Interactive Data Visualization for the Web PDF Download

Author: Scott Murray
Publisher: "O'Reilly Media, Inc."
ISBN: 1449339735
Category : Computers
Languages : en
Pages : 269

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Book Description
Create and publish your own interactive data visualization projects on the Web, even if you have no experience with either web development or data visualization. It’s easy with this hands-on guide. You’ll start with an overview of data visualization concepts and simple web technologies, and then learn how to use D3, a JavaScript library that lets you express data as visual elements in a web page. Interactive Data Visualization for the Web makes these skills available at an introductory level for designers and visual artists without programming experience, journalists interested in the emerging data journalism processes, and others keenly interested in visualization and publicly available data sources. Get a practical introduction to data visualization, accessible for beginners Focus on web-based tools that help you publish your creations quickly to a wide audience Learn about interactivity so you can engage users in exploring your data

Author: Matthew O. Ward
Publisher: CRC Press
ISBN: 1482257386
Category : Computers
Languages : en
Pages : 571

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Book Description
An Updated Guide to the Visualization of Data for Designers, Users, and ResearchersInteractive Data Visualization: Foundations, Techniques, and Applications, Second Edition provides all the theory, details, and tools necessary to build visualizations and systems involving the visualization of data. In color throughout, it explains basic terminology

Author: Scott Murray
Publisher: "O'Reilly Media, Inc."
ISBN: 1491921323
Category : Computers
Languages : en
Pages : 472

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Book Description
Author Scott Murray teaches you the fundamental concepts and methods of D3, a JavaScript library that lets you express data visually in a web browser.

Author: Carson Sievert
Publisher: CRC Press
ISBN: 0429824203
Category : Business & Economics
Languages : en
Pages : 278

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Book Description
The richly illustrated Interactive Web-Based Data Visualization with R, plotly, and shiny focuses on the process of programming interactive web graphics for multidimensional data analysis. It is written for the data analyst who wants to leverage the capabilities of interactive web graphics without having to learn web programming. Through many R code examples, you will learn how to tap the extensive functionality of these tools to enhance the presentation and exploration of data. By mastering these concepts and tools, you will impress your colleagues with your ability to quickly generate more informative, engaging, and reproducible interactive graphics using free and open source software that you can share over email, export to pdf, and more. Key Features: Convert static ggplot2 graphics to an interactive web-based form Link, animate, and arrange multiple plots in standalone HTML from R Embed, modify, and respond to plotly graphics in a shiny app Learn best practices for visualizing continuous, discrete, and multivariate data Learn numerous ways to visualize geo-spatial data This book makes heavy use of plotly for graphical rendering, but you will also learn about other R packages that support different phases of a data science workflow, such as tidyr, dplyr, and tidyverse. Along the way, you will gain insight into best practices for visualization of high-dimensional data, statistical graphics, and graphical perception. The printed book is complemented by an interactive website where readers can view movies demonstrating the examples and interact with graphics.

Author: Robert Grant
Publisher: CRC Press
ISBN: 135178174X
Category : Mathematics
Languages : en
Pages : 217

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Book Description
This is the age of data. There are more innovations and more opportunities for interesting work with data than ever before, but there is also an overwhelming amount of quantitative information being published every day. Data visualisation has become big business, because communication is the difference between success and failure, no matter how clever the analysis may have been. The ability to visualize data is now a skill in demand across business, government, NGOs and academia. Data Visualization: Charts, Maps, and Interactive Graphics gives an overview of a wide range of techniques and challenges, while staying accessible to anyone interested in working with and understanding data. Features: Focusses on concepts and ways of thinking about data rather than algebra or computer code. Features 17 short chapters that can be read in one sitting. Includes chapters on big data, statistical and machine learning models, visual perception, high-dimensional data, and maps and geographic data. Contains more than 125 visualizations, most created by the author. Supported by a website with all code for creating the visualizations, further reading, datasets and practical advice on crafting the images. Whether you are a student considering a career in data science, an analyst who wants to learn more about visualization, or the manager of a team working with data, this book will introduce you to a broad range of data visualization methods. Cover image: Landscape of Change uses data about sea level rise, glacier volume decline, increasing global temperatures, and the increasing use of fossil fuels. These data lines compose a landscape shaped by the changing climate, a world in which we are now living. Copyright © Jill Pelto (jillpelto.com).

