graphical representation of data in react

The top 11 React chart libraries for data visualization

In today’s data-driven world, presenting data in a visually compelling manner is paramount for user engagement and clarity.

React, a premier library for building UIs, has an extensive UI component ecosystem which offers many options for integrating charts. From business analytics dashboards to scientific data displays, the right charting library can transform numbers into insightful narratives.

As we delve into this article, we’ll explore the top charting libraries available for React, to help you make an informed decision for your next project.

The top 11 React chart libraries to consider

1. react-google-charts.

This library leverages the robustness of Google’s chart tools combined with a React-friendly experience. It is ideal for developers familiar with Google’s visualization ecosystem.

• Website: react-google-charts GitHub page
• Stats: 1.2k GitHub stars
• Google's proven reliability: Utilizing Google's charting ensures a robust and tested visualization tool.
• Seamless React integration: Designed with React in mind, it offers a smooth development experience.
• Dependency on external service: Reliance on Google Charts means any issues with Google's service can affect your charts.
• Limited Customization: While react-google-charts offers a range of styles and chart types, deep customization is limited. For example, creating unique chart types or intricate hover animations will come across as more complex when compared to more flexible libraries like D3.js.

➡️ See how to use Google Charts with React for dynamic data visualization

D3.js is the behemoth of data visualization, offering unparalleled flexibility in crafting almost any chart. It is often chosen for its sheer power and versatility.

• Website: D3.js official site
• Stats: 100k+ GitHub stars
• Unparalleled flexibility : As a low-level library, D3.js provides the tools to create almost any visualization imaginable.
• Strong community support: Its vast community ensures easy access to solutions, advice, and best practices.
• Optimized performance: D3.js handles vast amounts of data seamlessly - making it particularly useful for projects with large datasets.
• Steep learning curve: Its complex flexibility makes it challenging for beginners.
• Integration challenges: Integrating D3 with React can be less straightforward, often requiring manual DOM management.

3. Recharts

A blend of D3’s power with React’s elegance, make Recharts great for crafting responsive and stylish charts with an intuitive API.

• Website: Recharts Official Site
• Stats: 15k+ Github stars
• D3 and React synergy: Merges D3's capabilities with React's simplicity.
• Highly customizable: Offers extensive customization while being more user-friendy than raw D3.
• Library size: Its comprehensive nature might impact web application load times.
• Potential overhead: For basic charting, Recharts can introduce unnecessary complexity.

➡️ See how to use Recharts and Next.js to build an informational dashboard

4. Visx / Airbnb's Visx

This library strikes a balance between raw D3 and high-level libraries. It’s suitable for those looking for low-level utilities for customized charting.

• Website: Visx GitHub Page
• Stats: 12k Github stars
• Balanced approach: Sits between raw D3 and high-level libraries, offering customization with less complexity.
• Airbnb backing: Supported by Airbnb, ensuring quality and consistent updates.
• More manual input: Requires more hands-on work than other high-level libraries.
• Less ready-to-use: It isn't as plug-and-play as alternatives like Recharts.

5. react-flow-chart

Best suited for creating flowcharts or node-based diagrams with a customizable drag-and-drop interface.

• Website: react-flow-chart GitHub page
• Stats: 2.5k Github stars
• Flowchart specialization: Designed specifically for flowcharts, ensuring optimal tools for this need.
• Enhanced user experience: The drag-and-drop interface promotes user interaction and comprehension.
• Niche application: Its specialization can limit its use for broader charting needs.
• Feature limitations due to specialization: Since react-flow-chart specializes in flowcharts and diagrams for broader chart types, like bar or pie charts, you'd need another library.

6. react-financial-charts

Explicitly tailored for financial applications like stock trading or forex (FX) platforms, making it the go-to choice for financial visualizations.

• Website: react-financial-charts GitHub page
• Stats: 500+ GitHub stars
• Financial data focus: Provides tailored tools and charts for financial data, such as candlestick charts.
• High accuracy : Ensures precise representation of financial data, which is crucial for trading platforms.
• Narrowed use cases: Its financial focus might limit its applicability for other visualizations.
• Smaller community: Fewer resources and tutorials are available due to its niche audience.

7. react-charts

A simple and modern library, perfect for getting started without heavy configurations.

• Website: react-charts GitHub page
• Stats: 1.8k GitHub stars
• User-friendly: Designed for easy setup and use, ideal for quick projects or for beginners.
• Contemporary design: Delivers clean and modern visuals without extra design input.
• Potential feature gaps: react-charts excels at basic visualizations but lacks advanced features, such as the ability to smoothly animate mixed chart types (e.g. combining line and bar charts).
• Chart variety constraints: While react-charts offers basic chart types like line and bar charts, it does not support specialized types, such as radar or waterfall charts, limiting its versatility.

8. ant-design-charts

Part of the well-known Ant Design ecosystem, this library provides a consistent design language and multiple out-of-the-box charts.

• Website: ant-design-charts GitHub page
• Stats: 2.1k GitHub stars
• Consistent design language: Being part of the Ant Design ecosystem ensures a uniform design.
• Variety of pre-styled charts: Offers multiple ready-to-use charts, reducing design time.
• Integration with non-Ant design UIs: ant-design-charts is optimized for the Ant Design ecosystem. When integrating with other UI frameworks like Material-UI, Bootstrap, or Tailwind CSS, there might be design mismatches or additional styling adjustments needed to ensure visual consistency.
• Less flexibility: Does not offer the customization depth of D3 or Visx.

9. MUI X charts

This library seamlessly integrates with other MUI components and best suits those already invested in the MUI ecosystem.

• Website: MUI Official Site
• Stats: The entire MUI project has 70k+ GitHub stars
• MUI ecosystem integration: Designed to work seamlessly with other MUI components.
• Design consistency: Adheres to MUI’s design principles, ensuring a uniform look.
• Design limitations: MUI charts follows Material-UI's design. For projects with unique aesthetics, like a vintage-themed site, its default styles might not align, requiring tweaks to be made.
• Feature gaps: MUI charts is integrated with the Material-UI ecosystem, focusing on core chart types. It does not offer specialized charts like sunburst, treemaps, or advanced heatmaps. For such intricate visualizations, you'd need to complement with another library or tool.

10. react-chartkick

Aims for simplicity with a minimalistic API. It supports multiple charting engines like Chart.js, Google Charts, and Highcharts.

• Website: react-chartkick GitHub page
• Stats: 800+ GitHub stars
• Simplicity first: Offers a minimalistic API for quick and easy charting.
• Multiple-engine support: Compatible with various charting engines like Chart.js, Google Charts, and Highcharts.
• Customization depth: react-chartkick prioritizes simplicity, which means it limits customization. For intricate designs or specific chart behaviors, reliance on underlying engines like Chart.js or Highcharts might be necessary, introducing added layers of complexity.
• Dependency on other engines: Relies on other charting engines for rendering, which can introduce complexities.

11. react-chartjs-2

A React wrapper for Chart.js, known for its simplicity and lightweight design.

• Website: react-chartjs-2 GitHub page
• Stats: 2.7k GitHub stars
• Lightweight design: Known for its minimalistic and fast performance.
• Versatility: Inherits the flexibility and variety of Chart.js.
• Customization depth: While "react-chartjs-2" simplifies the Chart.js integration, it might limit advanced customization. For unique or complex visual styles, diving deeper into Chart.js itself may be required, potentially complicating the development process.
• Feature limitations: Depending on your project requirements, it might not be as comprehensive as libraries like D3 or Recharts. For instance, certain advanced animations, plugins, or interaction patterns might require direct manipulation of Chart.js, bypassing the React wrapper.

Choosing the right React chart library: 6 key considerations

Beyond just looking at features, when selecting a React chart library for your project, it’s essential to keep the following considerations in mind:

The nature of your project: The kind of project you are working on can significantly influence the library choice. For instance:

• Web Apps with Financial Data: Libraries such as react-financial-charts would be an ideal choice, thanks to their specialization in financial visualizations.
• Flowcharts and Diagrams: For apps where flowcharts are central, react-flow-chart offers specialized features tailored for this need.

Design consistency: If your project heavily relies on design systems like MUI or Ant Design, using libraries from the same ecosystem ensures a consistent look and feel. Hence, MUI charts or ant-design-charts would be preferable.

Performance and scalability: Performance becomes paramount for applications that deal with a large volume of dynamic data. Libraries like D3.js and Recharts are optimized for handling large datasets.

Customization and flexibility: Some projects require highly customized chart designs. D3.js offers the most flexibility but comes with a steeper learning curve. Visx sits in between, offering low-level utilities for high customization.

Integration and compatibility: Think about the other technologies and libraries you are using. Some chart libraries might have smoother integrations or built-in compatibility with specific backends, frameworks, or platforms.

The learning curve and documentation: Always consider the ease of learning. A well-documented library with an active community can significantly reduce development time, even if its API is complex.

6 practical scenarios and library recommendations

When choosing a React chart library, it’s worth considering a few real world scenarios to determine which would be the best fit for your needs.

Below are a few practical examples and recommendations based on the specific requirements of each situation:

Scenario 1: You’re building a personal project or prototype, and you need to quickly visualize some data without spending much time on setup and configurations.

• Recommendation: Opt for react-chartkick or react-google-charts. Both libraries are known for their ease of use and require minimal setup, making them perfect for quick visualizations.

Scenario 2: Your application is a trading platform, and you need to display candlestick charts, historical trading data, and other financial visualizations with high accuracy and detail.

• Recommendation: react-financial-charts is the ideal choice in this case. Tailored specifically for financial data visualization, it provides the tools and chart types most commonly needed in the trading sector.

Scenario 3: You’re working on an educational platform that aims to teach users about data structures and algorithms using interactive flowcharts and diagrams.

• Recommendation: react-flow-chart should be your go-to. With features tailored for flowcharts, including drag-and-drop capabilities, it can help make educational content more engaging.

Scenario 4: You’re developing an application where the design consistency of the charts with the rest of the UI is paramount. Your application already heavily relies on the Material-UI design system.

• Recommendation: MUI charts would be the best bet. Designed to work seamlessly with Material-UI, it will ensure your charts don’t look out of place.

Scenario 5: You’re working on a large-scale data analytics platform where the volume of data is immense, and performance optimization is a top priority.

• Recommendation: D3.js or Recharts should be on your radar. Both libraries are known for their performance optimization capabilities when handling large datasets.

Scenario 6: Your project requires charts that are not standard, and you need a library that provides the tools to craft custom, unique visualizations.

• Recommendation: Visx from Airbnb offers a collection of low-level primitives that allow for high customization, making it an excellent choice for creating bespoke visualizations.

Remember, while these recommendations provide a guideline, always test and evaluate libraries within your requirements and constraints.

The best library for a project often depends on a combination of factors, including the nature of the data, the specific visualization needs, and the surrounding tech stack.

Wrapping up

When deciding on a charting library for React, the choice often comes down to the project’s specific needs.

Some libraries offer more customization options, while others provide a smoother integration experience. The most crucial aspect is understanding your requirements and what each library brings. From D3’s unparalleled flexibility to the seamless integration of libraries like MUI charts, there’s a tool for every situation.

As you venture into data visualization with React, always prioritize understanding your data’s nature, the type of visualization required, and your audience’s needs.

Happy charting!

React chart library FAQs

1.Which is the simplest React chart library? While several libraries offer simplicity, react-chartkick and react-google-charts are notably beginner-friendly due to their straightforward APIs and minimal configuration needs.

