3f:null 40:["$","title",null,{"children":"Covid-19 derived chart | Jeff Baumes | Observable"}] 46:T5a2,md`# Original description This notebook intends to offer a complementary view on the global number of cases of Covid-19 infection published daily by [Johns Hopkins’ CSSE](https://systems.jhu.edu/). **Scale**. By using a logarithmic scale, we can visualize more countries—even if they are to this day less infected than the province of Hubei, China whence the epidemics originated. **Coordinates system**. By setting the *x* and *y* dimensions to something else than _confirmed cases_ vs _time_, we can compare the countries’ (or provinces’) trajectories in a different space. For example, while Italy and Iran presented roughly the same numbers of cases and deaths on March 3rd, the dynamics of the infection were clearly following different patterns. **Trend lines** help limit the clutter and focus on the current dynamics in each country. *These trends are not to be taken as a model that would persist in the future.* The data is also grouped in a format that might be easier to work with than the original format. _This notebook was prepared for the [LIRIS M2i project](https://projet.liris.cnrs.fr/mi2/), which researches (among other things) visualisation tools for cumulative data. Comments and suggestions welcome!_`47:T21d0,chart = function*(options) { options = options || {}; const w = options.width || width, height = options.height || w * 0.7, axisX = options.axisX || "date", axisY = options.axisY || "confirmed", perMillionX = axisX.indexOf("per million") >= 0, perMillionY = axisY.indexOf("per million") >= 0, groupBy = ((perMillionX || perMillionY) && options.groupBy !== "country" && options.groupBy !== "province") ? "country" : (options.groupBy || "country"), useLog = options.useLog || false; const svg = d3 .create("svg") .attr("class", "chart") .attr("viewBox", [0, 0, w, height]); yield svg.node(); const maxDate = d3.timeFormat("%Y-%m-%d")( Date.now() // new Date(Date.now() - 24 * 3600 * 1000 * 1) ); const series = d3 .rollups( data.filter(d => d.ymd <= maxDate), v => ({ confirmed: d3.sum(v.filter(d => d.type === "confirmed"), d => d[valueField]) || 0, "confirmed per million": d3.sum(v.filter(d => d.type === `confirmed per million (${groupBy})`), d => d[valueField]) || 0, "deaths per million": d3.sum(v.filter(d => d.type === `death per million (${groupBy})`), d => d[valueField]) || 0, deaths: d3.sum(v.filter(d => d.type === "death"), d => d[valueField]) || 0, recovered: d3.sum(v.filter(d => d.type === "recovered"), d => d[valueField]) || 0, country: v[0].country, population: v[0].population, province: v[0].province, region: v[0].region, continent: v[0].continent, ymd: v[0].ymd, date: parseDate(v[0].ymd) }), d => groupBy === 'total' ? 'Total' : d[groupBy], d => d.ymd ) .reverse(); const x = axisX === "date" ? d3.scaleTime().domain(d3.extent(data, d => d.date)) : d3[useLog ? "scaleLog" : "scaleLinear"]().domain([ perMillionX ? 0.001 : 1, d3.max(series, ([, s]) => d3.max(s, ([, d]) => d[axisX])) ]), y = d3[useLog ? "scaleLog" : "scaleLinear"]().domain([ perMillionY ? 0.001 : 1, d3.max(series, ([, s]) => d3.max(s, ([, d]) => d[axisY])) ]); x.range([120, w - 100]); y.range([height - 90, 40]); const zero = (perMillionX || perMillionY) ? 0.0006 : .6, // where to put our zeros on a log scale zeroAxis = (perMillionX || perMillionY) ? 0.0004 : .4; // where to put the axis const line = d3.line().curve(cumulative ? d3.curveMonotoneY : d3.curveLinear); const s = svg .selectAll("g") .data(series) .join("g"); const lines = s .append("path") .attr("d", d => { let points = d[1].map(d => [ x(d[1][axisX] || zero), y(d[1][axisY] || zero) ]); let transform = useLog ? Math.log10 : d => d; let pointsRaw = d[1].map(d => [ axisX === "date" ? Math.floor(d[1].date/8.64e7) : transform(d[1][axisX] || zero), axisY === "date" ? Math.floor(d[1].date/8.64e7) : transform(d[1][axisY] || zero) ]); if (useRegression) points = d3.regressionLinear()( points.slice( Math.max(0, points.length - 1 - regressionDelay - regressionPeriod), Math.max(0, points.length -regressionDelay) ) ); d.regression = d3.regressionLinear()( pointsRaw.slice( Math.max(0, pointsRaw.length - 1 - regressionDelay - regressionPeriod), Math.max(0, pointsRaw.length - regressionDelay) ) ); return line(points); }) .style("fill", "none") .style("stroke-width", 1.5) .style("stroke", d => color(groupBy === 'total' ? 1 : d[1][0][1][groupBy])); const circles = s .style("fill", d => color(groupBy === 'total' ? 1 : d[1][0][1][groupBy])) .selectAll("circle") .data(d => (showPoints ? d[1] : [])) .join("circle"); circles .attr("r", 2.5) .attr("cx", d => x(d[1][axisX] || zero)) .attr("cy", d => y(d[1][axisY] || zero)); const labels = svg.append("g"); labels .selectAll("text") .data(series) .join("text") .text(d => { return `${ d[0].replace(/Mainland China:/, "").replace(/:$/, "") } ${useRegression ? `${ (axisX === 'date' && useLog) ? (1/d.regression.a).toFixed(3) : d.regression.a.toFixed(3) } ${ useLog ? axisX === 'date' ? `days per 10x ${axisY}` : `10x ${axisY} per 10x ${axisX}` : axisX === 'date' ? `${axisY} per day` : `${axisY} per ${axisX}` }` : ''}`; }) .attr( "transform", d => `translate(${x(d[1][d[1].length - 1][1][axisX] || zero)},${y( d[1][d[1].length - 1][1][axisY] || zero ) - 5})` ) .attr("text-anchor", "middle") .attr("dx", -6) .attr("dy", -4) .style("fill", d => color(groupBy === 'total' ? 