36:["$","title",null,{"children":"COVID-19 en Argentina | Emmanuel Iarussi | Observable"}] 3e:T6fe,{ const line = vl.markLine({ interpolate: 'monotone' }) .encode( vl.x() .fieldT('date') .timeUnit('utcyearmonthday') .axis({ tickCount: 'month', format: '%b.', grid: false, tickSize: 6, labelColor: '#929292' }) .title(null), vl.y() .fieldQ('mean') .axis(null) .scale({ nice: false }) .title(null), vl.color({ value: colores_fijos(type) }), ); const text = vl.markText({ align: 'right', dx: -13, dy: -4, baseline: 'top', fontSize: 9 }) .encode( vl.text() .fieldQ('argmax.mean_ceil') .format(',d'), vl.x() .fieldT('argmax.date') .timeUnit('yearmonthday') .title(null), vl.y() .fieldQ('argmax.mean') .axis(null) .title(null), ) .transform( vl.calculate().as('mean_ceil').calculate(`ceil(datum.${type})`), vl.aggregate({ op: 'argmax', field: 'mean', as: 'argmax' }), ); const tick = text.markTick({ size: 10, xOffset: -5, align: 'right', fill: '#000' }); return vl.layer(line.markArea({ opacity: 0.2 }), line, text, tick) .width(168) .height(100) .facet(vl.facet().fieldN('state').title(null).sort({op: "sum", field: type, order: "descending"})) .columns(columns) .transform( vl.window({ op: 'mean', field: type, as: 'mean' }) .groupby('state') .frame([-3, 3]), ) .resolve({ axis: { x: 'independent' }, scale: { y: state_scale_type } }) .config({ style: { cell: { stroke: 'transparent' } }, header: { labelFontSize: 14, labelPadding: 0 }, }) .data(state_data .filter(d => d.total) .map(d => d.map(v => Object.assign({ state: d.state }, v))) .flat()) .render(); }3f:T572,make_chart = (data, width, domain) => { const bars = vl.markBar({ opacity: 0.1 }) .encode( vl.x() .fieldT('date') .timeUnit('utcyearmonthday') .scale({ domain: date_extent, paddingInner: 0.1, paddingOuter: 0 }) .axis({ tickCount: 'month', format: '%B', grid: false, tickSize: 6 }) .title(null), vl.y() .fieldQ(type) .scale({ domain, nice: false }) .axis({ tickCount: 4, domain: false, ticks: false, labelAlign: 'left', labelBaseline: 'bottom', }) .title(null), vl.color({ value: colores_fijos(type) }), ); // Push the line/area layers to the right edge of the bars (1 bandwidth) const xOffset = width / d3.timeDay.count(new Date(date_extent[0]), new Date(date_extent[1])); const line = bars.markLine({ interpolate: 'monotone', xOffset }) .encode(vl.y().fieldQ('mean')); const area = line.markArea({ opacity: 0.2, xOffset }); return vl.layer(bars, area, line) .data(data) .transform( vl.window({ op: 'mean', field: type, as: 'mean' }).frame([-3, 3]), ) .width(width) .height(custom_height) .autosize({ type: 'fit-x', contains: 'padding' }) .config({ style: { cell: { stroke: 'transparent' } }, axis: { labelColor: '#929292', gridColor: '#ececec' }, padding: { top: 5, right: 0, bottom: 5, left: 0 }, }) .render(); }40:T56a,make_chart_tests_diff = (data, width, domain) => { // Push the line/area layers to the right edge of the bars (1 bandwidth) const xOffset = width / d3.timeDay.count(new Date(date_extent[0]), new Date(date_extent[1])); const bars = vl.markBar({ opacity: 0.35, xOffset }) .encode( vl.x() .fieldT('date') .timeUnit('utcyearmonthday') .scale({ domain: date_extent, paddingInner: 0.1, paddingOuter: 0 }) .axis({ tickCount: 'month', format: '%B', grid: false, tickSize: 6 }) .title(null), vl.y() .fieldQ('val') .stack(normalize_diff) .scale({ domain, nice: false }) .axis({ tickCount: 4, domain: false, ticks: false, labelAlign: 'left', labelBaseline: 'bottom', }) .title(null), vl.color().fieldN("resultado").scale({ range: ["#1778B7","red"] }).legend({direction:'horizontal',orient:'top',symbolType:'circle',title:null}), vl.tooltip().field("rate") ); return vl.layer(bars) .data(data) .transform( vl.window({ op: 'mean', field: type, as: 'mean' }).frame([-3, 3]), ) .width(width) .height(custom_height/1.2) .autosize({ type: 'fit-x', contains: 'padding' }) .config({ style: { cell: { stroke: 'transparent' } }, axis: { labelColor: '#929292', gridColor: '#ececec' }, padding: { top: 10, right: 5, bottom: 10, left: 5 }, }) .render(); }41:T12b7,make_chart_tests = (data, width, domain) => { // Push the line/area layers to the right edge of the bars (1 bandwidth) const xOffset = width / d3.timeDay.count(new Date(date_extent[0]), new Date(date_extent[1])); const line_negativos = vl.markLine({ interpolate: 'monotone' ,xOffset}) .encode( vl.x().fieldT("date") .timeUnit('utcyearmonthday') .scale({ domain: date_extent, paddingInner: 0.1, paddingOuter: 0 }) .axis({ tickCount: 'day', format: '%B', grid: false, tickSize: 3 }) .title(null), vl.y().fieldQ("tests_negativos") .scale({ domain, nice: false }) .axis({ tickCount: 4, domain: false, ticks: false, labelAlign: 'left', labelBaseline: 'bottom', }), //vl.tooltip([{"field": "date", "type": "temporal"}, // {"field": "tests_negativos", "type": "quantitative"}]), vl.color({ value: "#1778B7" }),// .sort(vl.max("market cap").order("descending")) ); const line_positivos = vl.markLine({ interpolate: 'monotone' , xOffset}) .encode( vl.x().fieldT("date") .timeUnit('utcyearmonthday') .scale({ domain: date_extent, paddingInner: 0.1, paddingOuter: 0 }) .axis({ tickCount: 'day', format: '%B', grid: false, tickSize: 3 }) .title(null), vl.y().fieldQ("tests_positivos") .scale({ domain, nice: false }) .axis({ tickCount: 4, domain: false, ticks: false, labelAlign: 'left', labelBaseline: 'bottom', }) .title(null), //vl.tooltip([{"field": "date", "type": "temporal"}, // {"field": "tests_positivos", "type": "quantitative"}]), vl.color( { value: "#ff5733" } )// .sort(vl.max("market cap").order("descending")) ); // Interaction const hover_negativos = vl.selectSingle() .encodings('x') // limit selection to x-axis value .on('mouseover') // select on mouseover events .nearest(true) // select data point nearest the cursor .empty('none'); // empty selection includes no data points const hover_positivos = vl.selectSingle() .encodings('x') // limit selection to x-axis value .on('mouseover') // select on mouseover events .nearest(true) // select data point nearest the cursor .empty('none'); // empty selection includes no data points const base_negativos = line_negativos.transform(vl.filter(hover_negativos)); const base_positivos = line_positivos.transform(vl.filter(hover_negativos)); const mark_rule = vl.markRule({color: '#aaa', xOffset}) .transform(vl.filter(hover_negativos)) .encode(vl.x().fieldT('date')); const mark_circle_negativos = line_negativos.markCircle({xOffset}) .select(hover_negativos) // use as anchor points for selection .encode(vl.opacity().if(hover_negativos, vl.value(1)).value(0)); const mark_circle_positivos = line_positivos.markCircle({xOffset}) .select(hover_positivos) // use as anchor points for selection .encode(vl.opacity().if(hover_negativos, vl.value(1)).value(0)); const area_negativos = line_negativos.markArea({ opacity: 0.2 , xOffset}); const area_positivos = line_positivos.markArea({ opacity: 0.2 , xOffset}); // mark properties for text label layers const label = {align: 'left', dx: 5, dy: -5}; const label_date = {stroke: '#aaa', strokeWidth: 0.1, align: 'center', dx: 10, dy: (custom_height/1.5)+10}; const white = {stroke: 'white', strokeWidth: 2}; // add white stroked text to provide a legible background for labels const label_negativos_back = base_negativos.markText(label, white).encode(vl.text().fieldQ('tests_negativos'),xOffset); const label_negativos_front = base_negativos.markText(label,xOffset).encode(vl.text().fieldQ('tests_negativos'),xOffset) const label_positivos_back = base_positivos.markText(label, white).encode(vl.text().fieldQ('tests_positivos'),xOffset); const label_positivos_front = base_positivos.markText(label,xOffset).encode(vl.text().fieldQ('tests_positivos'),xOffset) const label_date_front = mark_rule.markText(label_date,xOffset).encode(vl.text().fieldT("date")) return vl.layer(line_negativos,mark_rule,mark_circle_negativos,area_negativos,line_positivos,mark_circle_positivos,area_positivos,label_negativos_back,label_negativos_front,label_positivos_back,label_positivos_front) .data(data) .transform( vl.window({ op: 'mean', field: type, as: 'mean' }).frame([-3, 3]), ) .width(width) .height(custom_height*1.5) .autosize({ type: 'fit-x', contains: 'padding' }) .config({ style: { cell: { stroke: 'transparent' } }, axis: { labelColor: '#929292', gridColor: '#ececec' }, padding: { top: 10, right: 5, bottom: 10, left: 5 }, }) .render(); }42:T5b9,raw_argentina_data = { let raw_data = [] // For dates let prev_positivos=0 let prev_negativos=0 let prev_contagios=0 let prev_tests =0 for(var d = 0; d < datos_viz.series_fechas.length-1;d++) { const tests = datos_viz.series_test_dia[0].val[d] const tests_negativos = datos_viz.series_test_neg_dia[0].val[d] const tests_positivos = datos_viz.series_acumulados_dia[0].val[d]// datos_viz.series_test_dia[0].val[d]-datos_viz.series_test_neg_dia[0].val[d] const contagios_totales = datos_viz.series_acumulados_dia[0].val[d] // A veces no cargan los tests para un dia y hay que atajarlo let day_data = {date:datos_viz.series_fechas[d], tests:tests, tests_negativos:tests_negativos, tests_positivos: tests_positivos, contagios_totales:contagios_totales, diff_positivos:tests_positivos-prev_positivos, diff_negativos:tests_negativos-prev_negativos, diff_contagios:contagios_totales-prev_contagios, diff_tests: (tests_positivos-prev_positivos)+(tests_negativos-prev_negativos)//tests-prev_tests }; prev_positivos = tests_positivos prev_negativos = tests_negativos prev_contagios = contagios_totales prev_tests = tests raw_data.push(day_data); } //raw_data.pop() return raw_data; }43:T113a,raw_argentina_data_diff = { // Previous values let prev_positivos= 0 let prev_negativos= 0 let prev_contagios= 0 let prev_tests = 0 // Current values let tests = 0 let tests_negativos = 0 let tests_positivos = 0 let contagios_totales = 0 // Compute diff instead of accumulates let raw_data_diff = [] for(var d = 0; d < datos_viz.series_fechas.length-1;d++) { // Get values (current day) tests = datos_viz.series_test_dia[0].val[d] tests_negativos = datos_viz.series_test_neg_dia[0].val[d] tests_positivos = datos_viz.series_test_dia[0].val[d]-datos_viz.series_test_neg_dia[0].val[d] contagios_totales = datos_viz.series_acumulados_dia[0].val[d] // Compute diff let day_data = {date:datos_viz.series_fechas[d], positivos:tests_positivos-prev_positivos,// contagios_totales-prev_contagios, negativos:tests_negativos-prev_negativos, tests:tests-prev_tests, contagios_totales:contagios_totales-prev_contagios}; // Current day values are now prev day prev_tests =tests prev_negativos=tests_negativos prev_positivos=tests_positivos prev_contagios=contagios_totales // push raw_data_diff.push(day_data) } // Now average (3 days) prev_positivos = 0 prev_negativos = 0 prev_contagios = 0 prev_tests = 0 let raw_data_diff_avg = [] for(var d = 0; d < datos_viz.series_fechas.length-1;d++) { // Get values (current day) tests = raw_data_diff[d].tests tests_negativos = raw_data_diff[d].negativos tests_positivos = raw_data_diff[d].positivos contagios_totales = raw_data_diff[d].contagios_totales // Get values next day let next_positivos = 0 let next_negativos = 0 let next_contagios = 0 let next_tests = 0 let divisor = 2 if(d { const output = data.map((d, i) => ({ date: d.date, tests: Math.max(d.tests, 0), // - (data[i - 1] ? data[i - 1].cases : 0) tests_negativos: Math.max(d.tests_negativos, 0), //Math.max(d.deaths - (data[i - 1] ? data[i - 1].deaths : 0), 0), tests_positivos: Math.max(d.tests_positivos, 0), contagios_totales: Math.max(d.contagios_totales, 0), diff_positivos:+d.diff_positivos, diff_negativos:+d.diff_negativos, diff_contagios:+d.diff_contagios, diff_tests:+d.diff_tests, })); // The line/area will be shifted to the right by one band, so prepend // a placeholder value with 0 to the left to make up for it //const dayBefore = new Date(output[0].date); //dayBefore.setDate(dayBefore.getDate() - 1); // output.unshift({ // date: d3.utcFormat('%Y-%m-%d')(dayBefore), // tests: 0, // tests_negativos: 0, // tests_positivos: 0, // contagios_totales: 