Plot.plot({
caption:
"The number of potential users are provided in a log scale for visual clarity. Numbers are computed from Global Burden of Disease estimated mental illness prevalence, and World Bank internet usage proportions.",
projection: "equal-earth",
width,
color: {
legend: true,
type: "log",
label: "Number of Potential Users",
tickFormat: "s",
scheme: "magma",
unknown: "white"
},
marks: [
Plot.geo(geo, {
fill: (d) => d.properties["Mentally Ill Adult Internet Users"],
stroke: "currentColor",
strokeWidth: 0.5
}),
Plot.sphere(),
Plot.tip(
geo.features,
Plot.pointer(
Plot.centroid({
channels: {
Country: (d) => d.properties["Name"],
"Mentally Ill Adult Internet Users": (d) =>
d.properties["Mentally Ill Adult Internet Users"],
"All-Time Potential Users": (d) =>
d.properties["All-Time Potential Users"],
"Active Potential Users": (d) =>
d.properties["Active Potential Users"]
},
format: {
Country: true,
"Mentally Ill Adult Internet Users": ".0s",
"All-Time Potential Users": ".0s",
"Active Potential Users": ".0s"
}
})
)
)
]
})
data
Type Table, then Shift-Enter. Ctrl-space for more options.
import { mentalDisordersOver20PopulationPerCountry2021 as gbd } from "@huw/gbd";
import { dataLatest as wbi } from "@huw/world-bank-internet"
import { countries110m as countries } from "@visionscarto/geo"
import { fixCode, codeToName } from "@huw/iso-3166-1"
data = aq
.from(gbd)
.join(aq.from(wbi), "Code")
.derive(
{
Name: aq.escape((d) => codeToName[d["Code"]]),
Code: (d) => d["Code"],
"Mentally Ill Adults": (d) => d["Population"],
"Internet Usage Proportion": (d) => d["Proportion"]
},
{ drop: true }
)
.derive({
"Mentally Ill Adult Internet Users": (d) =>
d["Internet Usage Proportion"] &&
d["Mentally Ill Adults"] * d["Internet Usage Proportion"]
})
.derive({
"All-Time Potential Users": (d) =>
d["Mentally Ill Adult Internet Users"] &&
d["Mentally Ill Adult Internet Users"] * 0.088,
"Active Potential Users": (d) =>
d["Mentally Ill Adult Internet Users"] &&
d["Mentally Ill Adult Internet Users"] * 0.008
})
.objects()
geo = ({
...countries,
features: countries.features.map((d1) => ({
...d1,
properties: {
...data.find((d2) => d2["Code"] === fixCode(d1.properties.a3)),
Name: codeToName[fixCode(d1.properties.a3)]
}
}))
})
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