2.Which React chart library offers the most customization? D3.js is renowned for its unmatched customization capabilities. Those wanting to combine D3’s power with React can consider Recharts and Visx.

3. Are any of these React chart libraries compatible with TypeScript? Many modern libraries, including Recharts, ant-design-charts, MUI charts, and Visx, have TypeScript definitions or are written in TypeScript. Always refer to the specific library’s documentation for details.

4. Do React chart libraries support realtime data updates? Many of the top React chart libraries support dynamic data updates, especially D3.js, allowing smooth transitions and animations upon data changes. To enable realtime updates in React there are many methods available. No matter which you choose, ensure you manage state efficiently in your React application for optimal realtime data rendering.

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Posted on Aug 27, 2020 • Originally published at devaradise.com

7 Best React Chart / Graph Libraries & How to Use Them (With Demo)

This post was originally published at https://www.devaradise.com/best-react-chart-graph-libraries

Chart or Graph is a common component in a web application used for data visualization. It turns raw data into information that can be used for decision making.

Creating chart components in the web-app from scratch is difficult and very time-consuming. Because of that, web developers usually create chart components using an external library.

In React, We can also use an external library to create chart components. There are many React chart libraries we can choose.

Here, We are going to discuss and review 7 top react chart/graph libraries so you can choose and decide what's best for project.

Related Tutorials

• React Datepicker Tutorial with Top 2 Datepicker Libraries
• React Tabs Tutorial: 3 Ways to Implement

We will see what each of these libraries can do, how easy to use & customize it, and how popular it is among developers.

For usage details, I include a code snippet and a working demo example for each library. Each demo example has the same data and case so to make it fair when we compare them.

We will visualize sales and leads data from January to June with Line and Bar Chart.

You can see the all demo examples in the link below.

Demo Examples

For the complete codes, you can see here on github . You can also clone the repository that contains demo for other tutorials as well. Don't forget to star it if you find it useful :D.

Now, let's see and review the 7 react chart libraries below.

1. Recharts

recharts / recharts

Redefined chart library built with react and d3.

Rechart is a simple, easy and highly-customizable open-source chart library for React.js. It supports line chart, bar chart, doughnut, pie chart, etc.

With more than 14k stars on github, Rechart is the most popular chart library built on top of React and D3.

Recharts is well documented and easy to implement. It also has minimal pre-designed charts that suit any design style.

How to use recharts

To use recharts, first you have to install it on your React project.

After it installed, you can create charts by using recharts components easily as follows.

As you see, the data object supplied for the charts is simple (line 5 - 12). It doesn't mix with options object like the other chart libraries. This makes recharts is easier and simple to implement.

In recharts, most of the chart elements like legends, grid, and tooltip also have their own component. So, we can just call include the in JSX markup if we want to show them.

If you want to see more recharts examples, you can see go to the official recharts examples page .

2. React-chartjs-2 (Chart.js wrapper for React)

reactchartjs / react-chartjs-2

React components for chart.js, the most popular charting library.

react-chartjs-2 is just a wrapper for Chart.js , the most popular javascript library for chart and graph with 50k+ stars on Github.

Chart.js is a great library to create highly customizable charts. It has many kinds of charts and a lot of options to customize it. It supports line chart, bar chart, doughnut & pie, scatter, radar, etc.

With react-chartjs-2, Implementing Chart.js in React becomes easier. React-chartjs-2 creates ready-to-use React chart components that can be used in JSX.

How to use chart.js in React

To use chart.js, you have to install chart.js and react-chartjs-2 as follows.

After that, you can import the chart components you want to implement, and use them. You can see the codes below.

With chart.js, the data object supplied for the chart has some properties to customize it, like fill , backgroundColor and borderColor .

The options object is used to set any configuration that not related to the data like chart layout, legend, animations, etc.

There are a lot of options to customize your chart with Chart.js. You can see the Chart.js official docs for that.

plouc / nivo

Nivo provides a rich set of dataviz components, built on top of the awesome d3 and react libraries.

Nivo is another best data visualization library for React, built on top of D3. It is highly customizable and has a lot of data visualization components with very good documentation .

It supports line chart, bar chart, bubble, heatmap, pie, radar and many more with the options to create them using SVG, Canvas and HTTP API.

Nivo also provides server side rendering ability and fully declarative charts.

How to use nivo

Nivo is modular. So, you don't have to install all packages on your project. Just install the components you want to add using yarn. You can find all the component list here .

After that, you can create nivo charts as follows.

The data object and options for each chart type are different. Nivo components has also many props for customization.

In the first look, that may seem intimidating. But, with Nivo modularity and very good documentation, you don't have to worry about all of those options.

4. Hightcharts-react (Highcharts wrapper for React)

highcharts / highcharts-react

The official highcharts supported wrapper for react.

Highchart is a popular javascript library for charts with 9k+ stars on Github. To make it easily implemented in React, the developer create highcharts-react as a react wrapper for highcharts.

Highcharts supports various line charts, time series, area chart, column/bar chart, pie chart, scatter, bubble, and many more. You can see the complete demo here .

How to use highcharts

To use highcharts, first, you have to install highcharts and highcharts-react-official as wrapper on your React project using npm.

After that, you can create the charts as follows.

As you see, you should create a mixed data and option object for each chart component. The data object structure are quite readable.

To customize it further, you can see the official documentation to add more options.

5. React-apexcharts (ApexCharts wrapper for React)

apexcharts / react-apexcharts

📊 react component for apexcharts.

Like Chart.js and Highchart, Apexcharts is also a popular javascript chart library that can be implemented to React with a wrapper. Apexcharts supports line, bar/column, area, timeline, mixed, candlestick, etc.

Among the other 6 chart libraries, Apexcharts is the most feature-rich and beautifully designed chart library. With minimum options, you can have a chart with zoom in/out feature, area selection, and import options to SVG, PNG, and CSV format.

But, it comes with a cost. Apexcharts has slower rendering charts compared to other chart libraries here.

How to use apexcharts

To use Apexcharts, first, you have to install it on your React project, as well as its wrapper.

Then, you can create chart components as follows.

You only need to import a Chart component and call it in JSX with some props.

The data and options object are similar with highcharts. For more details of the customization, you can go to the official documentation .

6. React-vis

uber / react-vis

Data visualization components.

React-vis is a collection of react components to render common data visualization charts.

It supports line/area/bar charts, heat maps, scatterplots, contour plots, hexagon heatmaps, pie and donut charts, sunbursts, radar charts, parallel coordinates, and treemaps.

React-vis is great if you want to create your own designed chart.

How to use React-vis

To use react-vis, first you have to install it on your React project.

After it installed, you can create charts as follows.

As you can see, it's quite simple to create charts with react-vis. The data supplied for the chart is also simple.

Similar to rechart, react-vis also has smaller collection of components for chart element like Grid, Line, XAxis, YAxis, etc that can be used in JSX markup.

As for the chart style/design, you should manually import the react-vis css file first. You can also add your own style to customize your chart components.

FormidableLabs / victory

A collection of composable react components for building interactive data visualizations.

Victory is a set of modular charting components for React and React Native. It makes it easy to get started without sacrificing flexibility.

Victory supports various chart components like line, bar, area, pie, candlestick, scatter, and many more.

How to use victory

To use victory, first you have to install it on your React project.

After it installed, you can create the victory charts as follows.

Initializing a chart component with victory is simple. But, it doesn't come with a predefined chart element like legend, tooltip, etc.

So, if you want to add those elements to your chart, you should add and design it manually. You can see the official documentation for that.

Conclusions

Among the 7 libraries above, I recommend the first 5 libraries if you want complete features and easy-to-use charts. But, if you want to create a very customized chart, and design it on your own, you can try the last 2 libraries.

After reading this post, i hope you can now choose and decide what chart library is best for your React project.

If you find this post useful, feel free to share it with your developer friends. If you have any questions, or maybe a suggestion, let me know in the comment section below!

Happy coding!

Top comments (1)

Templates let you quickly answer FAQs or store snippets for re-use.

• Joined Apr 9, 2021

Great List. Syncfusion React Charts missing here. React Charts - Rich & Interactive Graphs with Real-time data

• 30+ Chart types
• Fast paced performance
• Elegant animation
• Simple configuration and API
• Support all modern browsers
• Mobile-touch friendly and responsive
• Detailed documentation

Demo : ej2.syncfusion.com/react/demos/#/m... Documentation : ej2.syncfusion.com/react/documenta...

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MUI X Charts

A fast and extendable library of react chart components for data visualization.

• Bundle size

The @mui/x-charts is an MIT library for rendering charts relying on D3.js for data manipulation and SVG for rendering. And, like other MUI X components, charts are production-ready components that integrate smoothly into your app.

With a high level of customization, MUI X Charts provides three levels of customization layers: single components with great defaults, extensive configuration props , and subcomponents for flexible composition. Additionally, you can also use all the MUI System tools, such as the theme override or the sx prop.

Using the documentation

The MUI X Charts documentation has a slightly different structure than other MUI X components. Instead of having a long page for each, the pages are divided in two:

• General description of the built-in features the component provides.
• A set of examples demonstrating the component with customizations.

All MUI X Charts components

Scatter Chart

Radar Chart

Funnel Chart

Sankey Chart

Gantt Chart

Supported features.

The features that are shared across multiple Charts components, such as axes and legends, are also documented on separate pages.

Custom components

Tooltips and highlights.

• skip navigation All Products Product Bundles DevCraft All Telerik .NET tools and Kendo UI JavaScript components in one package. Now enhanced with: NEW : Design Kits for Figma

Data Visualization: Build React Graphs the Easy Way

Web applications often have to help the end user make sense of different kinds of data. One of the best ways to show data in a nice and easy to understand way is to visualize it using graphs (charts).

This article will demonstrate an efficient approach to creating React graphs by using the KendoReact Charts library, and we will implement Donut, Bar, Line and Sparkline graphs. We’re going with the React graph library that is part of KendoReact, as it is feature-rich and highly customizable. As part of a complete React UI components library, where this data visualization library really shines is its integration with the 90+ components within KendoReact, enabling developers to implement consistent styling and sleek design across an entire project.

Now, let’s get started!

Project Setup

Before we dive into implementation of our React graphs, let’s scaffold a new React project using Create React App . You can do so by running the command below in your terminal.

After the project is scaffolded, we need to install a few packages to use the KendoReact charts library.

The first package is kendo-react-charts library, which contains a variety of chart components for React. Next, we have kendo-theme-material . For this example, we will use the Material theme. KendoReact also offers Default (created by the team’s internal UX experts) and Bootstrap themes, but you can easily customize them or even build your own. Finally, we need to install Hammer.js , an open-source library for recognizing gestures made by touch, mouse and pointer events.

After installing new dependencies, let’s clean up the project. First, replace the contents of App.js and App.css files.

src/App.css

In the App.js, we removed the default code created by Create React App and instead imported KendoReact Material theme and Hammer. We also have two classes in the App.css file to add a bit of space between graphs that we will implement in a moment.

That’s it for the setup. Start the development server by running either npm run start or yarn start , and you should see a blank screen. Let’s add some graphs.