1 : d[1][0][1][groupBy])); labels.call(occlusion); // try a few times to move occluded labels down their lines // gives them a second chance at finding a spot for (let i = 1; i < 14; i++) { labels .selectAll(".occluded") .classed("occluded", false) .attr("transform", d => { const b = d[1].slice(-i)[0]; if (b) return `translate(${x(b[1][axisX] || zero)},${y( b[1][axisY] || zero )})`; }); labels.call(occlusion); } const axis = svg.append("g"); const axisB = d3.axisBottom(x), axisL = d3.axisLeft(y); const formatN = d3.format(","), logTickFormat = d => ((Math.log10(d) - Math.floor(Math.log10(d))) === 0 ? formatN(d) : ""); if (useLog) { axisL.tickFormat(logTickFormat); if (axisX !== "date") axisB.tickFormat(logTickFormat); } axis .append("g") .call(axisB) .attr("transform", `translate(0, ${y(zeroAxis)})`) .append("text") .text(axisX === "date" ? axisX : `${cumulative ? 'cumulative' : 'daily'} ${axisX}`) .attr("fill", `currentColor`) .attr("text-anchor", `end`) .attr("transform", `translate(${x.range()[1]}, ${-2})`); axis .append("g") .call(axisL) .attr("transform", `translate(${x.range()[0] - 30}, 0)`) .append("text") .text(`${cumulative ? 'cumulative' : 'daily'} ${axisY}`) .attr("fill", `currentColor`) .attr("text-anchor", `start`) .attr("transform", `translate(${2}, ${y.range()[1]})`); const grid = svg.insert("g", "g"); grid .append("g") .call(axisL) .call(g => g .selectAll(".tick line") .attr("x2", x.range()[0]) .attr("x1", x.range()[1]) .style("stroke", "#ddd") .style("stroke-width", .5) ) .call(g => g.selectAll(".domain,text").remove()); grid .append("g") .call(axisB) .call(g => g .selectAll(".tick line") .attr("y2", y.range()[0]) .attr("y1", y.range()[1]) .style("stroke", "#ddd") .style("stroke-width", .5) ) .call(g => g.selectAll(".domain,text").remove()); const eventLines = svg.append('g'); eventLines .selectAll('line') .data(events) .join('line') .attr('x1', d => x(d.date)) .attr('x2', d => x(d.date)) .attr('y1', d => y(zeroAxis)) .attr('y2', d => y(y.domain()[1])) .style('visibility', axisX === 'date' ? 'visible' : 'hidden') .style('stroke', '#aaa') .style('stroke-width', 1); const eventLabels = svg.append('g'); eventLabels .selectAll('text') .data(events) .join('text') .text(d => d.name) .style('visibility', axisX === 'date' ? 'visible' : 'hidden') .attr('x', 4) .attr('y', -4) .attr('transform', d => `translate(${x(d.date)},${y(zeroAxis)})rotate(-90)`) .style('fill', '#aaa'); const tooltip = svg.append("g"), xy = []; circles.each(function() { xy.push([+this.getAttribute("cx"), +this.getAttribute("cy") + 2]); }); const delaunay = d3.Delaunay.from(xy); const numformat = d3.format(",d"); svg.on("touchmove mousemove", function() { const m = d3.mouse(this), i = delaunay.find(...m), dist = Math.hypot(m[0] - xy[i][0], m[1] - xy[i][1]), d = circles.data()[i]; if (dist < 10) tooltip.attr("transform", `translate(${xy[i]})`).call( callout, `${groupBy === 'total' ? 'Total' : d[1][groupBy]} ${d[0]} c: ${numformat(d[1].confirmed)} d: ${numformat(d[1].deaths)} r: ${numformat(d[1].recovered)} cpm: ${numformat(d[1]['confirmed per million'])} dpm: ${numformat(d[1]['deaths per million'])}` ); else tooltip.call(callout, null); }); svg.on("touchend mouseleave", () => tooltip.call(callout, null)); }48:T518,data = { let raw = rawData.filter( d => ( filter.includes("All") || filter.length === 0 || (filter.includes("Outside China") && d.country !== 'China') || filter.includes(d.country) ) && ( countryRank[d.country] < topPopulation ) ); raw.forEach(d => { d.continent = countryStats(d.country).continentalRegion; d.region = countryStats(d.country).statisticalRegion; }); let country = raw.map(d => { let n = {...d}; n.type = `${d.type} per million (country)`; n.caseCount = d.cases; n.cases = 1e6 * d.cases / countryStats(d.country).pop2019; n.total = 1e6 * d.total / countryStats(d.country).pop2019; return n; }); let province = raw.filter(d => provincePopulationMap[d.province] !== undefined || populationMap[d.province.replace(':','')] !