import { Plot } from "@tututwo/rough-js-plugin-for-observable-plot"
chart = {
const legend = Plot.plot({
color: {
range: scheme,
transform: ([a, b]) => 3 * a + b,
unknown: "#ccc" // See Valdez-Cordova, Alaska
},
axis: null,
margin: 0,
inset: 18,
width: 106,
height: 106,
style: "overflow: visible;",
marks: [
Plot.dot(d3.cross([0, 1, 2], [0, 1, 2]), {
x: ([a, b]) => b - a,
y: ([a, b]) => b + a,
symbol: "square",
rotate: 45,
r: 14,
fill: (d) => d,
title: ([a, b]) => `Diabetes${label(a)}\nObesity${label(b)}`,
tip: true
}),
Plot.text(["Obesity →"], {
frameAnchor: "right",
fontWeight: "bold",
rotate: -45,
dy: 10
}),
Plot.text(["← Diabetes"], {
frameAnchor: "left",
fontWeight: "bold",
rotate: 45,
dy: 10
})
]
});
const color = legend.scale("color");
const index = new Map(data.map(({ county, ...rest }) => [county, rest]));
return Plot.plot({
width: width,
height: 800,
projection: "identity",
color,
marks: [
Plot.geo(
topojson.feature(us, us.objects.counties),
Plot.centroid({
stroke: "white",
rough: {
id: "I-am-an-id",
roughness: 1,
strokeWidth: 0.3,
fillWeight: 0.51,
bowing: 1,
fillStyle: "cross-hatch",
hachureGap: 2
},
fill: (d) => bivariateClass(index.get(d.id)),
title: (d) => {
const name = `${d.properties.name}, ${
states.get(d.id.slice(0, 2)).name
}`;
const value = index.get(d.id);
if (!value || (isNaN(value.diabetes) && isNaN(value.obesity)))
return `${name}\nno data`;
const [dc, oc] = bivariateClass(value);
return `${name}\n${
isNaN(value.diabetes) ? "No Data" : value.diabetes
}% Diabetes${label(dc)}\n${
isNaN(value.obesity) ? "No Data" : value.obesity
}% Obesity${label(oc)}`;
},
tip: true
})
)
,
() => svg`<g transform="translate(835,410)">${legend}`
],
style: "overflow: visible;"
});
}
Select a data source…
Type SQL, then Shift-Enter. Ctrl-space for more options.
labels = ["low", "", "high"]
label = i => labels[i] ? ` (${labels[i]})` : ""
data = FileAttachment("cdc_diabetes_obesity_2020.csv").csv()
.then((data) => {
data.forEach((d) => {
d.obesity = +d.obesity; // type as numeric
d.diabetes = +d.diabetes;
});
return data;
})
diabetes_thresholds = d3.scaleQuantile(data.map(d => d.diabetes), [0, 1, 2]).quantiles()
obesity_thresholds = d3.scaleQuantile(data.map(d => d.obesity), [0, 1, 2]).quantiles()
bivariateClass = {
const d = diabetes_thresholds;
const o = obesity_thresholds;
return (value) => {
const { diabetes: a, obesity: b } = value;
return [
isNaN(a) ? a : +(a > d[0]) + (a > d[1]),
isNaN(b) ? b : +(b > o[0]) + (b > o[1])
];
};
}
scheme = ["#e8e8e8","#ace4e4","#5ac8c8","#dfb0d6","#a5add3","#5698b9","#be64ac","#8c62aa","#3b4994"]
us = FileAttachment("counties-albers-10m.json").json()
states = new Map(us.objects.states.geometries.map(d => [d.id, d.properties]))
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