2d:null 2e:["$","title",null,{"children":"What do COVID-19 vaccinations, case rates, and deaths rates look like across community typologies? | US Covid Atlas | Observable"}] 33:T489,viewof mainPlot = html`

${addTooltips(Plot.plot({ width: width, color: { type: "ordinal", domain: acpKeys.map(f => f.Key), range: acpKeys.map(f => f.Color), }, x: { domain: [0,100], label: "Percent Vaccinated", grid: true }, y: { type:yaxisScale === "Log" ? 'log' : 'linear', [yaxisScale === "Log" ? null : 'domain']: yScale, label: timescale.includes("Cumulative") ? `Cumulative ${measure} Per 100,000 People` : `Monthly ${measure} Per 100,000 People`, grid: true, }, marks: [ Plot.dot(scatterData.filter(f => !selectedType.length || selectedType.includes(f['Type Number'])), { x: "vaccinationPct", y: yCol, fill: "Type Number", title: (d) => `${d.County}\n${yCol}: ${d[yCol]}\n% Vaccinated: ${d.vaccinationPct}\n % Unvaccinated: ${d.pctUnvaccinated}\n ACP Type: ${d.acpName}`, r:2 }), ...lineData.map((d, i) => Plot.line(d.data, { x: "x", y: "y", stroke: selectedType.length ? d.color : 'gray' }) ) ] }))}

`34:T70f,viewof historicalChart = addTooltips(Plot.plot({ width: 1200, height:500, marginRight:0, y: { grid: true, label: "↑ Vaccinated (%)" }, x: { label:"Date", tickFormat: f => f.slice(-2,) === '01' ? f.slice(-5,-3) +'/'+f.slice(2,4) : null }, marks: [ ...acpKeys.map(acp => Plot.line( historicalData.summarizedData[acp.Key], { x: "date", y: "val", strokeWidth: !selectedType.length ? 2 : !selectedType.length || selectedType.includes(acp.Key) ? 4 : 0.5, stroke:acp.Color, } )), ...acpKeys.map(acp => Plot.text( historicalData.benchMarks[acp.Key], { x: "date", y: "val", text: f => selectedType.includes(acp.Key) ? f.date : '', dx:-35, dy: -5, fontWeight:'bold', fill: 'black', } )), ...acpKeys.filter(f => !selectedType.length || selectedType.includes(f.Key)) .map(acp => Plot.dot( historicalData.summarizedData[acp.Key], { x: "date", y: "val", fontWeight:'bold', fill: 'none', stroke: 'none', r: 5, title: (d) => `${acpKeys.find(f => f.Key === acp.Key)['New Names']}\n${d.date}: ${d.val.toFixed(2)}% `, } ) ), ...acpKeys.map(acp => Plot.dot( historicalData.benchMarks[acp.Key], { x: "date", y: "val", r: f => selectedType.includes(acp.Key) ? 200 : 0, fill: 'black' } )), Plot.ruleY( lineMarks, { y: "val", stroke:'gray' }), // Plot.ruleX([vaccinations.dates[date]]), Plot.text( lineMarks, { x: 'date', y: "val", text:'text', dy:-10 } ) ] }))35:T8c5,viewof historicalTable = table( Object.values(historicalData.monthlyData), { style: 'compact', columns: { 'date': { formatter(val, i){return `${val.slice(6,7)}/${val.slice(2,4)}`} }, 'Graying America': { formatter(val, i){return selectedType.includes('2') ? html`${val}`: val} }, 'African American South': { formatter(val, i){return selectedType.includes('3') ? html`${val}`: val} }, 'Evangelical Hubs': { formatter(val, i){return selectedType.includes('4') ? html`${val}`: val} }, 'Working Class Country': { formatter(val, i){return selectedType.includes('5') ? html`${val}`: val} }, 'Military Posts': { formatter(val, i){return selectedType.includes('6') ? html`${val}`: val} }, 'Urban Suburbs': { formatter(val, i){return selectedType.includes('7') ? html`${val}`: val} }, 'Hispanic Centers': { formatter(val, i){return selectedType.includes('8') ? html`${val}`: val} }, 'Native American Lands': { formatter(val, i){return selectedType.includes('9') ? html`${val}`: val} }, 'Rural Middle America': { formatter(val, i){return selectedType.includes('10') ? html`${val}`: val} }, 'College Towns': { formatter(val, i){return selectedType.includes('11') ? html`${val}`: val} }, 'LDS Enclaves': { formatter(val, i){return selectedType.includes('12') ? html`${val}`: val} }, 'Aging Farmlands': { formatter(val, i){return selectedType.includes('13') ? html`${val}`: val} }, 'Big Cities': { formatter(val, i){return selectedType.includes('14') ? html`${val}`: val} }, 'Middle Suburbs': { formatter(val, i){return selectedType.includes('15') ? html`${val}`: val} }, Exurbs: { formatter(val, i){return selectedType.includes('15') ? html`${val}`: val} }, } } )36:Tbac,historicalData = { const countyKeys = Object.keys(vaccineData) let summarizedData = { '1':[], '2':[], '3':[], '4':[], '5':[], '6':[], '7':[], '8':[], '9':[], '10':[], '11':[], '12':[], '13':[], '14':[], '15':[], } let popData = { '1':0, '2':0, '3':0, '4':0, '5':0, '6':0, '7':0, '8':0, '9':0, '10':0, '11':0, '12':0, '13':0, '14':0, '15':0, } let benchMarks = { '1':[], '2':[], '3':[], '4':[], '5':[], '6':[], '7':[], '8':[], '9':[], '10':[], '11':[], '12':[], '13':[], '14':[], '15':[], } let monthlyData = {} for (let i=0;i +f.FIPS === +countyKeys[i]) const geoData = countiesGeojson.features.find(f => f.properties.GEOID === +countyKeys[i]) if (acpInfo === undefined || geoData === undefined) { console.log('Missing data: ' + countyKeys[i]) continue } const currentAcpType = acpInfo['Type Number'] const iterationDataOp = summarizedData[currentAcpType].length ? (val, n) => summarizedData[currentAcpType][n].val += val : (val, n) => summarizedData[currentAcpType].push({val, date: vaccinations.dates[n]}) for (let n=0; n +f.Key === i)['New Names']] = summarizedData[i][n].val.toFixed(2) } if (summarizedData[i][n].val > 10 && benchMarks[i].length === 0){ benchMarks[i].push({ date:vaccinations.dates[n], val: summarizedData[i][n].val, text: '(10%)', color: acpKeys.find(f => +f.Key === i).Color.trim() }) } if (summarizedData[i][n].val > 25 && benchMarks[i].length === 1){ benchMarks[i].push({ date:vaccinations.dates[n], val: summarizedData[i][n].val, text: '(25%)', color: acpKeys.find(f => +f.Key === i).Color.trim() }) } if (summarizedData[i][n].val > 50 && benchMarks[i].length === 2){ benchMarks[i].push({ date:vaccinations.dates[n], val: summarizedData[i][n].val, text: '(50%)', color: acpKeys.find(f => +f.Key === i).Color.trim() }) } } } return { summarizedData, benchMarks, monthlyData } }37:T791,scatterData = { let returnData = [] const vaccineIndex = date const caseIndex = cases.dates.indexOf(vaccinations.dates[date]) const deathIndex = deaths.dates.indexOf(vaccinations.dates[date]) for (let i=0;i +f.FIPS === geoid) if (!(vaccineData.hasOwnProperty(geoid))) continue const vaccinated = vaccineData[geoid][vaccineIndex] const vaccinationPct = Math.round((vaccinated/pop)*1000)/10 const cumulativeDeaths = deathData[geoid][deathIndex] const monthlyDeaths = cumulativeDeaths - deathData[geoid][deathIndex-30] const monthlyDeathsPer100k = Math.round((monthlyDeaths/pop)*100000*10)/10 const cumulativeDeathsPer100k = Math.round((cumulativeDeaths/pop)*100000*10)/10 const cumulativeCases= caseData[geoid][caseIndex] const monthlyCases = cumulativeCases - caseData[geoid][caseIndex-30] const monthlyCasesPer100k = Math.round((monthlyCases/pop)*100000*10)/10 const cumulativeCasesPer100k = Math.round((cumulativeCases/pop)*100000*10)/10 if (vaccinated === undefined || acp === undefined) continue // || vaccinationPct > 100 || monthlyDeathsPer100k > 100 const acpKey = acpKeys.find(f => f['Key'] === acp['Type Number']) returnData.push({ ...acp, ...countiesGeojson.features[i].properties, monthlyDeaths, monthlyDeathsPer100k, cumulativeDeaths, cumulativeDeathsPer100k, monthlyCases, monthlyCasesPer100k, cumulativeCases, cumulativeCasesPer100k, vaccination: vaccinated, vaccinationPct, pctUnvaccinated: 100 - Math.round((vaccinated/pop)*1000)/10, acpName: acpKey['New Names'], acpColor: acpKey['Color'] }) } return returnData }38:T424,Rows = { var Rows = {}; var Entry = {}; Entry.read = function (pbf, end) { return pbf.readFields(Entry._readField, {geoid: 0, vals: []}, end); }; Entry._readField = function (tag, obj, pbf) { if (tag === 1) obj.geoid = pbf.readVarint(true); else if (tag === 2) pbf.readPackedVarint(obj.vals, true); }; Entry.write = function (obj, pbf) { if (obj.geoid) pbf.writeVarintField(1, obj.geoid); if (obj.vals) pbf.writePackedVarint(2, obj.vals); }; Rows.read = function (pbf, end) { return pbf.readFields(Rows._readField, {dates: [], row: []}, end); }; Rows._readField = function (tag, obj, pbf) { if (tag === 1) obj.dates.push(pbf.readString()); else if (tag === 2) obj.row.push(Entry.read(pbf, pbf.readVarint() + pbf.pos)); }; Rows.write = function (obj, pbf) { if (obj.dates) for (var i = 0; i < obj.dates.length; i++) pbf.writeStringField(1, obj.dates[i]); if (obj.row) for (i = 0; i < obj.row.length; i++) pbf.writeMessage(2, Entry.write, obj.row[i]); }; return Rows }39:[{"id":"0e278cae2d502e565ff40f4b74be5d953aaf9234b1a5ee672fa2698cad57463f2398d2218eabc15e941cff90e40c6234f29b96f94eadcd4a2a53bb2db02b8772","href":"https://static.observableusercontent.com/files/0e278cae2d502e565ff40f4b74be5d953aaf9234b1a5ee672fa2698cad57463f2398d2218eabc15e941cff90e40c6234f29b96f94eadcd4a2a53bb2db02b8772","name":"acp_types.csv","type":"text/csv","lastModified":1633375989743.477,"size":64946},{"id":"dc7789cd674b29f3753757075964fd76e445c3e4a227a478c5f337fb3993c9e90f5d86548975a4764727c605460feeb46e72a15d571000d7864ad96dbf55ac53","href":"https://static.observableusercontent.com/files/dc7789cd674b29f3753757075964fd76e445c3e4a227a478c5f337fb3993c9e90f5d86548975a4764727c605460feeb46e72a15d571000d7864ad96dbf55ac53","name":"county_usfacts.geojson","type":"application/geo+json","lastModified":1633375989743.477,"size":1791452}] 31:{"id":"44663de5431e53af","slug":"vaccination-community-type","title":"What do COVID-19 vaccinations, case rates, and deaths rates look like across community typologies?","thumbnail":"f66fcbfb805ad2cc29489bb212595601e3eaa5856965307f3e24c06da6b404d8","access_level":"public","create_time":"$D2021-10-04T19:33:09.743Z","update_time":"$D2021-10-04T19:34:02.387Z","view_time":null,"first_public_time":"$D2021-10-04T19:33:47.392Z","has_unpublished_edits":null,"fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"4dc4fbd64737052c","login":"uscovidatlas","name":"US Covid Atlas","avatar_url":"https://avatars-next.observableusercontent.com/bfb7cd47a84b0863345144ac2bcb88913efa39af89b14b52e95fbb2f154250c3","tier":"starter_2024"},"version":1734,"publish_version":1734,"role":null,"authored":false,"authors":[{"type":"individual","id":"6d95e296026ed58f","login":"uscovidatlas","name":"US Covid Atlas","avatar_url":"https://avatars-next.observableusercontent.com/bfb7cd47a84b0863345144ac2bcb88913efa39af89b14b52e95fbb2f154250c3","tier":null}],"members":[{"type":"individual","id":"6d95e296026ed58f","login":"uscovidatlas","name":"US Covid Atlas","avatar_url":"https://avatars-next.observableusercontent.com/bfb7cd47a84b0863345144ac2bcb88913efa39af89b14b52e95fbb2f154250c3","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"title":"What do COVID-19 vaccinations, case rates, and deaths rates look like across community typologies?","cells":[{"id":0,"value":"md`# What do COVID-19 vaccinations, case rates, and deaths rates look like across community typologies?\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":760,"value":"md`This notebook outlines a few key plots the start understanding an exploring how different American Communities Proejct typologies have experienced COVID-19 vaccinations, cases, and deaths. This is a *preliminary* exploratory data analysis and graphic presentation, but is subject to change. \n\nKeep in mind that no community typology or county is a monolith. Within and between similar communities, access and response to vaccinations efforts may be very different.\n\nTo start, choose a metric of interest. By default, we show vaccination rates (percent fully vaccinated, all people) against the last 30 days of cases. You can also choose mortality rates and cumulative values.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":764,"value":"viewof measure = Inputs.radio([\"Cases\", \"Deaths\"], {value:'Cases', label:\"Measure\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":709,"value":"viewof timescale = Inputs.radio([\"Cumulative\", \"Last 30 Days\"], {value:'Last 30 Days', label:\"Time scale\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":717,"value":"viewof yaxisScale = Inputs.radio([\"Linear\", \"Log\"], {value:'Linear', label:\"Yaxis scale\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1341,"value":"md`\nNext, you can use the time slider to see how this relationship has changed over time. Below this chart, you can click on a typology name and color to filter for only that community type.