applications, and to obtain dashboards it is not sufficient to click on menus but you have to go deeper into the inner logic and mechanisms. It’s then that the expression of the interlocutor is generally crossed by a shadow of concern and you can play the ace up your sleeve by saying that in data visualization there are also maps, geographical maps, sure, those are made by data too: spatial data and geographical data, and the maps can be produced with the many available widgets such as zoom, flags, and colored areas; and we even go beyond simple maps, because there are also cartographic maps with layers of cartographic quality, such as maps of Rome, of Venice, of New York, of the most famous, and also not-so-famous cities and places, possibly with very detailed geographical information. At that point the interlocutor has likely lost the references she or he had from the usual experience with office automation products and doesn’t really know what this data visualization is, only that there seems to be a lot to say, enough to fill an entire book. If anyone recognizes themselves in this imaginary interlocutor (imaginary up to a certain point, to be honest), know that you are in

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xvi Introduction good company. Good in a literal not figurative sense, because data visualization is a little like the Cinderella of data science that many admire from a certain distance, it arrives last in a project and sometimes it does not receive the attention it deserves. Yet there are many who, given the right opportunity to study and practice it, sense that it could be interesting and enjoyable, it could certainly prove useful and applicable in an infinite number of areas, situations, and results. This is due to a property that data visualization has and is instead absent in traditional data analysis or code development: it stimulates visual creativity together with logic. Even statisticians and programmers use creativity, those who deny it have never really practiced one of those disciplines, but that is logical creativity. With data visualization, another dimension of data science that is otherwise neglected comes into play, the visual language combined with computational logic, the data represented with an expressive form that is no longer just logical and formal, but also perceptive, and sensorial, comes into play with shapes, colors, use and projections of space, and it is always accompanied with meaning that the originator wish to convey and the observers will interpret, often subjectively. Data visualization conveys different knowledge and logic for an expressive form that always has a double soul: computational for the data that feeds it, visual and sometimes interactive for the language it uses to communicate with the observer. Data visualization has always a double nature: it is a key part of data science for its methods, techniques, and tools, and it is storytelling; who produces visual representations from data tells a story that may have different guises and may produce different reactions. There is enough to fill not just a single book. Organization of the Work: Foundations and Advanced Contents The text is divided into four parts already mentioned in the previous introduction. The first part presents the fundamentals of data visualization with Python and R, the two reference languages and environments for data science, employed to create static graphs as a direct result of a previous data wrangling (import, transformation) and analysis activity. The reference libraries for this first part are Seaborn for Python and ggplot2 for R. They are both modern open-source graphics libraries and in constant evolution, both produced by the core developers and with the contributions of the respective communities, very large and lively in engaging in continuous innovations. Seaborn is the more recent of the two and partly repre- sents an evolved interface of Python’s traditional matplotlib graphics library, made more functional and enriched with features and graph types popular in modern data visualization. Ggplot2 is the traditional graphic library for R, unanimously recognized as one of the best ever, both in the open-source and proprietary world. Ggplot is full of high-level features and constantly evolving, it receives contri- butions from researchers and developers from various scientific and application

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Introduction xvii fields. A simply unavoidable tool for anyone approaching data visualization. The two have different settings, more traditional Seaborn, with a collection of functions and options for the different types of charts supported. Instead, ggplot is organized by overlapping graphic levels, according to a setting that goes by the name of grammar of graphics, shared by some of the most widespread digital graphics tools, and suitable for developing even unconventional types of graphics, thanks to the extreme flexibility it allows. This first part covers about a third of the work. The second part introduces Altair, a Python library capable of producing inter- active graphics in HTML and JSON format, as well as static versions in bitmap (PNG and JPG) and vector (SVG) formats. Altair is a young but solid graphic library because in all respects it represents a modern interface of Vega-Lite, a graphic library with an established tradition for web applications thanks to the declara- tive syntax in JSON format. Altair offers the same web-oriented functionality as Vega-Lite for typical data science use, with a syntax that supports the definition of overlapping graphical layers and aesthetics composed with a syntax that is easy to use and common to similar tools. This second part presents a higher level of dif- ficulty than the first, but certainly within reach for those who have acquired the fundamental knowledge given by the first part. The first and second parts cover approximately half of the work. The third and fourth parts represent advanced data visualization contents. The difficulty increases and so does the commitment required, on the other hand, we face two real worlds: that of web dashboards and of spatial data and maps. The term dashboard may be new to many, but dashboards are not. Whenever you access environments on the web that show menus and configurable graphic objects according to user’s choices and content in the form of data or graphs, what you are using is most likely a dashboard. If you access Open Data of a large institution, such as the Organisation for Economic Co-operation and Development (OECD) or the United Nations, or even an internal company application that displays graphs and statistics, you are most likely using a dashboard. Numerous systems and prod- ucts for creating dashboards with different technologies are available, it is a vast market. In data science environments with Python and R, there are two formidable tools, Plotly/Dash and Shiny, respectively. They are professional tools, and the list of relevant organizations using them is long. They are also irreplaceable teaching tools for learning the logic and basic mechanisms of a dashboard, which, in its final form, is a web application, therefore integrated with the typical technology of pages and websites. However, a Dash or Shiny dashboard is also something else, it is the terminal point of a pipeline that begins with the fundamentals of data science, data import, data wrangling, data analysis, and then static and dynamic graphs. The dashboard is the final end in which everything is concentrated and integrated: logic, mechanisms, requirements, and creativity. Technically they are challenging due to the presence of reactive logic which allows them to be

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xviii Introduction dynamic and interactive and due to the integration of various components. The text discusses and develops examples of medium complexity, with different solu- tions, from web scraping of online content to the integration of Altair interactive graphics. The second world that opens up, that of geographical maps, is undeniably fascinating. Spatial data, choropleth maps, the simplest ones with the colored areas (such as maps with areas colored according to the coalition that won the elections or the rate of unemployment by province, region, or nation), but also maps based on cartography data are the declination given by data science of a discipline that has very ancient roots and still constitutes an almost independent environment composed of high-resolution maps and geographic information systems (GIS), with its specializations and professional skills. Until a few years ago, data science tools could not even touch that world, but today they have come surprisingly close. This is thanks to extraordinary progress in open-source systems and tools, Python but above all R, which is now offering formidable tools capable of also using shape files from a technical cartography and geographic coordinate systems according to international standards. In the examples presented, geo- graphic and cartographic files from Venice, Rome, and New York were used with the aim of showing the impressive potential offered by the Python and R tools. Who is it Aimed at? It is simple to specify to whom this text is addressed: it is addressed to everyone. Anyone who finds data visualization interesting, and images useful for their work, study, and the skills they are building, will find a learning path that starts from the fundamentals and goes up to cartography and web applications. Of course, saying “it’s aimed at everyone” is simple, then doubts may arise in the reader, “but am I also part of those everyone?” Trying to make a list of those included in this “everyone” will inevitably leave out someone, but we could certainly mention students, researchers, and instructors of social, political, and economic sciences. In addition to many generic data, they may have spatial data to represent (e.g. movement of people and goods, global supply chains, logistics, spatial or ethnographic analyses). Next, students, researchers, and instructors of marketing, communication, public relations, journalism, media, and advertising, for whom interactive representations via the web and graphics in general are important, as products and skills. Also, students, researchers, and instructors of scientific and medical disciplines could be interested, they often deal with sophisticated graphic representations, for example in biology or epidemiology, without forgetting that the graphic contributions from the genomics and molecular biology community are among the most numerous. Students, researchers, and instructors of engi- neering, management, or bioengineering, for example, use data science tools and