Computational biologist. Using transcriptomics data to learn about inflammation and cancer.
Public
Edited
Jul 21, 2020
2 forks
4 stars
viewof width = html`<input type=range min=300 max=900>`
chart = {
const svg = d3.select(DOM.svg(width, height));
const g = svg.append("g")
.selectAll("g")
.data(bins)
.join("g");
g.append("path")
.attr("stroke", "currentColor")
.attr("d", d => `
M${x(d.range[0])},${y((d.x0 + d.x1) / 2)}
H${x(d.range[1])}
`);
g.append("path")
.attr("fill", "#ddd")
.attr("d", d => `
M${x(d.quartiles[2])},${y(d.x0) - 1}
V${y(d.x1)}
H${x(d.quartiles[0])}
V${y(d.x0) - 1}
Z
`);
g.append("path")
.attr("stroke", "currentColor")
.attr("stroke-width", 2)
.attr("d", d => `
M${x(d.quartiles[1])},${y(d.x0) - 1}
V${y(d.x1)}
`);
g.append("g")
.attr("fill", "currentColor")
.attr("fill-opacity", 0.2)
.attr("stroke", "none")
.attr("transform", d => `translate(0,${y((d.x0 + d.x1) / 2)})`)
.selectAll("circle")
.data(d => d.outliers)
.join("circle")
.attr("r", 2)
.attr("cy", () => (Math.random() - 0.5) * 4)
.attr("cx", d => x(d.x));
svg.append("g")
.call(xAxis);
svg.append("g")
.call(yAxis);
return svg.node();
}
bins = d3.histogram()
.thresholds(n)
.value(d => d.y)
(data)
.map(bin => {
bin.sort((a, b) => a.x - b.x);
const values = bin.map(d => d.x);
const min = values[0];
const max = values[values.length - 1];
const q1 = d3.quantile(values, 0.25);
const q2 = d3.quantile(values, 0.50);
const q3 = d3.quantile(values, 0.75);
const iqr = q3 - q1; // interquartile range
const r0 = Math.max(min, q1 - iqr * 1.5);
const r1 = Math.min(max, q3 + iqr * 1.5);
bin.quartiles = [q1, q2, q3];
bin.range = [r0, r1];
bin.outliers = bin.filter(v => v.x < r0 || v.x > r1); // TODO
return bin;
})
data = d3.csvParse(await FileAttachment("diamonds.csv").text(), ({carat, price}) => ({
y: +carat, x: +price
}))
x = d3.scaleLinear()
.domain([d3.min(bins, d => d.range[0]), d3.max(bins, d => d.range[1])]).nice()
.range([margin.left, width - margin.right])
xAxis = g => g
.attr("transform", `translate(0,${height - margin.bottom})`)
.call(d3.axisBottom(x).ticks(null, "s"))
.call(g => g.select(".domain").remove())
y = d3.scaleLinear()
.domain([d3.min(bins, d => d.x0), d3.max(bins, d => d.x1)])
.rangeRound([height - margin.bottom, margin.top])
yAxis = g => g
.attr("transform", `translate(${margin.left},0)`)
.call(d3.axisLeft(y).ticks(n).tickSizeOuter(0))
n = width / 40
height = 600
margin = ({top: 20, right: 20, bottom: 30, left: 40})
d3 = require("d3@5")
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