Creating React Graphs (Charts)

Imagine you are working on a job applications manager platform. This platform allows companies to create job postings to which job seekers can apply. Employees working at a company that created a job posting should be able to see a variety of details, such as how many candidates applied, the status of applications, how many applications were accepted or rejected, and so on. As I mentioned before, we are going to implement four types of graphs:

React Donut chart – to show applications status for the current month

React Bar chart – to show applications status for the last three months

React Line chart – to show how many positions were fulfilled and how many are still left

React Sparkline – to show how many applications are left to process to reach the minimum target

React Donut Chart

First, we need to create two new files. The first file, constants.js , contains colors that are used for the graphs.

src/constants.js

In the second file, we will implement the Donut chart. In the src directory, create a new folder called charts , and inside of it, create a file called Donut.js .

src/components/charts/Donut.js

As you can see, we import a few different React components from the KendoReact library. These are used to compose the Donut chart.

The <Chart> component is a container for all other components. <ChartTitle> , as the name suggests, is used to display a title for the graphic. The <ChartLegend> component can be used to indicate if a chart should display a legend for the categories. In this example, we have the visible prop set to false , but you can switch it to true to display it.

<ChartSeries*> are the main components, as they are responsible for rendering the data. In this case, we specify that we want the donut chart by passing type="donut" prop to the <ChartSeriesItem> component. For a full list of available options, you can check out the documentation .

Before we can see the Donut graph on the screen, we need to update the App.js file, so it imports and renders the <Donut> component.

Now you should see the graph below on your screen.

That was quick and easy, and the graphs look great. However, we can’t see the actual percentages. Let’s update the chart and add a tooltip that will show the percentage distribution for each application status. Let’s head back to the Donut.js file and make the following changes:

• Add ChartTooltip import.
• Below imports, create a function called renderTooltip that renders the markup, which will be displayed in a tooltip. This function will be passed to the <ChartTooltip> component.
• Render the <ChartTooltip> component.

Below you can see the tooltips in action.

We have the first graph done. However, what if we would like to see results for the last few months? We can deal with that by implementing a React Bar chart.

React Bar Chart

As we’ve done previously, we need to create a new file for the graph— Bar.js .

src/components/charts/Bar.js

You might have spotted that a lot of things are similar, for example, Chart components. The Bar graph is specified by passing type="bar" prop to the <ChartSeriesItem> component.

We also have new components— <ChartCategoryAxis*> . These are used for modifying the content of a bar on each axis. In this case, we are providing months for categories, as well as the “Months” label on the Y-axis. We’ve also set the <ChartLegend> to be visible, as, in comparison to the Donut graph, we do not have status labels. Nevertheless, by providing the labels prop we can tell the ChartSeriesItem component to display values on each bar line.

Before we can see the Bar chart on the screen, we need to add it to the App.js file.

The image below shows how the Bar chart should look now.

We can see the status of applications for the last three months. A great thing about React graph library part of KendoReact is that the graphs can be made interactive. For instance, we can make them pannable and zoomable by adding zoomable and pannable props to the <Chart> like this:

What’s more, it’s also possible to export KendoReact graphs to different formats such as image or PDF. This can be very useful if you want to allow users to create reports with charts. Let’s have a look at how we can add an export PDF functionality.

How to Export a React Graph

We can add export functionality by taking advantage of kendo-drawing and kendo-file-saver packages. Let’s install them.

The next step is to update the Bar.js file. First, we need to import exportVisual method from @progress/kendo-react-charts , exportPDF from @progress/kendo-drawing and saveAs from @progress/kendo-file-saver . We also need to import useRef hook as we need to get access to the instance of the <Chart> component. This instance will be passed to the exportVisual method. See the updated code below.

Bar component

We’ve added a button that calls onPDFExportClick function. The onPDFExportClick function first prepares chart information that has to be exported. It is then passed to the exportPDF method that prepares the file and converts it into a dataURI . Last but not least, the dataURI is passed to the saveAs method, which triggers the file download.

You can apply this logic to other KendoReact charts as well. If you would like to read more about available export options, you can visit the detailed documentation .

React Line Chart

So far, we have implemented two types of graphs: Donut and Bar. Let’s look at how we can utilize a Line chart to display information about the number of fulfilled and pending jobs during the last three months. Again, we need to create a new file, this time called Line.js .

src/components/Line.js

We are using the same components as in previous charts, with an addition of the <ChartValueAxis> component, which is used to add a title text and specify minimum and maximum values to display in the graph. Finally, import the Line component in App.js .

Below you can see a gif that showcases the Line graph comparing the total number of jobs as well as how many are pending and fulfilled.

React Sparklines

Sparklines are simple small graphs that can be embedded in tables, headlines or text. We will use a bullet sparkline to show how many applications were processed, as well as the monthly target. As you will see in the code below, the Sparkline graph implementation is a bit different from previous graphs, as it does not use <Chart*> components, but rather the <Sparkline> and <ChartValue*> components.

src/components/charts/Sparkline.js

You can configure the look of the graph by modifying props passed to the <ChartValueAxisItem> component. In this case, we are spreading the bulletValueAxis object. The last thing to do is to import the Sparkline component and render it in the App.js file.

Below you can see how the final result looks.

To see a full list of chart types that a Sparkline can render, you can check the KendoReact Sparkline documentation page .

We have covered how to create React graphs using as an example these four graph types from the KendoReact UI and data visualization library: Donut, Bar, Line and Sparkline. Building graphs with KendoReact is very easy, as it provides a lot of ready-made components that are feature-rich and easy to customize.

If you would like to access the full code example for this article, you can find it in this GitHub repository .

Thomas Findlay

Thomas Findlay is a 5-star rated mentor, full-stack developer, consultant, technical writer and the author of “ React - The Road To Enterprise ” and “ Vue - The Road To Enterprise .” He works with many different technologies such as JavaScript, Vue, React, React Native, Node.js, Python, PHP and more. Thomas has worked with developers and teams from beginner to advanced and helped them build and scale their applications and products. Check out his Codementor page, and you can also find him on Twitter .

Related Posts

How to build forms with react the easy way, kendoreact: adding a grid, dropdown list and more, kendoreact: using charts and react hooks, make your react apps pop with data visualizations, all articles.

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React Topics

React js charts.

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Table of Contents

Introduction

What are reactjs charts, react chart libraries, creating a bar chart with chart.js, tips and tricks.

• Visual data representation is a vital part of modern web applications. It helps in the understanding and interpretation of data by presenting it in a more digestible and intuitive format. When working with a JavaScript library like React, you may need to integrate charting capabilities into your applications. This is where ReactJS charts come into the picture.
• ReactJS Charts are graphical representations of data using React. They can be in various forms like bar charts, line charts, pie charts, etc., to visually represent and analyze complex categories of data.
• There are many libraries available for creating charts in React. Some of the most popular ones include Chart.js, Recharts, Victory, Nivo, etc. These libraries have their own pros and cons, but they all provide a way to create responsive and customizable charts in React applications.
• While working with ReactJS charts, it is important to understand the underlying data structures and how to manipulate them to fit your needs. Make sure to read the documentation of the library you are using to understand how to customize your charts.
• One common error when working with charts is mutating the state directly. Always use setState or hooks to update your state in React. Directly mutating the state can lead to unexpected behavior.
• Another common error is trying to render a chart before the data is available. Always ensure the data is available before rendering the chart. This can be done using conditional rendering or loading states.
• The use of charts in a React application can provide a more intuitive and interactive user experience. With a variety of charting libraries available, it's easy to create beautiful and responsive charts, tailored to your specific needs. By keeping the above tips and tricks in mind, you can avoid common pitfalls and make the most out of your React charting experience.

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Data Visualization in React Application

Veronika Rovnik

React is one of the trendiest frameworks for building single-page UI-first applications. Its popularity is increasing for the second year in a row and there are reasons for that.

Getting started with React is easy both for beginners and experienced developers thankfully to the supportive community and detailed documentation — it covers pretty much every aspect of working with React — from basics to advanced concepts. To start developing a web application with a high-performance presentation layer, you only need minimal knowledge of HTML and JavaScript.

Features you’ll love about React:

• Fast learning curve.
• A declarative approach to building UIs which makes the code more predictable.
• Component-based architecture . Components are totally reusable, therefore, maintaining your code is an unchallenging task.
• Performance . The rendering with virtual DOM is fast — the diffing algorithm under the hood of React optimizes expensive DOM operations that are required to update the view. Therefore, the elements are cheap to create.
• Effective event handling system that is implemented through event delegation. It’s compatible with the W3C Document object model .
• Ecosystem . You can take advantage of React Developer Tools that can be installed as Chrome extensions.
• API stability. If something is changed, migration paths are clear.
• A custom rendered called React Native that enables writing native apps for iOS and Android.
• Community . It’s backed by Facebook and a strong community of developers from all around the world. Also, you can find plenty of tutorials, guides and StackOverflow discussions.
• Great for apps with real-time data .

As I am a huge fan of both React and data visualization , I’ve decided to share with you my experience of adding a custom dashboard to the React app. Analysts acknowledge a dashboard as the handiest tool for communicating data because it helps them to speed up the decision-making process.

If you are about to develop an application and enhance it with analytical features, you should definitely take a look at these two JS libraries I’m going to build a dashboard with.

The first one is a pivot table — it transforms the raw data into an aggregated one, the second — charting library for insightful and interactive visualization. Together they make up a great responsive and customizable dashboard that fits any project.

Choosing Pivot Table

If you were looking for data analysis and visualization tools for React, you know how tricky the entire process is. From my experience, the choice depends mostly on the project requirements. In particular, when considering a pivot table component for web reporting, the main aspects to which you should pay attention are the following ones:

• Smooth integration with the existing infrastructure.
• Easy connecting to the data sources and switching between them.
• Reporting functionality: aggregation functions, multiple filtering options (date filtering is of special importance), sorting , and grouping .
• Rapid performance . It should leverage both data loading speed and grid rendering.
• Configurable appearance: changing styles and multiple layouts.
• Interactivity.
• Conditional and number formatting. It’s an essential feature as it makes perception of the information easier and helps to focus on the most important data.
• Extensive API.
• Compatibility with all modern browsers and mobile devices.
• Built-in exporting to various formats.

My choice fell on the web pivot table component that has all implemented features inside.

Flexmonster is a JavaScript pivot table control. It’s designed for analysis of data that comes from different data sources: databases, Elasticsearch , simple JSON / CSV files , and OLAP cubes. Also, it has the pivot charts that allow visualizing the aggregated data.

The first thing I liked about it was the UI — it’s elegant, well-organized and allows keeping all the necessary functionality at hand. After I dug deeper into the functionality, I’ve revealed that the pivot table component fully corresponds to the requirements of my analytical project.

To my mind, its strongest feature is the ease of connecting to the data sources and handling a large number of records without leading to app’s performance degradation.

Built-in exporting of reports to Excel and PDF adds an extra advantage as end-users often need to share the results of their analysis with teammates. I didn’t need to install any third-party plugins to enable the exporting feature.

Also, I was pleasantly surprised by the ability to add multiple aggregations to the same measure and create custom formulas for calculating the values. These features make this component flexible and customizable upon the needs.

Choosing Charts

From the perspectives of a developer and an end-user, charts need to satisfy these requirements:

• Interactivity . You should be able to enable/disable elements on the chart easily.
• Responsiveness . Charts should run smoothly across all devices.
• Customization options that include configuring of tooltips, legends, titles, axes labels, zooming, themes, and more.
• Fast performance.
• Drill-down charts for visualization of multi-level data.
• Support of events and data binding for keeping multiple charts synchronized.

