Principal Web Developer at Freedom of the Press Foundation
statesSummary = aq.fromCSV(
await (await fetch(`https://api.covidactnow.org/v2/states.csv?apiKey=${API_KEY}`)).text()
)
data = aq.fromCSV(
await (await fetch(`https://api.covidactnow.org/v2/states.timeseries.csv?apiKey=${API_KEY}`)).text()
)
.join(statesSummary.select(['state', 'population']), ['state', 'state'])
.groupby('state')
.derive({
casesPer100k: aq.rolling(d => op.average(d['actuals.newCases']) * 100000 / d['population'], [-6, 0]),
deathsPer5m: aq.rolling(d => op.average(d['actuals.newDeaths']) * 5000000 / d['population'], [-6, 0]),
hospitalizationsPer250k: d => d['actuals.hospitalBeds.currentUsageCovid'] * 250000 / d['population'],
laggedDate: aq.escape(d => d3.timeDay.offset(d.date, -lagDays)),
// Interpolate previous vaccine values where data is missing
vaccineRatio: d => op.fill_down(d['metrics.vaccinationsInitiatedRatio'], 0),
vaccineRatio2: d=> op.fill_down(d['metrics.vaccinationsCompletedRatio'], 0),
})
.ungroup()
keyVaccinationDates = Array.from(d3.group(data.objects(), d => d.state)).reduce((acc, [state, data]) => {
return acc.concat(
d3.range(VACCINE_MILESTONE_FREQUENCY, 1, VACCINE_MILESTONE_FREQUENCY).map(p => {
const closestDataPoint = data[d3.bisectLeft(data.map(d => settings.vaccinesCompleted ? d.vaccineRatio2 : d.vaccineRatio), p)]
return {
state,
approxVaccineRatio: p,
date: closestDataPoint ? closestDataPoint.date : null,
}
})
)
}, [])
extents = data.rollup({
caseMax: op.max('casesPer100k'), deathMax: op.max(settings.hospitalizations ? 'deathsPer5m' : 'hospitalizationsPer250k') }).object(0)
DATE_STRIPE_FREQUENCY = width > 600 ? 4 : 7
VACCINE_MILESTONE_FREQUENCY = width > 600 ? 0.1 : 0.2
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