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jwilber/.block

Last active May 4, 2019 23:27
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reusable joyplot
Raw
.block
license: mit
Raw
index.html
<!DOCTYPE html>
<head>
<meta charset="utf-8">
<script src="https://d3js.org/d3.v4.min.js"></script>
<script src="joyplot.js"></script>
<style>
body { margin:0;position:fixed;top:0;right:0;bottom:0;left:0; }
.point {
fill: #000;
stroke: transparent; c
stroke-width: 5px;
}
.line {
fill: none;
stroke: #aaa;
}
.axis {
.domain,
.tick line {
display: none;
}
}
</style>
</head>
<body>
<!-- here's the div our chart will be injected into -->
<div class="chart-container" style="max-width: 1000px;"></div>
<script>
const chart = new Chart({
element: document.querySelector('.chart-container'),
data: "iris.csv",
x: 'day',
y: 'retweets'
});
</script>
</body>
Raw
iris.csv
sepal_length sepal_width petal_length petal_width species
5.1 3.5 1.4 0.2 setosa
4.9 3 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
4.6 3.1 1.5 0.2 setosa
5 3.6 1.4 0.2 setosa
5.4 3.9 1.7 0.4 setosa
4.6 3.4 1.4 0.3 setosa
5 3.4 1.5 0.2 setosa
4.4 2.9 1.4 0.2 setosa
4.9 3.1 1.5 0.1 setosa
5.4 3.7 1.5 0.2 setosa
4.8 3.4 1.6 0.2 setosa
4.8 3 1.4 0.1 setosa
4.3 3 1.1 0.1 setosa
5.8 4 1.2 0.2 setosa
5.7 4.4 1.5 0.4 setosa
5.4 3.9 1.3 0.4 setosa
5.1 3.5 1.4 0.3 setosa
5.7 3.8 1.7 0.3 setosa
5.1 3.8 1.5 0.3 setosa
5.4 3.4 1.7 0.2 setosa
5.1 3.7 1.5 0.4 setosa
4.6 3.6 1 0.2 setosa
5.1 3.3 1.7 0.5 setosa
4.8 3.4 1.9 0.2 setosa
5 3 1.6 0.2 setosa
5 3.4 1.6 0.4 setosa
5.2 3.5 1.5 0.2 setosa
5.2 3.4 1.4 0.2 setosa
4.7 3.2 1.6 0.2 setosa
4.8 3.1 1.6 0.2 setosa
5.4 3.4 1.5 0.4 setosa
5.2 4.1 1.5 0.1 setosa
5.5 4.2 1.4 0.2 setosa
4.9 3.1 1.5 0.1 setosa
5 3.2 1.2 0.2 setosa
5.5 3.5 1.3 0.2 setosa
4.9 3.1 1.5 0.1 setosa
4.4 3 1.3 0.2 setosa
5.1 3.4 1.5 0.2 setosa
5 3.5 1.3 0.3 setosa
4.5 2.3 1.3 0.3 setosa
4.4 3.2 1.3 0.2 setosa
5 3.5 1.6 0.6 setosa
5.1 3.8 1.9 0.4 setosa
4.8 3 1.4 0.3 setosa
5.1 3.8 1.6 0.2 setosa
4.6 3.2 1.4 0.2 setosa
5.3 3.7 1.5 0.2 setosa
5 3.3 1.4 0.2 setosa
7 3.2 4.7 1.4 versicolor
6.4 3.2 4.5 1.5 versicolor
6.9 3.1 4.9 1.5 versicolor
5.5 2.3 4 1.3 versicolor
6.5 2.8 4.6 1.5 versicolor
5.7 2.8 4.5 1.3 versicolor
6.3 3.3 4.7 1.6 versicolor
4.9 2.4 3.3 1 versicolor
6.6 2.9 4.6 1.3 versicolor
5.2 2.7 3.9 1.4 versicolor
5 2 3.5 1 versicolor
5.9 3 4.2 1.5 versicolor
6 2.2 4 1 versicolor
6.1 2.9 4.7 1.4 versicolor
5.6 2.9 3.6 1.3 versicolor
6.7 3.1 4.4 1.4 versicolor
5.6 3 4.5 1.5 versicolor
5.8 2.7 4.1 1 versicolor
6.2 2.2 4.5 1.5 versicolor
5.6 2.5 3.9 1.1 versicolor
5.9 3.2 4.8 1.8 versicolor
6.1 2.8 4 1.3 versicolor
6.3 2.5 4.9 1.5 versicolor
6.1 2.8 4.7 1.2 versicolor
6.4 2.9 4.3 1.3 versicolor
6.6 3 4.4 1.4 versicolor
6.8 2.8 4.8 1.4 versicolor
6.7 3 5 1.7 versicolor
6 2.9 4.5 1.5 versicolor
5.7 2.6 3.5 1 versicolor
5.5 2.4 3.8 1.1 versicolor
5.5 2.4 3.7 1 versicolor
5.8 2.7 3.9 1.2 versicolor
6 2.7 5.1 1.6 versicolor
5.4 3 4.5 1.5 versicolor
6 3.4 4.5 1.6 versicolor
6.7 3.1 4.7 1.5 versicolor
6.3 2.3 4.4 1.3 versicolor
5.6 3 4.1 1.3 versicolor
5.5 2.5 4 1.3 versicolor
5.5 2.6 4.4 1.2 versicolor
6.1 3 4.6 1.4 versicolor
5.8 2.6 4 1.2 versicolor
5 2.3 3.3 1 versicolor
5.6 2.7 4.2 1.3 versicolor
5.7 3 4.2 1.2 versicolor
5.7 2.9 4.2 1.3 versicolor
6.2 2.9 4.3 1.3 versicolor
5.1 2.5 3 1.1 versicolor
5.7 2.8 4.1 1.3 versicolor
6.3 3.3 6 2.5 virginica
5.8 2.7 5.1 1.9 virginica
7.1 3 5.9 2.1 virginica
6.3 2.9 5.6 1.8 virginica
6.5 3 5.8 2.2 virginica
7.6 3 6.6 2.1 virginica
4.9 2.5 4.5 1.7 virginica
7.3 2.9 6.3 1.8 virginica
6.7 2.5 5.8 1.8 virginica
7.2 3.6 6.1 2.5 virginica
6.5 3.2 5.1 2 virginica
6.4 2.7 5.3 1.9 virginica
6.8 3 5.5 2.1 virginica
5.7 2.5 5 2 virginica
