ggplot2 VS Plotly

Compare ggplot2 VS Plotly and see what are their differences

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ggplot2

Application and Data, Libraries, and Charting Libraries

Plotly

Low-Code Data Apps

Landing page //
2023-02-27

Landing page //
2023-07-31

ggplot2

Website: ggplot2.tidyverse.org
Pricing URL: -
$ Details

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Plotly

Website: plotly.com
Pricing URL: Official Plotly Pricing
$ Details: -

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ggplot2 features and specs

Elegant Data Visualization
ggplot2 offers a high level of abstraction for creating complex multi-layered graphics using a coherent and consistent syntax.
Grammar of Graphics
It is built around the Grammar of Graphics, allowing users to build plots by combining different components in a clear and structured manner.
Extensibility
ggplot2 is highly extensible, enabling users and developers to create their own geoms, stats, and themes.
Integration with Tidyverse
It is part of the tidyverse, thus integrating smoothly with other tools and packages for data manipulation and cleaning, like dplyr and tidyr.
Wide Range of Plot Types
Users can create a diverse array of plots from simple bar graphs to complex multi-panel plots seamlessly.

Possible disadvantages of ggplot2

Steep Learning Curve
New users may find the syntax and grammar approach unfamiliar and difficult initially, which can make simple plotting tasks feel complicated.
Performance with Large Datasets
Rendering plots with ggplot2 can be slower than base R graphics when dealing with very large datasets.
Complex Customization
While customization is robust, it can become cumbersome and verbose for users needing plots with high levels of customization.
Limited Interactivity
ggplot2 primarily focuses on static plotting; additional libraries are needed to create interactive plots.

Plotly features and specs

Interactivity
Plotly offers highly interactive plots that allow users to pan, zoom, and hover over data points for more information. This enhances the user experience and provides deeper insights.
High-quality visualizations
It provides aesthetically pleasing and highly customizable charts, making it suitable for publication-quality visuals.
Versatility
Plotly supports multiple chart types including line charts, scatter plots, bar charts, and 3D plots, making it suitable for a wide range of applications.
Python integration
Plotly is well-integrated with Python and works seamlessly with other popular data science libraries like Pandas, NumPy, and Scikit-learn.
Web-based
The plots can be easily embedded in web applications or dashboards, making it ideal for sharing insights over the internet.
Open-source
Plotly offers an open-source version, which allows users to create and share visualizations without any cost.

Possible disadvantages of Plotly

Performance
Rendering very large datasets can sometimes be slow, which may not be suitable for real-time data visualization requirements.
Learning curve
Even though the library is well-documented, the extensive range of features can have a steep learning curve for beginners.
Cost for advanced features
While the basic functionality is free, more advanced features, such as export to certain formats and additional customizable options, require a paid subscription.
Dependency management
Plotly has a number of dependencies that need to be managed properly, which can sometimes complicate the setup process.
Complexity
For simple visualizations, Plotly might be overkill and simpler libraries like Matplotlib or Seaborn could be more appropriate.

Analysis of Plotly

Overall verdict

Overall, Plotly is a strong choice for those looking to create dynamic and interactive data visualizations, thanks to its range of features and ease of integration with web technologies.

Why this product is good

Plotly is considered good because it offers a comprehensive suite of tools for creating interactive visualizations that can be used in web applications, reports, and dashboards. It supports many different types of plots, is easy to use for both beginners and experienced developers, and integrates well with popular programming languages like Python, R, and JavaScript.

Recommended for

Plotly is recommended for data scientists, analysts, and developers who need to create interactive and visually appealing data visualizations. It's particularly useful for those who work with Python or R and want the ability to embed their visualizations in web applications or dashboards.

ggplot2 videos

+ Add

Learn R: An Introduction to ggplot2

Plotly videos

+ Add

Create Real-time Chart with Javascript | Plotly.js Tutorial

Category Popularity

0-100% (relative to ggplot2 and Plotly)

ggplot2

Plotly

Data Visualization

12 12%

Data Visualization

88% 88

Data Dashboard

13 13%

Data Dashboard

87% 87

Charting Libraries

0 0%

Charting Libraries

100% 100

Technical Computing

100 100%

Technical Computing

0% 0

User comments

Share your experience with using ggplot2 and Plotly. For example, how are they different and which one is better?

Reviews

These are some of the external sources and on-site user reviews we've used to compare ggplot2 and Plotly

ggplot2 Reviews

5 Best Python Libraries For Data Visualization in 2023

ggplot is a system for creating graphics declaratively. It’s based on the Grammar of Graphics of R programming language and is tightly integrated with Pandas. ggplot just requires you to declare how to map the variables to aesthetics and primitives to use and handles the rest automatically. Remember, ggplot is not recommended for creating highly customized graphics.

