d3 vs leaflet vs mapbox-gl vs victory vs plotly.js vs deck.gl vs react-vis
Data Visualization Libraries Comparison
1 Year
d3leafletmapbox-glvictoryplotly.jsdeck.glreact-visSimilar Packages:
What's Data Visualization Libraries?

Data visualization libraries are essential tools in web development that allow developers to create interactive and informative graphical representations of data. These libraries provide various functionalities, from simple charts to complex geographic visualizations, enabling users to gain insights from data through visual means. Each library has its unique strengths, making them suitable for different types of projects and data representation needs.

Package Weekly Downloads Trend
Github Stars Ranking
Stat Detail
Package
Downloads
Stars
Size
Issues
Publish
License
d34,081,059110,611871 kB20a year agoISC
leaflet1,368,90042,7273.74 MB5262 years agoBSD-2-Clause
mapbox-gl1,342,33411,60254.8 MB1,3807 days agoSEE LICENSE IN LICENSE.txt
victory240,38611,1452.28 MB924 months agoMIT
plotly.js223,99717,56697.2 MB7053 months agoMIT
deck.gl131,50512,6984.71 MB433a day agoMIT
react-vis75,1678,7572.18 MB3432 years agoMIT
Feature Comparison: d3 vs leaflet vs mapbox-gl vs victory vs plotly.js vs deck.gl vs react-vis

Customization

  • d3:

    D3.js offers unparalleled customization capabilities, allowing developers to create virtually any type of visualization. It provides low-level access to SVG elements, enabling detailed manipulation of styles, transitions, and interactions, but requires a strong understanding of web standards.

  • leaflet:

    Leaflet is designed to be simple and extensible, allowing developers to customize maps easily through plugins. While it provides basic mapping functionalities out of the box, additional features can be added through a wide range of community plugins.

  • mapbox-gl:

    Mapbox GL JS offers a high degree of customization for map styles and layers. Developers can use Mapbox Studio to design custom map styles and integrate them into applications, allowing for visually stunning and tailored map experiences.

  • victory:

    Victory offers a modular approach to chart building, allowing developers to customize individual components easily. It supports theming and provides a consistent API for styling, making it straightforward to create visually appealing charts.

  • plotly.js:

    Plotly.js provides a variety of customizable chart types and options, enabling developers to adjust colors, labels, and interactivity. It is particularly useful for creating dashboards where different visualizations need to be harmonized in appearance.

  • deck.gl:

    deck.gl allows for extensive customization of visual layers and interactions, particularly for geospatial data. Developers can create custom layers and integrate them seamlessly with existing Mapbox styles, offering flexibility in how data is presented on maps.

  • react-vis:

    React-Vis provides a set of customizable components that can be easily styled using props. While it is not as flexible as D3, it allows for quick adjustments to chart properties to fit the application's design requirements.

Learning Curve

  • d3:

    D3.js has a steep learning curve due to its complexity and the need for a solid understanding of SVG, DOM manipulation, and data binding concepts. Beginners may find it challenging but rewarding once mastered.

  • leaflet:

    Leaflet is beginner-friendly and easy to learn, making it an excellent choice for developers new to mapping. Its straightforward API allows for quick implementation of interactive maps with minimal setup.

  • mapbox-gl:

    Mapbox GL JS has a moderate learning curve, especially for developers familiar with JavaScript and web development. Its extensive documentation and examples help ease the learning process, but mastering its advanced features may take time.

  • victory:

    Victory is also beginner-friendly for React developers, offering a straightforward API and clear documentation. It allows for quick prototyping of charts, making it suitable for those new to data visualization.

  • plotly.js:

    Plotly.js is relatively easy to learn, especially for those with a background in data visualization. Its declarative syntax allows for quick chart creation, making it accessible for developers of all skill levels.

  • deck.gl:

    deck.gl has a moderate learning curve, especially for those familiar with React and WebGL. Its API is designed to be intuitive for developers who have experience with mapping libraries, but understanding WebGL concepts can be beneficial.

  • react-vis:

    React-Vis is designed for React developers and has a gentle learning curve. Its components are intuitive to use, and developers can quickly create visualizations without deep knowledge of the underlying principles.

Performance

  • d3:

    D3.js can become performance-intensive with large datasets due to its direct manipulation of the DOM. Developers need to optimize their code and use techniques like data joins and transitions carefully to maintain performance.

