Splom in plotly.js

How to make D3.js-based splom in Plotly.js.

Splom of Iris Dataset

The Iris dataset contains four data variables, sepal length, sepal width, petal length petal width, for 150 iris flowers. The flowers are labeled as Iris-setosa, Iris-versicolor, Iris-virginica.

Plotly.d3.csv('https://raw.githubusercontent.com/plotly/datasets/master/iris-data.csv', function(err, rows){

    function unpack(rows, key) {
        return rows.map(function(row) { return row[key.replace('.',' ')]; });
    }

    colors = []
    for (i=0; i < unpack(rows, 'class').length; i++) {
      if (unpack(rows, 'class')[i] == "Iris-setosa") {
        colors.push(0)
      } else if (unpack(rows, 'class')[i] == "Iris-versicolor") {
        colors.push(0.5)
      } else if (unpack(rows, 'class')[i] == "Iris-virginica") {
        colors.push(1)
      }
    }

    var pl_colorscale=[
               [0.0, '#19d3f3'],
               [0.333, '#19d3f3'],
               [0.333, '#e763fa'],
               [0.666, '#e763fa'],
               [0.666, '#636efa'],
               [1, '#636efa']
    ]

    var axis = () => ({
      showline:false,
      zeroline:false,
      gridcolor:'#ffff',
      ticklen:4
    })

    var data = [{
      type: 'splom',
      dimensions: [
        {label:'sepal length', values:unpack(rows,'sepal length')},
        {label:'sepal width', values:unpack(rows,'sepal width')},
        {label:'petal length', values:unpack(rows,'petal length')},
        {label:'petal width', values:unpack(rows,'petal width')}
      ],
      text: unpack(rows, 'class'),
      marker: {
        color: colors,
        colorscale:pl_colorscale,
        size: 7,
        line: {
          color: 'white',
          width: 0.5
        }
      }
    }]

    var layout = {
      title:'Iris Data set',
      height: 800,
      width: 800,
      autosize: false,
      hovermode:'closest',
      dragmode:'select',
      plot_bgcolor:'rgba(240,240,240, 0.95)',
      xaxis:axis(),
      yaxis:axis(),
      xaxis2:axis(),
      xaxis3:axis(),
      xaxis4:axis(),
      yaxis2:axis(),
      yaxis3:axis(),
      yaxis4:axis()
    }

    Plotly.react('graph', data, layout)

});

Diabetes dataset is downloaded from kaggle. It is used to predict the onset of diabetes based on 8 diagnostic measures. The diabetes file contains the diagnostic measures for 768 patients, that are labeled as non-diabetic (Outcome=0), respectively diabetic (Outcome=1). The splom associated to the 8 variables can illustrate the strength of the relationship between pairs of measures for diabetic/nondiabetic patients.

Plotly.d3.csv('https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv', function(err, rows){

    function unpack(rows, key) {
        return rows.map(function(row) { return row[key]; });
    }

    text = []
    for (i=0; i < unpack(rows, 'Outcome').length; i++) {
      if (unpack(rows, 'Outcome')[i] == "0") {
        text.push("Diabetic")
      } else {
        text.push("Non-Diabetic")
      }
    }

    var pl_colorscale=[
      [0.0, '#119dff'],
      [0.5, '#119dff'],
      [0.5, '#ef553b'],
      [1, '#ef553b']
    ]

    var axis = () => ({
      showline:false,
      zeroline:false,
      gridcolor:'#ffff',
      ticklen:2,
      tickfont:{size:10},
      titlefont:{size:12}
    })

    var data = [{
      type: 'splom',
      dimensions: [
        {label:'Pregnancies', values:unpack(rows, 'Pregnancies')},
        {label:'Glucose', values:unpack(rows, 'Glucose')},
        {label:'BloodPressure', values:unpack(rows, 'BloodPressure')},
        {label:'SkinThickness', values:unpack(rows, 'SkinThickness')},
        {label:'Insulin', values:unpack(rows, 'Insulin')},
        {label:'BMI', values:unpack(rows, 'BMI')},
        {label:'DiabPedigreeFun', values:unpack(rows, 'DiabetesPedigreeFunction')},
        {label:'Age', values:unpack(rows, 'Age')}
      ],
      text:text,
      marker: {
        color: unpack(rows, 'Outcome'),
        colorscale:pl_colorscale,
        size: 5,
        line: {
          color: 'white',
          width: 0.5
        }
      }
    }]

    var layout = {
      title:"Scatterplot Matrix (SPLOM) for Diabetes Dataset<br>Data source: <a href='https://www.kaggle.com/uciml/pima-indians-diabetes-database/data'>[1]</a>",
      height: 1000,
      width: 1000,
      autosize: false,
      hovermode:'closest',
      dragmode:'select',
      plot_bgcolor:'rgba(240,240,240, 0.95)',
      xaxis:axis(),
      yaxis:axis(),
      xaxis2:axis(),
      xaxis3:axis(),
      xaxis4:axis(),
      xaxis5:axis(),
      xaxis6:axis(),
      xaxis7:axis(),
      xaxis8:axis(),
      yaxis2:axis(),
      yaxis3:axis(),
      yaxis4:axis(),
      yaxis5:axis(),
      yaxis6:axis(),
      yaxis7:axis(),
      yaxis8:axis()
    }

    Plotly.react('graph', data, layout);

});

Splom of Iris Dataset

Splom of Diabetes Dataset