== undefined).map(d => { let n = {...d}; n.type = `${d.type} per million (province)`; n.caseCount = d.cases; n.cases = 1e6 * d.cases / (provincePopulationMap[d.province] || populationMap[d.province.replace(':','')].pop2019); n.total = 1e6 * d.total / (provincePopulationMap[d.province] || populationMap[d.province.replace(':','')].pop2019); return n; }); return [...raw, ...country, ...province]; // return [...province]; }49:T39f6,populationData = `country,continentalRegion,statisticalRegion,pop2018,pop2019,change China,Asia,Eastern Asia,"1,427,647,786","1,433,783,686",+0.43% India,Asia,Southern Asia,"1,352,642,280","1,366,417,754",+1.02% US,Americas,Northern America,"327,096,265","329,064,917",+0.60% Indonesia,Asia,South-eastern Asia,"267,670,543","270,625,568",+1.10% Pakistan,Asia,Southern Asia,"212,228,286","216,565,318",+2.04% Brazil,Americas,South America,"209,469,323","211,049,527",+0.75% Nigeria,Africa,Western Africa,"195,874,683","200,963,599",+2.60% Bangladesh,Asia,Southern Asia,"161,376,708","163,046,161",+1.03% Russia,Europe,Eastern Europe,"145,734,038","145,872,256",+0.09% Mexico,Americas,Central America,"126,190,788","127,575,529",+1.10% Japan,Asia,Eastern Asia,"127,202,192","126,860,301",−0.27% Ethiopia,Africa,Eastern Africa,"109,224,414","112,078,730",+2.61% Philippines,Asia,South-eastern Asia,"106,651,394","108,116,615",+1.37% Egypt,Africa,Northern Africa,"98,423,598","100,388,073",+2.00% Vietnam,Asia,South-eastern Asia,"95,545,962","96,462,106",+0.96% Congo (Kinshasa),Africa,Middle Africa,"84,068,091","86,790,567",+3.24% Germany,Europe,Western Europe,"83,124,418","83,517,045",+0.47% Turkey,Asia,Western Asia,"82,340,088","83,429,615",+1.32% Iran,Asia,Southern Asia,"81,800,188","82,913,906",+1.36% Thailand,Asia,South-eastern Asia,"68,863,514","69,037,513",+0.25% United Kingdom,Europe,Northern Europe,"67,141,684","67,530,172",+0.58% France,Europe,Western Europe,"64,990,511","65,129,728",+0.21% Italy,Europe,Southern Europe,"60,627,291","60,550,075",−0.13% South Africa,Africa,Southern Africa,"57,792,518","58,558,270",+1.33% Tanzania,Africa,Eastern Africa,"56,313,438","58,005,463",+3.00% Myanmar,Asia,South-eastern Asia,"53,708,320","54,045,420",+0.63% Kenya,Africa,Eastern Africa,"51,392,565","52,573,973",+2.30% "Korea, South",Asia,Eastern Asia,"51,171,706","51,225,308",+0.10% Colombia,Americas,South America,"49,661,048","50,339,443",+1.37% Spain,Europe,Southern Europe,"46,692,858","46,736,776",+0.09% Argentina,Americas,South America,"44,361,150","44,780,677",+0.95% Uganda,Africa,Eastern Africa,"42,729,036","44,269,594",+3.61% Ukraine,Europe,Eastern Europe,"44,246,156","43,993,638",−0.57% Algeria,Africa,Northern Africa,"42,228,408","43,053,054",+1.95% Sudan,Africa,Northern Africa,"41,801,533","42,813,238",+2.42% Iraq,Asia,Western Asia,"38,433,600","39,309,783",+2.28% Afghanistan,Asia,Southern Asia,"37,171,921","38,041,754",+2.34% Poland,Europe,Eastern Europe,"37,921,592","37,887,768",−0.09% Canada,Americas,Northern America,"37,074,562","37,411,047",+0.91% Morocco,Africa,Northern Africa,"36,029,093","36,471,769",+1.23% Saudi Arabia,Asia,Western Asia,"33,702,756","34,268,528",+1.68% Uzbekistan,Asia,Central Asia,"32,476,244","32,981,716",+1.56% Peru,Americas,South America,"31,989,260","32,510,453",+1.63% Malaysia,Asia,South-eastern Asia,"31,528,033","31,949,777",+1.34% Angola,Africa,Middle Africa,"30,809,787","31,825,295",+3.30% Mozambique,Africa,Eastern Africa,"29,496,004","30,366,036",+2.95% Yemen,Asia,Western Asia,"28,498,683","29,161,922",+2.33% Ghana,Africa,Western Africa,"28,206,728","28,833,629",+2.22% Nepal,Asia,Southern Asia,"28,095,714","28,608,710",+1.83% Venezuela,Americas,South America,"28,887,118","28,515,829",−1.29% Madagascar,Africa,Eastern Africa,"26,262,313","26,969,307",+2.69% North Korea,Asia,Eastern Asia,"25,549,604","25,666,161",+0.46% "Cote d'Ivoire",Africa,Western Africa,"25,069,230","25,716,544",+2.58% Cameroon,Africa,Middle Africa,"25,216,267","25,876,380",+2.62% Australia,Oceania,Australia and New Zealand,"24,898,152","25,203,198",+1.23% Taiwan*,Asia,Eastern Asia,"23,726,460","23,773,876",+0.20% Niger,Africa,Western Africa,"22,442,822","23,310,715",+3.87% Sri Lanka,Asia,Southern Asia,"21,228,763","21,323,733",+0.45% Burkina Faso,Africa,Western Africa,"19,751,466","20,321,378",+2.89% Mali,Africa,Western Africa,"19,077,749","19,658,031",+3.04% Romania,Europe,Eastern Europe,"19,506,114","19,364,557",−0.73% Malawi,Africa,Eastern Africa,"18,143,217","18,628,747",+2.68% Chile,Americas,South America,"18,729,160","18,952,038",+1.19% Kazakhstan,Asia,Central Asia,"18,319,618","18,551,427",+1.27% Zambia,Africa,Eastern Africa,"17,351,708","17,861,030",+2.94% Guatemala,Americas,Central America,"17,247,849","17,581,472",+1.93% Ecuador,Americas,South America,"17,084,358","17,373,662",+1.69% Netherlands,Europe,Western Europe,"17,059,560","17,097,130",+0.22% Syria,Asia,Western Asia,"16,945,057","17,070,135",+0.74% Cambodia,Asia,South-eastern Asia,"16,249,792","16,486,542",+1.46% Senegal,Africa,Western Africa,"15,854,323","16,296,364",+2.79% Chad,Africa,Middle Africa,"15,477,729","15,946,876",+3.03% Somalia,Africa,Eastern Africa,"15,008,226","15,442,905",+2.90% Zimbabwe,Africa,Eastern Africa,"14,438,802","14,645,468",+1.43% Guinea,Africa,Western Africa,"12,414,293","12,771,246",+2.88% Rwanda,Africa,Eastern Africa,"12,301,970","12,626,950",+2.64% Benin,Africa,Western Africa,"11,485,044","11,801,151",+2.75% Tunisia,Africa,Northern Africa,"11,565,201","11,694,719",+1.12% Belgium,Europe,Western Europe,"11,482,178","11,539,328",+0.50% Bolivia,Americas,South