Bokeh vs ggplot2

Overview

Bokeh

Stacks95

Followers183

Votes12

GitHub Stars20.2K

Forks4.2K

ggplot2

Stacks124

Followers70

Votes0

GitHub Stars6.8K

Forks2.1K

Bokeh vs ggplot2: What are the differences?

Introduction

When comparing Bokeh and ggplot2, it is important to understand the key differences between these two popular visualization libraries in Python and R.

Language: Bokeh is primarily used in Python, offering interactive visualization capabilities, while ggplot2 is a part of the R programming language, known for its grammar of graphics implementation within R.
Usage: Bokeh is often favored for creating interactive web-based visualizations, suitable for dashboards and web applications, whereas ggplot2 is commonly used for static, publication-quality graphics for data exploration and analysis.
Customization: Bokeh provides a higher level of customization with its tools and widgets for interactivity, allowing users to create dynamic plots easily, whereas ggplot2 offers a more structured and layered approach to building graphics using the grammar of graphics concepts.
Integration: Bokeh seamlessly integrates with other Python libraries such as Pandas, NumPy, and SciPy for data manipulation and analysis, while ggplot2 works well with the tidyverse ecosystem in R, promoting a tidy data approach for data visualization.
Documentation: Bokeh has extensive documentation and examples for users to get started quickly, with a focus on accessibility and user-friendly guides, whereas ggplot2 has comprehensive documentation with a steep learning curve due to its detailed grammar of graphics principles.

Summary

In summary, Bokeh typically excels in interactive web-based visualizations in Python, while ggplot2 shines in creating static, publication-ready graphics within the R programming language.

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CLI (Node.js)

Manual

Detailed Comparison

Bokeh	ggplot2
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 a general scheme for data visualization which breaks up graphs into semantic components such as scales and layers.
interactive visualization library ; versatile graphics ; open source; https://github.com/bokeh/bokeh	Axis titles; Tickmarks; Margins and points in ggplot2 look cooler
Statistics
GitHub Stars 20.2K	GitHub Stars 6.8K
GitHub Forks 4.2K	GitHub Forks 2.1K
Stacks 95	Stacks 124
Followers 183	Followers 70
Votes 12	Votes 0
Pros & Cons
Pros 12 Beautiful Interactive charts in seconds	No community feedback yet
Integrations
Bootstrap Flask NGINX React Django Python Jupyter Tornado Streamlit	MATLAB React Python SageMath Jupyter

What are some alternatives to Bokeh, ggplot2?

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.

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.

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.

ZingChart

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

amCharts

amCharts is an advanced charting library that will suit any data visualization need. Our charting solution include Column, Bar, Line, Area, Step, Step without risers, Smoothed line, Candlestick, OHLC, Pie/Donut, Radar/ Polar, XY/Scatter/Bubble, Bullet, Funnel/Pyramid charts as well as Gauges.

CanvasJS

Lightweight, Beautiful & Responsive Charts that make your dashboards fly even with millions of data points! Self-Hosted, Secure & Scalable charts that render across devices.

AnyChart

AnyChart is a flexible JavaScript (HTML5) based solution that allows you to create interactive and great looking charts. It is a cross-browser and cross-platform charting solution intended for everybody who deals with creation of dashboard, reporting, analytics, statistical, financial or any other data visualization solutions.

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Bokeh vs ggplot2: What are the differences?

Introduction

When comparing Bokeh and ggplot2, it is important to understand the key differences between these two popular visualization libraries in Python and R.

Language: Bokeh is primarily used in Python, offering interactive visualization capabilities, while ggplot2 is a part of the R programming language, known for its grammar of graphics implementation within R.
Usage: Bokeh is often favored for creating interactive web-based visualizations, suitable for dashboards and web applications, whereas ggplot2 is commonly used for static, publication-quality graphics for data exploration and analysis.
Customization: Bokeh provides a higher level of customization with its tools and widgets for interactivity, allowing users to create dynamic plots easily, whereas ggplot2 offers a more structured and layered approach to building graphics using the grammar of graphics concepts.
Integration: Bokeh seamlessly integrates with other Python libraries such as Pandas, NumPy, and SciPy for data manipulation and analysis, while ggplot2 works well with the tidyverse ecosystem in R, promoting a tidy data approach for data visualization.
Documentation: Bokeh has extensive documentation and examples for users to get started quickly, with a focus on accessibility and user-friendly guides, whereas ggplot2 has comprehensive documentation with a steep learning curve due to its detailed grammar of graphics principles.

Summary

In summary, Bokeh typically excels in interactive web-based visualizations in Python, while ggplot2 shines in creating static, publication-ready graphics within the R programming language.