All these features are well embodied in the following charting library.

FusionCharts is a lightweight JavaScript charting library which offers a collection of responsive charts, graphs, and maps. It comes with a wide range of integration options: you can use this library with the most popular server-side languages and JavaScript frameworks.

I find it convenient that FusionCharts supports React through an open source wrapper component because anyone can contribute to its development.

The first thing I liked the most is its comprehensive documentation that is full of ready-to-use demos . It’s user-friendly and guides through the process of creating the first chart.

With its rich API, I’ve managed to customize the charts the way I needed — chart components can be configured through attributes. Also, interaction with charts is possible via events, therefore, I’ve managed to attach event listeners to some actions with the chart.

Let me show you one of the interactive samples of charts:

Here you can see data binding in action — the charts are completely synchronized.

How to integrate a pivot table and a charting library

To experience the advantages of components simultaneously, you need to integrate them.

Let’s take a closer look at the process of building a responsive dashboard in React application.

To achieve the best results, I’ve read two guides on integrating the components with React application. You can find links to them at the end of the tutorial.

Firstly, I’ve installed a pivot table as an npm package, loaded the data into the component, and configured the report. This React Integration guide helped me a lot. Secondly, I’ve added the charts to the page and passed the data from the table to them. The entire process of getting all things done took up at most half an hour.

But let’s do it in a step-by-step manner.

Setting up Flexmonster

Assuming that you’ve created a React app , install an npm package of Flexmonster, add a <div> container for a pivot table element and render it:

Once you’ve got the component rendered, fetch the raw data from the database and load it into the pivot table for further aggregation.

Connecting to the database requires using a utility called Compressor that should be installed on the server side. Tests have shown that it considerably speeds up transferring of large amounts of data from the database to the browser. This tutorial helped me to establish communication between the component and the PostgreSQL database.

Now it’s time to build a report .

Firstly, set a slice — a part of a report that specifies which hierarchies to put to the rows, columns, and measures. If you are creating a quarterly report, at this step you can filter the data by the last quarter.

Here is a sample of the slice:

Another option is to add filters dynamically via UI as well:

As a result, you can see the rendered pivot table that displays the aggregated data.

Setting up FusionCharts

Follow all the instructions from the Integration with React guide :

• install an npm package and import the component into your app
• create a <div> node where the chart will be displayed
• import themes’ scripts

Don’t forget to add the Flexmonster connector for integration with FusionCharts. It performs data processing for specific types of charts.

Real-time interaction

To create charts once the pivot table is ready to show the data, I’ve attached an event handler to the reportcomplete event of Flexmonster.

To make the two components interact, you need to do the following:

• Define a function which draws and updates the chart.
• Call it when the reportcomplete event is fired.

Flexmonster provides a comfy way to add event listeners:

The next step is to initialize the chart, get the aggregated data from the pivot table and pass it to the chart.

I’ve used flexmonster.fusioncharts.getData() method for this. It handles all the data processing. Firstly, you need to tell the method for which type of chart you want to prepare the data and specify a slice to display on the chart (optionally). After, you need to pass a callbackHandler function that is called once the pivot table is ready to act as a data provider and an updateHandler function that is fired every time some changes occur in the report (filtering, sorting, updating the data). callbackHandler receives the pre-processed JSON data, sets it to the chart and renders the chart instance, updateHandler sets the updated data to the existing chart.

And here you go! Now you can see an interactive dashboard composed of the pivot table and the chart. You can add multiple charts in a similar way. These charts are instantly updated based on the changes in the report. That’s what I call a real-time interaction :)

Try manipulating the report: drag and drop the hierarchies, filter the data and see how your actions are reflected in the charts.

Configuring chart’s appearance

You can tune the appearance of each chart through attributes . I’ve disabled tooltips, added captions and added prefixes for numbers on the chart.

Also, I recommend setting a theme for the chart. There are five of them and all of them are awesome. But my favorite one is a dark theme. Here is how you can apply it:

Styling the pivot table

Another cherry on the top is Flexmonster themes . I’ve decided to harmonize the components and apply a dark theme to the pivot table as well by including its styles:

With this tutorial, you’ve learned in practice how to create an interactive analytical dashboard based on the data source of your choice. As dashboards are incredibly instrumental in guiding business strategies, the presented solution can serve as a part of an embedded BI in your React app.

To my mind, both Flexmonster and FusionCharts simplify the developer’s life a lot when it comes to featuring your React-powered application with reporting and charting functionality. I encourage you to try using them with your data and create your unique way of expressing the insights.

Thank you for reading!

📊 Try it on CodePen

Recommended reading

• Flexmonster: Integration with React
• Flexmonster: Integration with FusionCharts
• React component for FusionCharts
• FusionCharts Gallery

Written by Veronika Rovnik

Passionate about mathematics, machine learning, and technologies. Studying approaches in the field of data analysis and visualization. Open for new ideas :)

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Using Google Charts for React Data Visualization

To truly understand data, try using charts and other types of data visualization instead of raw numbers. In web apps, charts are particularly useful for creating dashboards that visualize site activities. In this tutorial, we'll learn how to use Google Charts to visualize React data. React Google Charts is a thin, typed React wrapper with a free charting service and one of Google’s JavaScript libraries.

Google Charts is a free, easy-to-use interactive web service that developers use to visualize data. Google Charts has a ton of customization options, ranging from simple line graphs to more complex map trees. To create a chart, the user inputs data and the service returns the requested chart images.

Why Google Charts?

The Google Charts library is highly customizable and easy to use, making it an excellent choice for React developers. Unlike other libraries, it’s also compatible with all platforms, ranging from mobile to desktop. It uses HTML and SVG at its core, and therefore doesn’t depend on any additional library or plugin.

Setting Up a Project

We’ll start by setting up our React project. First, run the command below in your terminal:

Once the command above finishes downloading the required libraries for the React app, navigate to the project directory using the command below:

Installing React Google Charts

Install the react-google-chart plugin using the command below:

Building Components

To begin, let’s build our first components. First, create a file called Chart.jsx in the src/ folder:

Paste the code below:

With the code block above, we imported the Chart components from the react-google-charts previously installed plugin. The Chart component contains props that enable graphical representation. For example, the chartType prop gives us the ability to change the chart display type, the data prop accepts our input data, and the options prop accepts a map of options that contains the title , color , backgroundcolor , etc.

Updating App.js

Let’s clean up our App.js by replacing it with the code block below:

The code block above simply imports and renders our Charts components.

To see what we’ve built so far, run the code on your browser using the command below:

Here, we have a Google chart showing the various test scores of five students.

Populating Google Charts with React Hooks

Before diving into working with React Hooks , let’s first get a grip on what hooks are.

React Hooks are state managers introduced in React 16.8. They allow us to use features like useState() and more without having to write a class.

We’ll be simulating responses from an external source or API, and then using React Hooks to manage the states and keep track of the data.

Working with JSON

First, create a data.json file in the src/ folder:

Next, paste the JSON code below into the newly created file:

The code above is in JSON format and will serve as our API response.

Creating States

We’ll be managing two states: the loading state (when the data is coming from the API) and the data state (when displaying the data in our chart). Paste the code block below into the App.js file:

In the code block above, we imported the useState and useEffect components from the React library and then created two states: one for our loading gif, and the other to display the response data with useState .

Manipulating our State

Now, let’s manage our state by updating the code in the App() function with the code block below:

With the code block above, we import the data.json as a response that is passed into a timer function. This creates a 3-second delay to simulate a fetch request, after which we set our loading to false (stop the spinner) and set the data to the response data.

Using Multiple Charts

React Google Charts has a variety of chart types at our disposal.

In this section, we’ll be implementing a few of the charts to visualize our data from the data.json file.

When loading, we want to display a loading gif. Import the loading gif into the App.js file:

Now, let's populate our app with other chart types. Update the return block by copying and pasting the code block below:

We’re looping through the response stored in the data hook and passing it to our Charts components as props while listening to the loading state. While the data is loading, the loading gif is displayed, and after 3 seconds, the data is displayed.

Updating the Google Charts Component

In the previous section, we successfully mapped and passed our response as props into the Charts component. Let’s update our Charts component to receive the props and render the charts. To achieve this, update the code in src/Charts.jsx :

In the code block above, we destructure data , options , and chartType from the response prop, and then assign their corresponding property to the Chart component.

We’ve successfully built our chart application! Let’s refresh it to see the charts as shown below:

Use Cases for Data Visualization

Keeping track of user data and activities can be rigorous for users, admins, and developers since data is often presented in several rows and columns. This makes reading and interpreting data a strenuous task for the analyst. With visual data, anyone can easily understand it, thus making it easy for the brain to capture and process.

Visual charts are most often used in:

• Admin dashboards
• Network mapping
• Real-time data flow in stock markets

In this tutorial, we explored React Google Charts, its uses, and how to use it to create dynamic charts for our applications. We also broadened our expertise by looking at other techniques for building complicated applications with React Hooks and the react-google-charts package.

Read the React Google Chart documentation for more implementation information and check out the complete source code for this article.

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• Best React Charts Libraries

Best React Charts Libraries 2023

Since we now live in the age of information proliferation, data continues to accumulate at an exponential rate. These massive datasets, however, are diffuse and difficult to read. That's why it's important to visualize data so it can be readily accepted and used. It is crucial to utilize the right charts when visualizing data, but visualization is more intuitive and relevant.

The term data visualization, simply put, refers to the practice of presenting information visually, such as through charts and graphs. It provides a visual representation of analytics, helping decision-makers better understand complex topics and spot novel patterns. With interactive visualization, you can modify the data you see and the way it's processed in real-time, expanding on the original concept by leveraging technology to dig deeper into charts and graphs.

In this article, we explore the best React chart libraries in 2023 to help boost your data visualization efforts. 

Boost Your Data Visualization 

Charts, graphs, infographics, and even animations are all examples of data visualization methods. With the help of these visual representations, even the most intricate data relationships and data-driven insights may be conveyed with ease.

It's crucial to remember that data teams aren't the only ones who can benefit from data visualization. It is used by management to communicate company structure and reporting relationships, and by analysts and scientists to uncover and clarify trends and patterns.

Data visualization is a critical aspect of the data science process. It helps teams and individuals explain data to their colleagues and decision-makers more clearly. Teams that are in charge of managing reporting systems usually use predefined template views to keep an eye on how things are going. But performance dashboards aren't the only way to show data visually. During text mining, for example, an analyst might use a word cloud to find important ideas, trends, and hidden connections in this unstructured data. They could also use a graph structure to show how entities in a knowledge graph are related to each other. There are many ways to show different kinds of data, and it's important to keep in mind that this is a skill that should be used by more people than just your core analytics team.

The 4 key ways data visualization helps make sense of data for businesses include: 

1. Learn to swiftly grasp concepts. Using visual representations of data, businesses may observe massive volumes of data in a unified, understandable format, and extrapolate meaningful insights. Because graphical data analysis is so much quicker than spreadsheet data analysis, it allows firms to respond rapidly to issues and inquiries.

2. Find connections and patterns. When presented visually, even massive volumes of complex data begin to make sense, allowing organizations to easily identify strongly connected factors. Although some connections will be immediately apparent, others will require more thought. In order to achieve their most essential objectives, businesses need to zero in on the factors most likely to affect them, and this can be done by identifying the links between various factors.

