Introduction to D3.js
D3.js stands for Data-Driven Documents, is a front-end visualization library in JavaScript for creating interactive and dynamic web-based data visualizations; it also has an active community behind it, for which it is very famous. It uses HTML, CSS, and SVG to bring data to life, and mainly, it is for manipulating DOM objects, focusing on objects and concepts, not just the pre-built charts and graphs. It is mostly compatible with popular web browsers, like Chrome or Firefox. It can even create different shapes arcs, lines, arcs, rectangles, and points.
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D3.js's essential feature is that it provides beautiful, fully customized visualizations. It is a suite of small modules for data visualization and analysis. These modules work well together, but we should pick and choose the parts that we only need. D3's most complex concept is selections, and if we are using a virtual DOM framework like React (and don’t need transitions), we don’t need selections at all.
How to use D3.js?
So, we need to create a visualization with d3, set up a workspace inside a container, create x and y-axes, process data, and draw graphs and charts using functions. We can also add different attributes and styles for data points or lines. When creating basic charts and graphs, D3 is not complicated, for customization need to add more code. More complex visualizations need lots of logic, layouts, and data formatting as these are the keys we want our visualization to speak for. D3 can also be paired with a WebGL library, which provides more standard capabilities in terms of dynamic and interactiveness. We can even animate one element, based on transitions similar to those done in CSS.
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Why do we use D3.js?
Today, different charting libraries and BI tools are available for creating visualizations, so the question arises: why use d3.js for creating visualizations? D3.js is chosen because of its key features: versatility, full customization, and interactive visualization, even the exact data visualization that can be made by graphic designers. Data visualization is more complex than we think.
It is easy to draw shapes, but sometimes visualizations require full customization, such as bringing subtle ticks and different smooth curves between data points. That is where d3.js comes in, which allows us to explore the design space quickly. It is not only a charting library but also a versatile coding environment that can be best used for making complex visualizations.
When to use D3.js?
As d3.js becomes complicated sometimes, programming in d3 should be done from scratch and requires a steep learning curve, but due to its significant advantages, we need to use it and decide when to use that library. We should use D3.js when our web application is interacting with data. We can explore D3.js for its graphing capabilities and make it more usable. It can be added to the front end of our web application as the backend server will generate the data, and the front end of the application is where the user interacts with data using D3.js.
Some of the use cases of D3.js We above discussed data visualization basics, d3.js front-end visualization library concepts, and where and when to approach the d3.js library; we go through some of the use cases of d3 as d3 with complex code also provides reusable code that can be used again in other visualizations, d3 also can be used to react, storytelling with customized and visualizations the most crucial use case can also be achieved with d3. Some of the use cases of the d3 library are discussed below:
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Reusable Charts with D3.js
When creating visualizations, we also need to consider the reusability of the charts or anything made in visualization. Let's discuss how the D3.js library provides reusable charts. First, we should know what a reusable chart is and some of its characteristics. We need to build charts in an independent way. We need to make all chart elements associated with data points of the dataset independently. This has to do with the way D3 associates data instances with DOM elements.
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Repeatable - Instantiate the chart more than one time, as chart visualizing chart with different datasets.
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Modifiable - The source code of charts can be easily refactored by other developers according to different needs.
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Configurable- We need to modify only the appearance and behaviour of the graph without changing the code.
Some of the best practices to make reusable charts with d3.js
Built charts in an independent way. To make the chart reusable with d3.js, it should be repeatable, modifiable, configurable and extensible. To make the chart repeatable in d3.js, we can use the object-oriented property and approach chart.prototype.render and also use this property during a call to the functions. To make the modifiable chart, make source code with simple transformations with d3.js built-in functions so that the path to modification in the system becomes straightforward for the other developers to modify.
For example, we need to make some modifications to our dataset. We add one more data item to the array, as our bar chart still contains only four bars of data, and at that time, we choose the data element from entering the selection. A room visualization, with several chairs that are DOM elements and guests that are data items, sits on the chairs that are data joined with DOM elements. The enter selection is the waiting area for data items that enter the room but cannot be seated, as there are not enough chairs. As to arranging more chairs, where to create a new bar div and add it to DOM is done by d3 selector enter.
exit() selection—We discussed how we could add new items to a data set dynamically and update the visualization. In the same way, we can remove items from the data set and allow D3 to deal with the corresponding DOM elements or, following our room/chair method, remove those chairs that are no longer needed because some guests decided to leave. The exit selection does this. It contains only those data items about to leave the data set.
Configurable - Consider the visualization of the bubble chart. To make it reusable, only the size of the chart and input dataset need to be customized. Define Sthe ize of the chart
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var bubbleChart = function () {
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var width = 500,
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height = 500;
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function bubblechart(select){
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}
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return chart;
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}
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bubble.html
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var chart = bubbleChart().width(500).height(500);
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// bubble_chart.js
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var bubbleChart = function () {
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var width = 500
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height = 500;
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function chart(select){
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}
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chart.width = function(val) {
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if (!argu.len) { return width; }
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width = val;
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return chart;
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}
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chart.height = function(val) {
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if (!argu.len) { return height; }
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height = val;
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return chart;
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}
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return chart;
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}
Role of Data Visualization in D3.js
Over the last few years, data visualization has grown more and more of a day-to-day experience. We see data visualization daily on our social media, in news articles, and daily at work. Data visualization translates quantitative data into graphic representations using libraries, i.e. D3.js Library. Data visualization includes tasks of designing, creating and deploying, which is not just a single profession at all but shows a combination of designers, analysts, engineers, and journalists. As for data visualization tasks, engineers use different javascript libraries, and analysts use various business intelligence tools.
