Bokeh vs ggvis

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

Bokeh is a Development solution with tags like python, data-visualization, interactive, graphics, web-browser.

It boasts features such as Interactive data visualization, Supports streaming data, Python library, Targets modern web browsers, Elegant and concise graphics, High-performance interactivity, Can handle large datasets and pros including 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.

On the other hand, ggvis is a Data Visualization product tagged with r, ggplot2, interactive, data-visualization, graphics, web-browser.

Its standout features include 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 it shines with pros like 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.

To help you make an informed decision, we've compiled a comprehensive comparison of these two products, delving into their features, pros, cons, pricing, and more. Get ready to explore the nuances that set them apart and determine which one is the perfect fit for your requirements.

Bokeh

Bokeh

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.

Categories:
Development Data Visualization
python data-visualization interactive graphics web-browser

Bokeh Features

  1. Interactive data visualization
  2. Supports streaming data
  3. Python library
  4. Targets modern web browsers
  5. Elegant and concise graphics
  6. High-performance interactivity
  7. Can handle large datasets

Pricing

  • Open Source

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

ggvis

ggvis

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.

Categories:
Data Visualization Interactive Graphics
r ggplot2 interactive data-visualization graphics web-browser

Ggvis Features

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

Pricing

  • Open Source

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