Data visualization software enginner: working to make visualization more accessible, equitable, and impactful.
Plot.plot({
x: { axis: null },
y: { tickFormat: "s", grid: true },
color: { scheme: "spectral", legend: true },
fy: {
label: null,
axis: null
},
marks: [
Plot.barY(dataByGender, {
x: "key",
y: "population",
fill: "key",
fx: "state",
fy: "gender",
sort: { x: null, color: null, fx: { value: "-y", reduce: "sum" } }
}),
Plot.ruleY([0]),
Plot.text(
dataByGender,
Plot.selectFirst({
text: (d) => d.gender,
fx: ["CA"],
frameAnchor: "top-left",
dy: 3,
dx: 3
})
)
]
})
Plot.plot({
x: { axis: null },
y: { tickFormat: "s", grid: true },
color: { scheme: "spectral", legend: true },
fy: {
// data: dataByGender,
// label: null,
// align: "left"
},
marks: [
Plot.barY(dataByGender, {
x: "key",
y: "population",
fill: "key",
fx: "state",
fy: "gender",
sort: { x: null, color: null, fx: { value: "-y", reduce: "sum" } }
}),
Plot.ruleY([0]),
Plot.text(
dataByGender,
Plot.selectFirst({
text: (d) => d.gender,
fy: "gender",
facetAnchor: "left",
dy: 3,
dx: 3
})
)
]
})
data = FileAttachment("population-state-age.csv").csv({typed: true})
// Making fake data to get a category for fy
dataByGender = ["M", "F"]
.map((gender) => {
return data.map((d) => {
return { ...d, gender, population: d.population * (1 + Math.random()) };
});
})
.flat()
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