What is Recharts and what are its top alternatives?
Top Alternatives to Recharts
- vx
vx is collection of reusable low-level visualization components. vx combines the power of d3 to generate your visualization with the benefits of react for updating the DOM. ...
- Victory
A collection of composable React components for building interactive data visualizations. ...
- 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. ...
- 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. ...
- 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. ...
- 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. ...
- ApexCharts
A modern JavaScript charting library to build interactive charts and visualizations with simple API. ...
Recharts alternatives & related posts
related vx posts
related Victory posts
Server side
We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.
Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.
Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.
Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.
Client side
UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.
State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.
Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.
Cache
- Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.
Database
- Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.
Infrastructure
- Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.
Other Tools
Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.
Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.
As a frontend engineer on the Algorithms & Analytics team at Stitch Fix, I work with data scientists to develop applications and visualizations to help our internal business partners make data-driven decisions. I envisioned a platform that would assist data scientists in the data exploration process, allowing them to visually explore and rapidly iterate through their assumptions, then share their insights with others. This would align with our team's philosophy of having engineers "deploy platforms, services, abstractions, and frameworks that allow the data scientists to conceive of, develop, and deploy their ideas with autonomy", and solve the pain of data exploration.
The final product, code-named Dora, is built with React, Redux.js and Victory, backed by Elasticsearch to enable fast and iterative data exploration, and uses Apache Spark to move data from our Amazon S3 data warehouse into the Elasticsearch cluster.
- Beautiful visualizations195
- Svg103
- Data-driven92
- Large set of examples81
- Data-driven documents61
- Visualization components24
- Transitions20
- Dynamic properties18
- Plugins16
- Transformation11
- Makes data interactive7
- Open Source4
- Enter and Exit4
- Components4
- Exhaustive3
- Backed by the new york times3
- Easy and beautiful2
- Highly customizable1
- Awesome Community Support1
- Simple elegance1
- Templates, force template1
- Angular 41
- Beginners cant understand at all11
- Complex syntax6
related D3.js posts
We use Plotly (just their open source stuff) for Zulip's user-facing and admin-facing statistics graphs because it's a reasonably well-designed JavaScript graphing library.
If you've tried using D3.js, it's a pretty poor developer experience, and that translates to spending a bunch of time getting the graphs one wants even for things that are conceptually pretty basic. Plotly isn't amazing (it's decent), but it's way better than than D3 unless you have very specialized needs.
Hi,
I am looking at integrating a charting library in my React frontend that allows me to create appealing and interactive charts. I have basic familiarity with ApexCharts with React but have also read about D3.js charts and it seems a much more involved integration. Can someone please share their experience across the two libraries on the following dimensions:
- Amount of work needed for integration
- Amount of work or ease for creating new charts in either of the libraries.
Regards
Amit
amCharts
- Mock-up tools18
- Each element can be Customized3
- Amcharts upgrade often need to rewrite all code1
related amCharts posts
Highcharts
- Low learning curve and powerful34
- Multiple chart types such as pie, bar, line and others17
- Responsive charts13
- Handles everything you throw at it9
- Extremely easy-to-parse documentation8
- Built-in export chart as-is to image file5
- Easy to customize color scheme and palettes5
- Export on server side, can be used in email1
- Expensive9
related Highcharts posts
Here is my stack on #Visualization. @FusionCharts and Highcharts are easy to use but only free for non-commercial. Chart.js and Plotly are two lovely tools for commercial use under the MIT license. And D3.js would be my last choice only if a complex customized plot is needed.
- Bindings to popular languages like Python, Node, R, etc16
- Integrated zoom and filter-out tools in charts and maps10
- Great support for complex and multiple axes9
- Powerful out-of-the-box featureset8
- Beautiful visualizations6
- Active user base4
- Impressive support for webgl 3D charts4
- Charts are easy to share with a cloud account3
- Webgl chart types are extremely performant3
- Interactive charts2
- Easy to use online editor for creating plotly.js charts2
- Publication quality image export2
- Terrible document18
related Plotly.js posts
We use Plotly (just their open source stuff) for Zulip's user-facing and admin-facing statistics graphs because it's a reasonably well-designed JavaScript graphing library.
If you've tried using D3.js, it's a pretty poor developer experience, and that translates to spending a bunch of time getting the graphs one wants even for things that are conceptually pretty basic. Plotly isn't amazing (it's decent), but it's way better than than D3 unless you have very specialized needs.
Here is my stack on #Visualization. @FusionCharts and Highcharts are easy to use but only free for non-commercial. Chart.js and Plotly are two lovely tools for commercial use under the MIT license. And D3.js would be my last choice only if a complex customized plot is needed.
- East to implement7
- Smaller learning curve6
- Free to use5
- Vue Compatible4
- Very customizable3
- Angular compatible3
- React compatible2
- Support is in chinese2
related ECharts posts
- Provides zooming capabilities4
- Interactive charts4
- Graphs renders in SVG3
- Open source with MIT license3
- Multiple chart types such as pie, bar, line and others2
- Slow rendering4
related ApexCharts posts
Hi,
I am looking at integrating a charting library in my React frontend that allows me to create appealing and interactive charts. I have basic familiarity with ApexCharts with React but have also read about D3.js charts and it seems a much more involved integration. Can someone please share their experience across the two libraries on the following dimensions:
- Amount of work needed for integration
- Amount of work or ease for creating new charts in either of the libraries.
Regards
Amit