3. Identify developing tendencies. Businesses can gain an edge over the competition and positively impact the bottom line by using data visualization to uncover trends in the business and the market. Problems with product quality or client retention can be caught early by monitoring for unusual fluctuations.

4. Communicate. Once a company has used visual analytics to get new insights, the next step is to share those findings with the appropriate stakeholders. Visual representations of data, such as charts and graphs, are useful here because they capture attention and convey information rapidly.

Role of Charts in Data Visualization

To better understand massive volumes of data, data visualization charts provide a visual representation that tells a story using symbols. Visual data metaphors, such as charts, more effectively engage human visual processes and augment human cognition than semantic data alone. The charts and graphs used in data visualization reduce massive amounts of complex, disorganized data to easily digestible graphics that reveal previously unseen insights.

Using charts or graphs to visualize vast amounts of complex data is easier than poring over spreadsheets or reports because of the way the human brain absorbs information. Quickly and easily conveying thoughts in a general fashion, data visualization also allows you to experiment with various scenarios by making little alterations.

Further, visualizing data can:

• Point out the weak spots so they can be strengthened.
• Determine which variables have the most impact on how customers behave.
• The ability to know where to put what things is a huge help.
• In other words, try to guess sales numbers.

There are many different kinds of charts that can help us understand large, complicated data sets better. But each chart used to show data has a different purpose, and not every chart is right for every project. Using the wrong type of chart to show data can make it even harder to understand. The type of data being analyzed and the questions being asked to determine which data visualization chart is best.

There are 5 main kinds of charts that are used to show data

1. Multidimensional: These charts show at least two or more variables and create 3D visualizations with several layers at the same time.

• Pie charts are a type of data visualization that uses a single circle cut into "slices." Each "slice" shows a certain percentage of the value of the whole circle.
• Venn diagrams : In a Venn diagram, overlapping circles, each of which represents a different variable, show how logically related two sets are.
• Stacked bar graphs : Bar charts are a type of data visualization that uses horizontal columns to compare numbers between groups. A waterfall chart is a type of multidimensional bar chart that uses floating bars to show how different factors can change an initial value in both good and bad ways. Bullet graphs are another way to show data with bars. They have a single main measure that is layered with different colors to show actual value, target value, and ranges.

Histograms show how data is spread out over a continuous interval or a certain amount of time. They do this by putting vertical bars on a horizontal line to show where most of the values are.

2. Temporal: Linear and one-dimensional, these data visualizations are usually made up of lines that start at a certain time and end at a certain time. Some temporal data visualization chart examples include:

• Scatter plots : This chart shows two variables as data points. The value of each variable determines which way each data point is facing. The data visualization bubble chart is a popular variation on the scatter plot. In this chart, the area of each data point bubble shows a third value.
• Polar area diagrams are basically line charts that show data by filling in the space between the x-axis with colors. This helps show how parts relate to the whole.
• Time series sequences show data points at different points in time by using lines, steps, or column charts, where the horizontal axis shows time and the vertical axis shows the values being measured.
• Timelines show the order in which things happened on a scale of time.
• Line graphs are a type of data visualization that shows data as points connected by a line. These points show how a variable changes over time.

3. Network: These charts show how different datasets are connected in a network:

• Matrix charts : A matrix diagram shows in grid form how two or more groups of elements are related to each other. At least two variables must be put in both the X and Y categories. Different colors are used to show variables after the first two.
• Node-link diagrams are the standard way to draw a graph, with the vertices shown as disks, boxes, or text labels and the edges shown as line segments, polylines, or curves.
• Word clouds , which are also called "tag clouds," make the most common words in a text or the most-searched words bigger or smaller depending on how often they show up. Larger words are used more often.
• Alluvial diagrams show how the structure of a network changes over time. They are also called flow diagrams.

4. Geospatial: Spatial data visualizations put points of information on maps. The best charts for showing data in a geospatial context are:

• Flow map : Linear symbols show how something moves from its starting point to its endpoint. The width of the line shows how much flow is increasing or decreasing.
• When the number of data points in a certain area goes up or down, it shows how concentrated a feature is. This is called a " density map ."
• Cartograms distort the real borders of a geographic area to show different variables, which will either make the borders bigger or smaller depending on the value of the variable.
• In a heat map , the size of an event is shown by color in a two-dimensional space, and the map is either cluster or spatial.

5. Hierarchical: These charts show how smaller groups fit into larger ones. Some of the best types of charts for displaying hierarchical data are:

• Tree diagrams show the relationship between tasks and subtasks, or between parents and children.
• Ring charts : A ring chart, also called a sunburst diagram, is a type of data visualization that shows hierarchy as a set of concentric rings, with each ring representing a level in the hierarchy.

Best React Data Visualization Libraries for Plotting Charts

React Chart Libraries, also popularly known as React apps, work on visualizing the data, managing the libraries, allowing users to build and design the components, and supporting them in keeping the data. These adaptable and scalable apps use frameworks that are highly functional and sustainable in the long run.

React Chart Libraries are a useful resource for web developers who want to add insightful data visualizations to their websites. These apps are mostly used to aid web developers in demonstrating the current or future state of a specific application or feature. Because of this, site developers can now concentrate on creating useful content rather than worrying about how it will look to users.

The chart is a standard feature of both mobile and web-based software. Various kinds of charts reveal various kinds of data. The popularity of digital charts can be attributed to the fact that we can read and collect important data in a variety of formats that best suit our needs. Line charts, bar charts, pie charts, Gantt charts, and bubble charts are the most common types of the best React charts library, and each is intuitive and simple to use.

In the same way that React apps are used to create charts, a Reactjs charts library can be used to create charts. These programs are adaptable and reusable since they are built on extensible frameworks. To better incorporate meaningful data into online projects, web developers can use chart libraries for React.

Recharts is known as one of the most reliable React charting libraries, so professionals and web developers use it the most. This React graph visualization library has a light dependence on different D3 modules, but it also supports SVG natively. The parts that Recharts use are all presentational, in part because they are all declarative.

Recharts offers beautiful charts right out of the box, and you can modify them by adjusting component props and putting in your own components. The given reusable components can also be modified by the user to better suit their own purposes. Unfortunately, the library's charts are not designed to work well on mobile devices. Putting them inside a responsive wrapper component will fix this issue by automatically adjusting their width to match the width of the viewing device.

React-chartjs-2

If you know about the charting library chartjs, you know that React-chartjs-2 is not a typical React graph library. It's better to think of it as a React wrapper for chartjs, which is a JavaScript library for making charts.

For those who don't know, chartjs is a React chart library that is easy for beginners to use. It uses HTML5 Canvas to build chart components that are different from each other in their types and kinds. To use React-chartjs-2, you should know how Chartjs works and how to read its documentation. Since the framework is easy to understand, it shouldn't be too hard to do the same. 

D3.js is a JavaScript library that lets you use data to change documents. D3 lets you use HTML, SVG, and CSS to bring data to life. By focusing on web standards, the D3 Reactjs graph library lets you use all of the features of modern browsers without being tied to a proprietary framework. It does this by combining powerful visualization components with a data-driven way of manipulating the DOM.

Flexible and simple, Chart.js is a JavaScript charting library that delivers cool features for the modern web such as colors plugin, tree-shaking, scale stacking, subtitle plugin, line segment styling, advanced animations, and more. 

Victory is a popular React chart library that can be used with both React and React Native. It has a set of charting components and the same API for all applications. Users can make charts out of data for both Android and iOS apps using the same API. The library makes it easy to make charts that work on different platforms. It is also easy to learn and use.

It also makes it easy to add charts like line, pie, bar, and candlestick charts to web and React Native apps .

Nivo gives web developers a unique way to make charts and other data on the server side. For data visualization and presentations, there are many different and creative templates from which to choose. The chart library has unique and eye-catching ways to show important information. Nivo is a charting library that gives you SVG, HTML, and Canvas charts, as well as transitions (or motions). Nivo is a one-stop shop for you if you are looking for a precise and flexible way to visualize your data.

React Vis is one of the best React charting libraries. It was made by the web developers at Uber Open Source. It gives you a pretty easy and quick way to figure out how to chart the parts you need for your data visualization needs.

Some of the most common charts for visualizing data are line charts, area charts, bar charts, pie charts, donut charts, contour plots, heat maps, hexagon heatmaps, scatterplots, pie and donut charts, sunbursts, radar charts, parallel coordinates, and treemaps, to name a few.

VISX is a new name in the field of React Chart Libraries. It was made by Airbnb and has an active repository that combines the best parts of React DOM and D3.js in a well-thought-out way. 

This library uses the power of both D3.js and React. D3.js is used to do complicated calculations behind the scenes, and React is used to update the DOM. It is broken up into several small packages. This keeps it from getting too big because developers can only download the packages they need. It is also neutral because it lets developers use their own libraries for animation and state management. Since VISX assumes you're using React, you can choose any animation library that works with React.

VISX is easy to use for developers who know React, and the developer can make changes as needed by making their own chart components that can be reused. You can build complex visualizations by using small D3-based primitive visualization components. It comes with well-organized documentation and a gallery of examples to make it easy for you to get started.

Everyone knows about the tech giant Alibaba who are the ones behind BizCharts . BizCharts is a React Chart Library with an impressive library and plenty of different uses. BizCharts is also known for having the largest collection of charts so far. This makes it a great place to start when designing and visualizing data. 

React-stock charts

The React-stockcharts library is a must-have React chart library for anyone who wants to see their stock chart data in a variety of styles that are both flexible and easy to understand. React stock charts are a one-stop shop for web developers who want a framework for displaying financial data.

You can track and show the stock price of any company over a certain time period using charting and range tools. The component has zoom, pan, tooltips, crosshairs, trackballs, period selectors, range selectors, and events to make stock charts more interactive.

Rumble Charts

Rumble charts are the best React chart library to use if you want to make anonymous charts with React. It's a simple app with templates from today. Rumble Charts can be thought of as a way to use react components to build flexible charts that are easy to put together. 

React time series charts

For time series components, the React time series charts are a React chart library that is always available and gives out a well-organized set of charting features that are chosen to fit the needs of modules. These charting parts make it easier to see network traffic and time-series data in a logical way.

Ant Design Charts

Ant Design Charts is the next best charting library for React. It is much easier for students of the React technology stack to use. It gives you high-quality charts right out of the box and asks you to look into how the default charts affect the user experience. Users can easily change the details of charts to meet the needs of their business. This is in part because the documentation is so good and has so many examples, rules, and variations. It's also a great platform for making changes to existing themes and components.

Ant Design is used by companies such as Lenovo and Toyota. It's a great choice for business projects at a high level. It's a great choice for beginners because it has so many resources.

eCharts for React

ECharts is a powerful, easy-to-use, interactive charting and visualizing tool that works well for business presentations. ECharts is a robust open-source charting library that makes it easy for developers to incorporate interactive, customizable, and responsive charts into their React websites. With this charting library, developers need only describe the chart's settings in an object and send that object to the ReactEcharts component's prop in order to get started. The lack of walkthroughs in the documentation can make it difficult for newcomers to understand and use, and some developers may feel that the documentation is inadequate.

React-financial-charts

React-financial charts are dedicated to finance. With many improvements throughout the years, this viable option delivers options to present financial data where users can customize thickness, brushes, and outlines of Stock Charts for composite series.

