ggvis vs Bokeh

Struggling to choose between ggvis and Bokeh? 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, Bokeh is a Development product tagged with python, data-visualization, interactive, graphics, web-browser.

Its standout features include Interactive data visualization, Supports streaming data, Python library, Targets modern web browsers, Elegant and concise graphics, High-performance interactivity, Can handle large datasets, and it shines with pros like Very flexible and customizable visualizations, Integrates well with other Python data tools like NumPy and Pandas, Open source and free, Good performance even with large datasets, Nice web-based interface for sharing visualizations.

Feature	ggvis	Bokeh
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.

Bokeh

Description: Bokeh is an interactive data visualization library for Python that targets modern web browsers for presentation. It offers elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets.

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

Bokeh Features

• Interactive data visualization
• Supports streaming data
• Python library
• Targets modern web browsers
• Elegant and concise graphics
• High-performance interactivity
• Can handle large datasets

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

Bokeh

Pros

• Very flexible and customizable visualizations
• Integrates well with other Python data tools like NumPy and Pandas
• Open source and free
• Good performance even with large datasets
• Nice web-based interface for sharing visualizations

Cons

• Steeper learning curve than some visualization libraries
• Visualizations can be more complex to build
• Limited built-in statistical analysis features
• Requires knowledge of Python and web development
• Not as simple as drag-and-drop visualization builders

Pricing Comparison

ggvis

• Open Source

Bokeh

• Open Source