30:T1f49,{ const weeksWindow = 12; // Número de semanas recientes a mostrar const target100K = 2; const targets100K = [ { "paso":"Transición", meta100K: 10}, { "paso":"Apertura Inicial", meta100K: 5}, { "paso":"Apertura Avanzada", meta100K: 2} ] const fechas = fechasMetropolitana; const lastWeek = _.last(fechas); const mostRecentDates = _.takeRight(fechas,weeksWindow+1); const groupedData = dataGroupedByLocation; const recordComuna = _.chain(groupedData).find(d => d.location == comunaSeleccionada).value(); recordComuna.items.forEach(d => { d.weeklyNewCasesEstimate = d.weeklyNewCasesEstimate < 0 ? 0 : d.weeklyNewCasesEstimate; }) const lastItem = _.find(recordComuna.items, e=> e.date == lastWeek) function weeks2Target(d) { const speed = reductionSpeed(d); const delta = d.lastWeekCases - d.weekTarget; return delta/speed; } function reductionSpeed(d) { const windowItems = _.takeRight(d.items,weeksWindow); const first = _.first(windowItems)["lastWeekCases"]; const last = _.last(windowItems)["lastWeekCases"]; const speed = (first-last)/(windowItems.length-1); return speed; } const data = { location:recordComuna.location, region: recordComuna.region, poblacion: recordComuna.poblacion, date: lastWeek, dateLastWeek: lastItem.dateLastWeek, maxCases: _.max(recordComuna.items.map(e => e.totalCases_today)), positividad: positividadXComunas[aliasComunas[recordComuna.location] || recordComuna.location], dayTarget: target100K * recordComuna.poblacion/100000, weekTarget: 7 * target100K * recordComuna.poblacion/100000, lastWeekCases: lastItem && (lastItem.weeklyNewCasesEstimate), dailyAverage: lastItem && (lastItem.weeklyNewCasesEstimate)/7, dailyAveragePoblacion: lastItem && 100000*(lastItem.weeklyNewCasesEstimate)/recordComuna.poblacion/7, items: recordComuna.items.filter(e => mostRecentDates.includes(e.date)).map(d => ({ date: d.date, total_cases: d.total_cases, lastWeekCases: d.weeklyNewCasesEstimate, lastWeekCasesPoblación: 100000*(d.weeklyNewCasesEstimate)/d.poblacion, dailyAverage: (d.weeklyNewCasesEstimate)/7, dailyAveragePoblacion: 100000*(d.weeklyNewCasesEstimate)/d.poblacion/7, })) } data["weeksToTarget"] = weeks2Target(data); data["reductionSpeed"] = reductionSpeed(data); data["targetDate"] = moment(lastWeek).add(weeks2Target(data),"weeks").format("YYYY-MM-DD") const targets = targets100K.map(d => ({ paso : d.paso, meta : d.meta100K*data.poblacion/100000, meta100K : d.meta100K })) //return targets const color = d3.scaleLinear().range(colorRange).domain(colorDomainPorPoblacion); function getCellContent(items, date) { const metric = _.find(items, f=> f.date == date) && _.find(items, f=> f.date == date).lastWeekCases return metric >= 0 && (metric > 11 ? d3.format(".0f")(metric) : d3.format(".1f")(metric)) || `` } function growing(record) { const sortedItems = _.chain(record.items).sortBy(f=> f.date == date).reverse().value(); const lastItem = sortedItems[0]; const beforeLastItem = sortedItems[1]; return lastItem.dailyAverage > beforeLastItem.dailyAverage } function warning(record) { const sortedItems = _.chain(record.items).sortBy(f=> f.date == date).reverse().value(); const lastItem = sortedItems[0]; return lastItem.dailyAverage > 10; } function softwarning(record) { const sortedItems = _.chain(record.items).sortBy(f=> f.date == date).reverse().value(); const lastItem = sortedItems[0]; return lastItem.dailyAverage >= 5; } //return mostRecentDates const out = html`

${data.location} (${moment(data["dateLastWeek"]).format("DD MMM")} al ${moment(data["date"]).format("DD MMM YYYY")})

${data.location} tuvo ${d3.format(".0f")(data.lastWeekCases)} casos en la semana y la meta es llegar a los siguientes umbrales
${targets.map(d => `

${d3.format(".0f")(d.meta*7)} casos semanales para ${d.paso}

`)}

En últimas ${weeksWindow} semanas hay ${data.reductionSpeed < 0 ? "un aumento" : "una reducción"} promedio de ${d3.format(".1f")(Math.abs(data.reductionSpeed))} nuevos casos semanales

${ data.lastWeekCases <= data.weekTarget ? `Ya llegó al umbral asociado a Apertura Avanzada` : data.reductionSpeed > 0 ? `De mantener el ritmo reciente de reducción de contagios, llegará al umbral definido para Apertura Avanzada en ${moment(data["targetDate"]).format("MMM YYYY")}` : `De mantener el ritmo reciente de aumento en contagios, no llegará al umbral asociado a Apertura Avanzada` }

${ data.positividad > .08 ? `OJO: ${data.location} tiene una positividad alta (${d3.format(".1%")(data.positividad)}) y esto puede afectar la precisión de los reportes de casos (podrían ser mayores y/o tener una tasa diferentre de aumento/disminución)` : `` }

Detalles para ${data.location}

Población: ${d3.format(".0f")(data.poblacion)} habitantes
Incidencia actual: ${d3.format(".1f")(data.dailyAveragePoblacion)} casos diarios x 100 mil habitantes (al ${moment(lastWeek).format("DD MMM YYYY")})
Positividad: ${d3.format(".1%")(data.positividad)} (al ${moment(datePositividad).format("DD MMM YYYY")})

Umbral Transición: ${d3.format(".0f")(10*data.weekTarget/2)} (nuevos casos en la semana) ${d3.format(".1f")(10*data.dayTarget/2)} (promedio diario)
Equivale a 10 casos diarios x 100 mil habitantes
Umbral Apertura Inicial: ${d3.format(".0f")(5*data.weekTarget/2)} (nuevos casos en la semana) ${d3.format(".1f")(5*data.dayTarget/2)} (promedio diario)
Equivale a 5 casos diarios x 100 mil habitantes
Umbral Apertura Avanzada: ${d3.format(".0f")(data.weekTarget)} (nuevos casos en la semana) ${d3.format(".1f")(data.dayTarget)} (promedio diario)
Equivale a 2 casos diarios x 100 mil habitantes

