Mirror density plot in d3.js




This post describes how to build a mirror density chart in d3.js. Two variables are represented face to face. This kind of chart can be handy to compare 2 variables that have overlapping distribution. It is often used to compare sexes for instance. This example works with d3.js v4 and v6


Density plot section

Notes:

  • This chart is an extension of the previous basic density chart.

  • It splits the dataset in 2 parts thanks to the filter() function. See more on data manipulation. Density is estimated and plotted for each part.

  • Instead of sharing the Y axis, each group has its own scale and axis.

  • Ticks position is hardcoded to avoid an overlapping 0 value.
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<!DOCTYPE html>
<meta charset="utf-8">

<!-- Load d3.js -->
<script src="https://d3js.org/d3.v4.js"></script>

<!-- Create a div where the graph will take place -->
<div id="my_dataviz"></div>


<!DOCTYPE html>
<meta charset="utf-8">

<!-- Load d3.js -->
<script src="https://d3js.org/d3.v6.js"></script>

<!-- Create a div where the graph will take place -->
<div id="my_dataviz"></div>


<script>

// set the dimensions and margins of the graph
var margin = {top: 30, right: 30, bottom: 30, left: 60},
    width = 460 - margin.left - margin.right,
    height = 400 - margin.top - margin.bottom;

// append the svg object to the body of the page
var svg = d3.select("#my_dataviz")
  .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 the data
d3.csv("https://raw.githubusercontent.com/holtzy/D3-graph-gallery/master/DATA/data_doubleHist.csv", function(data) {

  // add the x Axis
  var x = d3.scaleLinear()
      .domain([-10,15])
      .range([0, width]);
  svg.append("g")
      .attr("transform", "translate(0," + height + ")")
      .call(d3.axisBottom(x));

  // add the first y Axis
  var y1 = d3.scaleLinear()
            .range([height/2, 0])
            .domain([0, 0.12]);
  svg.append("g")
    .attr("transform", "translate(-20,0)")
    .call(d3.axisLeft(y1).tickValues([0.05, 0.1]));

  // add the first y Axis
  var y2 = d3.scaleLinear()
            .range([height/2, height])
            .domain([0, 0.12]);
  svg.append("g")
      .attr("transform", "translate(-20,0)")
      .call(d3.axisLeft(y2).ticks(2).tickSizeOuter(0));

  // Compute kernel density estimation
  var kde = kernelDensityEstimator(kernelEpanechnikov(7), x.ticks(60))
  var density1 =  kde( data
      .filter( function(d){return d.type === "variable 1"} )
      .map(function(d){  return d.value; }) )
  var density2 =  kde( data
      .filter( function(d){return d.type === "variable 2"} )
      .map(function(d){  return d.value; }) )

  // Plot the area
  svg.append("path")
      .attr("class", "mypath")
      .datum(density1)
      .attr("fill", "#69b3a2")
      .attr("opacity", ".6")
      .attr("stroke", "#000")
      .attr("stroke-width", 1)
      .attr("stroke-linejoin", "round")
      .attr("d",  d3.line()
        .curve(d3.curveBasis)
          .x(function(d) { return x(d[0]); })
          .y(function(d) { return y1(d[1]); })
      );

  // Plot the area
  svg.append("path")
      .attr("class", "mypath")
      .datum(density2)
      .attr("fill", "#404080")
      .attr("opacity", ".6")
      .attr("stroke", "#000")
      .attr("stroke-width", 1)
      .attr("stroke-linejoin", "round")
      .attr("d",  d3.line()
        .curve(d3.curveBasis)
          .x(function(d) { return x(d[0]); })
          .y(function(d) { return y2(d[1]); })
      );

});

// Handmade legend
svg.append("circle").attr("cx",290).attr("cy",30).attr("r", 6).style("fill", "#69b3a2")
svg.append("circle").attr("cx",290).attr("cy",60).attr("r", 6).style("fill", "#404080")
svg.append("text").attr("x", 310).attr("y", 30).text("variable A").style("font-size", "15px").attr("alignment-baseline","middle")
svg.append("text").attr("x", 310).attr("y", 60).text("variable B").style("font-size", "15px").attr("alignment-baseline","middle")