0, // diff_positivos:0, // diff_negativos:0, // diff_contagios:0, // diff_tests:0 // }); Object.assign(output, data[0], { past_week: output[output.length - 1]["tests"]-output[output.length - 7]["tests"], //d3.sum(output.slice(-7), d => d[type]), total: output[output.length - 1][`tests`], }); ['date', 'tests', 'tests_negativos', 'tests_positivos', 'contagios_totales','diff_positivos','diff_negativos','diff_contagios','diff_tests'].forEach(key => delete output[key]); return output; };45:[] 3a:{"id":"9a37aa2607e28b05","slug":"covid-19-en-argentina/3","title":"COVID-19 en Argentina","thumbnail":"8224704f8fed89c0219dd53a477438aef65f14fea06c11e5f4caeda1dd1ab843","access_level":"public","create_time":"$D2020-04-05T19:31:36.077Z","update_time":"$D2020-04-13T02:27:18.390Z","view_time":null,"first_public_time":"$D2020-04-05T21:20:04.947Z","has_unpublished_edits":null,"fork":{"id":"6abf0ed1e76ab124","slug":"covid-19-curves-u-s","title":"COVID-19 Curves (U.S.)","owner":{"id":"71e001d943b9a4ca","login":"autopaideia","name":"Nick Bosman"}},"stars":3,"starred":false,"owner":{"type":"team","id":"b99d4b1edfb0cda9","login":"eiarussi","name":"Emmanuel Iarussi","avatar_url":"https://avatars-next.observableusercontent.com/1e2ac1492eebef5a646755f105a47fd84a4ad5c6f8fcdb86a639453d6626257a","tier":"starter_2024","bio":"I'm a Computer Scientist at CONICET. My research interests focus on tools for interactive design in the context of 2D and 3D content manipulation.","followed_by_you":false,"follows_you":false},"version":2996,"publish_version":2996,"role":null,"authored":false,"authors":[{"type":"individual","id":"688b326db60f2dfc","login":"eiarussi","name":"Emmanuel Iarussi","avatar_url":"https://avatars-next.observableusercontent.com/1e2ac1492eebef5a646755f105a47fd84a4ad5c6f8fcdb86a639453d6626257a","tier":null,"bio":"I'm a Computer Scientist at CONICET. My research interests focus on tools for interactive design in the context of 2D and 3D content manipulation.","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"688b326db60f2dfc","login":"eiarussi","name":"Emmanuel Iarussi","avatar_url":"https://avatars-next.observableusercontent.com/1e2ac1492eebef5a646755f105a47fd84a4ad5c6f8fcdb86a639453d6626257a","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":true,"trashed":false,"lapsed":null,"subscribed":null,"forks":3,"collection":null,"type":"notebook","body":{"titling":"auto","title":"COVID-19 en Argentina","cells":[{"id":273,"value":"md`# COVID-19 en Argentina\n\nPequeño intento en formato notebook de visualizar el avance del Covid-19 en la Argentina. \nLas barras muestran el total de cada día. La linea resalta la tendencia general de evolución de casos.\n\nDatos: [Sistemas Mapache](https://bit.ly/2wTzB1o).\n\n*Ultima actualización ${d3.utcFormat('%B %e, %Y')(new Date(d3.max(datos_csv, d => d.fecha)))}*.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1859,"value":"viewof type = radio({\n title: 'Tipo',\n options: [\n { label: 'Casos', value: 'casos' },\n { label: 'Fallecimientos', value: 'muertes' },\n ],\n value: 'casos',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1931,"value":"md`## Total `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1511,"value":"make_figure('Argentina', us_data, width);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1516,"value":"md`## Por Provincia`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1201,"value":"viewof state = select({\n title: 'Seleccionar',\n options: Array.from(new Set(raw_state_data_argentina.map(d => d.state))).sort(),\n value: 'CABA',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1064,"value":"{\n const stateData = state_data.find(d => d.state === state);\n\n return make_figure(stateData.state, stateData, width);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1541,"value":"md`## Panel general de Provincias\nVista del acumulado de casos para cada provincia.