Nuevos casos

${data.items.map((d,i) => ` `)}

Total casos confirmados según datos publicados en Informes Epidemiológicocs para la fecha respectiva
Fecha	Total casos confirmados	Nuevos casos semanales	Nuevos casos diarios
${moment(d.date).format("D MMM")}	${d.total_cases}	${i && d.lastWeekCases && d3.format(".0f")(d.lastWeekCases) \|\| ""}	${i && d3.format(".1f")(d.dailyAverage) \|\| ""}

En promedio, la últimas ${weeksWindow} semanas hay ${data.reductionSpeed < 0 ? "un aumento" : "una reducción"} de ${d3.format(".1f")(Math.abs(data.reductionSpeed))} nuevos casos semanales
${ data.lastWeekCases <= data.weekTarget ? `Ya llegó al umbral asociado a Apertura Avanzada` : data.reductionSpeed > 0 ? `A este ritmo se llegará a umbral asociado a Apertura Avanzada en ${d3.format(".1f")(data.weeksToTarget)} semanas (en ${moment(data["targetDate"]).format("MMM YYYY")})` : `A este ritmo no se llegará umbral asociado a Apertura Avanzada` }

` return out }31:T20da,{ const weeksWindow = 3; // Número de semanas recientes a mostrar const target100K = 2; const fechas = fechasMetropolitana; const lastWeek = _.last(fechas); const mostRecentDates = _.takeRight(fechas,weeksWindow); const groupedData = dataGroupedByLocation; const recordComuna = _.chain(groupedData).find(d => d.location == comunaSeleccionada).value(); recordComuna.items.forEach(d => { d.weeklyNewCasesEstimate = d.weeklyNewCasesEstimate < 0 ? 0 : d.weeklyNewCasesEstimate; }) const lastItem = _.find(recordComuna.items, e=> e.date == lastWeek) function weeks2Target(d) { const speed = reductionSpeed(d); const delta = d.lastWeekCases - d.weekTarget; return delta/speed; } function reductionSpeed(d) { const first = _.first(d.items)["lastWeekCases"]; const last = _.last(d.items)["lastWeekCases"]; const speed = (first-last)/(d.items.length-1); return speed; } function buildRecord(recordComuna) { const lastItem = _.find(recordComuna.items, e=> e.date == lastWeek) const data = { location:recordComuna.location, region: recordComuna.region, poblacion: recordComuna.poblacion, date: lastWeek, maxCases: _.max(recordComuna.items.map(e => e.totalCases_today)), positividad: positividadXComunas[aliasComunas[recordComuna.location] || recordComuna.location], dayTarget: target100K * recordComuna.poblacion/100000, weekTarget: 7 * target100K * recordComuna.poblacion/100000, lastWeekCases: lastItem && (lastItem.weeklyNewCasesEstimate), dailyAverage: lastItem && (lastItem.weeklyNewCasesEstimate)/7, dailyAveragePoblacion: lastItem && 100000*(lastItem.weeklyNewCasesEstimate)/recordComuna.poblacion/7, items: recordComuna.items.filter(e => mostRecentDates.includes(e.date)).map(d => ({ date: d.date, lastWeekCases: d.weeklyNewCasesEstimate, lastWeekCasesPoblación: 100000*(d.weeklyNewCasesEstimate)/d.poblacion, dailyAverage: (d.weeklyNewCasesEstimate)/7, dailyAveragePoblacion: 100000*(d.weeklyNewCasesEstimate)/d.poblacion/7, })) } data["weeksToTarget"] = weeks2Target(data); data["reductionSpeed"] = reductionSpeed(data); data["targetDate"] = moment(lastWeek).add(weeks2Target(data),"weeks").format("YYYY-MM-DD") return data } const data = buildRecord(recordComuna) //const groupedData = fuenteDatos == "regionales" ? dataGroupedByLocationSEREMIS : dataGroupedByLocation; function incidencia2Cases(valor, poblacion) { return valor * poblacion / 100000; } const data2 = _.chain(groupedData) .groupBy(d => d.region) .map((items, region) => { return { region: region, items: _.map(items, d => buildRecord(d)) } }) .sortBy(d => ordenRegiones[d.region]) .value() return html` ${ _.map(data2, (itemRegion) => `

${itemRegion.region}

${_.map(itemRegion.items, d => ` `).join("")}

				Meta diaria				Meta semanal
Comuna	Población	Incidencia (x 100 mil habs.)	Casos Diarios	Transición	Apertura Inicial	Apertura Avanzada	Casos Semanales	Transición	Apertura Inicial	Apertura Avanzada
${d.location}	${d.poblacion}	${d3.format(".1f")(d.dailyAveragePoblacion)}	${d3.format(".1f")(d.dailyAverage)}	${d3.format(".1f")(incidencia2Cases(10,d.poblacion))}	${d3.format(".1f")(incidencia2Cases(5,d.poblacion))}	${d3.format(".1f")(incidencia2Cases(2,d.poblacion))}	${d3.format(".0f")(d.lastWeekCases)}	${d3.format(".1f")(incidencia2Cases(10,d.poblacion)*7)}	${d3.format(".1f")(incidencia2Cases(5,d.poblacion)*7)}	${d3.format(".1f")(incidencia2Cases(2,d.poblacion)*7)}

`) } ` const color = d3.scaleLinear().range(colorRange).domain(colorDomainPorPoblacion); function getCellContent(items, date) { const metric = _.find(items, f=> f.date == date) && _.find(items, f=> f.date == date).lastWeekCases return metric >= 0 && (metric > 11 ? d3.format(".0f")(metric) : d3.format(".1f")(metric)) || `` } function growing(record) { const sortedItems = _.chain(record.items).sortBy(f=> f.date == date).reverse().value(); const lastItem = sortedItems[0]; const beforeLastItem = sortedItems[1]; return lastItem.dailyAverage > beforeLastItem.dailyAverage } function warning(record) { const sortedItems = _.chain(record.items).sortBy(f=> f.date == date).reverse().value(); const lastItem = sortedItems[0]; return lastItem.dailyAverage > 10; } function softwarning(record) { const sortedItems = _.chain(record.items).sortBy(f=> f.date == date).reverse().value(); const lastItem = sortedItems[0]; return lastItem.dailyAverage >= 5; } //return mostRecentDates const out = html`