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":899,"value":"viewof date = slider({\n min: 30, \n max: dateList.length-1, \n step: 1, \n value: dateList.length-1, \n title: \"Date Select\", \n format: v => dateList[v],\n description: `Choose a date. Data will include the 30 days before that date or cumulative values.`\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":756,"value":"md`### Vaccinations Rates (x) vs ${measure} Rate (y)`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":330,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":365,"value":"viewof legendButtons = Inputs.button(\n [...acpKeys.map(\n type => [\n html`\n \n ${type['New Names']}\n `, \n () => selectedType.includes(type.Key)\n ? mutable selectedType = remove(selectedType, type.Key)\n : mutable selectedType = [...selectedType, type.Key]\n ]\n ),\n ['Reset', () => mutable selectedType = []]\n ]\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1363,"value":"viewof historicalChartText = md`### Vaccination Histories\nThe chart below shows how different ACP typology counties have progressed in vaccination efforts starting in 2021. The percentages represent total population vaccinated--adults, seniors, and children. Hover over a line for more information. Select a typology from the legend above to filter.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1020,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1654,"value":"viewof histoicalText = md`The table below includes the percent of all people vaccinated at the start of each month.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1503,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":245,"value":"md`\n#### Data Notes\n*Due to incomplete data, Texas and Hawaii have been excluded*`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1730,"value":"md`This notebook is a product of the ongoing collaboration between the [American Communities Project](https://www.americancommunities.org/) and the US COVID Atlas. We stratify cleaned and aggregated vaccination data from the [US COVID Atlas](https://theuscovidatlas.org/) (source: CDC, USAFacts) by ACP community type to investigate trends in vaccination rates over time and geography.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1111,"value":"md`

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":576,"value":"md`#### Data Processing`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1133,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1257,"value":"lineMarks = [\n {\n date: vaccinations.dates[30],\n val: 10,\n text: '10% Vaccinated'\n },\n {\n date: vaccinations.dates[30],\n val: 25,\n text: '25% Vaccinated'\n },\n {\n date: vaccinations.dates[30],\n val: 50,\n text: '50% Vaccinated'\n }\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1000,"value":"regressionData = {\n let returnObject = {};\n selectedType.forEach(t => returnObject[t] = [])\n if(!selectedType.length) returnObject[0] = []\n for (let i=0; i !selectedType || f['Type Number'] === selectedType)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":94,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":477,"value":"lineFn = Object.values(regressionData).map(d => ss.linearRegressionLine(ss.linearRegression(d)))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":799,"value":"yScale = colDict[timescale][measure].scale","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":741,"value":"yCol = colDict[timescale][measure].col","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":783,"value":"colDict = ({\n \"Cumulative\": {\n \"Cases\":{ col: 'cumulativeCasesPer100k', scale:[0.01,25000]},\n \"Deaths\":{ col: 'cumulativeDeathsPer100k', scale:[0.01,1000]}\n },\n \"Last 30 Days\": {\n \"Cases\":{ col: 'monthlyCasesPer100k', scale:[0.01,5000]},\n \"Deaths\":{col: 'monthlyDeathsPer100k', scale:[0.01,100]}\n },\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":470,"value":"lineData = lineFn.map((fn, i) => {\n return {\n data: [{x:0,y:fn(0)},{x:100,y:fn(100)}],\n color: selectedType.length ? acpKeys.find(f=>+f.Key === +Object.keys(regressionData)[i]).Color : '',\n name: selectedType.length ? acpKeys.find(f=>+f.Key === +Object.keys(regressionData)[i])['New Names'] : '',\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":80,"value":"acpTypes = FileAttachment(\"acp_types.