ggvis vs D3.js

Struggling to choose between ggvis and D3.js? Both products offer unique advantages, making it a tough decision.

ggvis is a Data Visualization solution with tags like r, ggplot2, interactive, data-visualization, graphics, web-browser.

It boasts features such as Grammar of Graphics-based visualization using the ggplot2 API, Interactivity through linking graphical elements to data, Built on top of Shiny for reactive programming, Can embed plots in R Markdown documents and Shiny apps, Supports faceting, zooming, panning, etc., Exporting plots to SVG and PNG format and pros including Leverages ggplot2 syntax for easy plotting, Interactivity enables exploration of data, Tight integration with Shiny apps, Can create standalone visualizations to embed in web pages.

On the other hand, D3.js is a Development product tagged with javascript, data-binding, svg, graphs, charts.

Its standout features include Data-Driven DOM Manipulation, Powerful Visualization Components, Animated Transitions, Highly Customizable and Flexible, Wide Browser Support, and it shines with pros like Open source and free, Large and active community support, Integrates well with other JS libraries, High performance with canvas rendering, Supports large datasets and real-time updates.

Feature	ggvis	D3.js
Overall Score	N/A	N/A
Primary Category	Data Visualization	Development

Product Overview

ggvis

Description: ggvis is an R package for creating interactive data visualizations and graphics in a web browser. It builds on the popular ggplot2 package but allows users to add interactivity, make visualizations reusable, and embed them in web pages.

D3.js

Description: D3.js is a JavaScript library for visualizing data with HTML, SVG, and CSS. It allows developers to bind arbitrary data to DOM elements and apply data-driven transformations to the document. Common uses are for creating interactive graphs, charts, maps, and data visualizations.

Key Features Comparison

ggvis Features

• Grammar of Graphics-based visualization using the ggplot2 API
• Interactivity through linking graphical elements to data
• Built on top of Shiny for reactive programming
• Can embed plots in R Markdown documents and Shiny apps
• Supports faceting, zooming, panning, etc.
• Exporting plots to SVG and PNG format

D3.js Features

• Data-Driven DOM Manipulation
• Powerful Visualization Components
• Animated Transitions
• Highly Customizable and Flexible
• Wide Browser Support

Pros & Cons Analysis

ggvis

Pros

• Leverages ggplot2 syntax for easy plotting
• Interactivity enables exploration of data
• Tight integration with Shiny apps
• Can create standalone visualizations to embed in web pages

Cons

• Limited adoption compared to static ggplot2
• Interactivity requires knowledge of reactivity in Shiny
• Less customizable than D3.js for web-based graphics

D3.js

Pros

• Open source and free
• Large and active community support
• Integrates well with other JS libraries
• High performance with canvas rendering
• Supports large datasets and real-time updates

Cons

• Steep learning curve
• Complex documentation
• Requires knowledge of SVG
• Not optimized for mobile platforms
• Version 3.0 has breaking changes from 2.0

Pricing Comparison

ggvis

• Open Source

D3.js

• Open Source