Dash by Plotly vs Shiny

Struggling to choose between Dash by Plotly and Shiny? Both products offer unique advantages, making it a tough decision.

Dash by Plotly is a Development solution with tags like python, plotly, dashboard, visualization, analytics.

It boasts features such as Interactive data visualization, Built on top of Plotly.js, React, and Flask, Supports many chart types like scatter plots, histograms, box plots, heatmaps, etc, Linked views for cross-filtering, Supports callbacks for dynamic updates and interactions, Layouts with CSS grid and flexbox, Authentication and role-based access control, Works with Pandas DataFrames and pros including Open-source and free to use, Great for building analytical web apps quickly, Large library of customizable visualizations, Python-based, so easy for Python developers, Active community support.

On the other hand, Shiny is a Development product tagged with r, web-development, data-visualization.

Its standout features include Reactive programming framework, Built on R, Easily create dashboards and web apps, Host apps on Shiny Server or Shinyapps.io, Integrate with JavaScript, CSS, and HTML, and it shines with pros like Open source and free, Large user community, Extensive documentation and tutorials, Integrates seamlessly with R and R packages, Highly customizable and extensible.

Feature	Dash by Plotly	Shiny
Overall Score	N/A	N/A
Primary Category	Development	Development

Product Overview

Dash by Plotly

Description: Dash by Plotly is an open-source Python framework for building analytical web applications. It makes it easy to build reactive, customizable dashboards by leveraging Flask, Plotly.js, and React.js.

Shiny

Description: Shiny is an open-source R package that provides an elegant and powerful web framework for building web applications using R. It makes it easy to build interactive web apps straight from R.

Key Features Comparison

Dash by Plotly Features

• Interactive data visualization
• Built on top of Plotly.js, React, and Flask
• Supports many chart types like scatter plots, histograms, box plots, heatmaps, etc
• Linked views for cross-filtering
• Supports callbacks for dynamic updates and interactions
• Layouts with CSS grid and flexbox
• Authentication and role-based access control
• Works with Pandas DataFrames

Shiny Features

• Reactive programming framework
• Built on R
• Easily create dashboards and web apps
• Host apps on Shiny Server or Shinyapps.io
• Integrate with JavaScript, CSS, and HTML

Pros & Cons Analysis

Dash by Plotly

Pros

• Open-source and free to use
• Great for building analytical web apps quickly
• Large library of customizable visualizations
• Python-based, so easy for Python developers
• Active community support

Cons

• Steeper learning curve than some other BI tools
• Advanced customization requires knowledge of React
• Hosting and deployment may require DevOps skills
• Not as feature-rich as commercial BI platforms

Shiny

Pros

• Open source and free
• Large user community
• Extensive documentation and tutorials
• Integrates seamlessly with R and R packages
• Highly customizable and extensible

Cons

• Can have a steep learning curve
• Hosting and scaling apps takes more work
• Not as robust for large complex apps as other frameworks
• Limited UI widget options compared to JavaScript frameworks

Pricing Comparison

Dash by Plotly

• Open Source
• Freemium

Shiny

• Open Source
• Free
• Subscription-Based (Shinyapps.io)