America,"11,353,142","11,513,100",+1.41% Cuba,Americas,Caribbean,"11,338,134","11,333,483",−0.04% Haiti,Americas,Caribbean,"11,123,178","11,263,770",+1.26% South Sudan,Africa,Eastern Africa,"10,975,927","11,062,113",+0.79% Burundi,Africa,Eastern Africa,"10,524,117","10,864,245",+3.23% Dominican Republic,Americas,Caribbean,"10,627,141","10,738,958",+1.05% Czechia,Europe,Eastern Europe,"10,665,677","10,689,209",+0.22% Greece,Europe,Southern Europe,"10,522,246","10,473,455",−0.46% Portugal,Europe,Southern Europe,"10,256,193","10,226,187",−0.29% Jordan,Asia,Western Asia,"9,965,318","10,101,694",+1.37% Azerbaijan,Asia,Western Asia,"9,949,537","10,047,718",+0.99% Sweden,Europe,Northern Europe,"9,971,638","10,036,379",+0.65% United Arab Emirates,Asia,Western Asia,"9,630,959","9,770,529",+1.45% Honduras,Americas,Central America,"9,587,522","9,746,117",+1.65% Hungary,Europe,Eastern Europe,"9,707,499","9,684,679",−0.24% Belarus,Europe,Eastern Europe,"9,452,617","9,452,411",0.00% Tajikistan,Asia,Central Asia,"9,100,835","9,321,018",+2.42% Austria,Europe,Western Europe,"8,891,388","8,955,102",+0.72% Papua New Guinea,Oceania,Melanesia,"8,606,323","8,776,109",+1.97% Serbia,Europe,Southern Europe,"8,802,754","6,963,764",−0.35% Kosovo,Europe,Southern Europe,"8,802,754","1,810,463",−0.35% Switzerland,Europe,Western Europe,"8,525,611","8,591,365",+0.77% Israel,Asia,Western Asia,"8,381,516","8,519,377",+1.64% Togo,Africa,Western Africa,"7,889,093","8,082,366",+2.45% Sierra Leone,Africa,Western Africa,"7,650,150","7,813,215",+2.13% Hong Kong,Asia,Eastern Asia,"7,371,730","7,436,154",+0.87% Laos,Asia,South-eastern Asia,"7,061,507","7,169,455",+1.53% Paraguay,Americas,South America,"6,956,066","7,044,636",+1.27% Bulgaria,Europe,Eastern Europe,"7,051,608","7,000,119",−0.73% Lebanon,Asia,Western Asia,"6,859,408","6,855,713",−0.05% Libya,Africa,Northern Africa,"6,678,559","6,777,452",+1.48% Nicaragua,Americas,Central America,"6,465,501","6,545,502",+1.24% El Salvador,Americas,Central America,"6,420,746","6,453,553",+0.51% Kyrgyzstan,Asia,Central Asia,"6,304,030","6,415,850",+1.77% Turkmenistan,Asia,Central Asia,"5,850,901","5,942,089",+1.56% Singapore,Asia,South-eastern Asia,"5,757,499","5,804,337",+0.81% Denmark,Europe,Northern Europe,"5,752,126","5,771,876",+0.34% Finland,Europe,Northern Europe,"5,522,576","5,532,156",+0.17% Slovakia,Europe,Eastern Europe,"5,453,014","5,457,013",+0.07% Congo (Brazzaville),Africa,Middle Africa,"5,244,359","5,380,508",+2.60% Norway,Europe,Northern Europe,"5,337,962","5,378,857",+0.77% Costa Rica,Americas,Central America,"4,999,441","5,047,561",+0.96% Palestine,Asia,Western Asia,"4,862,979","4,981,420",+2.44% Oman,Asia,Western Asia,"4,829,473","4,974,986",+3.01% Liberia,Africa,Western Africa,"4,818,973","4,937,374",+2.46% Ireland,Europe,Northern Europe,"4,818,690","4,882,495",+1.32% New Zealand,Oceania,Australia and New Zealand,"4,743,131","4,783,063",+0.84% Central African Republic,Africa,Middle Africa,"4,666,368","4,745,185",+1.69% Mauritania,Africa,Western Africa,"4,403,313","4,525,696",+2.78% Panama,Americas,Central America,"4,176,869","4,246,439",+1.67% Kuwait,Asia,Western Asia,"4,137,312","4,207,083",+1.69% Croatia,Europe,Southern Europe,"4,156,405","4,130,304",−0.63% Moldova,Europe,Eastern Europe,"4,051,944","4,043,263",−0.21% Georgia,Asia,Western Asia,"4,002,942","3,996,765",−0.15% Eritrea,Africa,Eastern Africa,"3,452,786","3,497,117",+1.28% Uruguay,Americas,South America,"3,449,285","3,461,734",+0.36% Bosnia and Herzegovina,Europe,Southern Europe,"3,323,925","3,301,000",−0.69% Mongolia,Asia,Eastern Asia,"3,170,216","3,225,167",+1.73% Armenia,Asia,Western Asia,"2,951,745","2,957,731",+0.20% Jamaica,Americas,Caribbean,"2,934,847","2,948,279",+0.46% Puerto Rico,Americas,Caribbean,"3,039,596","2,933,408",−3.49% Albania,Europe,Southern Europe,"2,882,740","2,880,917",−0.06% Qatar,Asia,Western Asia,"2,781,682","2,832,067",+1.81% Lithuania,Europe,Northern Europe,"2,801,264","2,759,627",−1.49% Namibia,Africa,Southern Africa,"2,448,301","2,494,530",+1.89% Gambia,Africa,Western Africa,"2,280,094","2,347,706",+2.97% Botswana,Africa,Southern Africa,"2,254,068","2,303,697",+2.20% Gabon,Africa,Middle Africa,"2,119,275","2,172,579",+2.52% Lesotho,Africa,Southern Africa,"2,108,328","2,125,268",+0.80% North Macedonia,Europe,Southern Europe,"2,082,957","2,083,459",+0.02% Slovenia,Europe,Southern Europe,"2,077,837","2,078,654",+0.04% Guinea-Bissau,Africa,Western Africa,"1,874,303","1,920,922",+2.49% Latvia,Europe,Northern Europe,"1,928,459","1,906,743",−1.13% Bahrain,Asia,Western Asia,"1,569,446","1,641,172",+4.57% Trinidad and Tobago,Americas,Caribbean,"1,389,843","1,394,973",+0.37% Equatorial Guinea,Africa,Middle Africa,"1,308,975","1,355,986",+3.59% Estonia,Europe,Northern Europe,"1,322,920","1,325,648",+0.21% Timor-Leste,Asia,South-eastern Asia,"1,267,974","1,293,119",+1.98% East Timor,Asia,South-eastern Asia,"1,267,974","1,293,119",+1.98% Mauritius,Africa,Eastern Africa,"1,189,265","1,198,575",+0.78% Cyprus,Asia,Western Asia,"1,170,125","1,179,551",+0.81% Eswatini,Africa,Southern