Gain insights into creating interactive web data visualizations with D3.js. Learn about SVGs, selections, advanced scatter plots, scales, animations, and interactivity to transform data into meaningful insights.

In this course, you will learn to create your own interactive data visualizations for the web with D3.js, a very popular JavaScript library. 

Starting with the fundamentals, you'll learn about SVG, selections, classes, and styles, as well as displaying data. This will give you the base to start working with more advanced concepts. 

Once you’ve got the basics out of the way, you will start practicing drawing various scatter plots, where you’ll learn all the best practices for creating stunning visualizations.

Lastly, you will dive into scales and one of the more fun parts: adding animation and interactivity.

By the time you finish this course, you will have a powerful new library to add to your resume, and you’ll be able to transform data into meaningful insights.

Master D3.js for Data Visualization

The first project for this section is to draw a histogram. **Histograms are great at showing a distribution of values.** Let’s look at why we might want to draw a histogram.

#chart{
  width: 800px;
  height: 800px;
  background-color: #f7f7f7;
  margin: 25px auto;
  position: relative
}

#tooltip {
  text-align: center;
  border: 1px solid #ccc;
  position: absolute;
  padding: 10px;
  background-color: #fff;
  display: none;
}

#tooltip .metric-date {
  text-decoration: underline;
}

.axis {
  font-size: 18px;
  shape-rendering: geometricPrecision;
}


<!DOCTYPE html>
<html lang="en">

<head>
  <meta charset="UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>D3 Scatterplot</title>
  
</head>

<body>
  <div id="chart"></div>

  <script src="https://d3js.org/d3.v6.min.js"></script>
  
</body>

</html>

async function draw() {
  // Data
  const dataset = await d3.json('/udata/1kL2GBJw8VB/data-4-1.json')

  const xAccessor = (d) => d.currently.humidity
  const yAccessor = (d) => d.currently.apparentTemperature
  
  // Dimensions
  let dimensions = {
    width: 800,
    height: 800,
    margin: {
      top: 50,
      right: 50,
      bottom: 50,
      left: 50
    }
  }

  dimensions.ctrWidth = dimensions.width - dimensions.margin.left - dimensions.margin.right;
  dimensions.ctrHeight = dimensions.height - dimensions.margin.top - dimensions.margin.bottom;

  // Draw Image
  const svg = d3.select("#chart")
    .append("svg")
    .attr("width", dimensions.width)
    .attr("height", dimensions.height)

  const ctr = svg.append("g") // <g>
    .attr(
      "transform",
      `translate(${dimensions.margin.left}, ${dimensions.margin.top})`
    )

  // Scales
  const xScale = d3.scaleLinear()
    .domain(d3.extent(dataset, xAccessor))
    .rangeRound([0, dimensions.ctrWidth])
    .clamp(true)

  const yScale = d3.scaleLinear()
    .domain(d3.extent(dataset, yAccessor))
    .rangeRound([dimensions.ctrHeight, 0])
    .nice()
    .clamp(true)

  // Draw circles
  ctr.selectAll('circle')
    .data(dataset)
    .join('circle')
    .attr("cx", d => xScale(xAccessor(d)))
    .attr("cy", d => yScale(yAccessor(d)))
    .attr('data-temp', yAccessor)
    .attr("r", 5)
    .attr("fill", 'red')

  // Axes
  const xAxis = d3.axisBottom(xScale)
    .ticks(5)
    .tickFormat(d => d * 100 + "%")

  const xAxisGroup = ctr.append("g")
    .call(xAxis)
    .style("transform", `translateY(${dimensions.ctrHeight}px)`)
    .classed('axis', true)

  xAxisGroup.append("text")
    .attr("x", dimensions.ctrWidth / 2)
    .attr("y", dimensions.margin.bottom - 10)
    .attr("fill", "black")
    .text("Humidity")
    .style("text-anchor", "middle")


  xAxisGroup.append("text")
    .attr("x", dimensions.ctrWidth / 2)
    .attr("y", dimensions.margin.bottom - 10)
    .attr("fill", "black")
    .text("Humidity")

  const yAxis = d3.axisLeft(yScale);

  const yAxisGroup = ctr.append("g")
    .call(yAxis)
    .classed("axis", true);

  yAxisGroup.append("text")
    .attr("x", -dimensions.ctrHeight / 2)
    .attr("y", -dimensions.margin.left + 15)
    .attr("fill", "black")
    .html("Temperature (&deg;F)")
    .style("transform", "rotate(270deg)")
    .style("text-anchor", "middle")
}

draw()

codemirror

Here is the scatterplot we drew in an earlier lesson. Scatterplots are great at showing the relationships between two metrics. It answers the question of whether or not one metric affects the other.

# Frequency

What if our question was how frequently a value appears? It’s sort of hard to tell because the data is all over the place. Different questions require different visualizations. We can answer this question with a histogram.

Let’s pretend we had an array of 10 numbers. The numbers range from 10 to 100. 

```javascript
[10, 10, 10, 10, 30, 50, 50, 80, 80, 100]
```

One possible question is, how often does the number `10` appear in the array? What about `50`? Or `100`? We can answer these questions by counting the numbers in the array. However, that will not suffice for more complex data structures. We can draw the frequency of each number with a histogram.

Histograms are a type of chart that shows the frequency of a set of values within a dataset.

Overview

SVG Basics

D3 Fundamentals

Drawing a Scatter Plot

Scales

Animations & Interactivity

Even More Charts!

Conclusion

What is a Histogram?

Frequency