Teletherapy Usage Ceilings per CountryHow many psychiatrists are there in each country?Which countries are the most depressed?Which countries are highly depressed and cheap to operate in?Where is all the depression?Average Life Satisfaction per CountryCumulative distribution of global wellbeing
Which countries are more depressed than they are dissatisfied?
Plot.plot({
caption:
"Data on prevalence is estimated from real data sources and statistical models for the region. Data from the WHO Global Health Observatory (2015).",
projection: "equal-earth",
width,
color: {
legend: true,
type: "log",
label: "Ratio of Depression Prevalence to Life Satisfaction",
scheme: "magma",
unknown: "white"
},
marks: [
Plot.geo(dataGeo, {
fill: (d) =>
d.properties["Ratio of Depression Prevalence to Life Satisfaction"],
stroke: "currentColor",
strokeWidth: 0.5
}),
Plot.sphere(),
Plot.tip(
dataGeo.features,
Plot.pointer(
Plot.centroid({
channels: {
Country: (d) => d.properties["Name"],
Ratio: (d) =>
d.properties[
"Ratio of Depression Prevalence to Life Satisfaction"
]
}
})
)
)
]
})
import { depressionPrevalenceProportionPerCountry2015 as gho } from "@huw/gho"
import { dataSnapshot as whr } from "@huw/whr"
import { countries110m as countries } from "@visionscarto/geo"
import { fixCode, codeToName } from "@huw/iso-3166-1"
data = aq
.from(whr)
.select(aq.not("Year"))
.join(aq.from(gho), "Code", [
aq.all(),
{ "Proportion of Depression": (d) => d["Proportion"] }
])
.derive({
"Ratio of Depression Prevalence to Life Satisfaction": (d) =>
d["Proportion of Depression"] / d["Cantril Ladder Score"]
})
.objects()
data
Type Table, then Shift-Enter. Ctrl-space for more options.
dataGeo = ({
...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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