Bokeh vs Shiny

Overview

Bokeh

Stacks95

Followers183

Votes12

GitHub Stars20.2K

Forks4.2K

Shiny

Stacks208

Followers228

Votes13

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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CLI (Node.js)

Manual

Detailed Comparison

Bokeh	Shiny
Bokeh is an interactive visualization library for modern web browsers. It provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets.	It is an open source R package that provides an elegant and powerful web framework for building web applications using R. It helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge.
interactive visualization library ; versatile graphics ; open source; https://github.com/bokeh/bokeh	-
Statistics
GitHub Stars 20.2K	GitHub Stars -
GitHub Forks 4.2K	GitHub Forks -
Stacks 95	Stacks 208
Followers 183	Followers 228
Votes 12	Votes 13
Pros & Cons
Pros 12 Beautiful Interactive charts in seconds	Pros 8 R Compatibility 3 Free 2 Highly customizable and extensible
Integrations
Bootstrap Flask NGINX React Django Python Jupyter Tornado Streamlit	No integrations available

What are some alternatives to Bokeh, Shiny?

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.

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

Highcharts

Highcharts currently supports line, spline, area, areaspline, column, bar, pie, scatter, angular gauges, arearange, areasplinerange, columnrange, bubble, box plot, error bars, funnel, waterfall and polar chart types.

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.

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

Chart.js

Visualize your data in 6 different ways. Each of them animated, with a load of customisation options and interactivity extensions.

Recharts

Quickly build your charts with decoupled, reusable React components. Built on top of SVG elements with a lightweight dependency on D3 submodules.

ECharts

It is an open source visualization library implemented in JavaScript, runs smoothly on PCs and mobile devices, and is compatible with most current browsers.

Cube

Cube: the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power your data apps and AI with context.

ZingChart

The most feature-rich, fully customizable JavaScript charting library available used by start-ups and the Fortune 100 alike.

Related Comparisons

Bokeh vs Shiny: What are the differences?

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.

Bokeh vs Shiny

Overview