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1908,"value":"viewof state_scale_type = radio({\n title: 'Escala',\n options: [\n { label: 'Compartida', value: 'shared' },\n { label: 'Independiente', value: 'independent' },\n ],\n value: 'independent',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1274,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2367,"value":"md`## Testeos\nCantidad acumulada de tests con resultados negativos y positivos. Se muestra el conteo hasta el día ${d3.utcFormat('%B %e, %Y')(argentina_data[argentina_data.length-1].date)}. Abajo se muestra la proporción diaria de los resultados de los tests, calculado como el promedio en una ventana de 3 días. `","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2551,"value":"plot({\n autosize: \"pad\",\n data: [\n {\n name: \"values\",\n values: [\"Negativos\", \"Positivos\"]\n }\n ],\n scales: [\n {\n name: \"symbolColor\",\n type: \"ordinal\",\n domain: {data: \"values\", field: \"data\"},\n range: [\"#1778B7\",\"#ff5733\" ]\n },\n {\n name: \"symbolSize\",\n type: \"ordinal\",\n domain: {data: \"values\", field: \"data\"},\n range: [130, 130]\n }\n ],\n legends: [Object.assign({offset: 0}, symbolLegendDef)]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2370,"value":"make_figure_tests('Tests (acumulado)', argentina_data, width);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2690,"value":"make_figure_tests_diff('Tests (diarios)', raw_argentina_data_diff, width);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2763,"value":"viewof normalize_diff = radio({\n title: 'Normalizado',\n options: [\n { label: 'Normalizado', value: 'normalize' },\n { label: 'Total', value: 'count' },\n ],\n value: 'normalize',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":920,"value":"md`## Apéndice\nFunciones para graficar y procesar los datos.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":858,"value":"process_data = data => {\n const output = data.map((d, i) => ({\n date: d.date,\n casos: Math.max(d.casos, 0), // - (data[i - 1] ? data[i - 1].cases : 0) \n muertes: Math.max(d.muertes, 0), //Math.max(d.deaths - (data[i - 1] ? data[i - 1].deaths : 0), 0),\n total_casos: +d.casos,\n total_muertes: +d.muertes,\n }));\n\n // The line/area will be shifted to the right by one band, so prepend\n // a placeholder value with 0 to the left to make up for it\n const dayBefore = new Date(output[0].date);\n dayBefore.setDate(dayBefore.getDate() - 1);\n\n output.unshift({\n date: d3.utcFormat('%Y-%m-%d')(dayBefore),\n casos: 0,\n muertes: 0,\n total_casos: 0,\n total_muertes: 0,\n });\n\n Object.assign(output, data[0], {\n past_week: output[output.length - 1][type]-output[output.length - 7][type], //d3.sum(output.slice(-7), d => d[type]),\n total: output[output.length - 1][`total_${type}`],\n });\n\n ['date', 'casos', 'muertes'].forEach(key => delete output[key]);\n\n return output;\n};","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1492,"value":"us_data = process_data(Array.from(d3.group(raw_state_data_argentina, d => d.date))\n .map(([date, data]) => ({\n date,\n casos: d3.sum(data, d => d.casos),\n muertes: d3.sum(data, d => d.muertes),\n })))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1258,"value":"state_data = Array.from(d3.group(raw_state_data_argentina, d => d.state))\n .map(([, rows]) => process_data(rows))\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":909,"value":"date_extent = {\n const extent = d3.extent(raw_state_data_argentina, d => d.date)\n .map(d => new Date(d));\n extent[0].setDate(extent[0].getDate() - 1);\n extent[1].setDate(extent[1].getDate() + 1);\n\n return extent;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1058,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1499,"value":"make_figure = async (title, data, width, domain) => html`

${title}

\n ${data.past_week.toLocaleString('es')} NUEVOS ${type.toUpperCase()} EN LA ULTIMA SEMANA
\n ${data.total.toLocaleString('es')} ${type.toUpperCase()} TOTALES\n

\n ${await make_chart(data, width, domain)}\n`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2385,"value":"make_figure_tests = async (title, data, width, domain) => html`