${data.location} (al ${moment(data["date"]).format("DD MMM YYYY")})

${data.location} tuvo ${data.lastWeekCases} casos en la semana y la meta es llegar a ${d3.format(".0f")(data.weekTarget)} casos semanales

${ data.lastWeekCases <= data.weekTarget ? `Ya llegó a la meta` : data.reductionSpeed > 0 ? `De mantener el ritmo de reducción de contagios, llegará a la meta en ${moment(data["targetDate"]).format("MMM YYYY")}` : `De mantener el ritmo de aumento en contagios, no llegará a la meta` }

Detalles para ${data.location}

Población: ${d3.format(".0f")(data.poblacion)} habitantes
Meta: ${d3.format(".0f")(data.weekTarget)} (nuevos casos en la semana) ${d3.format(".1f")(data.dayTarget)} (promedio diario)
Equivale a 2 casos diarios x 100 mil habitantes
Positividad: ${d3.format(".1%")(data.positividad)} (al ${moment(datePositividad).format("DD MMM YYYY")})

Nuevos casos

${data.items.map(d => ` `)}

Fecha	Nuevos casos semanales	Nuevos casos diarios
${moment(d.date).format("D MMM")}	${d.lastWeekCases}	${d3.format(".1f")(d.dailyAverage)}

En promedio, la últimas ${weeksWindow} semanas hay ${data.reductionSpeed < 0 ? "un aumento" : "una reducción"} de ${d3.format(".1f")(Math.abs(data.reductionSpeed))} casos nuevos semanales

${ data.lastWeekCases <= data.weekTarget ? `Ya llegó a la meta` : data.reductionSpeed > 0 ? `A este ritmo se llegará a la meta en ${d3.format(".1f")(data.weeksToTarget)} semanas (en ${moment(data["targetDate"]).format("MMM YYYY")})` : `A este ritmo no se llegará a la meta` }

` return out }32:Ta8a,{ const color = d3.scaleLinear().range(colorRange).domain(colorDomainPorPoblacion); const data = _.chain(dataGroupedByLocation).map(d => { const lastWeek = _.last(epiDates); const lastItem = _.find(d.items, e=> e.date == lastWeek) return ({ location:d.location, region: d.region, poblacion: d.poblacion, maxCases: _.max(d.items.map(e => e.totalCases_today)), lastWeekCases: lastItem && (lastItem.totalCases_today-lastItem.totalCases_1weeksAgo), items: d.items.filter(e => epiDates.includes(e.date)).map(d => ({ date: d.date, lastWeekCases: 10000*(d.totalCases_today - d.totalCases_1weeksAgo)/d.poblacion })) }) }) .filter(d => regionSeleccionada ? d.region == regionSeleccionada : true) .filter(d => d.maxCases >=0) .sortBy(d => -d.lastWeekCases/d.poblacion) .value() const out = html` ${data.map((d,i) => ` ${ i % 20 == 0 ? ` ${ epiDates.map(e => ` `).join("") } `: ``} ${ epiDates.map(e => ` `).join("") } `).join("")}

Comuna (población)	${moment(e).format("ddd DD MMM")}
${d.location} (${d3.format(".2s")(d.poblacion)})	${ _.find(d.items, f=> f.date == e) ? d3.format(".1f")(_.find(d.items, f=> f.date == e).lastWeekCases) : `` }	${[null].map(d => { const inlineVis = html`HOLA`; return inlineVis.innerHtml; }) }