React-timeseries-charts

React time series charts give you a React chart library with time series components. This gives you a set of well-organized charting features that meet the needs of modules.

React time series charts help you see network traffic and other data that changes over time in an organized way. They add clear examples and clear documentation to the platform, which makes the low maintenance worth it.

This library was made for React so that network traffic and time series data can be seen. The library for React is mostly about time series data charts. They were made with d3 and React at a higher level of being able to be put together. Even though the library isn't actively updated or maintained, it can be used for a lot of examples and documentation.

Storytelling using React Graphing Libraries

As humans are presented with an ever-increasing data flood, visual storytelling is more crucial than ever. Visual, interactive metaphors are more successful in engaging human perceptual processes than static number figures and text. Incorporating visuals into information has been shown to greatly enhance engagement.

Information can be communicated more quickly using visual aids than through a lengthy spreadsheet or lengthy report. Metaphors in visual form serve as a bridge between speakers of different languages. Using data visualization charts makes data more digestible, which in turn aids in the rapid extraction of insights, the enhancement of decision-making, the exposure of previously hidden patterns and relationships, the anticipation of emerging trends, the facilitation of visual analytics, and the enhancement of collaboration.

Svitla Systems has rich experience and expertise in web solutions development , including React as well as in building high-end data visualization on React. If you care to learn more, please contact our representatives for more information.

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Data Visualization

Data Visualization with React and GraphQL | Quick Guide

Navdeep Singh Gill | 04 April 2023

Table of Content

In this article, additional resources.

• Top Data Modelling Techniques and Tools 
• Data Fabric Vs Data Mesh | Detailed Comparison
• Data Validation Testing Tools and Techniques
• Data Visualisation Consulting Services
• Enterprise DataOps Readiness Assessment
• Talk to a Data Visualization Expert

Introduction to Data Visualization

A picture is worth a million words – especially when you are trying to understand and discover insights from data. Visuals are especially helpful when you’re trying to find relationships among hundreds or thousands of variables to determine their relative importance – or if they are important at all. Data Visualization , as its name Data and Visualization, describes the term. Data Visualization means to visualize the raw data so that one can understand the information easily. Some elements of data visualization are:

1. Simple Analysis

2. Clear Handling and Keys

3. Summaries of Key Points

4. Add Design Elements

3D Dimensional data which provides the perception of depth, breadth, and height. Click to explore about, What is 3D Data Visualization?

Why is Data Visualization Important?

So here is the answer: most industries like the health industry , IT sector, HR, logistics, marketing, and so many of them require that data should be carried in such a way that they can track insights daily, weekly, monthly, and quarterly and yearly basis. So the solution for that problem is a dashboard with data visualization charts and graphs. As we know, dashboards give a clear vision and perspective to decide whether it is normal or crucial for your organization easily. For example, you can check this screen recording for a sample dashboard with data visualization. With the help of graphs and charts, you can easily understand the purpose of this dashboard. There are four main reasons we visualize data-

1. Digest large amounts of data at scale.

What are the best Data Visualization Techniques?

To create meaningful visuals output of your data, there are some basic techniques and tips you should consider. Size of the data and composition play an important role when selecting graphs to represent your information. Before we move further here are some of the tips that you need to keep in your mind:

1. Word Clouds

Word clouds give corporations the idea of how frequently a word is used. The words in the cloud are of various sizes. The larger the scale – the higher the frequency. This technique may be helpful, for a model, for sentiment analysis of the customers’ social media posts.

2. Connectivity Charts

Connectivity charts show the links between aspects or events. The chart below shows the connections between machinery failures and their triggers, as well as the strength of these connections. Visualization techniques that work for both traditional and big data Some of traditional data visualization techniques accommodate big data as well.

3. Line Charts

Line charts allow watching on the behavior of one or several objects variable over time and recognizing the trends. In traditional Business Intelligence, line charts can show sales, revenue development and profit for the last one year. When running with big data, organizations can use this visualization technique to track total website clicks by weeks, the average number of complaints/requests to the call center by weeks or by months, etc.

4. Pie charts

5. bar charts.

Data Visualization Techniques uses charts and graphs to visualize large amounts of complex data. Click to explore about, Advanced Data Visualization Techniques and its Features

Data Visualization for Big Data, Data Science, and IoT

Data visualization can be helpful in many sectors. Here are some of the popular sectors: By using data visualization , it became easier for business owners to understand their extensive data in a simple format. The visualization method is also time-saving, so business does not have to spend much time to make a report on any work or solve a query. They can quickly do it in less time and in a more attracting way. Visual analytics shows a story to the viewers by their work. By using charts and images or graphs, a person can easily represent the whole concept. As well the viewers would be able to understand the entire thing easily. The most complicated data will seem easy when it undergoes the process of visualization.

Big data report gets transformed into a simple format. And it helps us to understand the concept in a natural way. With the Data visualization process, it gets more comfortable for the business manager to understand their product growth. The visualization tools can be very useful to monitor an email campaign. Or company’s initiative regarding something. Market competition in a better way.

What are the benefits of Dashboard?

The benefits of dashboard are listed below:

1. Data Accessibility

You can easily access your data and set your tasks, actions, and decisions as per the priorities.

2. Data Visualization

You can get an exact and better visualization of data from your dashboard’s home screen.

3. Better Decision Making

Now we know that if the data and visuals are crystal clear and understandable, it will help us make the right call for the organization.

4. Accountability

Now, if the right decisions are made, we can say that the accountability will be improved and increased of the organization.

5. Interactivity

In an organization, multiple heads of the organization from different departments check the dashboards. With the help of the dashboard data visuals, it will be easy to communicate between internal team heads. It can mutually take a decision that will be beneficial for the organization and its growth.

6. Gamification

As we know, data are displayed like charts, graphs, and multiple visualizations to give a gamified view.

What is the role of Reactjs in Data Visualization?

As I told that React works on components. With the help of components, we can do the small modules that can be stateful or stateless, and pass the data to them with the props. The benefit of making a component is to use the component again and again in the same project. We can make reusable component so that if we have some data in our app, we don’t need to create a new component every time. We all know that technology is changing day by day and people’s interest is also evolving with it. So, for a company or an organization has to be updated with the people interest.

A company whether it is selling any product or has to arrange its employee record, it needs to have a database. But the problem doesn’t resolve here. A person can not understand the significant amount of raw data. To overcome this situation we started using charts and graphs. So one can easily assume the information what the data has hidden in it. There are so many javascript libraries , available free of cost for creating graphs, charts or treemaps by the raw data. D3.js, Canvas.js , Highcharts.js, etc. are the best example of it.

Significance of ReactJs for dashboards and data visualizations in modern businesses

1. component reusability.

With ReactJs for dashboards and data visualization, you can create modular and self-organized code to get your dashboard app development off to a good start. You have to visualize the features and aspects you want in your dashboard. ReactJS allows you to reuse components, making them self-contained and organized.

Explore more about the concept of ReactJs.

2. Virtual DOM

Because of ReactJS's virtual DOM structure, you can get updates to your dashboard quickly and easily. Dashboards are designed to be accurate in real-time, so real-time data is displayed on the screen in front of the user. Because of React's virtual DOM architecture, component re-rendering is possible. ReactJS dashboards provide real-time notifications, continuous updates, and data charts on the fly.

3. Isomorphic JavaScript

A dashboard should function similarly to a client-side single-page application (SPA) with fast rendering. When a user has to wait for a few seconds, it becomes unappealing, and isomorphic javascript allows for fast rendering of web pages.

Isomorphic javascript overcomes business concerns such as:

- Inadequate SEO

- Poor performance

- Maintainability is excellent.

Further Investigate about JavaScript.

4. Continuous Development of Library

React developers keep you up to date on the most recent features and react libraries. As a result, you can be confident that using ReactJS for dashboards and data visualization will propel your company to the forefront of cutting-edge trends.

5. Great Ecosystem

React provides you with a large community of pre-built solutions to reuse. The majority of reactJS developers quickly find solutions to their problems. You can reuse charts, documentation tools, graphics, and other components.

A query based language for REST APIs used to retrieve the exact data needed Click to explore about, Overview of GraphQL Features and Tools

Examples regarding React Js for Dashboards and Data Visualization 

1. marketing kpi dashboards .

These dashboards display critical business information quickly and interactively, assisting marketing personnel in making critical business decisions.

2. Sales Dashboards

To remain competitive, your company must cultivate a data-driven culture. The ReactJS sales dashboard directs your sales time with the appropriate performance indicators and business metrics, ensuring that your company remains at the top of the charts.

3. Executive Dashboards 

Using the digestible data from the dashboards, business executives can monitor, track, and report, allowing them to execute flawless business actions.

4. Stripe Dashboards 

An incredible dashboard design responds to relevant user queries and displays ideal fit margins for each card. Users easily and quickly understand the graphical images.

5. Weather App Mobile Dashboard 

An easy-to-understand React JS mobile app dashboard connects the breadcrumbs with flawless navigation.

6. Instagram Professional Profile Mobile Dashboard

If you are using a professional account on Instagram, it will help users track your insights and reach of your account. It is the best example of a Mobile Dashboard with millions of Data.

What is GraphQL?

GraphQL is a query language for the APIs for fetching your data and runtime for satisfying those queries with your current data. It is an alternative for the REST APIs. GraphQL is a multi-platform language. It works for all type of clients including Android, iOS or web. GraphQL provides comprehensive and understandable information of the data in your API, gives clients the endowment to ask for exactly what they want and nothing more their need, makes it easier to evolve APIs over time, and enables powerful developer tools. GraphQL works on a single endpoint principle. You send a query to that endpoint/API by using a special Query language syntax. The server responds to a question by providing a JSON object. Let’s see an example of such a query. GET /graphql?query={ person(id: "1") { name } } It will return JSON data like this: {     “Name”: “BOB” }

Get more information regarding GraphQL which will be beneficial for you.

What is Rest?

A single endpoint.

GraphQL has only one parameter, where you send all your queries. With a REST approach, you create multiple endpoints and use HTTP verbs to distinguish read actions (GET) and write actions (POST, PUT, DELETE). GraphQL does not use HTTP verbs to determine the request type. GraphQL makes it simple to monitor for fields usage With REST, except forcing sparse fieldsets, there is no way to determine if clients server uses a variable, so when it comes to deprecating or refactoring, it’s impossible to decide on actual usage. GraphQL makes it possible to monitor which fields are used by clients.

Tailored to your needs

The REST API will usually return you much more data than what you need unless you constrain the API server as well, and you customize your responses for each different request. With GraphQL you explicitly request just the information you need, you don’t “opt out” from the full response default, but it’s mandatory to pick the fields you want. This helps saving resources on the server, since you most probably need less processing, and also network savings since the payload to transfer smaller.

A method of translating raw data into visual forms like charts, graphs, and maps. Click to explore about, Best Practices for Data Visualization

What is the Role of GraphQL in Data Visualization?

To link the react with the GraphQL we should have known about GraphQL.  You all might be thinking that GraphQL might be libraries for plotting graph. But you are wrong. It is not a library for graphs. GraphQL is a query language for the API and server-side runtime. It is a type system which we define for our data to execute the queries over the API. GraphQL isn't connected to any specific database or storage system and is instead backed by your existing code and data.

