In today’s environment, having access to data provides people and businesses with a significant competitive advantage. Every business, large or small, strives to gather as much data as possible about its market, customers, and goods. All of this information enables them to make well-informed decisions that will help them increase their production and revenues, among other things.
There are obvious reasons for its appeal, which become plain once you see what the library can accomplish. All of the visualisations in the library is done with HTML, CSS, and SVG. You can use it to express your data in a variety of interesting and unique ways. You can probably construct that with D3.js if you can picture it.
D3 demo by eyan.
As you learn more about the library, you’ll find that it doesn’t force you to represent your data in a specific way. This has its own set of benefits and drawbacks. On the one hand, the library provides a great deal of versatility in terms of what you can do with it. The negative is that the library has a steep learning curve, and you’ll have to write more code than libraries that specialise in specialised data visualisations, such as bar carts. The line chart sample on the D3.js website demonstrates this.
For extremely simple things like line and bar charts, I recommend that you look into using other libraries. However, you should absolutely check out the D3 gallery to see what kind of visualisations the library can produce.
You could get excited about all the things you can accomplish with D3.js after reading about it and experiencing it. However, it may disappoint those who wish to build charts to visualise data but find D3 to be too difficult. In this scenario, the C3.js library would be an excellent choice.
C3 demo by Bantsmore.
When it comes to creating a chart, C3.js is based on D3.js and does all the heavy lifting for you. It’s just a D3.js wrapper with all the necessary code to make a chart. The good news is that the charts you make can still be completely customised. C3.js adds some classes to each element it uses to produce the charts. By directly using D3, you can create your own styling and incorporate additional functions.
You also have complete control over the chart rendering thanks to APIs and callbacks. This enables you to access the state of the chart even after it has been rendered, allowing you to make any necessary modifications.
Recharts is a React and D3-based library. The library was created to make it easy to incorporate charts into your React apps. With native SVG support and a dependency on only a few D3 submodules, the library delivers excellent speed.
Recharts demo by StarNavi Team.
The charts are easy to alter by changing component props and handing in custom components, and they can be made with reusable React components. Recharts have a clear separation of components, making them self-contained. A line chart, for example, is made up of independent elements such as the x-axis, tooltip, grid, and others.
On the website, there are numerous examples of typical chart types reproduced in all of their variations. All of these charts are explained in great length in the documentation.
One of the most common ways to visualise data is through charts. Almost all charting libraries will produce bars, lines, and arcs that are far too flawless. Because you want everything to look clean and exact, this is actually purposeful and highly beneficial. What if that isn’t the case, and you still want your charts to have a “hand-drawn” feel to them?
Chart.xkcd demo by Timqian.
This is exactly what the chart.xkcd library will do for you. It will give your plots a rough, cartoony vibe that will distinguish them apart from other libraries’ charts. The method of creating the charts is really simple, and you won’t need to write much code.
Line charts, bar charts, pie charts, and radar charts are among the six chart kinds supported by the library. They all have different configuration options for changing their appearance. During initialization, you can optionally turn off the hand-drawn effect.
The DataMaps package is a fantastic tool for creating dynamic data visualisation maps. A single file contains all of the code needed to build the maps. The maps are created using SVG and may scale to any screen size. Out-of-the-box, the library supports choropleths and bubble maps.
DataMaps demo by Andy Wise.
Include D3.js and Topojson on your page to get started with the maps. After that, simply include the Datamaps.js code and specify a map container. By using the topo.json file found here, you can create a map of any country. The documentation page goes into great detail about how to make choropleths and bubble maps.
For items like labels and popups, the library includes a variety of customization choices. You can also use resizing events to listen to different events and make the maps more responsive.
The Sigma.js package is suitable for creating graphs with hundreds of nodes and edges to render.
Since version 2.0, the library has changed its attention to graph display management. Rendering and interactivity are examples of this. Other libraries are used to manage graph data structures, layout algorithms, and metrics, among other things.
WebGL is used to render graphs in Sigma.js. When drawing larger graphs, this gives you a performance edge over SVG and canvas. However, there is a disadvantage in that modifying the graphs becomes more difficult. The library includes a React wrapper, allowing you to utilise it within your React apps.
Sigma is wonderful for huge graphs, however, D3.js is a better option if your graphs will only have a few hundred nodes or if you wish to tweak them extensively.
You may use the deck.gl package to graphically evaluate huge datasets. It’s built on WebGL and strives to be extremely fast. When it comes to visualising the data you give it, it takes a layered approach.
Deck.gl demo by Andy Wise.
Existing layers are combined to create complex visuals. The deck has layers. gl is a visualisation type that accepts a data collection and assigns positions, colours, and extrusions to it before showing it on a map. You can use the same data to render numerous layers. Bitmaps, columns, and contours are some of the well-tested and supported layer types in the library.
For enhanced efficiency, the library makes use of the device’s GPU to conduct high-precision computations. The library can be used directly in your website, but it also works with the React framework. It’s also simple to interface with major base map services such as Google Maps, Mapbox, and ArcGIS.
Just look at some of the samples on the website and you’ll be blown away.
Cytoscape.js is a graph theory library that aims to make data display and analysis for programmers and scientists as simple as feasible. It makes it simple to model and visualise relational data.
Cytoscape demo by Justin Mathieu.
The library includes important functions that allow users to interact with various graphs. You can then listen to those user events and, if necessary, take appropriate action. It’s compatible with both desktop and mobile browsers. In fact, it supports movements like pinch-to-zoom and panning right out of the box.
Cytoscape is highly optimised for performance and includes a wide range of features such as automatic or manual node positioning, stylesheet-based separation of presentation from actual data, graph theory algorithms, and set theory operations.
You should look over all of the examples on the website and engage with them.
Our Final Thoughts
In a nutshell, D3.js may be used to visualise any type of data. There are also D3.js-based specialist libraries that can help you complete specific tasks more rapidly, such as making charts. Other libraries, such as deck.gl, can assist you in visualising location-based data on maps.
Hopefully, at least one of the libraries listed below will be valuable to you. You can also look through the GitHub repositories for a free data visualisation library.
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