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  1. Stackups
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  5. Plotly vs Shiny

Plotly vs Shiny

OverviewComparisonAlternatives

Overview

Plotly.js
Plotly.js
Stacks399
Followers694
Votes69
GitHub Stars17.9K
Forks1.9K
Shiny
Shiny
Stacks208
Followers228
Votes13

Plotly vs Shiny: What are the differences?

Plotly and Shiny are both popular tools for creating interactive visualizations and dashboards in data science. While they serve similar purposes, there are key differences between them that make them distinct in their own ways.
  1. Programming Language: The first major difference between Plotly and Shiny is the programming language they are built on. Plotly is primarily built on Python, while Shiny is built on R. This means that users familiar with Python would find Plotly easier to use, while users familiar with R would find Shiny more suitable for their needs.

  2. Data Visualization Library: Another significant difference is the data visualization library that each tool uses. Plotly utilizes its own open-source JavaScript library called Plotly.js for creating interactive charts and graphs. On the other hand, Shiny utilizes the ggplot2 library, which is a popular data visualization library in R. This difference in libraries affects the types of visuals that can be created and the level of customization available to users.

  3. Ease of Use: Plotly and Shiny also differ in terms of ease of use. Plotly is known for its user-friendly interface and intuitive APIs, making it relatively easy for users to create interactive visualizations. Shiny, while powerful, has a steeper learning curve and requires a good understanding of R programming concepts. So, users with minimal programming experience might find Plotly more accessible.

  4. Deployment Options: When it comes to deployment, Plotly offers more flexibility. Plotly charts can be deployed as web applications, embedded within websites, or shared as standalone HTML files. In contrast, Shiny is primarily designed for deployment as web applications using the Shiny Server. This distinction in deployment options can influence the way users choose to share and distribute their interactive visualizations.

  5. Support for Machine Learning: Plotly has more extensive support for machine learning algorithms and tools. It offers features like built-in machine learning dashboards, support for TensorFlow, and integration with popular libraries like scikit-learn and PyTorch. Shiny, while capable of implementing machine learning models, does not have the same level of built-in support and lacks some of the advanced features provided by Plotly.

  6. Community and Documentation: The community and documentation surrounding Plotly and Shiny are also different. Plotly has a large and active community with well-maintained documentation and numerous examples and tutorials available. Shiny, being an R-based tool, benefits from the extensive R community and has its own documentation and resources. However, Plotly's community and documentation are generally considered to be more extensive and comprehensive.

In Summary, Plotly and Shiny differ in the programming language they are built on, the data visualization libraries they use, ease of use, deployment options, support for machine learning, and the size and quality of their respective communities and documentation.

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Detailed Comparison

Plotly.js
Plotly.js
Shiny
Shiny

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.

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.

Feature parity with MATLAB/matplotlib graphing; Online chart editor; Fully interactive (hover, zoom, pan); SVG and WebGL backends; Publication-quality image export
-
Statistics
GitHub Stars
17.9K
GitHub Stars
-
GitHub Forks
1.9K
GitHub Forks
-
Stacks
399
Stacks
208
Followers
694
Followers
228
Votes
69
Votes
13
Pros & Cons
Pros
  • 16
    Bindings to popular languages like Python, Node, R, etc
  • 10
    Integrated zoom and filter-out tools in charts and maps
  • 9
    Great support for complex and multiple axes
  • 8
    Powerful out-of-the-box featureset
  • 6
    Beautiful visualizations
Cons
  • 18
    Terrible document
Pros
  • 8
    R Compatibility
  • 3
    Free
  • 2
    Highly customizable and extensible
Integrations
Python
Python
React
React
MATLAB
MATLAB
Jupyter
Jupyter
Julia
Julia
No integrations available

What are some alternatives to Plotly.js, Shiny?

D3.js

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

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

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.

Superset

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

Chart.js

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

Recharts

Recharts

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

ECharts

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

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

ZingChart

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

Power BI

Power BI

It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.

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