// Function to compute density
function kernelDensityEstimator(kernel, X) {
  return function(V) {
    return X.map(function(x) {
      return [x, d3.mean(V, function(v) { return kernel(x - v); })];
    });
  };
}
function kernelEpanechnikov(k) {
  return function(v) {
    return Math.abs(v /= k) <= 1 ? 0.75 * (1 - v * v) / k : 0;
  };
}

</script>
<script>

// set the dimensions and margins of the graph
const margin = {top: 30, right: 30, bottom: 30, left: 60},
    width = 460 - margin.left - margin.right,
    height = 400 - margin.top - margin.bottom;

// append the svg object to the body of the page
const svg = d3.select("#my_dataviz")
  .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 the data
d3.csv("https://raw.githubusercontent.com/holtzy/D3-graph-gallery/master/DATA/data_doubleHist.csv").then( function(data) {

  // add the x Axis
  const x = d3.scaleLinear()
      .domain([-10,15])
      .range([0, width]);
  svg.append("g")
      .attr("transform", `translate(0, ${height})`)
      .call(d3.axisBottom(x));

  // add the first y Axis
  const y1 = d3.scaleLinear()
            .range([height/2, 0])
            .domain([0, 0.12]);
  svg.append("g")
    .attr("transform", "translate(-20,0)")
    .call(d3.axisLeft(y1).tickValues([0.05, 0.1]));

  // add the first y Axis
  const y2 = d3.scaleLinear()
            .range([height/2, height])
            .domain([0, 0.12]);
  svg.append("g")
      .attr("transform", "translate(-20,0)")
      .call(d3.axisLeft(y2).ticks(2).tickSizeOuter(0));

  // Compute kernel density estimation
  const kde = kernelDensityEstimator(kernelEpanechnikov(7), x.ticks(60))
  const density1 =  kde( data
      .filter( function(d){return d.type === "variable 1"} )
      .map(function(d){  return d.value; }) )
  const density2 =  kde( data
      .filter( function(d){return d.type === "variable 2"} )
      .map(function(d){  return d.value; }) )

  // Plot the area
  svg.append("path")
      .attr("class", "mypath")
      .datum(density1)
      .attr("fill", "#69b3a2")
      .attr("opacity", ".6")
      .attr("stroke", "#000")
      .attr("stroke-width", 1)
      .attr("stroke-linejoin", "round")
      .attr("d",  d3.line()
        .curve(d3.curveBasis)
          .x(function(d) { return x(d[0]); })
          .y(function(d) { return y1(d[1]); })
      );

  // Plot the area
  svg.append("path")
      .attr("class", "mypath")
      .datum(density2)
      .attr("fill", "#404080")
      .attr("opacity", ".6")
      .attr("stroke", "#000")
      .attr("stroke-width", 1)
      .attr("stroke-linejoin", "round")
      .attr("d",  d3.line()
        .curve(d3.curveBasis)
          .x(function(d) { return x(d[0]); })
          .y(function(d) { return y2(d[1]); })
      );

});

// Handmade legend
svg.append("circle").attr("cx",290).attr("cy",30).attr("r", 6).style("fill", "#69b3a2")
svg.append("circle").attr("cx",290).attr("cy",60).attr("r", 6).style("fill", "#404080")
svg.append("text").attr("x", 310).attr("y", 30).text("variable A").style("font-size", "15px").attr("alignment-baseline","middle")
svg.append("text").attr("x", 310).attr("y", 60).text("variable B").style("font-size", "15px").attr("alignment-baseline","middle")

// Function to compute density
function kernelDensityEstimator(kernel, X) {
  return function(V) {
    return X.map(function(x) {
      return [x, d3.mean(V, function(v) { return kernel(x - v); })];
    });
  };
}
function kernelEpanechnikov(k) {
  return function(v) {
    return Math.abs(v /= k) <= 1 ? 0.75 * (1 - v * v) / k : 0;
  };
}
</script>

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