D3.js is a JavaScript library that enables you to create dynamic data visualizations in web browsers. It specializes in visualizing large data sets in an understandable and interactive way. The D3.js library stands out as one of the best data visualization tools for front-end developers because of its core features, such as DOM manipulation, dynamic properties, and animations.
Today, we’re going to show you how to build your first bar chart using D3.js. Let’s get started!
We’ll cover:
- Project overview
- Initial setup
- Create your dataset
- Set dimensions and margins
- Append SVG element
- Gather and format data
- Add bars
- Next steps
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Project overview
Today, we’re going to build a bar chart using D3.js. This is a great project because it allows you to practice your D3.js data visualization skills in a practical way.
Bar charts are a useful and effective way to compare data between different groups. They improve readability, allowing you to easily identify patterns or trends in your data.
D3.js is great for data visualization for many reasons, such as:
- Visuals: works with HTML, CSS, and SVG so no new tools are required
- Animations: allows you to animate and modify page elements
- Supported in all major browsers: works on the web making visuals easy to share and publish
- Flexible: works well with existing web technologies and can manipulate any part of the DOM
- Scalable: works well with large sets of data
Initial setup
There are three things we need to do before creating our bar chart:
- Set up the document’s head and body
- Set up the CSS
- Load the d3.js library
They can all be done with the following code:
<!DOCTYPE html><meta charset="utf-8"><style> /* set the CSS */.bar { fill: steelblue; }</style><body><!-- load the d3.js library --><script src="https://d3js.org/d3.v6.min.js"></script><script>
In the <style> section of the code, we set the styling for the color of the bars. Placing the style at the beginning of the code is optional, but it is convenient to do early.
name,amountsFoo, 33Rishab, 12Alexis, 41Tom, 16Courtney, 59Christina, 38Jack, 21Mickey, 25Paul, 30
We will use this data to make a vertical bar chart that stores the values of the amounts.
// Set graph margins and dimensionsvar margin = {top: 20, right: 20, bottom: 30, left: 40},width = 960 - margin.left - margin.right,height = 500 - margin.top - margin.bottom;
Now, we’ll determine the ranges of the x and y domains.
// Set rangesvar x = d3.scaleBand().range([0, width]).padding(0.1);var y = d3.scaleLinear().range([height, 0]);
var svg = d3.select("body").append("svg").attr("width", width + margin.left + margin.right).attr("height", height + margin.top + margin.bottom).append("g").attr("transform","translate(" + margin.left + "," + margin.top + ")")
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// Get datad3.csv("amounts.csv").then(function(data) {// Formatdata.forEach(function(d) {d.amounts = +d.amounts;});
Scale domains
Before we add our bars, let’s work through our x and y data to make sure it’s scaled to our set domains:
// Scale the range of the data in the domainsx.domain(data.map(function(d) { return d.name; }));y.domain([0, d3.max(data, function(d) { return d.amounts; })]);
// Append rectangles for bar chartsvg.selectAll(".bar").data(data).enter().append("rect").attr("class", "bar").attr("x", function(d) { return x(d.name); }).attr("width", x.bandwidth()).attr("y", function(d) { return y(d.amounts); }).attr("height", function(d) { return height - y(d.amounts); });
The last thing we need to do is append our axes, and then we can check out the finished result.
// Add x axissvg.append("g").attr("transform", "translate(0," + height + ")").call(d3.axisBottom(x));// Add y axissvg.append("g").call(d3.axisLeft(y));
This is what our code should look like in its entirety:
<!DOCTYPE html><meta charset="utf-8"><style> /* set the CSS */.bar { fill: steelblue; }</style><body><!-- load the d3.js library --><script src="https://d3js.org/d3.v6.min.js"></script><script>// Set graph margins and dimensionsvar margin = {top: 20, right: 20, bottom: 30, left: 40},width = 960 - margin.left - margin.right,height = 500 - margin.top - margin.bottom;// Set rangesvar x = d3.scaleBand().range([0, width]).padding(0.1);var y = d3.scaleLinear().range([height, 0]);var svg = d3.select("body").append("svg").attr("width", width + margin.left + margin.right).attr("height", height + margin.top + margin.bottom).append("g").attr("transform","translate(" + margin.left + "," + margin.top + ")");// Get datad3.csv("amounts.csv").then(function(data) {// Format datadata.forEach(function(d) {d.amounts = +d.amounts;});// Scale the range of the data in the domainsx.domain(data.map(function(d) { return d.name; }));y.domain([0, d3.max(data, function(d) { return d.amounts; })]);// Append rectangles for bar chartsvg.selectAll(".bar").data(data).enter().append("rect").attr("class", "bar").attr("x", function(d) { return x(d.name); }).attr("width", x.bandwidth()).attr("y", function(d) { return y(d.amounts); }).attr("height", function(d) { return height - y(d.amounts); });// Add x axissvg.append("g").attr("transform", "translate(0," + height + ")").call(d3.axisBottom(x));// Add y axissvg.append("g").call(d3.axisLeft(y));});</script></body>
This is our finished bar chart:
Next steps
Congrats on taking your first steps with D3.js data visualization! Bar charts are a great way to visualize large data sets in an understandable and visually appealing way. There’s still a lot to learn about the D3.js library such as:
- Tree diagrams
- Sankey diagrams
- Mapping
- And much more
To learn about these concepts and learn more about bar graphs in D3.js, check out Educative’s course D3 Tips and Tricks: Interactive Data Visualization. In this course, you’ll explore D3.js, starting with simple line graphs and moving into more advanced concepts such as histograms and drawing elements. By the end, you’ll be able to create awesome data visualizations with D3.
Happy learning!
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