Africa,"1,136,281","1,148,130",+1.04% Djibouti,Africa,Eastern Africa,"958,923","973,560",+1.53% Fiji,Oceania,Melanesia,"883,483","889,953",+0.73% Reunion,Africa,Eastern Africa,"882,526","888,927",+0.73% Comoros,Africa,Eastern Africa,"832,322","850,886",+2.23% Guyana,Americas,South America,"779,006","782,766",+0.48% Bhutan,Asia,Southern Asia,"754,388","763,092",+1.15% Solomon Islands,Oceania,Melanesia,"652,857","669,823",+2.60% Macau,Asia,Eastern Asia,"631,636","640,445",+1.39% Montenegro,Europe,Southern Europe,"627,809","627,987",+0.03% Luxembourg,Europe,Western Europe,"604,245","615,729",+1.90% Western Sahara,Africa,Northern Africa,"567,402","582,463",+2.65% Suriname,Americas,South America,"575,990","581,372",+0.93% Cabo Verde,Africa,Western Africa,"543,767","549,935",+1.13% Cape Verde,Africa,Western Africa,"543,767","549,935",+1.13% Maldives,Asia,Southern Asia,"515,696","530,953",+2.96% Guadeloupe,Americas,Caribbean,"446,928","447,905",+0.22% Malta,Europe,Southern Europe,"439,248","440,372",+0.26% Brunei,Asia,South-eastern Asia,"428,963","433,285",+1.01% Belize,Americas,Central America,"383,071","390,353",+1.90% Bahamas,Americas,Caribbean,"385,637","389,482",+1.00% Martinique,Americas,Caribbean,"375,673","375,554",−0.03% Iceland,Europe,Northern Europe,"336,713","339,031",+0.69% Vanuatu,Oceania,Melanesia,"292,680","299,882",+2.46% Barbados,Americas,Caribbean,"286,641","287,025",+0.13% New Caledonia,Oceania,Melanesia,"279,993","282,750",+0.98% French Guiana,Americas,South America,"275,713","282,731",+2.55% French Polynesia,Oceania,Polynesia,"277,679","279,287",+0.58% Mayotte,Africa,Eastern Africa,"259,531","266,150",+2.55% São Tomé and Príncipe,Africa,Middle Africa,"211,028","215,056",+1.91% Samoa,Oceania,Polynesia,"196,129","197,097",+0.49% Saint Lucia,Americas,Caribbean,"181,889","182,790",+0.50% Guernsey and Jersey,Europe,Northern Europe,"170,499","172,259",+1.03% Guam,Oceania,Micronesia,"165,768","167,294",+0.92% Curaçao,Americas,Caribbean,"162,752","163,424",+0.41% Kiribati,Oceania,Micronesia,"115,847","117,606",+1.52% F.S. Micronesia,Oceania,Micronesia,"112,640","113,815",+1.04% Grenada,Americas,Caribbean,"111,454","112,003",+0.49% Tonga,Oceania,Polynesia,"110,589","110,940",+0.32% Saint Vincent and the Grenadines,Americas,Caribbean,"110,211","110,589",+0.34% Aruba,Americas,Caribbean,"105,845","106,314",+0.44% U.S. Virgin Islands,Americas,Caribbean,"104,680","104,578",−0.10% Seychelles,Africa,Eastern Africa,"97,096","97,739",+0.66% Antigua and Barbuda,Americas,Caribbean,"96,286","97,118",+0.86% Isle of Man,Europe,Northern Europe,"84,077","84,584",+0.60% Andorra,Europe,Southern Europe,"77,006","77,142",+0.18% Dominica,Americas,Caribbean,"71,625","71,808",+0.26% Cayman Islands,Americas,Caribbean,"64,174","64,948",+1.21% Bermuda,Americas,Northern America,"62,756","62,506",−0.40% Marshall Islands,Oceania,Micronesia,"58,413","58,791",+0.65% Greenland,Americas,Northern America,"56,564","56,672",+0.19% Northern Mariana Islands,Oceania,Micronesia,"56,882","56,188",−1.22% American Samoa,Oceania,Polynesia,"55,465","55,312",−0.28% Saint Kitts and Nevis,Americas,Caribbean,"52,441","52,823",+0.73% Faroe Islands,Europe,Northern Europe,"48,497","48,678",+0.37% Sint Maarten,Americas,Caribbean,"41,940","42,388",+1.07% Monaco,Europe,Western Europe,"38,682","38,964",+0.73% Turks and Caicos Islands,Americas,Caribbean,"37,665","38,191",+1.40% Liechtenstein,Europe,Western Europe,"37,910","38,019",+0.29% San Marino,Europe,Southern Europe,"33,785","33,860",+0.22% Gibraltar,Europe,Southern Europe,"33,718","33,701",−0.05% British Virgin Islands,Americas,Caribbean,"29,802","30,030",+0.77% Caribbean Netherlands,Americas,Caribbean,"25,711","25,979",+1.04% Palau,Oceania,Micronesia,"17,907","18,008",+0.56% Cook Islands,Oceania,Polynesia,"17,518","17,548",+0.17% Anguilla,Americas,Caribbean,"14,731","14,869",+0.94% Tuvalu,Oceania,Polynesia,"11,508","11,646",+1.20% Wallis and Futuna,Oceania,Polynesia,"11,661","11,432",−1.96% Nauru,Oceania,Micronesia,"10,670","10,756",+0.81% "Saint Helena, Ascension and Tristan da Cunha",Africa,Western Africa,"6,035","6,059",+0.40% Saint Pierre and Miquelon,Americas,Northern America,"5,849","5,822",−0.46% Montserrat,Americas,Caribbean,"4,993","4,989",−0.08% Falkland Islands,Americas,South America,"3,234","3,377",+4.42% Niue,Oceania,Polynesia,"1,620","1,615",−0.31% Tokelau,Oceania,Polynesia,"1,319","1,340",+1.59% Holy See,Europe,Southern Europe,801,799,−0.25% Diamond Princess,Asia,Eastern Asia,"3,700","3,700",0.00% World,,,"7,631,091,040","7,713,468,100",+1.08% `4a:T17cc,provincePopulationData = `rank2019,rank2010,state,pop2019,pop2010,percentChange,"Absolute change, 2010-2019","Total seats in the U.S. House of Representatives, 2013–2023","Estimated population per electoral vote, 2019[note 2]","Estimated population per House seat, 2019","Census population per House seat, 2010","Percent of the total U.S. population, 2018[note 3]" 1,1,California,"39,512,223","37,254,523",6.1%,"+2,257,700",53,"718,404","745,514","702,885",11.96% 