Source: www.technotification.com

Top 8 Python Libraries for Data Visualization

Ggplot is a Python data visualization library that is based on the implementation of ggplot2 which is created for the programming language R. Ggplot can create data visualizations such as bar charts, pie charts, histograms, scatterplots, error charts, etc. using high-level API. It also allows you to add different types of data visualization components or layers in a single...

Source: www.geeksforgeeks.org

Plotly Reviews

Best 8 Redash Alternatives in 2023 [In Depth Guide]

Plotly is specifically designed for companies who want to build and deploy analytic applications like dashboards using Python, Julia, or R without needing DevOps or Javascript developers.

Source: www.datapad.io

5 Best Python Libraries For Data Visualization in 2023

Plotly is a web-based data visualization toolkit that comes with unique functionalities such as dendrograms, 3D charts, and also contour plots, which is not very common in other libraries. It has a great API offering scatter plots, line charts, bar charts, error bars, box plots, and other visualizations. Plotly can even be accessed from a Python Notebook.

Source: www.technotification.com

Top 8 Python Libraries for Data Visualization

Plotly is a free open-source graphing library that can be used to form data visualizations. Plotly (plotly.py) is built on top of the Plotly JavaScript library (plotly.js) and can be used to create web-based data visualizations that can be displayed in Jupyter notebooks or web applications using Dash or saved as individual HTML files. Plotly provides more than 40 unique...

Source: www.geeksforgeeks.org

5 top picks for JavaScript chart libraries

Plotly is a graphing library that’s available for various runtime environments, including the browser. It supports many kinds of charts and graphs that we can configure with a variety of options.

Source: blog.logrocket.com

Social recommendations and mentions

Based on our record, Plotly should be more popular than ggplot2. It has been mentiond 34 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

ggplot2 mentions (12)

Plotnine
Plotnine is heavily inspired by the ggplot2 library, which uses the + operator in the same way: https://ggplot2.tidyverse.org/#usage. - Source: Hacker News / about 2 months ago
Ask HN: What plotting tools should I invest in learning?
For random, quick and dirty, ad-hoc plotting tasks my default is GNUPlot[1]. Otherwise I tend to use either Python with matplotlib, or R with ggplot2. I keep saying I'm going to invest the time to properly learn D3[4] or something similar for doing web-based plotting, but somehow never quite seem to find time to do it. sigh [1]: http://www.gnuplot.info/ [2]: https://matplotlib.org/ [3]:... - Source: Hacker News / about 3 years ago
Relative frequency of letters in five-letter English words (Wordle aid) [OC]
I got the list of five-letter words from the words package in R, created the QWERTY keyboard grid with base R and tibble, and visualized the data with geom_tile in the ggplot2 package. Source: about 3 years ago
[OC] U.S. News & World Report Best Colleges: 2002 to 2023
Thanks, it's an interesting idea! I definitely could implement this with scale_fill_gradientn) in ggplot2. Source: about 3 years ago
Facts about Aaron Boone's Ejections as Manager
I used the ggplot2 package in R to create these figures. Source: about 3 years ago

Plotly mentions (34)

How to Analyze 47 Million Hacker News Posts: A Data Scientist's Dream Dataset Just Got Better
Let's dive into some practical examples. First, you'll need to set up your environment with the right tools. I recommend using pandas for data manipulation and plotly for visualization. - Source: dev.to / 5 months ago
Python for Data Visualization: Best Tools and Practices
Plotly is perfect for interactive visualizations. You can create interactive charts and graphs that allow users to hover, click, and zoom in. Plotly is also great for web-based visuals, making it easy to share your findings online. - Source: dev.to / over 1 year ago
Generative AI Powered QnA & Visualization Chatbot
Front End: A React application that leverages React-Chatbotify library to easily integrate a chatbot GUI. It also uses the Plotly library to display the charts/visualizations. The generative AI implementation and details are entirely abstracted from the front end. The front-end application depends on a single REST endpoint of the backend application. - Source: dev.to / over 1 year ago
Build a Stock Dashboard in less than 40 lines of Python code!🤓
In this tutorial, Mariya Sha will guide you through building a stock value dashboard using Taipy, Plotly, and a dataset from Kaggle. - Source: dev.to / over 1 year ago
Essential Deep Learning Checklist: Best Practices Unveiled
How to Accomplish: Utilize visualization libraries like Matplotlib, Seaborn, or Plotly in Python to create histograms, scatter plots, and bar charts. For image data, use tools that visualize images alongside their labels to check for labeling accuracy. For structured data, correlation matrices and pair plots can be highly informative. - Source: dev.to / about 2 years ago

What are some alternatives?

When comparing ggplot2 and Plotly, you can also consider the following products

Matplotlib - matplotlib is a python 2D plotting library which produces publication quality figures in a variety...

D3.js - D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS.

Bokeh - Bokeh visualization library, documentation site.

RAWGraphs - RAWGraphs is an open source app built with the goal of making the visualization of complex data...

Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.

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.

Matplotlib vs ggplot2