Nowadays, we can collect and analyze more data than ever. Big data is a hot topic even now and a major to study. But to be able to understand and digest all these kind of numbers, we need visualization and a platform or framework that makes all kinds of visualizations possible, no matter how much data there is need to process, that's where d3.js and other visualizations tools come in. Plotting charts with visualization libraries and tools is not enough; we also need storytelling art here. As most of the libraries and tools that exist here do not provide the effective display of quantitative information, where D3.js is the most successful library comes in that already tells half of the story when we start developing the code for it.
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Data Visualization using D3.js with React
The D3 enter, exit, and update pattern provides full control to the developer for managing the DOM. We can manage when an element should be added to the screen, when it should be removed, and how to update the element. It works fine when the updates of data elements are simple, but it gets complex when there are a lot of data elements to keep track of, and the data elements to update vary from one user action to another.
One solution to manage the DOM data elements is to count which elements require updates manually, but it also becomes complex. We cannot keep the count in our heads, as manually defining the DOM tree is not recommended, so we need to integrate react and D3 together for complex visualizations. As a result, the updates of the virtual DOM are precisely like the D3 enter, exit, and update patterns. So let's use d3 with react, react for what to enter and exit operations and d3 for update patterns. Discuss react with d3 implementation in a few steps -
Enter and Exit pattern with React
React follows the concept of dynamic children (reuse code between components and inheritance), similar to the D3 data binding property. It allows passing in a key per children to track order of children as like vital function pass into d3. data and uses that to calculate what should be added and removed when data changes. Consider the example here, as we need to render two rectangles and a text element with D3. The code for it in D3 looks like the below -
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var graph = d3.select(‘svg’).append(‘g’)
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.class(‘graph’, true);
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var expenses_graph = graph.selectAll(‘g.expense’);
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var enter_ele = expenses_graph
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.data(expensesData, (expense_graph) => expense_graph.id)
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.enter().append(‘g’)
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.class(‘expense_graph’, true);
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enter_ele.append(‘rect’)
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.class(‘expenseRect’, true);
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enter_ele.append(‘text’);
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expenses.exit().remove();
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class ExpenseComponent extends React.Component {
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render() {
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return (
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);
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}
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}
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class GraphComponent extends React.Component {
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render() {
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var expenses_graph = expensesData && expensesData.map((expense) => {
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return ();
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});
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return (
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{expenses_graph}
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);
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}
Firstly, when we see the react code, it doesn't look very easy, but it also provides some great things, such as - Allowing to make components for elements, so it becomes easier to reuse code; that kind of reusability code is also done in old D3 code, but react makes it explicit. It allows us to keep track of what components look like and reflect the structure of the DOM. Another thing that can be achieved is that we do not need to consider entering and exiting again. When we show and hide parts of components depending on data, react will only draw the elements that we need; when we need it accordingly in a straight manner, the below code in react will show -
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class ExpComponent extends React.Component {
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render() {
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return (
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{this.props.data.name && ()}
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{this.props.data.name && ()}
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);
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}
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}
Updating and transitioning with D3
With entering and exit selections, we have a structure of the components. We need to fill in the attributes.D3 also manages to update the attributes. In a React component, call the enter code from component() and update code from update(). In this way, as soon as the elements are inserted into the DOM, we can use D3 to set the starting attributes. When data changes, we will use D3 transition patterns to move the element to the next set of attributes.
We should also remember that whatever React keeps tracking of D3 cannot be manipulated. So, the update and transition pattern helps to maintain ownership between these two, where React manages the structure, and D3 helps support the attributes. That way, D3 will transition elements, update their positions, and fill colours and sizes without conflicting with the React workflow.
Storytelling with d3.js Interactive Visualizations
We will discuss the d3.js library, its features, why we need it and its reusability. We need to remember the target audience when making any visualization because our task is not only to render visualization, but it should be fully explainable so our approached audience can understand it wisely and how the audience can understand it. That can only be achieved with storytelling. D3.js' interactive and beautiful visualizations help with the fantastic narration of data.
Let's discuss the chord diagrams complex visualization in a different interactive way and see how it helps in storytelling - Consider the problem here: as all people in India are using phones, many will switch to a new phone after some time, a question arises how do users change and how this differs per brand, these kinds of different problems can be answered by visualizing the dataset by using chord diagrams visualization in d3.js. The below chord diagram shows the relationship in terms of switching behaviour between different phone brands. The circle is divided into eight brands - the arc length of every group shows the brand's market share.
The outer side rim of the chord diagram shows the percentage per brand. This indicates that Samsung shares 38%, Apple is second with a share of 19%, and Nokia is third with a 16% share. The chords are directed in a diagram, as 8.7% of users who now have Samsung used to have Nokia, and only 1.2% of users have the opposite. The chords between the arcs visualize users switching behaviour between all brands in both directions. For example, the blue chord connecting Samsung and Nokia in the left section shows the users that moved from Samsung to Nokia and from Nokia to Samsung. The visualization shows that Nokia lost its share to Samsung, as 8.7% of all users that used Nokia now own Samsung
Insights from a mobile consumer survey chord diagram visualization
When we made the customized visualization visualizing the flow of brands, we came to more conclusions and insights that are discussed below --
Both Apple and Samsung are capturing users from Nokia and other brands.
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Only a few users losing by Apple, the number of users gained is twice the number of users lost.
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HTC is acquiring users from Nokia and LG brands and losing users to Samsung and Huawei brands.
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Nokia's brand acquires more users than it loses the users.
Approaches to Data Visualization
Data visualization helps users translate quantitative data into graphic representations using leading Data Visualization Techniques. To know more about Data Visualization, we recommend taking the following steps
- Understanding How 3D Data Visualization Works
- Discover how to scale Data Visualization using GraphQL and ReactJS.
More Ways to Explore Us
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