2,2,Texas,"28,995,881","25,145,561",15.3%,"+3,850,320",36,"763,050","805,441","698,503",8.68% 3,4,Florida,"21,477,737","18,801,310",14.2%,"+2,676,427",27,"740,611","795,472","696,468",6.44% 4,3,New York,"19,453,561","19,378,102",0.4%,"+75,459",27,"670,812","720,502","717,707",5.91% 5,6,Pennsylvania,"12,801,989","12,702,379",0.8%,"+99,610",18,"640,099","711,222","705,715",3.87% 6,5,Illinois,"12,671,821","12,830,632",-1.2%,"−158,811",18,"633,591","703,990","712,864",3.85% 7,7,Ohio,"11,689,100","11,536,504",1.3%,"+152,596",16,"649,394","730,569","721,032",3.53% 8,9,Georgia,"10,617,423","9,687,653",9.6%,"+929,770",14,"663,589","758,387","691,975",3.18% 9,10,North Carolina,"10,488,084","9,535,483",10.0%,"+952,601",13,"699,206","806,776","733,498",3.14% 10,8,Michigan,"9,986,857","9,883,640",1.0%,"+103,217",14,"624,179","713,347","705,974",3.02% 11,11,New Jersey,"8,882,190","8,791,894",1.0%,"+90,296",12,"634,442","740,183","732,658",2.69% 12,12,Virginia,"8,535,519","8,001,024",6.7%,"+534,495",11,"656,578","775,956","727,366",2.58% 13,13,Washington,"7,614,893","6,724,540",13.2%,"+890,353",10,"634,574","751,489","672,454",2.28% 14,16,Arizona,"7,278,717","6,392,017",13.9%,"+886,700",9,"661,702","808,746","710,224",2.17% 15,14,Massachusetts,"6,949,503","6,547,629",5.3%,"+344,874",9,"626,591","765,834","727,514",2.09% 16,17,Tennessee,"6,833,174","6,346,105",7.6%,"+483,069",9,"620,834","758,797","705,123",2.05% 17,15,Indiana,"6,732,219","6,483,802",3.8%,"+248,417",9,"612,020","748,024","720,422",2.02% 18,18,Missouri,"6,137,428","5,988,927",2.5%,"+148,501",8,"613,743","767,179","748,615",1.85% 19,19,Maryland,"6,045,680","5,773,552",4.7%,"+272,128",8,"604,568","755,710","721,694",1.83% 20,20,Wisconsin,"5,822,434","5,686,986",2.4%,"+135,448",8,"582,243","727,804","710,873",1.76% 21,22,Colorado,"5,758,736","5,029,196",14.5%,"+729,540",7,"639,860","822,677","720,704",1.72% 22,21,Minnesota,"5,639,632","5,303,925",6.3%,"+335,707",8,"563,963","704,954","662,991",1.70% 23,24,South Carolina,"5,148,714","4,625,364",11.3%,"+523,350",7,"572,079","735,531","660,766",1.54% 24,23,Alabama,"4,903,185","4,779,736",2.6%,"+123,449",7,"544,798","700,455","682,819",1.48% 25,25,Louisiana,"4,648,794","4,533,372",2.5%,"+115,422",6,"581,099","774,799","755,562",1.41% 26,26,Kentucky,"4,467,673","4,339,367",3.0%,"+128,306",6,"558,459","744,612","723,228",1.35% 27,27,Oregon,"4,217,737","3,831,074",10.1%,"+386,663",5,"602,534","843,547","766,215",1.27% 28,28,Oklahoma,"3,956,971","3,751,351",5.5%,"+205,620",5,"565,282","791,394","750,270",1.19% 29,30,Connecticut,"3,565,287","3,574,097",-0.2%,"−8,810",5,"509,327","713,057","714,824",1.08% 30,35,Utah,"3,205,958","2,763,885",16.0%,"+442,073",4,"534,326","801,490","690,972",0.96% 32,31,Iowa,"3,155,070","3,046,355",3.6%,"+108,715",4,"525,845","788,768","761,717",0.95% 31,29,Puerto Rico,"3,193,694","3,725,789",-14.3%,"−532,095",1 (non-voting),—,"3,193,694","3,725,789",0.97% 33,36,Nevada,"3,080,156","2,700,551",14.1%,"+379,605",4,"513,359","770,039","675,173",0.92% 34,33,Arkansas,"3,017,825","2,915,918",3.5%,"+101,886",4,"502,967","754,451","728,990",0.91% 35,32,Mississippi,"2,976,149","2,967,297",0.3%,"+8,852",4,"496,024","744,037","742,026",0.90% 36,34,Kansas,"2,913,314","2,853,118",2.1%,"+60,196",4,"485,552","728,329","713,280",0.88% 37,37,New Mexico,"2,096,829","2,059,179",1.8%,"+37,650",3,"419,366","698,943","686,393",0.63% 38,39,Nebraska,"1,934,408","1,826,341",5.9%,"+108,067",3,"386,882","644,803","608,780",0.58% 39,40,Idaho,"1,792,065","1,567,582",13.9%,"+218,483",2,"446,516","893,033","783,826",0.53% 40,38,West Virginia,"1,787,147","1,852,994",-3.3%,"−60,820",3,"358,435","597,391","617,670",0.55% 41,41,Hawaii,"1,415,872","1,360,301",4.1%,"+55,571",2,"353,968","707,936","680,151",0.43% 42,43,New Hampshire,"1,359,711","1,316,470",3.3%,"+43,241",2,"339,928","679,856","658,233",0.41% 43,42,Maine,"1,344,212","1,328,361",1.2%,"+15,851",2,"336,053","672,106","664,181",0.40% 44,45,Montana,"1,068,778","989,415",8.0%,"+79,363",1,"356,259","1,068,778","989,417",0.32% 45,44,Rhode Island,"1,059,361","1,052,567",0.6%,"+6,794",2,"264,840","529,681","526,466",0.32% 46,46,Delaware,"973,764","897,934",8.4%,"+75,830",1,"324,588","973,764","897,934",0.29% 47,47,South Dakota,"884,659","814,180",8.7%,"+70,479",1,"294,886","884,659","814,180",0.27% 48,49,North Dakota,"762,062","672,591",13.3%,"+89,471",1,"254,021","762,062","672,591",0.23% 49,48,Alaska,"731,545","710,231",3.0%,"+21,314",1,"243,848","731,545","710,231",0.22% 50,51,District of Columbia,"705,749","601,723",17.3%,"+104,026",1 (non-voting),"235,250",—,—,0.21% 51,50,Vermont,"623,989","625,741",-0.2%,"-1,752",1,"207,996","623,989","625,741",0.19% 52,52,Wyoming,"578,759","563,626",2.7%,"+15,133",1,"192,920","578,759","563,626",0.17% 53,53,Guam,"165,718","159,358[4]",4.0%,"+6,360",1 (non-voting),,—,—,0.05% 54,54,U.S. Virgin Islands,"104,914","106,405[5]",-1.4%,"−1,491",1 (non-voting),,—,—,0.03% 55,55,American Samoa,"55,641","55,519[6]",0.22%,+122,1 (non-voting),,—,—,0.02% 56,56,Northern Mariana Islands,"55,194","53,883[7]",2.4%,"+1,311",1 (non-voting),,—,—,0.02% —,—,Contiguous United States,"325,386,357","306,675,006",6.2%,"+19,011,351",432,"616,262","753,209","708,285",98.38% —,—,The fifty states,"327,533,795","308,143,836",6.3%,"+19,389,959",435,"612,213","752,951","708,405",99.02% —,—,Fifty states + D.C.,"328,239,523","308,745,538",6.3%,"+19,493,985",435 (+ 1 non-voting),"610,111",—,—,99.24% —,—,Total U.S. (including D.C. and territories),"331,814,684","312,846,492",5.7%,"+17,897,562",435 (+ 6 non-voting),,—,—,100.00% `4b:[] 44:{"id":"f7c1ffcb03550eff","slug":"covid-19-derived-chart","title":"Covid-19 derived chart","thumbnail":"89e9737177784e8b66e2e229ba742efb69896b91c3fb80219a687e4238831b92","access_level":"public","create_time":"$D2020-03-15T17:31:08.445Z","update_time":"$D2020-03-30T16:34:41.692Z","view_time":null,"first_public_time":"$D2020-03-15T20:38:32.850Z","has_unpublished_edits":null,"fork":{"id":"b6d5370c0863b90e","slug":"covid-19-derived-chart","title":"Covid-19 derived chart","owner":{"id":"da2a32154638c457","login":"fil","name":"Fil"}},"stars":2,"starred":false,"owner":{"type":"team","id":"e2d656a3355b8613","login":"jeffbaumes","name":"Jeff Baumes","avatar_url":"https://avatars-next.observableusercontent.com/d6a8e1a24059997c2c6b6b13f9e886f9c8b779b2eef91ecb0ea60d93ce69518e","tier":"starter_2024","bio":"Producing open source analytics and vis @Kitware.","following":false},"version":1039,"publish_version":1039,"role":null,"authored":false,"authors":[{"type":"individual","id":"431f4e2db5423400","login":"jeffbaumes","name":"Jeff Baumes","avatar_url":"https://avatars-next.observableusercontent.com/d6a8e1a24059997c2c6b6b13f9e886f9c8b779b2eef91ecb0ea60d93ce69518e","tier":null,"bio":"Producing open source analytics and vis @Kitware.","following":false}],"members":[{"type":"individual","id":"431f4e2db5423400","login":"jeffbaumes","name":"Jeff Baumes","avatar_url":"https://avatars-next.observableusercontent.com/d6a8e1a24059997c2c6b6b13f9e886f9c8b779b2eef91ecb0ea60d93ce69518e","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":2,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Covid-19 derived chart","cells":[{"id":710,"value":"md`# Covid-19 derived chart\n\nNote that trend line labels are *not to be taken literally*. They are very likely *not* the best way to interpret these numbers, and do not take into account many important considerations. Trends should *not* to be expected to follow forward at the same rate into the future.\n\n[@jeffbaumes](/@jeffbaumes) updates:\n* Trend line interpretation text\n* Historical trend line\n* Filter by country\n* Group by total\n* Include \"per million\" dimensions using Wikipedia country and US state population\n* Daily or cumulative\n* Group by continent or statistical region\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":0,"value":"$46","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":832,"value":"md`\n**Note:** Selecting \"per million\" dimensions only work when grouping by country or province (currently only US province populations are available).\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":376,"value":"html`

\n\n${viewof axisX}\n${viewof axisY}\n${viewof groupBy}\n${viewof cumulative}\n${viewof useLog}\n${viewof showPoints}\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":874,"value":"html`

\n\n${viewof filter}\n${viewof topPopulation}\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":597,"value":"html`

\n\n${viewof useRegression}\n${viewof regressionPeriod}\n${viewof regressionDelay}\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":481,"value":"chart({ axisX, axisY, groupBy, useLog })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":19,"value":"$47","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":451,"value":"md`## Data\n\nThe dataset is fetched directly from Johns Hopkins’ CSSE [dashboard](https://gisanddata.maps.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6) github repo. This notebook makes of course no claim re: the accuracy of the numbers.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2,"value":"import { data as rawData } from \"@fil/ncov2019-data\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":652,"value":"countries = rawData.map(d => d.country).filter((value, index, self) => self.indexOf(value) === index).sort();","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":902,"value":"countryRank = {\n const rank = {};\n countries.sort((a, b) => countryStats(b).pop2019 - countryStats(a).pop2019).forEach((d, i) => rank[d] = i);\n return rank;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":637,"value":"$48","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":733,"value":"events = [\n // {\n // name: 'China begins lockdown',\n // date: parseDate('2020-01-23'),\n // },\n {\n name: 'WHO declares pandemic',\n date: parseDate('2020-03-11'),\n },\n // {\n // name: 'US declares national emergency',\n // date: parseDate('2020-03-13'),\n // },\n]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":892,"value":"md`2019 population data from [Wikipedia](https://en.wikipedia.org/wiki/List_of_countries_by_population_%28United_Nations%29). A few country names were adjusted to match the JHU dataset.