Bitemporal coronavirus casesSensitivity and Specificity (sketch)Coronavirus orbitsVarianceSmoothing with simple moving average vs. binomial filterCoronavirus logistic isoplethsRecreating John Burn-Murdoch’s Coronavirus trackerBuilding up to John Burn-Murdoch’s Coronavirus tracker
Covid Tracking Project data
data = _.flow([
mapEach(mapEach(transformEntry)),
mapEach(normalizeDomain),
mapEach(mapZip(transformTimeseries))
])(dataMap)
tidyData = flatMapMapMap(data)
getCovidDataGrouped({
statistic: "inIcuCurrently",
transformation: "log",
derivative: 1,
smoothing: 7
})
getCovidDataTidy({
statistic: "inIcuCurrently",
transformation: "log",
derivative: 1,
smoothing: 7
})
rawData = fetch("https://api.covidtracking.com/v1/states/daily.json")
.then(resp => resp.json())
.then(data =>
data
.map(({ date, ...rest }) => ({
date: dateFormat(dateParseRaw(date)),
...rest
}))
.sort((a, b) => a.date.localeCompare(b.date))
)
dataMap = d3.rollup(rawData, d => d[0], d => d.state, d => d.date)
transformEntry = _.flow([
d => d[statistic],
transformation === "logarithmic" ? d => Math.log(d) : _.identity
])
transformTimeseries = _.flow([differences(derivative), sma(smoothing)])
normalizeDomain = map => new Map(dates.map(date => [date, map.get(date)]))
flatMapMapMap = map =>
Array.from(map.entries()).flatMap(([state, value]) =>
Array.from(value.entries()).map(([date, value]) => ({ state, date, value }))
)
defaults = ({ statistic, transformation, derivative, smoothing })
// aliasing bc we lack version pinning on imports
getCovidDataGrouped = transformData
getCovidDataTidy = params => flatMapMapMap(getCovidDataGrouped(params))
Purpose-built for displays of data
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