` /* ${[null].map(d => { const out = html` `; debugger; const visEl = d3.select(out).select("td svg"); const visObj = new InlineVis(visEl.node()); visObj.height(20).width(100).render([10,50,30]); return out.innerHtml; })}*/ //return out return html`` }33:T643,html``34:T8dd,class InlineVis { constructor(el, metric) { this._data = []; this._margin = {left: 2, right:2, top:2, bottom: 2}; this._totalWidth = width; this._totalHeight = 800 < screen.height ? 800 : screen.height; this.svgContainer = d3.select(el); this.startup(); } startup() { this._width = this._totalWidth - this._margin.left - this._margin.right; this._height = this._totalHeight - this._margin.top - this._margin.bottom; this.xScale = d3.scaleLinear() .range([0,this._width]) //this.yScale = d3.scaleLog() this.yScale = d3.scaleLinear() .range([this._height,0]) this.yAxis = d3.axisLeft(this.yScale) .ticks(10) .tickFormat(d3.format(",")); this.xAxis = d3.axisBottom(this.xScale) //.ticks(4); this.svgContainer .attr("width",this._width+this._margin.left+this._margin.right) .attr("height",this._height+this._margin.top+this._margin.bottom) this.svgContainer.selectAll("g").remove(); this.container = this.svgContainer.selectAll("g.container") .data([1]) .join("g") .attr("class", "container inlineVis") .attr("transform", `translate(${this._margin.left},${this._margin.top})`) this.line = d3.line() .x((d, i) => this.xScale(i)) // set the x values for the line generator .y((d) => this.yScale(d)) // set the y values for the line generator .curve(d3.curveMonotoneX) // apply smoothing to the line } render(data) { debugger; // change... const self = this; const yDomain = d3.extent(data, (d,i) => d);; const xDomain = d3.extent(data, (d,i) => i); // Update scale domoins according to data values this.xScale.domain(xDomain) //this.yScale.domain([minValue, maxValue]) this.yScale.domain(yDomain) // Path line for each country const line = this.container.selectAll("path.line") .data([data]) .join("path") .attr("class",d => `line`) .attr("d",this.line) } height(_) { this._totalHeight = _; this.startup(); return this; } width(_) { this._totalWidth = _; this.startup(); return this; } }35:T8f8,dataGroupedByLocation = { debugger; function estimateAverageWeeklyNewCases(record) { const location = record.location const date = record.date function estimateWeeklyCases(date1, date2) { if (date1 == "2020-10-30") { debugger; } const record1 = dictDataComunas[location][date1]; const record2 = dictDataComunas[location][date2]; const elapsedDays = moment(date1).diff(moment(date2), "days"); const changeCases = record1 && record2 && record1.total_cases - record2.total_cases || null; return changeCases && elapsedDays && 7 * changeCases / elapsedDays || null; } if (record.weekAgoRecord) { return record.total_cases - record.weekAgoRecord.total_cases } else if (date == "2020-10-30") { // Caso especial en que informe epidemiológico fue publicado Sabado 24 de Octubre en vez de Viernes 23 const weekAgoDate = moment(date).subtract(6, "days").format("YYYY-MM-DD"); const olderDates = _.chain(dictDataComunas[location]) .keys() .filter(d => d <= weekAgoDate) .sortBy() .reverse() .value(); const closestDateToWeekAgo = _.first(olderDates); return estimateWeeklyCases(date, closestDateToWeekAgo) } else { debugger; const weekAgoDate = moment(date).subtract(1, "week").format("YYYY-MM-DD"); const olderDates = _.chain(dictDataComunas[location]) .keys() .filter(d => d <= weekAgoDate) .sortBy() .reverse() .value(); const closestDateToWeekAgo = _.first(olderDates); return estimateWeeklyCases(date, closestDateToWeekAgo) } } // Filter records only before the selected max date const _data = dataComunas const newData = _.chain(_data) .groupBy(d => d.location) .map((items, location) => { return ({ location: location, region: items[0].region, items: items.map(item => { item.weeklyNewCasesEstimate = estimateAverageWeeklyNewCases(item); return item }), lockdowns: cuarentenasByCode[items[0].location_code] || [], poblacion: items[0].poblacion }) }) .filter(d => !d.location.match(/Desconocido/)) .value() return newData }36:Teab,dataComunas = { const data = dataComunasRaw; // Datos Informe Epidemiológico //const data = dataComunasRaw1; // Datos de fuentes regionales const sortedDates = _.chain(data).map(d => d.date).uniq().sortBy().reverse().value(); const prevDateDict = {}; sortedDates.forEach((d,i) => { prevDateDict[d] = sortedDates[i+1] || null; }) debugger; const comunaDateDict = {}; data.forEach(d => { comunaDateDict[d.location] = comunaDateDict[d.location] || {}; comunaDateDict[d.location][d.date] = d; }) data.forEach(d => { d.dateLastWeek = moment(d.date).subtract(7, "days").format("YYYY-MM-DD"); const recordLastWeek = comunaDateDict[d.location][d.dateLastWeek]; d.valueLastWeek = recordLastWeek && recordLastWeek.total_cases d.deltaLastWeek = recordLastWeek && (+d.total_cases - recordLastWeek.total_cases) d.dailyAverage = d.deltaLastWeek / 7; d.weekAgoRecord=recordLastWeek; d.growthFactor = recordLastWeek && (+d.total_cases / +recordLastWeek.total_cases) }) //return data; function getCasesWeekAgo(item,n) { if (n == 0) { return item && item.total_cases || null } else { return item && getCasesWeekAgo(item.weekAgoRecord, n-1) || null } } function getCasesDatePointsAgo(item,n) { if (n == 0) { return item && item.total_cases || null } else { return item && getCasesDatePointsAgo(comunaDateDict[item.location][prevDateDict[item.date]], n-1) || null } } function getCasesDaysPointsAgo(item,n) { if (n == 0) { return 0 } else { const prevDate = item && prevDateDict[item.date] || null; const daysSincePrevDAte = item && moment(item.date).diff(moment(prevDate), "days") return item && daysSincePrevDAte + getCasesDaysPointsAgo(comunaDateDict[item.location][prevDateDict[item.date]], n-1) || 0 } } data.forEach(d => { d.referenceDate = refDate; d.deltaOnReferenceDate = comunaDateDict[d.location][refDate] && comunaDateDict[d.location][refDate].deltaLastWeek ? +comunaDateDict[d.location][refDate].deltaLastWeek : null; d.dailyAvgOnReferenceDay = d.deltaOnReferenceDate / 7; d.dailyAverageDiffAgainstReferenceDay = d.dailyAverage - d.dailyAvgOnReferenceDay; d.relativeIncrease = d.dailyAverage / d.dailyAvgOnReferenceDay; d.dailyAverageLAstWeek = d.weekAgoRecord && d.weekAgoRecord.weekAgoRecord ? (d.weekAgoRecord.total_cases - d.weekAgoRecord.weekAgoRecord.total_cases)/7 : null d.totalCases_today = getCasesWeekAgo(d,0) d.totalCases_1weeksAgo = getCasesWeekAgo(d,1) d.totalCases_2weeksAgo = getCasesWeekAgo(d,2) d.totalCases_3weeksAgo = getCasesWeekAgo(d,3) d.totalCases_4weeksAgo = getCasesWeekAgo(d,4) d.totalCases_5weeksAgo = getCasesWeekAgo(d,5) d.totalCases_6weeksAgo = getCasesWeekAgo(d,6) d.totalCases_2Datepointsago = getCasesDatePointsAgo(d,2) d.totalCases_4Datepointsago = getCasesDatePointsAgo(d,4) d.daysSince2Datepointsago = getCasesDaysPointsAgo(d,2) d.daysSince4Datepointsago = getCasesDaysPointsAgo(d,4) d.avgWeekyCases_2Datepointsago = getCasesDaysPointsAgo(d,2) ? 7*(d.total_cases - getCasesDatePointsAgo(d,2))/getCasesDaysPointsAgo(d,2) : 0; d.avgWeekyCases_4Datepointsago = getCasesDaysPointsAgo(d,4) ? 14*(d.total_cases - getCasesDatePointsAgo(d,4))/getCasesDaysPointsAgo(d,4) : 0; //d.poblacion = +poblacionPorComuna[+d.location_code]; }) /* _.chain(data).groupBy(d => d.location).each((items,key) => { identifySlope(items) }).value() */ return data }37:T5e4,/*dataGroupedByLocationSEREMIS = { function estimateAverageWeeklyNewCases(record) { const location = record.location const date = record.date function estimateWeeklyCases(date1, date2) { const record1 = dictDataComunasSEREMIS[location][date1]; const record2 = dictDataComunasSEREMIS[location][date2]; const elapsedDays = moment(date1).diff(moment(date2), "days"); const changeCases = record1 && record2 && record1.total_cases - record2.total_cases || null; return changeCases && elapsedDays && 7 * changeCases / elapsedDays || null; } if (record.weekAgoRecord) { return record.total_cases - record.weekAgoRecord.total_cases } else { debugger; const weekAgoDate = moment(date).subtract(1, "week").format("YYYY-MM-DD"); const olderDates = _.chain(dictDataComunasSEREMIS[location]) .keys() .filter(d => d <= weekAgoDate) .sortBy() .reverse() .value(); const closestDateToWeekAgo = _.first(olderDates); return estimateWeeklyCases(date, closestDateToWeekAgo) } } const grouped = _.chain(dataComunasSEREMIS) .groupBy(d => d.location) .map((items, location) => ({ location: location, poblacion: items[0].poblacion, region: items[0].region, items: items.map(item => { item.weeklyNewCasesEstimate = estimateAverageWeeklyNewCases(item); return item }), })) .value() return grouped } */38:T895,/*dataComunasSEREMIS = { //const data = dataComunasRaw; // Datos Informe Epidemiológico const data = dataComunasRawSEREMIS; // Datos de fuentes regionales const comunaDateDict = {}; data.forEach(d => { comunaDateDict[d.location] = comunaDateDict[d.location] || {}; comunaDateDict[d.location][d.date] = d; }) data.forEach(d => { d.dateLastWeek = moment(d.date).subtract(7, "days").format("YYYY-MM-DD"); const recordLastWeek = comunaDateDict[d.location][d.dateLastWeek]; d.valueLastWeek = recordLastWeek && recordLastWeek.total_cases d.deltaLastWeek = recordLastWeek && (+d.total_cases - recordLastWeek.total_cases) d.dailyAverage = d.deltaLastWeek / 7; d.weekAgoRecord=recordLastWeek; d.growthFactor = recordLastWeek && (+d.total_cases / +recordLastWeek.total_cases) }) //return data; function getCasesWeekAgo(item,n) { if (n == 0) { return item && item.total_cases || null } else { return item && getCasesWeekAgo(item.weekAgoRecord, n-1) || null } } data.forEach(d => { d.referenceDate = refDate; d.deltaOnReferenceDate = comunaDateDict[d.location][refDate] && comunaDateDict[d.location][refDate].deltaLastWeek ? +comunaDateDict[d.location][refDate].deltaLastWeek : null; d.dailyAvgOnReferenceDay = d.deltaOnReferenceDate / 7; d.dailyAverageDiffAgainstReferenceDay = d.dailyAverage - d.dailyAvgOnReferenceDay; d.relativeIncrease = d.dailyAverage / d.dailyAvgOnReferenceDay; d.dailyAverageLAstWeek = d.weekAgoRecord && d.weekAgoRecord.weekAgoRecord ? (d.weekAgoRecord.total_cases - d.weekAgoRecord.weekAgoRecord.total_cases)/7 : null d.totalCases_today = getCasesWeekAgo(d,0) d.totalCases_1weeksAgo = getCasesWeekAgo(d,1) d.totalCases_2weeksAgo = getCasesWeekAgo(d,2) d.totalCases_3weeksAgo = getCasesWeekAgo(d,3) d.totalCases_4weeksAgo = getCasesWeekAgo(d,4) d.totalCases_5weeksAgo = getCasesWeekAgo(d,5) d.totalCases_6weeksAgo = getCasesWeekAgo(d,6) d.poblacion = +poblacionPorComuna[+d.location_code]; }) return data } */39:T4ac,function checkbox(config = {}) { let { value: formValue, title, description, submit, options } = Array.isArray(config) ? {options: config} : config; options = options.map( o => (typeof o === "string" ? { value: o, label: o } : o) ); const form = input({ type: "checkbox", title, description, submit, getValue: input => { if (input.length) return Array.prototype.filter .call(input, i => i.checked) .map(i => i.value); return input.checked ? input.value : false; }, form: html` ` }); form.output.remove(); return form; }3a:[{"id":"c047f0f796d31c89f0b2d555a91513818b11df236fca8c2d96a0114057226777cee2669ecb33e55b08b13edd7ef197b5d8dfb02ced61589c9a00c758a1b96773","href":"https://static.observableusercontent.com/files/c047f0f796d31c89f0b2d555a91513818b11df236fca8c2d96a0114057226777cee2669ecb33e55b08b13edd7ef197b5d8dfb02ced61589c9a00c758a1b96773","name":"circle.png","type":"image/png","lastModified":1600608640588.828,"size":28081}] 2e:{"title":"¿Cuánto falta?","cells":[{"id":0,"value":"md`# ¿Cuánto falta?`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5931,"value":"html`