For example, we have to fetch the users details of first 100 people of our organization, so we can set the limit parameter using GraphQL so that our API returns only 100 users details. A GraphQL service is generated by defining types and fields on those types, then providing functions for each field on each type. For example, a GraphQL service that tells us who the logged in user is (me) as well as that user's age might look something like this:

type Query {   me: User } type User {   id: ID   name: String   age: Integer }   Along with functions for each field on each type:

function Query_me(request) {   return request.auth.user; }

function User_age(user) {   return user.getAge(); }

Once a GraphQL service is running, it can be sent GraphQL queries to execute and validate. A received query is first verified to ensure it only relates to the fields and types defined, then runs the provided functions to produce a result. For example the query:

{   me {     name   } }

{   "me": {    "name": "Luke Skywalker"   } }

To link the GraphQL with React.js app, we can use Relay. A relay is a javascript framework for building the data-driven React applications powered by GraphQL. It is designed from the ground up to be easy to use, extensible and, most of all, performant. It is also developed by the Facebook. React allows designs to be defined as components where every component is responsible for rendering a part of the HTML. Composing other components is how to build complex UI designs. Each React component doesn't need to know the internal workings of the composed components.

Relay links React with GraphQL and develop the idea of encapsulation further. It allows components to define what data they need and the Relay framework gives the data. This makes the data needs of inner components covered and allows composition of that reqData Visualization with GraphQL and React.jsuirements. Thinking about what data an app request becomes localized to the component making it easier to reason about what fields are needed or no longer needed. Relay life cycle has three points.

1. Declarative - Declare the data your component needs with GraphQL, Relay manages how and when to fetch your data. 2. Colocation - GraphQL is written next to the component that relies on them. Relay aggregates query into efficient network requests 3. Mutations - Write GraphQL mutations and Relay offers automatic data consistency, optimistic updates, and error handling. 4. Declarative - Declare the data your component needs with GraphQL, Relay manages how and when to fetch your data.

5. Colocation - GraphQL is written next to the component that relies on them. Relay aggregates query into efficient network requests.

6. Mutations - Write GraphQL mutations and Relay offers automatic data consistency, optimistic updates, and error handling. Recently Facebook launched the Relay Modern, that is the latest version of Relay. Relay Modern is designed to be easier to use, more extensible and, most of all, able to improve performance on mobile devices. It accomplishes this with static queries and ahead-of-time code generation.

A javascript library for building declarative, efficient, and interactive user interfaces. Click to explore about, Learn React | Advanced Guide for Angular Developers

Runtime Architecture of the Relay

The Relay runtime is a full-featured GraphQL client that is developed for high performance even on low-end mobile/tablets devices and is equipped for scaling to vast, complex applications. The runtime API isn't expected to be utilized straightforwardly in the product code, yet instead to give an establishment to building more elevated amount product APIs, for example, React/Relay. This framework includes-

Know more about the Importance of APIs Testing.

Core primitives for building offline/persisted caching. Example Data Flow: Fetching Query Data The query is fetched from the network/backend. The query and response are traversed together, extracting the results into Record objects which are added to a fresh RecordSource. This fresh RecordSource would then be published to the store: Publishing the results updates the store but does not immediately notify any subscribers. This is accomplished by calling notify()..which requests the callbacks for any subscribe()-ers whose results have changed.

Each subscription is tested as follows: First, the list of data IDs that have been changed since the last notify() is compared corresponding data IDs listed in the subscription's latest Snapshot. If there is not any overlap, the subscription's results cannot possibly have been changed. In this case, the subscription is ignored. Unless processing continues. Second, any subscriptions that have overlapping data IDs are re-read, and the new/previous results are compared.

If the result has not been changed, the subscription is ignored (this can occur if a field of a record changed that is not relevant to the subscription's selector), otherwise processing continues. Finally, subscriptions whose data changed are notified via their callback.

React JS is a great Framework with some essential features for developing high-quality applications. Immediate visualization of work result, high rendering speed and proficient operation of components associated with the Reactjs is the most significant advantage of this tool. As far as some needs are concerned, we can summarize that React JS was designed from the start as an excellent representation layer and it just performs this primary role with excellence. React holds an extensive and highly developed ecosystem which consists of compatible add-ons, plugins, libraries, and frameworks. On the contrary, great thanks to specifics of its DOM manipulation system, using React in parallel with any other tool which changes DOM or part, therefore, is quite a challenging task.

• What are the importance of Tableau Data Visualization?
• Read here about Data Visualization Best Practices
• Explain the concept of Data Visualization with Microsoft Power BI.

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Build an analytics app with React and Cube.js

In the world of big data, data analytics and data visualization go hand in hand. With data visualization tools and technologies, we can easily analyze massive amounts of information to make data-driven decisions.

By providing a graphical representation of data through data visualization types like charts, tables, maps, or graphs, decision-making can be streamlined.

In this article, we’ll learn how to build an analytics app to monitor and analyze raw data with React and Cube.js. This app will monitor raw product data from a database connected to a React dashboard through Cube.js, showcasing how much the product sells and how many customers the product sells to.

What is Cube.js?

Cube.js is an open source analytics platform that helps users create a semantic API layer on top of data to manage access control and aggregate the data. Because Cube.js is visualization agnostic, users can build frontends with any framework or library of choice.

Note that Cube.js is not a database; rather it is a headless API layer that connects databases to the frontend so users can build data applications faster.

It also manages caching and query queuing, and exposes the API for the frontend to build dashboards and other analytics features.

React and Cube.js tutorial prerequisites

Before we continue, you must have a basic understanding of React, PostgreSQL, and Recharts . Also, note that every command in this article runs on Linux, so the command variations in Windows and Mac may vary.

PostgresSQL database setup

Cube.js requires a database to store and get the analytics data, so we will use Postgre SQL as our database for this tutorial .

Downloading Postgres

Let’s start by downloading PostgreSQL, or Postgres, and creating our database:

With Postgres installed, we can log in and create a database, which we will later connect to Cube.js.

There are two ways we can create a Postgres database: we either use the postgres role to create the database or create a database as a new user. Let’s see how to use both.

Using the postgres role to create a database

To connect to Postgres using the postgres role, we must switch over to the Postgres account on the server by typing the following:

This prompts us to enter the password for the current user; in this case, it will be your computer’s password.

Then, we can access PostgreSQL using psql by typing the following command:

Now, run this command to create a new database:

We have successfully created an ecom database.

Using a new user to create a database

With this method, we do not need to log into Postgres; we can simply run this command on the home terminal:

This command creates a new Postgres user called testuser . You can name yours anything you see fit.

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We can then create the database by running the following command:

With the newly created database named ecom , let’s proceed to grant all privileges to the user we just created. To grant privileges, we must first log into Postgres:

We can now try connecting remotely to our database by running the following command:

This command prompts for a password, so enter the database password to connect to the database. In our case, the password is testpassword .

Populating the database with data

Now that we have a working database, we can populate the database with an ecommerce sample dataset. We will use this dataset later when we configure our Cube.js service.

You can also use other sample datasets like this dvdrental , but be sure to read how to use it here .

To populate our database with the ecommerce dataset, run the following:

“Relation” is the mathematical name for tables. So, the user’s relations count is the total number of tables in the user’s relation.

We can also run \d+ to see the list of all relations in our database. And now, we’re done with the backend setup.

Cube.js setup

To scaffold our Cube.js project, run the following:

Now we can set up our Cube.js project by running the command below:

This creates a new project with the name analytics and postgres as our database.

Next, let’s add the following Postgres database credentials manually to the .env file. This allows us connect our Postgres database to Cube.js:

If you use different database names, such as user , fill it with your unique credentials.

Oftentimes we won’t see the .env file in our folder and we must check the options of seeing hidden files before we see the .env file. We can use the terminal to add the credentials by simply running the following commands:

After that, we cd into our folder and run the command below:

Let’s go to http://localhost:4000/ to view the Developer Playground where we also generate our data schema. You can read more on the Cube.js data schema .

Next, click the public dropdown menu, select products and orders , and click Generate Schema to generate the Order.js and Products.js schema files.

Note that in a situation where you stop the process using ctrl+z and try running it again, you might get an error. This is because the process still runs in the background while using the address.

To clear this error, kill the process and clear the address with the command below:

Setting up the React app

This is the last part of this tutorial. Give yourself a round of applause for coming this far, you deserve it.

Now, let’s go ahead and finish what we started. We can connect Cube.js to our React app in two ways:

• By using the Cube.js D eveloper P layground
• By using an existing React app

To use the Developer Playground, go to the Dashboard App section and either select any of the available templates or create a new app.

If you’re using an existing React app, you only need to install the packages or the dependencies you need and connect to Cube.js.

For this tutorial, we’ll use an existing React app, so we can go ahead and install the Recharts and Cube.js core packages:

Now, let’s import our installed packages into our Analytics.js file:

Finally, let’s import the three modules we installed, which are @cubejs-client/core , @cubejs-client/react , and recharts :

The @cubejs-client/core allows us to connect to our Cube.js backend, taking two parameters: our Cube.js secret token, which can be found in our .env file and the API URL, which is the Cube.js default URL in development mode.

@cubejs-client/react allows us to query our Cube.js backend from our functional React component, using the useCubeQuery Hook to execute this query. We can then pass the dimensions and measures we need to the Hook.

Also, here dimensions is our quantitative data like the number of units sold, the number of unique visits, or order counts, while measures is the categorical data like gender, product name, or units of time (like day, week, or month).

We can change the granularity query to week , month , or year , or change the dateRange to last {n} week , month , or year , respectively. We can also change the measures and dimensions according to the schemas generated.

With all our data, we can finally visualize it using BarChart , which is from the Recharts module. We can also use the LineChart , AreaChart , or PieChart that Recharts offers if desired.

We can use any database for our analytics app. To use MongoDB, simply download the mongodb instance and the MongoDB Connector for BI that helps write SQL queries on top of MongoDB.

We can also use any visualization packages that we are familiar with like D3.js or Charts.js.

Visit the Cube.js official website to learn how to authenticate your analytics app so users have different levels of access to the analytics data and utilize the many cool analytics features that Cube.js offers.

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Correlogram

In this blog post, we will be exploring how to build a correlogram with React and D3.js. A correlogram is a graphical representation of the correlation matrix for a given dataset. It is a useful tool for visualizing the relationships between different variables in a dataset, and can help identify potential correlations that may not be immediately obvious.

Building a correlogram with React and D3.js allows us to create a highly interactive and customizable visualization. We will be able to use React's powerful component-based approach to build our visualization, while leveraging the flexibility and power of D3.js to create a dynamic and engaging visual representation of our data.

Correlogram = scatter plot + histogram

A correlogram uses histograms to show the distribution of each numeric variable on the diagonal of the matrix. It uses scatter plots to show the relationship of each pair of variable on every other cells.

As a result, it is required to understand how to build a histogram and a scatter plot component using React and d3.js! In this post, we will just show how to leverage those reusable components to build a correlogram.

Learn how to build a histogram with react and d3.js

Scatter plot

Learn how to build a scatter plot with react and d3.js

Bubble plot

Learn how to build a bubble plot with react and d3.js

The dataset provides several numeric values for a set of data points. It can also add some categorical variables that can be added to customize the marker colors.

The suggested data structure is an array of object , where each object is a data point. It can have as many numeric properties as needed.

Here is a minimal example of the data structure:

Basic correlogram

Once the 2 Histogram and ScatterPlot components are available, it's just a matter of using them for each pair of variable in the dataset.

