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Bokeh

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Shiny

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Followers224

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Bokeh vs Shiny: What are the differences?

Bokeh and Shiny are two popular libraries used for creating interactive data visualization applications in Python and R, respectively. While both libraries serve the same purpose, there are several key differences between them.

Language Compatibility: Bokeh is designed for Python, while Shiny is designed for R. This means that if you are more comfortable with Python, Bokeh will be the preferred choice, and if you are more comfortable with R, Shiny will be the preferred choice.
Development Environment: Bokeh provides a Pythonic programming interface, allowing developers to write code in Python and create interactive visualizations using familiar Python libraries. On the other hand, Shiny provides a more integrated development environment within RStudio, making it seamless to develop interactive applications directly within the R ecosystem.
Backend Support: Bokeh supports multiple backends, including HTML, JavaScript, and WebGL, allowing you to choose the most suitable backend for your application. Shiny, on the other hand, is built on top of the R web framework, and its backend is tightly coupled with R, limiting the backend options.
Deployment Options: Bokeh provides flexible deployment options, allowing you to host your applications on a variety of platforms, including standalone web servers, Bokeh server, or embedded in Flask or Django applications. Shiny, on the other hand, can be deployed on the Shiny Server or hosted on the ShinyApps.io platform.
Community and Ecosystem: Bokeh has a large and vibrant community that actively contributes to its development, with plenty of examples, tutorials, and extensions available. Shiny also has a strong community, but it is relatively smaller compared to Bokeh. Additionally, R's ecosystem provides a wide range of statistical and data manipulation packages that can be seamlessly integrated with Shiny applications.

In Summary, Bokeh and Shiny differ in their language compatibility, development environment, backend support, deployment options, and community and ecosystem size. These differences make each library more suitable for specific use cases and programming preferences.

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Pros of Bokeh

Pros of Shiny

12
Beautiful Interactive charts in seconds

8
R Compatibility
3
Free
2
Highly customizable and extensible

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What companies use Bokeh?

What companies use Shiny?

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What tools integrate with Bokeh?

What tools integrate with Shiny?

No integrations found

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What are some alternatives to Bokeh and Shiny?

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.

Matplotlib

It is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. It can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.

Dash

Dash is an API Documentation Browser and Code Snippet Manager. Dash stores snippets of code and instantly searches offline documentation sets for 150+ APIs. You can even generate your own docsets or request docsets to be included.

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.

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

Bokeh vs Shiny