ADVERTENCIA: Se solía hacer público un Informe Epidemiológicolos días Sábado, pero con fecha del día Viernes (ej 16 de Octubre), con datos correspondentes al día Jueves (ej 15 de Octubre).

\nEl Sábado 24 de Octubre se hizo público un informe Epidemiológico con fecha del mismo día 24 de Octubre y datos que se indica corresponden al Viernes 23 de Octubre.\n

\nAsumiremos que hay un cambio de día de publicación y - para este reporte - contamos con datos de 8 días (informe del 16 de Octubre). Se hará una estimación proporcional de los casos en la última semana (multiplicando la diferencia con el informe del 16 de Octubre por 7/8)\n
\n
\nEsta es una situación anómala y podría afectar la precisión de los cálculos si los datos no son correctos\n

\n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5699,"value":"md`## Situación de COVID-19 en comunas Chilenas`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4698,"value":"\nviewof regionSeleccionada = {\n const regiones = _.chain(dataToRender)\n .map(d => d.region)\n .uniq() \n .sortBy(d => ordenRegiones[d])\n .map(d => ({\n label: d,\n value:d\n }))\n .value()\n \n const options = _.concat(\n //[{label: \"Todas\", value:\"\"}],\n regiones\n )\n \n return select({\n title: \"Seleccionar una región\",\n options: options,\n placeholder: \"Seleccione una región . . .\",\n value: \"\"\n })\n}\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":5508,"value":"\nviewof comunaSeleccionada = {\n \n const comunasEnLaRegion = _.chain(dataGroupedByLocation)\n .filter(d => regionSeleccionada ? d.region == regionSeleccionada : true)\n .sortBy(d => d.location)\n .value()\n\n \n const comunaPopulosa = _.maxBy(comunasEnLaRegion, d => d.poblacion)[\"location\"]\n\n \n return select({\n title: \"Seleccionar una comuna\",\n options: comunasEnLaRegion.map(d => d.location),\n placeholder: \"Seleccione una región . . .\",\n value: comunaPopulosa\n })\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5517,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":5852,"value":"md`# Datos por comuna (al ${moment(maxDate).format(\"DD MMM YYYY\")})`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5787,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":5654,"value":"html`