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1647,"value":"acpKeys = [{\"Key\":\"1\",\"New Names\":\"Exurbs\",\"Color\":\"#fcb93a\"},{\"Key\":\"2\",\"New Names\":\"Graying America\",\"Color\":\"#2e547a\"},{\"Key\":\"3\",\"New Names\":\"African American South\",\"Color\":\"#457a59\"},{\"Key\":\"4\",\"New Names\":\"Evangelical Hubs\",\"Color\":\"#82477f\"},{\"Key\":\"5\",\"New Names\":\"Working Class Country\",\"Color\":\"#86563e\"},{\"Key\":\"6\",\"New Names\":\"Military Posts\",\"Color\":\"#abbf48 \"},{\"Key\":\"7\",\"New Names\":\"Urban Suburbs\",\"Color\":\"#f08031\"},{\"Key\":\"8\",\"New Names\":\"Hispanic Centers\",\"Color\":\"#1f8fba\"},{\"Key\":\"9\",\"New Names\":\"Native American Lands\",\"Color\":\"#eacd3f\"},{\"Key\":\"10\",\"New Names\":\"Rural Middle America\",\"Color\":\"#3a9c9b \"},{\"Key\":\"11\",\"New Names\":\"College Towns\",\"Color\":\"#c44244\"},{\"Key\":\"12\",\"New Names\":\"LDS Enclaves\",\"Color\":\"#3a2c70 \"},{\"Key\":\"13\",\"New Names\":\"Aging Farmlands\",\"Color\":\"#7a3842\"},{\"Key\":\"14\",\"New Names\":\"Big Cities\",\"Color\":\"#c43b82\"},{\"Key\":\"15\",\"New Names\":\"Middle Suburbs\",\"Color\":\"#699246 \"}]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"currentColumns = fetch('https://raw.githubusercontent.com/GeoDaCenter/covid/master/functions/meta/columns.json').then(r => r.json())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":36,"value":"countiesGeojson = FileAttachment(\"county_usfacts.geojson\").json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1370,"value":"remove = (arr, key) => {\n let tempArr = [...arr]\n tempArr.splice(arr.indexOf(key), 1)\n return tempArr\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":556,"value":"md`#### Mutable State`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":352,"value":"mutable selectedType = []","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":553,"value":"md`#### Style`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":373,"value":"viewof style = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":559,"value":"md`#### Imports`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":903,"value":"import {slider} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":455,"value":"ss = require('simple-statistics')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1552,"value":"import {addTooltips} from \"@mkfreeman/plot-tooltip\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":847,"value":"Pbf = require('pbf')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1506,"value":"import {table} from \"@tmcw/tables@513\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":895,"value":"md` #### Data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":869,"value":"parseData = (data) => {\n let dataObj = {}\n for (let i=0; i cases.dates.includes(date))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":878,"value":"caseData = parseData(cases.row)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":883,"value":"vaccinations = fetch('https://theuscovidatlas.org/pbf/vaccination_fully_vaccinated_cdc.pbf')\n .then(r => r.arrayBuffer())\n .then(ab => new Pbf(ab))\n .then(pbf => Rows.read(pbf))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":866,"value":"deaths = fetch('https://theuscovidatlas.org/pbf/covid_deaths_usafacts.pbf')\n .then(r => r.arrayBuffer())\n .then(ab => new Pbf(ab))\n .then(pbf => Rows.read(pbf))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":852,"value":"cases = fetch('https://theuscovidatlas.org/pbf/covid_confirmed_usafacts.pbf')\n .then(r => r.arrayBuffer())\n .then(ab => new Pbf(ab))\n .then(pbf => Rows.read(pbf))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":844,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W39","stdlib":"1","titling":"auto"}} 32:"$31:body"