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D3.js

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D3.js vs SVG.js: What are the differences?

Introduction

In this Markdown code, we will discuss the key differences between D3.js and SVG.js, two popular libraries used for creating interactive graphics on the web.

  1. D3.js: D3.js, also known as Data-Driven Documents, is a JavaScript library that provides a set of tools for manipulating data and creating data visualizations. It is widely used for creating complex and dynamic visuals by binding data to the Document Object Model (DOM) in HTML.

  2. SVG.js: SVG.js is a lightweight JavaScript library specifically designed for working with Scalable Vector Graphics (SVG). It simplifies the creation and manipulation of SVG elements in HTML by providing an intuitive API that closely resembles the native SVG syntax.

  3. Approach to Graphics Creation: D3.js follows a declarative approach, where developers define the desired outcome and let the library handle the rendering and animation. On the other hand, SVG.js takes an imperative approach, allowing developers to directly manipulate SVG elements using a simpler and more intuitive API.

  4. Learning Curve: D3.js has a steeper learning curve due to its extensive functionality and more complex syntax. It requires a deeper understanding of JavaScript and SVG to use effectively. In contrast, SVG.js has a shallower learning curve, making it easier to get started with for beginners or developers who prefer a simpler approach.

  5. Community and Ecosystem: D3.js has a larger community and a vast ecosystem of plugins and resources due to its popularity and wide usage. It has been around for a longer time and has been adopted by many data visualization projects. SVG.js, although not as widely adopted, still has an active community and a growing ecosystem of plugins and examples.

  6. Use Cases: D3.js is particularly suited for creating highly customized and interactive data visualizations, such as complex charts, graphs, and maps. It provides fine-grained control over the rendering and behavior of elements. SVG.js, on the other hand, is more suitable for simpler graphics, animations, and interactive effects that primarily rely on SVG elements.

In Summary, D3.js and SVG.js differ in their approach to graphics creation, learning curve, community and ecosystem, and use cases. While D3.js is more complex and powerful, SVG.js offers a simpler and more beginner-friendly experience, while still enabling the creation of dynamic SVG graphics.

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Pros of D3.js
Pros of SVG.js
  • 195
    Beautiful visualizations
  • 103
    Svg
  • 92
    Data-driven
  • 81
    Large set of examples
  • 61
    Data-driven documents
  • 24
    Visualization components
  • 20
    Transitions
  • 18
    Dynamic properties
  • 16
    Plugins
  • 11
    Transformation
  • 7
    Makes data interactive
  • 4
    Open Source
  • 4
    Enter and Exit
  • 4
    Components
  • 3
    Exhaustive
  • 3
    Backed by the new york times
  • 2
    Easy and beautiful
  • 1
    Highly customizable
  • 1
    Awesome Community Support
  • 1
    Simple elegance
  • 1
    Templates, force template
  • 1
    Angular 4
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    Cons of D3.js
    Cons of SVG.js
    • 11
      Beginners cant understand at all
    • 6
      Complex syntax
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      What is D3.js?

      It is a JavaScript library for manipulating documents based on data. Emphasises on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework.

      What is SVG.js?

      The lightweight library for manipulating and animating SVG. It has no dependencies and aims to be as small as possible.

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      Blog Posts

      Sep 8 2017 at 2:54PM

      Eventbrite-0

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      What are some alternatives to D3.js and SVG.js?
      three.js
      It is a cross-browser JavaScript library and Application Programming Interface used to create and display animated 3D computer graphics in a web browser.
      Plotly.js
      It is a standalone Javascript data visualization library, and it also powers the Python and R modules named plotly in those respective ecosystems (referred to as Plotly.py and Plotly.R). It can be used to produce dozens of chart types and visualizations, including statistical charts, 3D graphs, scientific charts, SVG and tile maps, financial charts and more.
      Highcharts
      Highcharts currently supports line, spline, area, areaspline, column, bar, pie, scatter, angular gauges, arearange, areasplinerange, columnrange, bubble, box plot, error bars, funnel, waterfall and polar chart types.
      Python
      Python is a general purpose programming language created by Guido Van Rossum. Python is most praised for its elegant syntax and readable code, if you are just beginning your programming career python suits you best.
      Tableau
      Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
      See all alternatives