Violin Plot

Overview

This function (processViolin()) allows you to create a violin chart using Highcharts:

Check codePen demo

Installation

You can either add the function direclty in your code (see violin-plot.js) or use the following link (see below):

<script src="https://marketing-demo.s3-eu-west-1.amazonaws.com/violinFunction/processViolin.js"></script>

Description

Here is the description of the function’s parameters:

• step is the minimum data set unit. The step is used to sample the data set and create the KDE.
• precision is used to refine the violin plot at the extremities and in the thin spots, the smallest this parameter is the more points you get on the extremities and the thin spots on the chart.
• densityWidth is used to widen the violin. This parameter should be equal to 1 to reflect the result of the KDE values. Nevertheless, for visibility purposes, you are free to change the densityWidth to get a wider and visible shape.
• args is one or many arrays that represent the data set. In our case, args is four arrays of weight athletes, one array for each discipline.

The function processViolin() returns a set of three arrays:

• xiData is the xAxis data generated using the step and the range of the athletes’ weights data.
• results includes all the violin charts data.
• stat is the array with all the descriptive statistical coefficients.

Example

Check codePen demo

let arr1 = [1,2,2,2,5,8,9,9], arr2=[3,4,5], arr3=[1,2,2,3];
let step = 1,
    precision = 0.00000000001,
    width = 3;
let data = processViolin(step,precision,width,arr1, arr2,arr3);
let violin1 = data.results[0],
      violin2 = data.results[1],
      violin3 = data.results[2];
Highcharts.chart("container", {
...
series: [
        {
          name: "Violin 1",
          color: "#ffa8d4",
          data: violin1
        },
        {
          name: "Violin 2",
          color: "#a8d4ff",
          data: violin2
        },
        {
          name: "Violin 3",
          color: "#ffa956",
          data: violin3
        }
      ]
});

Remark

The function is built around the kernel density estimation (KDE).