We can list the variable in a allVariables array, and map twice on it. We need to render a distribution for the diagonal, and a scatterplot otherwise. Something like:

Note that graphWidth and graphHeight can easily be computed from the total width and height, once we know how many variables there are.

Once the list of react nodes with all the individual charts is available ( allGraphs ), it just a matter of rendering them in a div with a grid display.

And voilà , a first decent correlogram for your data analysis pipeline 😊. It's not perfect yet. You probably want to give more love to axes and labels , add hover effect and tooltips . But hopefully that's a good template to get started.

A correlogram built with react and d3.js. It shows the relationship between the 4 numeric variables of the famous iris dataset.

Note: You can compare this code with the pure d3 alternative . I find it much more readable.

Correlation

Scatterplot

Connected Scatter

👋 Hey, I'm Yan and I'm currently working on this project! Feedback is welcome ❤️. You can fill an issue on Github, drop me a message on Twitter , or even send me an email pasting yan.holtz.data with gmail.com . You can also subscribe to the newsletter to know when I publish more content!

Find React Tree Graph Examples and Templates

Use this online react-tree-graph playground to view and fork react-tree-graph example apps and templates on codesandbox. click any example below to run it instantly or find templates that can be used as a pre-built solution.

• School Guide
• Class 9 Syllabus
• Maths Notes Class 9
• Science Notes Class 9
• History Notes Class 9
• Geography Notes Class 9
• Political Science Notes Class 9
• NCERT Soln. Class 9 Maths
• RD Sharma Soln. Class 9
• Math Formulas Class 9
• CBSE Class 9 Maths Revision Notes

Chapter 1: Number System

• Number System in Maths
• Natural Numbers | Definition, Examples, Properties
• Whole Numbers | Definition, Properties and Examples
• Rational Number: Definition, Examples, Worksheet
• Irrational Numbers- Definition, Identification, Examples, Symbol, Properties
• Real Numbers
• Decimal Expansion of Real Numbers
• Decimal Expansions of Rational Numbers
• Representation of Rational Numbers on the Number Line | Class 8 Maths
• Represent √3 on the number line
• Operations on Real Numbers
• Rationalization of Denominators
• Laws of Exponents for Real Numbers

Chapter 2: Polynomials

• Polynomials in One Variable - Polynomials | Class 9 Maths
• Polynomial Formula
• Types of Polynomials
• Zeros of Polynomial
• Factorization of Polynomial
• Remainder Theorem
• Factor Theorem
• Algebraic Identities

Chapter 3: Coordinate Geometry

• Coordinate Geometry
• Cartesian Coordinate System in Maths
• Cartesian Plane

Chapter 4: Linear equations in two variables

• Linear Equations in One Variable
• Linear Equation in Two Variables
• Graph of Linear Equations in Two Variables
• Graphical Methods of Solving Pair of Linear Equations in Two Variables
• Equations of Lines Parallel to the x-axis and y-axis

Chapter 5: Introduction to Euclid's Geometry

• Euclidean Geometry
• Equivalent Version of Euclid’s Fifth Postulate

Chapter 6: Lines and Angles

• Lines and Angles
• Types of Angles
• Pairs of Angles - Lines & Angles
• Transversal Lines
• Angle Sum Property of a Triangle

Chapter 7: Triangles

• Triangles in Geometry
• Congruence of Triangles |SSS, SAS, ASA, and RHS Rules
• Theorem - Angle opposite to equal sides of an isosceles triangle are equal | Class 9 Maths
• Triangle Inequality Theorem, Proof & Applications

Chapter 8: Quadrilateral

• Angle Sum Property of a Quadrilateral
• Quadrilateral - Definition, Properties, Types, Formulas, Examples
• Introduction to Parallelogram: Properties, Types, and Theorem
• Rhombus: Definition, Properties, Formula, Examples
• Kite - Quadrilaterals
• Properties of Parallelograms
• Mid Point Theorem

Chapter 9: Areas of Parallelograms and Triangles

• Area of Triangle | Formula and Examples
• Area of Parallelogram
• Figures on the Same Base and between the Same Parallels

Chapter 10: Circles

• Circles in Maths
• Radius of Circle
• Tangent to a Circle
• What is the longest chord of a Circle?
• Circumference of Circle - Definition, Perimeter Formula, and Examples
• Angle subtended by an arc at the centre of a circle
• What is Cyclic Quadrilateral
• Theorem - The sum of opposite angles of a cyclic quadrilateral is 180° | Class 9 Maths

Chapter 11: Construction

• Basic Constructions - Angle Bisector, Perpendicular Bisector, Angle of 60°
• Construction of Triangles

Chapter 12: Heron's Formula

• Area of Equilateral Triangle
• Area of Isosceles Triangle
• Heron's Formula
• Applications of Heron's Formula
• Area of Quadrilateral
• Area of Polygons

Chapter 13: Surface Areas and Volumes

• Surface Area of Cuboid
• Volume of Cuboid | Formula and Examples
• Surface Area of Cube
• Volume of a Cube
• Surface Area of Cylinder (CSA and TSA) |Formula, Derivation, Examples
• Volume of Cylinder
• Surface Area of Cone
• Volume of Cone | Formula, Derivation and Examples
• Surface Area of Sphere | CSA, TSA, Formula and Derivation
• Volume of a Sphere
• Surface Area of a Hemisphere
• Volume of Hemisphere

Chapter 14: Statistics

• Collection and Presentation of Data

Graphical Representation of Data

• Bar graphs and Histograms
• Central Tendency
• Mean, Median and Mode

Chapter 15: Probability

• Experimental Probability
• Empirical Probability
• CBSE Class 9 Maths Formulas
• NCERT Solutions for Class 9 Maths
• RD Sharma Class 9 Solutions

In today’s world of the internet and connectivity, there is a lot of data available and some or the other method is needed for looking at large data, the patterns, and trends in it. There is an entire branch in mathematics dedicated to dealing with collecting, analyzing, interpreting, and presenting the numerical data in visual form in such a way that it becomes easy to understand and the data becomes easy to compare as well, the branch is known as Statistics . The branch is widely spread and has a plethora of real-life applications such as Business Analytics, demography, astrostatistics, and so on. There are two ways of representing data, 

• Pictorial Representation through graphs.

They say, “A picture is worth the thousand words”.  It’s always better to represent data in graphical format. Even in Practical Evidence and Surveys, scientists have found that the restoration and understanding of any information is better when it is available in form of visuals as Human beings process data better in visual form than any other form. Does it increase the ability 2 times or 3 times? The answer is it increases the Power of understanding 60,000 times for a normal Human being, the fact is amusing and true at the same time. Let’s look at some of them in detail. 

Types of Graphical Representations

Comparison between different items is best shown with graphs, it becomes easier to compare the crux out of the data pertaining to different items. Let’s look at all the different types of graphical representations briefly: 

Line Graphs

A line graph is used to show how the value of particular variable changes with time. We plot this graph by connecting the points at different values of the variable. It can be useful for analyzing the trends in the data predicting further trends. 

A bar graph is a type of graphical representation of the data in which bars of uniform width are drawn with equal spacing between them on one axis (x-axis usually), depicting the variable. The values of the variables are represented by the height of the bars. 

Histograms 

This is similar to bar graphs, but it is based frequency of numerical values rather than their actual values. The data is organized into intervals and the bars represent the frequency of the values in that range. That is, it counts how many values of the data lie in a particular range. 

Line Plot 

It is a plot that displays data as points and checkmarks above a number line, showing the frequency of the point. 

Stem and Leaf Plot 

This is a type of plot in which each value is split into a “leaf”(in most cases, it is the last digit) and “stem”(the other remaining digits). For example: the number 42 is split into leaf (2) and stem (4).  

Box and Whisker Plot 

These plots divide the data into four parts to show their summary. They are more concerned about the spread, average, and median of the data. 

It is a type of graph which represents the data in form of a circular graph. The circle is divided such that each portion represents a proportion of the whole. 

Graphical Representations used in Maths

Graphs in maths are used to study the relationships between two or more variables that are changing. Statistical data can be summarized in a better way using graphs. There are basically two lines of thoughts of making graphs in maths: 

• Value-Based or Time Series Graphs

Frequency Based

Value-based or time series graphs .

These graphs allow us to study the change of a variable with respect to another variable within a given interval of time. The variables can be anything. Time Series graphs study the change of variable with time. They study the trends, periodic behavior, and patterns in the series. We are more concerned with the values of the variables here rather than the frequency of those values. 

Example: Line Graph

These kinds of graphs are more concerned with the distribution of data. How many values lie between a particular range of the variables, and which range has the maximum frequency of the values. They are used to judge a spread and average and sometimes median of a variable under study. 

Example: Frequency Polygon, Histograms.

Principles of Graphical Representations

All types of graphical representations require some rule/principles which are to be followed. These are some algebraic principles. When we plot a graph, there is an origin, and we have our two axes. These two axes divide the plane into four parts called quadrants. The horizontal one is usually called the x-axis and the other one is called the y-axis. The origin is the point where these two axes intersect. The thing we need to keep in mind about the values of the variable on the x-axis is that positive values need to be on the right side of the origin and negative values should be on the left side of the origin. Similarly, for the variable on the y-axis, we need to make sure that the positive values of this variable should be above the x-axis and negative values of this variable must be below the y-axis. 

Advantages and Disadvantages of using Graphical System

Advantages: 

• It gives us a summary of the data which is easier to look at and analyze.
• It saves time.
• We can compare and study more than one variable at a time.

Disadvantage: 

It usually takes only one aspect of the data and ignores the other. For example, A bar graph does not represent the mean, median, and other statistics of the data. 

General Rules for Graphical Representation of Data

We should keep in mind some things while plotting and designing these graphs. The goal should be a better and clear picture of the data. Following things should be kept in mind while plotting the above graphs: 

• Whenever possible, the data source must be mentioned for the viewer.
• Always choose the proper colors and font sizes. They should be chosen to keep in mind that the graphs should look neat.
• The measurement Unit should be mentioned in the top right corner of the graph.
• The proper scale should be chosen while making the graph, it should be chosen such that the graph looks accurate.
• Last but not the least, a suitable title should be chosen.

Frequency Polygon

A frequency polygon is a graph that is constructed by joining the midpoint of the intervals. The height of the interval or the bin represents the frequency of the values that lie in that interval. 

Sample Problems

Question 1: What are different types of frequency-based plots? 

Answer: 

Types of frequency based plots:  Histogram Frequency Polygon Box Plots

Question 2: A company with an advertising budget of Rs 10,00,00,000 has planned the following expenditure in the different advertising channels such as TV Advertisement, Radio, Facebook, Instagram, and Printed media. The table represents the money spent on different channels. 

Draw a bar graph for the following data. 

Solution: 

Steps:  Put each of the channels on the x-axis The height of the bars is decided by the value of each channel.

Question 3: Draw a line plot for the following data 

Steps:  Put each of the x-axis row value on the x-axis joint the value corresponding to the each value of the x-axis.

Question 4: Make a frequency plot of the following data: 

Steps:  Draw the class intervals on the x-axis and frequencies on the y-axis. Calculate the mid point of each class interval. Class Interval Mid Point Frequency 0-3 1.5 3 3-6 4.5 4 6-9 7.5 2 9-12 10.5 6 Now join the mid points of the intervals and their corresponding frequencies on the graph.  This graph shows both the histogram and frequency polygon for the given distribution.

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