Author: Christian Tominski
Publisher: CRC Press
ISBN: 1351648748
Category : Computers
Languages : en
Pages : 318

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Book Description
In the age of big data, being able to make sense of data is an important key to success. Interactive Visual Data Analysis advocates the synthesis of visualization, interaction, and automatic computation to facilitate insight generation and knowledge crystallization from large and complex data. The book provides a systematic and comprehensive overview of visual, interactive, and analytical methods. It introduces criteria for designing interactive visual data analysis solutions, discusses factors influencing the design, and examines the involved processes. The reader is made familiar with the basics of visual encoding and gets to know numerous visualization techniques for multivariate data, temporal data, geo-spatial data, and graph data. A dedicated chapter introduces general concepts for interacting with visualizations and illustrates how modern interaction technology can facilitate the visual data analysis in many ways. Addressing today’s large and complex data, the book covers relevant automatic analytical computations to support the visual data analysis. The book also sheds light on advanced concepts for visualization in multi-display environments, user guidance during the data analysis, and progressive visual data analysis. The authors present a top-down perspective on interactive visual data analysis with a focus on concise and clean terminology. Many real-world examples and rich illustrations make the book accessible to a broad interdisciplinary audience from students, to experts in the field, to practitioners in data-intensive application domains. Features: Dedicated to the synthesis of visual, interactive, and analysis methods Systematic top-down view on visualization, interaction, and automatic analysis Broad coverage of fundamental and advanced visualization techniques Comprehensive chapter on interacting with visual representations Extensive integration of automatic computational methods Accessible portrayal of cutting-edge visual analytics technology Foreword by Jack van Wijk For more information, you can also visit the author website, where the book's figures are made available under the CC BY Open Access license.

Author: Abha Belorkar
Publisher: Packt Publishing Ltd
ISBN: 1800201060
Category : Computers
Languages : en
Pages : 362

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Book Description
Create your own clear and impactful interactive data visualizations with the powerful data visualization libraries of Python Key FeaturesStudy and use Python interactive libraries, such as Bokeh and PlotlyExplore different visualization principles and understand when to use which oneCreate interactive data visualizations with real-world dataBook Description With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python. You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model. By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories. What you will learnExplore and apply different interactive data visualization techniquesManipulate plotting parameters and styles to create appealing plotsCustomize data visualization for different audiencesDesign data visualizations using interactive librariesUse Matplotlib, Seaborn, Altair and Bokeh for drawing appealing plotsCustomize data visualization for different scenariosWho this book is for This book intends to provide a solid training ground for Python developers, data analysts and data scientists to enable them to present critical data insights in a way that best captures the user's attention and imagination. It serves as a simple step-by-step guide that demonstrates the different types and components of visualization, the principles, and techniques of effective interactivity, as well as common pitfalls to avoid when creating interactive data visualizations. Students should have an intermediate level of competency in writing Python code, as well as some familiarity with using libraries such as pandas.

Author: Claus O. Wilke
Publisher: O'Reilly Media
ISBN: 1492031054
Category : Computers
Languages : en
Pages : 390

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Book Description
Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options. This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke teaches you the elements most critical to successful data visualization. Explore the basic concepts of color as a tool to highlight, distinguish, or represent a value Understand the importance of redundant coding to ensure you provide key information in multiple ways Use the book’s visualizations directory, a graphical guide to commonly used types of data visualizations Get extensive examples of good and bad figures Learn how to use figures in a document or report and how employ them effectively to tell a compelling story