  • leaflet:

    Leaflet performs well for simple maps and moderate datasets. However, performance may decrease with a high number of markers or layers, so developers should consider optimizations like clustering for large datasets.

  • mapbox-gl:

    Mapbox GL JS is designed for high performance, utilizing WebGL for rendering. It efficiently handles large datasets and provides smooth interactions, making it suitable for applications requiring dynamic map features.

  • victory:

    Victory is generally performant for typical use cases, but like React-Vis, it may face challenges with large datasets. Developers should be mindful of the number of rendered components to maintain performance.

  • plotly.js:

    Plotly.js performance can vary depending on the complexity of the visualizations. While it handles moderate datasets well, very large datasets may lead to slower rendering times, requiring optimization techniques like data aggregation.

  • deck.gl:

    deck.gl is optimized for performance, leveraging WebGL to render large datasets efficiently. It can handle thousands of data points without significant performance degradation, making it ideal for visualizing complex geospatial data.

  • react-vis:

    React-Vis is performant for standard charting needs, but may struggle with very large datasets. Developers should consider performance implications when rendering complex visualizations with many data points.

Integration

  • d3:

    D3.js can be integrated into any web application, but it requires manual setup for frameworks like React or Angular. Its flexibility allows it to work with various libraries, but this may involve additional complexity.

  • leaflet:

    Leaflet is easy to integrate into any web application, including those built with frameworks like React and Angular. Its simplicity and lightweight nature make it a popular choice for adding maps to projects.

  • mapbox-gl:

    Mapbox GL JS integrates well with various frameworks, including React and Angular. It provides a robust API for customizing maps and can be used alongside other libraries for enhanced functionality.

  • victory:

    Victory is also tailored for React, offering a collection of components that integrate smoothly into React applications. Its modular design allows for easy customization and reuse across projects.

  • plotly.js:

    Plotly.js can be integrated into any web application and works well with frameworks like React. It provides a straightforward way to create interactive charts and dashboards, making it a versatile choice for data visualization.

  • deck.gl:

    deck.gl integrates seamlessly with React and can work alongside Mapbox for enhanced mapping capabilities. Its design makes it easy to incorporate into modern web applications focused on geospatial data visualization.

  • react-vis:

    React-Vis is designed specifically for React applications, making integration seamless. It provides a set of components that can be easily used within React's component structure, allowing for quick visualization development.

How to Choose: d3 vs leaflet vs mapbox-gl vs victory vs plotly.js vs deck.gl vs react-vis
  • d3:

    Choose D3.js if you need fine-grained control over your visualizations and want to manipulate the DOM directly. D3 is highly flexible and can create any type of visualization, but it has a steeper learning curve and requires a good understanding of SVG and web standards.

  • leaflet:

    Opt for Leaflet if you need a lightweight, easy-to-use library for interactive maps. It is perfect for simple mapping needs and is highly extensible with plugins, making it suitable for projects that require basic map functionalities.

  • mapbox-gl:

    Choose Mapbox GL JS for high-performance, interactive maps with advanced features like 3D terrain and vector tiles. It is best suited for applications that require rich, customizable maps with detailed styling options.

  • victory:

    Choose Victory for a modular approach to building charts in React applications. It provides a collection of components that can be easily customized and is designed for building complex visualizations with a focus on accessibility.

  • plotly.js:

    Use Plotly.js if you need to create interactive charts and dashboards quickly. It provides a wide range of chart types and is particularly strong in scientific and statistical visualizations, making it suitable for data-heavy applications.

  • deck.gl:

    Select deck.gl for rendering large-scale, complex data visualizations on maps, especially when working with WebGL. It is ideal for visualizing geospatial data and integrates well with other libraries like Mapbox and React.

  • react-vis:

    Select React-Vis if you are building a React application and need a simple way to integrate visualizations. It offers a set of reusable React components for creating charts and is easy to use for developers familiar with React.

README for d3

D3: Data-Driven Documents

D3 (or D3.js) is a free, open-source JavaScript library for visualizing data. Its low-level approach built on web standards offers unparalleled flexibility in authoring dynamic, data-driven graphics. For more than a decade D3 has powered groundbreaking and award-winning visualizations, become a foundational building block of higher-level chart libraries, and fostered a vibrant community of data practitioners around the world.

Resources