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cdcDeathData = d3.csv(`https://gist.githubusercontent.com/nofurtherinformation/977077f4e1874333f570515007537ebb/raw/fde4cc565c4e6ce8e4200ef63fc23438792ac9e9/cdc_deaths.csv`)
rawPopulationData = d3.csv('https://gist.githubusercontent.com/nofurtherinformation/28547271cf378dc19984c9aeb4673b7f/raw/6614436c28e4c0b11d915b1216e1b1cb09ec190f/DS01_2018_C.csv')
populationData = {
const cdcGeoids = cdcDeathData.map(row => +row.fips)
return rawPopulationData.filter(row => cdcGeoids.includes(+row['GEOID']))
}
geography = fetch('https://cdn.jsdelivr.net/npm/us-atlas@3/counties-albers-10m.json').then(r => r.json())
// get the columns from the first data to select
populationColumns = Object.keys(populationData[0]).slice(3,)
normalizedDeathData = {
// declare return data object
let returnData = [];
// loop through CDC rows of counties
for (let i=0; i<cdcDeathData.length; i++){
// add a temporary object for this county
const currentGEOID = cdcDeathData[i].fips
let tempObj = {
'fips':currentGEOID
}
// find the county population data
const countyData = _.find(populationData, feature => +feature.GEOID == +currentGEOID)
// if missing population data, skip
if (countyData === undefined) {
continue
}
const countyPopulation = countyData?.totPopE
// list of date columns
const tempKeys = Object.keys(cdcDeathData[i]).slice(1,)
// loop through date columns to calculate 7-day average of deaths per 100k
for (let n=0; n<tempKeys.length; n++){
tempObj[tempKeys[n]] = (cdcDeathData[i][tempKeys[n]]/countyPopulation)*100_000
}
returnData.push(tempObj)
}
return returnData
}
import {table} from '@tmcw/tables'
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