Data science | Machine Learning | JavaScript
chart = {
const root = pack(data);
const svg = d3.create("svg")
.attr("viewBox", [0, 0, width, height])
.style("font", "10px sans-serif")
.attr("text-anchor", "middle");
const shadow = DOM.uid("shadow");
svg.append("filter")
.attr("id", shadow.id)
.append("feDropShadow")
.attr("flood-opacity", 0.3)
.attr("dx", 0)
.attr("dy", 1);
const node = svg.selectAll("g")
.data(d3.group(root.descendants(), d => d.height))
.join("g")
.attr("filter", shadow)
.selectAll("g")
.data(d => d[1])
.join("g")
.attr("transform", d => `translate(${d.x + 1},${d.y + 1})`);
node.append("circle")
.attr("r", d => d.r)
.attr("fill", d => color(d.height));
const leaf = node.filter(d => !d.children);
leaf.select("circle")
.attr("id", d => (d.leafUid = DOM.uid("leaf")).id);
leaf.append("clipPath")
.attr("id", d => (d.clipUid = DOM.uid("clip")).id)
.append("use")
.attr("xlink:href", d => d.leafUid.href);
leaf.append("text")
.attr("clip-path", d => d.clipUid)
.selectAll("tspan")
.data(d => d.data.name.split(/(?=[A-Z][a-z])|\s+/g))
.join("tspan")
.attr("x", 0)
.attr("y", (d, i, nodes) => `${i - nodes.length / 2 + 0.8}em`)
.text(d => d);
node.append("title")
.text(d => `${d.ancestors().map(d => d.data.name).reverse().join("/")}\n${format(d.value)}`);
return svg.node();
}
data = FileAttachment("output_LDA@1.json").json()
pack = data => d3.pack()
.size([width - 2, height - 2])
.padding(3)
(d3.hierarchy(data)
.sum(d => d.value)
.sort((a, b) => b.value - a.value))
width = 975
height = width
format = d3.format(",d")
color = d3.scaleSequential([0, 3], d3.interpolateBlues)
d3 = require("d3@6")
import {rasterize} from "@mbostock/saving-svg"
DOM.download(() => rasterize(chart), undefined, "Save as PNG")
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
