Splom in JavaScript
How to make D3.js-based splom in Plotly.js.
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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.
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: {
text: '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('myDiv', data, layout)
});
Click to copy
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.
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},
title:{font:{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: {
text: "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('myDiv', data, layout);
});
Click to copy