Author: Amelia Wattenberger
Publisher: Fullstack.IO
ISBN: 9780991344659
Category : Computers
Languages : en
Pages : 608

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Book Description
Build beautiful data visualizations with D3 The Fullstack D3 book is the complete guide to D3. With dozens of code examples showing each step, you can gain new insights into your data by creating visualizations. Learn how to quickly turn data into insights with D3 We have the data. But it needs to be understood by humans. The best way to convert this data into an understandable format is to mold it into a data visualization. And D3 is the best tool for job if you need to create custom data visualizations. With Fullstack D3 and Data Visualization you and your team will be able to share key insights, uncover problems before they start, and impress your boss by creating gorgeous visualizations. What's Inside Chapter 0: Introduction When would you want to use D3.js? There is a spectrum of libraries to create charts on the web: on one end, you have easy-to-use, basic libraries that will create a standard chart type. Chapter 1: Making your first chart In this chapter we make a line chart. Line charts are a great starting place because of their popularity, but also because of their simplicity. Chapter 2: Making a scatterplot When looking at the relationship between two metrics, a scatterplot is a good choice. In this chapter we show how to create a scatterplot. Chapter 3: Making a bar chart In this chapter we cover how to create a histogram, which is a bar chart that shows the distribution of one metric, with the metric values on the x axis and the frequency of values on the y axis. Chapter 4: Animations and Transitions When we update our charts, we can animate elements from their old to their new positions. These animations can be visually exciting, but more importantly, they have functional benefits. Chapter 5: Interactions The biggest advantage of creating charts with JavaScript is the ability to respond to user input. Chapter 6: Making a map Maps are also uniquely good at answering geography-based questions. In this chapter, we'll build a map and learn how to plot values within a location. Chapter 7: Data Visualization Basics Now that we're comfortable with how to create a chart, we should zoom out a bit and talk about what chart to create. Chapter 8: Common Charts In this chapter, we talk about common chart types and when to use them. Chapter 9: Dashboard Design A dashboard is any web interface that makes sense out of dynamic data, and in this chapter we learn how to make one. Chapter 10: Advanced Visualization: Marginal Histogram First, we'll focus on enhancing a chart we've already made: our scatter plot. This chart will have multiple goals, all exploring the daily temperature ranges in our weather dataset. Chapter 11: Advanced Visualization: Radial Weather Chart We talked about radar charts in Chapter 10. For this project, we'll build a more complex radar chart. Chapter 12: Advanced Visualization: Animated Sankey Diagram In this project, we'll be simulating real data and creating an animated diagram to engage our viewers. Chapter 13: D3 and React What's the best way to draw a chart within React? It turns out that there is a fair bit of overlap in functionality between a React and D3 - we'll discuss how we can create blazing fast charts using the two together. Chapter 14: D3 and Angular In this chapter we show how to create optimized SVG charts using D3 and Angular.

Author: Jack Dougherty
Publisher: O'Reilly Media
ISBN: 9781492086000
Category :
Languages : en
Pages : 275

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Book Description
Tell your story and show it with data, using free and easy-to-learn tools on the web. This introductory book teaches you how to design interactive charts and customized maps for your website, beginning with simple drag-and-drop tools such as Google Sheets, Datawrapper, and Tableau Public. You'll also gradually learn how to edit open source code templates like Chart.js, Highcharts, and Leaflet on GitHub. Hands-On Data Visualization for All takes you step-by-step through tutorials, real-world examples, and online resources. This hands-on resource is ideal for students, nonprofit organizations, small business owners, local governments, journalists, academics, and anyone who wants to take data out of spreadsheets and turn it into lively interactive stories. No coding experience is required. Build interactive charts and maps and embed them in your website Understand the principles for designing effective charts and maps Learn key data visualization concepts to help you choose the right tools Convert and transform tabular and spatial data to tell your data story Edit and host Chart.js, Highcharts, and Leaflet map code templates on GitHub Learn how to detect bias in charts and maps produced by others