Autor

Ernesto Laval (@elaval)

\n\n

Fuente de datos

\nDatos de casos confirmados por comunas en Informes epidemiológicos del Ministerio de Salud de Chile, publicados por el Ministerio de Ciencia en: https://github.com/MinCiencia/Datos-COVID19/blob/master/output/producto1/Covid-19_std.csv\n

\nDato de positividad, surge del trabajo colaborativo entre el Ministerio de Salud, el Ministerio de Ciencias, e investigadores del grupo ICOVID Chile: https://github.com/MinCiencia/Datos-COVID19/tree/master/output/producto55\n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4611,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":593,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":1402,"value":"md`## CSS Styles`","pinned":false,"hidden":false,"mode":"ojs"},{"id":85,"value":"style_bootstrap","pinned":false,"hidden":false,"mode":"ojs"},{"id":4097,"value":"style_fontawesome","pinned":false,"hidden":false,"mode":"ojs"},{"id":218,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":4832,"value":"md`# Vis`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4833,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":1407,"value":"md`## Config Params`","pinned":false,"hidden":false,"mode":"ojs"},{"id":5439,"value":"\nfuenteDatos = \"epi\"\n/*viewof fuenteDatos = radio({\n title: 'Fuente de datos',\n description: 'Seleccionar fuente de datos (por defecto son informes regionales)',\n options: [\n { label: 'Informes regionales (salvo R. Metropolitana)', value: 'regionales' },\n { label: 'Informes epidemiológicos para todas las regiones ', value: 'epi' },\n ],\n value: 'regionales'\n})\n*/","pinned":false,"hidden":false,"mode":"ojs"},{"id":5312,"value":"fechasMetropolitana = {\n const out = _.chain(epiDates)\n //.filter(d => d > \"2020-06-23\")\n .sortBy().reverse().filter((d,i) => i % 2 == 0).reverse().value();\n \n return out\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5250,"value":"/*fechasRegiones = {\n const out = _.chain(dataComunasSEREMIS).map(d => d.date).uniq().sortBy().reverse()\n .filter(d => d <= maxDate)\n .filter((d,i) => i % 7 == 0).take(6).reverse()\n .value()\n \n return out\n}\n*/","pinned":false,"hidden":false,"mode":"ojs"},{"id":5097,"value":"omitDates = [\"2020-06-08\",\"2020-06-12\",\"2020-06-15\", \"2020-06-19\", \"2020-06-23\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":4684,"value":"epiDates = _.chain(dataComunas)\n .map(d => d.date)\n .uniq()\n .filter(d => d <= maxDate)\n .filter(d => moment(maxDate).diff(moment(d), \"weeks\") < 12)// Last 6 weeks\n\n //.filter(d => moment(d).day()==1)// Lunes\n //.filter(d => moment(d).day()==5)\n //.filter(d => [5,6].includes(moment(d).day()))\n .sortBy(d => d)\n .value()","pinned":false,"hidden":false,"mode":"ojs"},{"id":4542,"value":"whiteTextThreshold = 800","pinned":false,"hidden":false,"mode":"ojs"},{"id":4626,"value":"whiteTextThresholdPorPoblacion = 60","pinned":false,"hidden":false,"mode":"ojs"},{"id":4516,"value":"maxDiff = _.max(dataGroupedByLocation.map(d => _.max(d.items.map(e => e.avgWeekyCases_2Datepointsago)) ))","pinned":false,"hidden":false,"mode":"ojs"},{"id":4619,"value":"maxDiffPorPoblacion = _.max(dataGroupedByLocation.map(d => _.max(d.items.map(e => 2*100000*(e.avgWeekyCases_2Datepointsago)/e.poblacion)) ))","pinned":false,"hidden":false,"mode":"ojs"},{"id":4504,"value":"colorRange = ['white', 'yellow',\"orange\",'red', \"purple\", \"black\"]","pinned":true,"hidden":false,"mode":"ojs"},{"id":4507,"value":"colorDomain = [0, 50,100, 200, 1000, maxDiff]","pinned":true,"hidden":false,"mode":"ojs"},{"id":4617,"value":"colorDomainPorPoblacion = [0, 5, 10, 50, 100, 200]","pinned":true,"hidden":false,"mode":"ojs"},{"id":2536,"value":"standardNames= ({\n \"Arica y Parinacota\": \"Arica y Parinacota\",\n\"Tarapaca\": \"Tarapacá\",\n\"Antofagasta\": \"Antofagasta\",\n\"Atacama\": \"Atacama\",\n\"Coquimbo\": \"Coquimbo\",\n\"Valparaiso\": \"Valparaíso\",\n\"Metropolitana\": \"Metropolitana\",\n\"Del Libertador General Bernardo O’Higgins\":\"O'Higgins\",\n\"O’Higgins\":\"O'Higgins\",\n\"Maule\": \"Maule\",\n\"Nuble\": \"Ñuble\",\n\"Biobio\": \"Biobío\",\n\"La Araucania\": \"Araucanía\",\n\"Los Rios\": \"Los Ríos\",\n\"Los Lagos\": \"Los Lagos\",\n\"Aysen\": \"Aysén\",\n\"Magallanes y la Antartica\": \"Magallanes\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2884,"value":"locale_es_ES = d3.json(\"https://raw.githubusercontent.com/d3/d3-time-format/master/locale/es-ES.json\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2883,"value":"function multiFormat(date) {\n const locale = d3.timeFormatLocale(locale_es_ES);\n const formatMillisecond = locale.format(\".%L\"),\n formatSecond = locale.format(\":%S\"),\n formatMinute = locale.format(\"%I:%M\"),\n formatHour = locale.format(\"%I %p\"),\n formatDay = locale.format(\"%a %d\"),\n formatWeek = locale.format(\"%b %d\"),\n formatMonth = locale.format(\"%B\"),\n formatYear = locale.format(\"%Y\");\n \n return (d3.timeSecond(date) < date ? formatMillisecond\n : d3.timeMinute(date) < date ? formatSecond\n : d3.timeHour(date) < date ? formatMinute\n : d3.timeDay(date) < date ? formatHour\n : d3.timeMonth(date) < date ? (d3.timeWeek(date) < date ? formatDay : formatWeek)\n : d3.timeYear(date) < date ? formatMonth\n : formatYear)(date);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":840,"value":"// Base trasition duration during transitions \nduration = 500","pinned":false,"hidden":false,"mode":"ojs"},{"id":2801,"value":"// locationIconUrl = \"https://qg70ohwn8vgx8gg1dm7bu6g9-wpengine.netdna-ssl.com/wp-content/uploads/2014/02/house-Icon.png\"\n\nlocationIconUrl = await FileAttachment(\"circle.png\").url();","pinned":false,"hidden":false,"mode":"ojs"},{"id":3413,"value":"metric = \"total_cases\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":3616,"value":"diagnosticMessages = ({\n 1: `no muestra reducción de nuevos casos con respecto a la semana anterior`,\n 2: `muestra reducción de nuevos casos con respecto a la semana anterior, pero no con respecto a 2 semanas atrás`,\n 3: `muestra reducción de casos con respecto a las 2 semanas anteriores`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3904,"value":"diagnosticMessagesPlural = ({\n 1: `no muestran reducción de nuevos casos con respecto a la semana anterior`,\n 2: `muestran reducción de nuevos casos con respecto a la semana anterior, pero no con respecto a 2 semanas atrás`,\n 3: `muestran reducción de casos con respecto a las 2 semanas anteriores`\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":3997,"value":"function enCuarentena(item) {\n return item.lockdowns.find(d => {\n const fromDate = moment(d.start_date);\n const toDate = moment(d.end_date).add(1, \"day\")\n return moment(new Date()).isBetween(fromDate, toDate)\n }) ? true : false\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3750,"value":"chartWidth = 1024 < width ? 1024 : width;","pinned":false,"hidden":false,"mode":"ojs"},{"id":1384,"value":"md`## Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3132,"value":"maxLocation_dailyAvg = {\n return _.maxBy(dataToRender, (d) => _.max(d.items.map(e => e.dailyAverage || 0)))\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3157,"value":"minLocation_dailyAvg = {\n return _.minBy(dataToRender, (d) => _.min(d.items.map(e => e.dailyAverage || 999)))\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":265,"value":"dataToRender = _.chain(dataGroupedByLocation)\n.map(d => {\n return {\n location : d.location,\n region : d.region,\n items: d.items.filter(e => e[\"date\"] >= refDate),\n lockdowns: d.lockdowns\n }\n})\n.filter(d => true)\n.value()","pinned":false,"hidden":false,"mode":"ojs"},{"id":3783,"value":"dataGroupedByLocation.find(d => d.location == \"Tierra Amarilla\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1211,"value":"dataGroupedByLocation_bkup = { \n \n debugger;\n \n // Filter records only before the selected max date\n const _data = dataComunas\n \n \n const newData = _.chain(_data)\n .groupBy(d => d.location)\n .map((items, location) => { \n return ({\n location: location,\n region: items[0].region,\n items: removeLastIfNoPrevWeek(items),\n lockdowns: cuarentenasByCode[items[0].location_code] || [],\n poblacion: items[0].poblacion\n })\n })\n .value()\n \n return newData\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5006,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":3932,"value":"function removeLastIfNoPrevWeek(items) {\n debugger;\n let lastItem = _.last(items);\n \n if (items.length <= 7 || lastItem.valueLastWeek) {\n return items\n } else {\n return removeLastIfNoPrevWeek(_.slice(items,0,items.length-1))\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2176,"value":"","pinned":true,"hidden":false,"mode":"ojs"},{"id":3019,"value":"dataCuarentenas_custom = {\n const data = (await d3.csv(\"https://raw.githubusercontent.com/elaval/covid_chile_regional/master/cuarentenas.csv\")).map(d => ({\n location: d.comuna,\n regions: d.region,\n start_date: d.fecha_inicio,\n end_date: d.fecha_termino\n }));\n return _.groupBy(data, d => d.location);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3870,"value":"cuarentenasByCode = _.groupBy(dataCuarentenas_minCencia, d => d.location_code)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3850,"value":"dataCuarentenas_minCencia = {\n \n const dataRaw = await d3.csv(\"https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto29/Cuarentenas-Totales.csv\");\n return _.chain(dataRaw)\n .map(d => ({\n \n location: d.Nombre,\n detail: d.Nombre,\n scope: d[\"Alcance\"],\n start_date: moment(d[\"Fecha de Inicio\"]).format(\"YYYY-MM-DD\"),\n end_date: d[\"Fecha de Término\"] && moment(d[\"Fecha de Término\"]).format(\"YYYY-MM-DD\") || moment(d[\"Fecha de Inicio\"]).add(30,\"days\").format(\"YYYY-MM-DD\"),\n location_code: d[\"Código CUT Comuna\"]\n }))\n .sortBy(d => d.location)\n .value()\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3772,"value":"dataComunas.filter(d => d.location == \"Huara\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":5429,"value":"_.chain(dictDataComunas).map((d,key) => key).filter(d => !d.match(/Desconocido/)).value()","pinned":false,"hidden":false,"mode":"ojs"},{"id":5410,"value":"dictDataComunas = {\n const dict = {};\n \n dataComunas.forEach(d => {\n const date = d.date;\n const location = d.location;\n \n dict[location] = dict[location] || {};\n dict[location][date] = d;\n })\n \n return dict\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5736,"value":"positividadXComunas = {\n const dict = {};\n _.chain(dataPositividadRaw)\n .groupBy(d => d.Comuna)\n .map((items,comuna) => ({\n location: comuna,\n positividad: _.chain(items).sortBy(d => d.date).last().value()[\"positividad\"]\n }))\n .each(d => dict[d.location] = +d.positividad)\n .value()\n \n return dict\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5763,"value":"datePositividad = _.maxBy(dataPositividadRaw, d => d.fecha)[\"fecha\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":5732,"value":"dataPositividadRaw = d3.csv(\"https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto55/Positividad_por_comuna.csv\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2652,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":2716,"value":"refDate = moment(maxDate).subtract(referenceDays,\"days\").format(\"YYYY-MM-DD\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2732,"value":"referenceDays = 14;","pinned":false,"hidden":false,"mode":"ojs"},{"id":2713,"value":"maxDate = {\n return d3.max(dataComunasRaw, d => d.date)\n //return \"2020-06-17\"\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3890,"value":"md`Datos de comunas `","pinned":false,"hidden":false,"mode":"ojs"},{"id":2933,"value":"dataComunasRaw = {\n const baseData = await d3.csv(\"https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto1/Covid-19_std.csv\")\n \n // Region,Codigo region,Comuna,Codigo comuna,Poblacion,Fecha,Casos confirmados\n\n return _.chain(baseData).map(d => ({\n date: moment(d[\"Fecha\"], \"YYYY-MM-DD\").format(\"YYYY-MM-DD\"),\n location: d[\"Comuna\"],\n location_code: d[\"Codigo comuna\"],\n region:d[\"Region\"],\n total_cases: +d[\"Casos confirmados\"],\n poblacion: +d[\"Poblacion\"]\n }))\n .sortBy(d => d.location)\n .value()\n \n /*\n 0: Object {\n Fecha: \"30-03-2020\"\n CUT: \"15101\"\n Region: \"AP\"\n Comuna: \"Arica\"\n Casos Confirmados: \"6\"\n}\n*/\n \n /*\n date: moment(d[\"Fecha\"], \"DD-MM-YYYY\").format(\"YYYY-MM-DD\"),\n location: d[\"Comuna\"],\n ISO_Region: d[\"Region\"],\n location_code: d[\"CUT\"],\n total_cases: +d[\"Confirmados Acumulados\"],\n new_cases: +d[\"Nuevos Confirmados\"]\n */\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5209,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":5206,"value":"/*dictDataComunasSEREMIS = {\n const dict = {};\n \n dataComunasSEREMIS.forEach(d => {\n const date = d.date;\n const location = d.location;\n \n dict[location] = dict[location] || {};\n dict[location][date] = d;\n })\n \n return dict\n}*/","pinned":false,"hidden":false,"mode":"ojs"},{"id":5201,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":5197,"value":"dataComunasRawSEREMIS_ = {\n const baseData = await d3.csv(\"https://raw.githubusercontent.com/ivanMSC/COVID19_Chile/master/historial_comunas.csv\")\n \n return _.chain(baseData).map(d => ({\n date: moment(d[\"Fecha\"], \"DD-MM-YYYY\").format(\"YYYY-MM-DD\"),\n location: d[\"Comuna\"],\n ISO_Region: d[\"Region\"],\n region: regiones[d[\"Region\"]] && regiones[d[\"Region\"]][\"nombreRegion\"],\n location_code: d[\"CUT\"],\n total_cases: +d[\"Confirmados Acumulados\"],\n new_cases: +d[\"Nuevos Confirmados\"]\n }))\n .sortBy(d => d.location)\n .value()\n \n /*\n Fecha: \"06-04-2020\"\n CUT: \"15101\"\n Region: \"AP\"\n Comuna: \"Arica\"\n Confirmados Acumulados: \"54\"\n Nuevos Confirmados: \"22\"\n}\n*/\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5203,"value":"poblacionPorComuna = {\n \n \n const data = await d3.csv(\"https://raw.githubusercontent.com/MinCiencia/Datos-COVID19/master/output/producto2/2020-06-08-CasosConfirmados.csv\");\n \n const dict = {};\n \n data.forEach(d => {\n dict[+d[\"Codigo comuna\"]] = +d[\"Poblacion\"]\n })\n \n return dict\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5199,"value":"regiones = {\n const data = await d3.csv(\"https://raw.githubusercontent.com/ivanMSC/COVID19_Chile/master/utils/regionesChile.csv\");\n const dict = {};\n \n data.forEach(d => dict[d.ISO_Region] = d);\n \n return dict\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5726,"value":"ordenRegiones = ({\n\"Arica y Parinacota\": 10,\n\"Tarapacá\": 20,\n\"Antofagasta\": 30,\n\"Atacama\": 40,\n\"Coquimbo\": 50,\n\"Valparaíso\": 60,\n\"Metropolitana\": 70,\n\"O’Higgins\": 80,\n\"Maule\": 90,\n\"Ñuble\":100,\n\"Biobío\": 110,\n\"Araucanía\": 120,\n\"Los Ríos\": 130,\n\"Los Lagos\": 140,\n\"Aysén\": 150,\n\"Magallanes\": 160\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":5746,"value":"import {aliasComunas} from \"@elaval/coronavirus-data\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":1397,"value":"md`## Aux functions`","pinned":false,"hidden":false,"mode":"ojs"},{"id":966,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":1387,"value":"md`## Code Libraries`","pinned":false,"hidden":false,"mode":"ojs"},{"id":83,"value":"import {style_bootstrap, style_fontawesome} from \"@elaval/utils\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":12,"value":"_ = require(\"lodash\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":7,"value":"d3 = require(\"d3\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":249,"value":"moment = require('moment@2.24.0/moment.js')","pinned":false,"hidden":false,"mode":"ojs"},{"id":2331,"value":"momentLocaleConfig = {\n moment.locale('es'); // 'en'\n \n moment.updateLocale('es', {\n weekdays : [\n \"Domingo\", \"Lunes\", \"Martes\", \"Miércoles\", \"Jueves\", \"Viernes\", \"Sábado\"\n ]\n});\n \n moment.updateLocale('es', {\n months : [\n \"Enero\", \"Febrero\", \"Marzo\", \"Abril\", \"Mayo\", \"Junio\", \"Julio\",\n \"Agosto\", \"Septiembre\", \"Octubre\", \"Noviembre\", \"Diciembre\"\n ]\n});\n \n moment.updateLocale('es', {\n monthsShort : [\n \"Ene\", \"Feb\", \"Mar\", \"Abr\", \"May\", \"Jun\",\n \"Jul\", \"Ago\", \"Sep\", \"Oct\", \"Nov\", \"Dic\"\n ]\n});\n \n moment.updateLocale('es', {\n weekdaysShort : [\"Dom\", \"Lun\", \"Mar\", \"Mie\", \"Jue\", \"Vie\", \"Sab\"]\n});\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":333,"value":"import {input, autoSelect,text, select, date, radio, button, number} from '@jashkenas/inputs'","pinned":false,"hidden":false,"mode":"ojs"},{"id":627,"value":"ga = {\n const ga = await require(\"https://www.google-analytics.com/analytics.js\").catch(() => window.ga);\n ga(\"create\", \"UA-143020946-10\", \"auto\");\n ga(\"send\", \"pageview\", html``.pathname);\n return ga;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":5308,"value":"moment(\"2020-06-15\").subtract(\"28\", \"days\").format(\"DD MMM\")","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W3a","stdlib":"1","titling":"auto"}