ggplot2 VS NVD3

Compare ggplot2 VS NVD3 and see what are their differences

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ggplot2

Application and Data, Libraries, and Charting Libraries

NVD3

This project is an attempt to build re-usable charts and chart components for d3.

Landing page //
2023-02-27

Landing page //
2021-07-31

ggplot2

Website: ggplot2.tidyverse.org
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NVD3

Website: nvd3.org
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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.

NVD3 features and specs

Reusable Charts
NVD3 provides a variety of pre-built chart types that are easy to reuse and customize, saving developers time when creating standard visualization needs.
Based on D3.js
Utilizing D3.js ensures a high level of flexibility and the ability to integrate complex data manipulations with aesthetic chart outputs.
Ease of Use
NVD3 simplifies the process of generating complex visualizations by providing an intuitive API for interacting with D3.js charts.
Responsive Design
Many of the chart components are designed with responsiveness in mind, allowing them to adjust to different screen sizes and devices.

Possible disadvantages of NVD3

Limited Updates
NVD3 is not frequently updated, which may lead to compatibility issues or lack of support for newer web technologies.
Learning Curve
While it simplifies some aspects of D3.js, users still need to have a solid understanding of D3.js to fully leverage NVD3's capabilities, which can be steep for new users.
Limited Customization
Compared to directly using D3.js, NVD3 offers a more limited range of customization options for charts, potentially restricting designers looking for highly unique visualizations.
Documentation Quality
The documentation for NVD3 can be sparse or outdated, making it harder for developers to troubleshoot issues or learn best practices.

Analysis of NVD3

Overall verdict

NVD3 is generally a good library if you are seeking convenience and speed in creating standard chart types. However, if your project requires extensive customization and more complex interactive features, sticking directly with D3.js could be more beneficial.

Why this product is good

NVD3 is built on top of D3.js, offering a simplified API for creating various types of charts. It's useful for developers who need to generate data visualizations quickly without delving deep into the complexities of D3. It provides a collection of reusable chart components, saving time in coding from scratch. However, since it's a layer on top of D3.js, it might not offer the same level of customization and flexibility for more unique visualizations.

Recommended for

Developers looking for quick, easy-to-implement charting solutions.
Projects that require standard charts with minimal customization.
Teams that want to leverage D3.js capabilities with a simpler API.

ggplot2 videos

+ Add

Learn R: An Introduction to ggplot2

NVD3 videos

No NVD3 videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to ggplot2 and NVD3)

ggplot2

NVD3

Data Visualization

45 45%

Data Visualization

55% 55

Charting Libraries

0 0%

Charting Libraries

100% 100

Data Dashboard

33 33%

Data Dashboard

67% 67

Technical Computing

100 100%

Technical Computing

0% 0

User comments

Share your experience with using ggplot2 and NVD3. 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 NVD3

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

NVD3 Reviews

The Best Data Visualization Tools - Top 30 BI Software

This project is an attempt to build reusable charts and chart components for d3.js without taking away the power that d3.js gives you. The goal of the project is to keep all your charts neat and customizable. NVD3 is developed by the front end engineers at Novus Partners and uses their insight in charting technology.

Source: improvado.io

Social recommendations and mentions

Based on our record, ggplot2 seems to be a lot more popular than NVD3. While we know about 12 links to ggplot2, we've tracked only 1 mention of NVD3. 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

NVD3 mentions (1)

Widely Used Data Display and Analysis Libraries
NVD3 is also on the list of the most popular libraries. Built upon D3.js like the others above, it does have a solid technical base. - Source: dev.to / almost 5 years ago