Ridgeline plot template in d3.js





This post provides a clean template for ridgeline plot with d3.js (also called joy plot). Color depends on average value, axes are labeled, and background is customized. See other examples in the ridgeline section of the gallery. This example works with d3.js v4 and v6


Ridgeline section

Steps:
|
<!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>

<!-- Style -->
<style>
.xAxis line {
  stroke: #B8B8B8;
}
</style>

<!-- Viridis color palette-->
<script src="https://d3js.org/d3-scale-chromatic.v1.min.js"></script>

<!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>

<!-- Style -->
<style>
.xAxis line {
  stroke: #B8B8B8;
}
</style>
    
<script>

// set the dimensions and margins of the graph
var margin = {top: 80, right: 30, bottom: 50, left:110},
    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 + ")");

//read data
d3.csv("https://raw.githubusercontent.com/zonination/perceptions/master/probly.csv", function(data) {

  // Get the different categories and count them
  var categories = ["Almost Certainly", "Very Good Chance", "We Believe", "Likely", "About Even", "Little Chance", "Chances Are Slight", "Almost No Chance" ]
  var n = categories.length

  // Compute the mean of each group
  allMeans = []
  for (i in categories){
    currentGroup = categories[i]
    mean = d3.mean(data, function(d) { return +d[currentGroup] })
    allMeans.push(mean)
  }

  // Create a color scale using these means.
  var myColor = d3.scaleSequential()
    .domain([0,100])
    .interpolator(d3.interpolateViridis);

  // Add X axis
  var x = d3.scaleLinear()
    .domain([-10, 120])
    .range([ 0, width ]);
  svg.append("g")
    .attr("class", "xAxis")
    .attr("transform", "translate(0," + height + ")")
    .call(d3.axisBottom(x).tickValues([0,25, 50, 75, 100]).tickSize(-height) )
    .select(".domain").remove()

  // Add X axis label:
  svg.append("text")
      .attr("text-anchor", "end")
      .attr("x", width)
      .attr("y", height + 40)
      .text("Probability (%)");

  // Create a Y scale for densities
  var y = d3.scaleLinear()
    .domain([0, 0.25])
    .range([ height, 0]);

  // Create the Y axis for names
  var yName = d3.scaleBand()
    .domain(categories)
    .range([0, height])
    .paddingInner(1)
  svg.append("g")
    .call(d3.axisLeft(yName).tickSize(0))
    .select(".domain").remove()

  // Compute kernel density estimation for each column:
  var kde = kernelDensityEstimator(kernelEpanechnikov(7), x.ticks(40)) // increase this 40 for more accurate density.
  var allDensity = []
  for (i = 0; i < n; i++) {
      key = categories[i]
      density = kde( data.map(function(d){  return d[key]; }) )
      allDensity.push({key: key, density: density})
  }

  // Add areas
  svg.selectAll("areas")
    .data(allDensity)
    .enter()
    .append("path")
      .attr("transform", function(d){return("translate(0," + (yName(d.key)-height) +")" )})
      .attr("fill", function(d){
        grp = d.key ;
        index = categories.indexOf(grp)
        value = allMeans[index]
        return myColor( value  )
      })
      .datum(function(d){return(d.density)})
      .attr("opacity", 0.7)
      .attr("stroke", "#000")
      .attr("stroke-width", 0.1)
      .attr("d",  d3.line()
          .curve(d3.curveBasis)
          .x(function(d) { return x(d[0]); })
          .y(function(d) { return y(d[1]); })
      )

})

// This is what I need to compute kernel density estimation
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: 80, right: 30, bottom: 50, left:110},
    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})`);

//read data
d3.csv("https://raw.githubusercontent.com/zonination/perceptions/master/probly.csv").then(function(data) {

  // Get the different categories and count them
  const categories = ["Almost Certainly", "Very Good Chance", "We Believe", "Likely", "About Even", "Little Chance", "Chances Are Slight", "Almost No Chance" ]
  const n = categories.length

  // Compute the mean of each group
  allMeans = []
  for (i in categories){
    currentGroup = categories[i]
    mean = d3.mean(data, function(d) { return +d[currentGroup] })
    allMeans.push(mean)
  }

  // Create a color scale using these means.
  const myColor = d3.scaleSequential()
    .domain([0,100])
    .interpolator(d3.interpolateViridis);

  // Add X axis
  const x = d3.scaleLinear()
    .domain([-10, 120])
    .range([ 0, width ]);
  svg.append("g")
    .attr("class", "xAxis")
    .attr("transform", "translate(0," + height + ")")
    .call(d3.axisBottom(x).tickValues([0,25, 50, 75, 100]).tickSize(-height) )
    .select(".domain").remove()

  // Add X axis label:
  svg.append("text")
      .attr("text-anchor", "end")
      .attr("x", width)
      .attr("y", height + 40)
      .text("Probability (%)");

  // Create a Y scale for densities
  const y = d3.scaleLinear()
    .domain([0, 0.25])
    .range([ height, 0]);

  // Create the Y axis for names
  const yName = d3.scaleBand()
    .domain(categories)
    .range([0, height])
    .paddingInner(1)
  svg.append("g")
    .call(d3.axisLeft(yName).tickSize(0))
    .select(".domain").remove()

  // Compute kernel density estimation for each column:
  const kde = kernelDensityEstimator(kernelEpanechnikov(7), x.ticks(40)) // increase this 40 for more accurate density.
  const allDensity = []
  for (i = 0; i < n; i++) {
      key = categories[i]
      density = kde( data.map(function(d){  return d[key]; }) )
      allDensity.push({key: key, density: density})
  }

  // Add areas
  svg.selectAll("areas")
    .data(allDensity)
    .join("path")
      .attr("transform", function(d){return(`translate(0, ${(yName(d.key)-height)})` )})
      .attr("fill", function(d){
        grp = d.key ;
        index = categories.indexOf(grp)
        value = allMeans[index]
        return myColor( value  )
      })
      .datum(function(d){return(d.density)})
      .attr("opacity", 0.7)
      .attr("stroke", "#000")
      .attr("stroke-width", 0.1)
      .attr("d",  d3.line()
          .curve(d3.curveBasis)
          .x(function(d) { return x(d[0]); })
          .y(function(d) { return y(d[1]); })
      )

})

// This is what I need to compute kernel density estimation
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>

Related blocks →