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Bubble Matrix
Plot.plot({
marginTop: 80,
marginLeft: 120,
r: {
type: "linear",
range: [5, 28]
},
color: {
scheme: "RdYlGn",
domain: [1, 5]
},
x: {
domain: ["Z-Score", "", "Key Skill", " ", ...fields],
axis: "top",
label: null,
tickSize: 0,
tickRotate: -35
},
y: {
domain: d3
.groupSort(
data,
// Sort by Z-Score, break ties by Key Skill * epsilon
(v) =>
v.find((d) => d.field === "Z-Score").value +
1e-6 * v.find((d) => d.field === "Key Skill").value,
(d) => d.name
)
.reverse(),
label: null,
tickSize: 0
},
marks: [
Plot.dot(data, {
filter: (d) => d.field !== "Z-Score",
x: "field",
y: "name",
r: "value",
fill: "value"
}),
Plot.dot(data, {
filter: (d) => d.field === "Z-Score",
x: "field",
y: "name",
r: (d) => d["value"] * 0.5,
fill: "steelblue",
fillOpacity: (d) => (d["value"] - 7) * 0.6
}),
Plot.text(data, {
x: "field",
y: "name",
text: "value",
fill: "white"
})
],
width: 700,
height: 500
})
candidates = FileAttachment("candidates.csv").csv({ typed: true })
fields = ["Z-Score"].concat(candidates.columns.slice(3))
// reshape
data = candidates
.map((d) =>
fields.map((field) => ({
name: d.Candidate,
value: field === "Z-Score" ? d["Z"] : d[field],
field
}))
)
.flat()
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Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
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