5.8 2.8 5.1 2.4 virginica
6.4 3.2 5.3 2.3 virginica
6.5 3 5.5 1.8 virginica
7.7 3.8 6.7 2.2 virginica
7.7 2.6 6.9 2.3 virginica
6 2.2 5 1.5 virginica
6.9 3.2 5.7 2.3 virginica
5.6 2.8 4.9 2 virginica
7.7 2.8 6.7 2 virginica
6.3 2.7 4.9 1.8 virginica
6.7 3.3 5.7 2.1 virginica
7.2 3.2 6 1.8 virginica
6.2 2.8 4.8 1.8 virginica
6.1 3 4.9 1.8 virginica
6.4 2.8 5.6 2.1 virginica
7.2 3 5.8 1.6 virginica
7.4 2.8 6.1 1.9 virginica
7.9 3.8 6.4 2 virginica
6.4 2.8 5.6 2.2 virginica
6.3 2.8 5.1 1.5 virginica
6.1 2.6 5.6 1.4 virginica
7.7 3 6.1 2.3 virginica
6.3 3.4 5.6 2.4 virginica
6.4 3.1 5.5 1.8 virginica
6 3 4.8 1.8 virginica
6.9 3.1 5.4 2.1 virginica
6.7 3.1 5.6 2.4 virginica
6.9 3.1 5.1 2.3 virginica
5.8 2.7 5.1 1.9 virginica
6.8 3.2 5.9 2.3 virginica
6.7 3.3 5.7 2.5 virginica
6.7 3 5.2 2.3 virginica
6.3 2.5 5 1.9 virginica
6.5 3 5.2 2 virginica
6.2 3.4 5.4 2.3 virginica
5.9 3 5.1 1.8 virginica
Raw
joyplot.js
class Chart {
constructor(opts) {
console.log('inside')
this.element = opts.element;
this.x = opts.x;
this.y = opts.y;
d3.csv(opts.data, (d) => {
this.data = d;
this.draw();
});
}
draw() {
this.margin = {
top: 200,
bottom: 50,
right: 50,
left: 100
};
this.width = 800 - this.margin.right - this.margin.left;
this.height = this.width / 1.5 - this.margin.top - this.margin.bottom;
this.element.innerHTML = '';
const svg = d3.select(this.element).append('svg');
svg.attr('width', this.width + this.margin.right + this.margin.left);
svg.attr('height', this.height + this.margin.top + this.margin.bottom);
this.plot = svg.append('g')
.attr('transform',
`translate(${this.margin.left}, ${this.margin.top})`)
this.createScales();
this.addAxes();
this.addDensities();
}
createScales() {
this.categories = this.data.columns.filter(d => !isNaN(this.data[0][d]))
this.n = this.categories.length
const {min, max} = csvExtent(this.data)
this.x = d3.scaleLinear()
.domain([min - 15, max + 15])
.range([ 0, this.width ]);
// Create a Y scale for densities
this.y = d3.scaleLinear()
.domain([0, .25])
.range([ this.height, 0]);
// Create the Y axis for names
this.yName = d3.scaleBand()
.domain(this.categories)
.range([0, this.height])
.paddingInner(1)
console.log(this.yName)
};
addAxes() {
this.plot.append("g")
.attr('class', 'x-axis')
.attr("transform", `translate(0, ${this.height})`)
.call(d3.axisBottom(this.x));
this.plot.append("g")
.attr('class', 'y-axis')
.call(d3.axisLeft(this.yName));
};
addDensities() {
console.log('r', this.yName)
const that = this;
// Compute kernel density estimation for each column:
var kde = kernelDensityEstimator(kernelEpanechnikov(7), this.x.ticks(40))
// increase this 40 for more accurate density.
var allDensity = []
for (var i = 0; i < this.n; i++) {
let key = this.categories[i]
let density = kde( this.data.map(function(d){ return d[key]; }) )
allDensity.push({key: key, density: density})
}
// Add areas
this.plot.selectAll("areas")
.data(allDensity)
.enter()
.append("path")
// .attr('transform',
// d => {
// `translate(0, ${(that.yName(d.key)-that.height)})`
// })
.attr("transform", function(d){
return("translate(0," + (that.yName(d.key)-that.height) +")" )
})
.datum(d => d.density)
.attr("fill", "skyblue")
.attr('opacity', 1)
.attr("stroke", "azure")
.attr("stroke-width", 1)
.attr("d", d3.line()
.curve(d3.curveStep)
.x(function(d) { return that.x(d[0]); })
.y(function(d) { return that.y(d[1]); })
)
};
}
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;
};
}
function csvExtent(data) {
const colNames = data.columns
const extents = colNames.map(col => d3.extent(data, d => +d[col]));
const csvMin = d3.min(extents, d => d[0])
const csvMax = d3.max(extents, d => d[1])
return {'min': csvMin, 'max': csvMax}
};
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