${title}

\n ${data.past_week.toLocaleString('es')} TESTS REALIZADOS EN LA ULTIMA SEMANA
\n ${data.total.toLocaleString('es')} TESTS TOTALES\n

\n ${await make_chart_tests(data, width, domain)}\n`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2700,"value":"make_figure_tests_diff = async (title, data, width, domain) => html`

${title}

\n TASA ACTUAL DE POSITIVOS ES DE ${data.last_pos_rate}%
\n TASA PROMEDIO DE POSITIVOS ES DE ${data.avg_pos_rate}%\n

\n ${await make_chart_tests_diff(data, width, domain)}\n`;","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2695,"value":"$40","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2388,"value":"$41","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2541,"value":"// The Vega legend definition.\nsymbolLegendDef = {\n return {\n type: \"symbol\",\n title: \"\",\n fill: \"symbolColor\",\n size: \"symbolSize\",\n direction: \"horizontal\",\n columns: undefined,\n rowPadding: 2,\n columnPadding: 10,\n clipHeight: +0,\n titleOrient: \"top\",\n symbolStrokeColor: '#fff',\n symbolStrokeWidth: 1\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2086,"value":"raw_state_data_argentina = {\n let state_data = []\n // For dates\n for(var d = 0; d < datos_viz.series_fechas.length;d++)\n {\n // For provincias\n for(var p = 0; p < datos_viz.series_acumulados_provincia.length;p++)\n {\n \n let day_data = {date:datos_viz.series_fechas[d],\n state:datos_viz.series_acumulados_provincia[p].nombre,\n fips: \"0\", \n casos: datos_viz.series_acumulados_provincia[p].val[d], \n muertes: datos_viz.series_muertes_acumulados_provincia[p].val[d]};\n \n state_data.push(day_data);\n \n }\n }\n \n return state_data;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2358,"value":"$42","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2727,"value":"$43","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2380,"value":"argentina_data = process_argentina_data(Array.from(d3.group(raw_argentina_data, d => d.date))\n .map(([date, data]) => ({\n date,\n tests: d3.sum(data, d => d.tests),\n tests_negativos: d3.sum(data, d => d.tests_negativos),\n tests_positivos: d3.sum(data, d => d.tests_positivos),\n contagios_totales: d3.sum(data, d => d.contagios_totales),\n diff_positivos: d3.sum(data, d => d.diff_positivos),\n diff_negativos: d3.sum(data, d => d.diff_negativos),\n diff_contagios: d3.sum(data, d => d.diff_contagios),\n diff_tests: d3.sum(data, d => d.diff_tests)\n })))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2378,"value":"$44","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2316,"value":"colores_fijos = d3.scaleOrdinal()\n .domain([\"casos\",\"muertes\"])\n .range([\"#ff5733\" ,\"#663399\"])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2327,"value":"columns = { if (width < 500) {return 2} else if (width < 800) {return 4} else {return 5} } ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2347,"value":"custom_height = { if (width < 500) {return width/3} else {return width/5} } ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"d3 = require(\"d3@5\", \"d3-array@2\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2916,"value":"function compareTotal(a, b) {\n\n let comparison = 0;\n if (a.total > b.total) {\n comparison = -1;\n } else if (a.total < b.total) {\n comparison = 1;\n }\n return comparison;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":111,"value":"import {vl} from '@vega/vega-lite-api'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1203,"value":"import { radio, select } from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2125,"value":"import {datos_viz} from \"@eiarussi/covid-19-data-loader\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2042,"value":"import {datos_csv} from \"@eiarussi/covid-19-data-loader\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2547,"value":"import {plot} from '@vega/vega-plot';","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W45","stdlib":"1"}} 35:null