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":758,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":760,"value":"population = d3.csvParse(populationData, d => { d.pop2018 = +(d.pop2018.replace(/,/g,'')); d.pop2019 = +(d.pop2019.replace(/,/g,'')); return d; });","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":769,"value":"populationMap = {\n let m = {};\n population.forEach(d => m[d.country] = d);\n return m;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":885,"value":"mismatchedCountries = countries.filter(d => populationMap[d] === undefined)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":931,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":934,"value":"provincePopulation = d3.csvParse(provincePopulationData, d => { d.pop2019 = +(d.pop2019.replace(/,/g,'')); return d; });","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":936,"value":"provincePopulationMap = {\n const map = {};\n provincePopulation.forEach(d => map[`US:${d.state}`] = d.pop2019);\n return map;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1030,"value":"function countryStats(country) {\n if (populationMap[country] !== undefined) {\n return populationMap[country];\n }\n return {\n pop2019: 1e3,\n statisticalRegion: 'unknown', \n continentalRegion: 'unknown',\n };\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":381,"value":"md`## Inputs`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":647,"value":"viewof filter = select({\n options: [\"All\", \"Outside China\", ...countries],\n value: \"all\",\n description: \"Countries\",\n multiple: true,\n size: 10\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":862,"value":"viewof topPopulation = slider({\n min: 1,\n max: 166,\n value: 166,\n step: 1,\n description: \"N most populous countries\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":146,"value":"viewof axisY = select({\n options: [\"confirmed\", \"deaths\", \"recovered\", \"confirmed per million\", \"deaths per million\"],\n value: \"confirmed\",\n description: \"Y dimension\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":208,"value":"viewof axisX = select({\n options: [\"date\", \"confirmed\", \"deaths\", \"recovered\", \"confirmed per million\", \"deaths per million\"],\n value: \"date\",\n description: \"X dimension\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":200,"value":"viewof groupBy = select({\n options: [\"country\", \"province\", \"continent\", \"region\", \"total\"],\n description: \"Group by\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":980,"value":"viewof cumulative = checkbox({\n options: [\"Cumulative\"],\n value: [\"Cumulative\"]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":986,"value":"valueField = cumulative ? 'total' : 'cases';","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":369,"value":"viewof useLog = checkbox({\n options: [\"Use log scales\"],\n value: [\"Use log scales\"]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":595,"value":"viewof showPoints= checkbox({\n options: [\"Show points\"],\n value: [\"Show points\"]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":594,"value":"viewof useRegression = checkbox({\n options: [\"Trend lines\"],\n value: []\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":593,"value":"viewof regressionPeriod = slider({\n min: 3,\n max: 30,\n value: 0,\n step: 1,\n description: \"Days\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":697,"value":"viewof regressionDelay = slider({\n min: 0,\n max: 60,\n value: 0,\n step: 1,\n description: \"Days from latest\"\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":389,"value":"md`## Utilities`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":64,"value":"color = (groupBy,\nd3.scaleOrdinal(\n d3.schemeCategory10.concat(d3.schemeSet2).concat(d3.schemeDark2)\n))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":277,"value":"parseDate = d3.timeParse(\"%Y-%m-%d\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"d3 = require(\"d3@5\", \"d3-array@2\", \"d3-delaunay@5\", \"d3-regression\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":148,"value":"import { checkbox, select, slider } from \"@jashkenas/inputs\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":243,"value":"import { occlusion } with { d3 } from \"@fil/occlusion\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":249,"value":"html``","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":536,"value":"import { callout } from \"@d3/line-chart-with-tooltip\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":621,"value":"import { Scrubber } from \"@mbostock/scrubber\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4b","stdlib":"1"}} 45:"$44:body"