2b:null 2c:["$","title",null,{"children":"Covid Charts | alastairorchard | Observable"}] 31:T450,viewof national_chart = vl.data(final_world_data .filter(d => +d["new_cases"] > 0) .filter(d => +d["new_deaths"] > 0) .filter(d => d.location === selectedCountry) ) .title(selectedCountry + " Daily Cases & Deaths") .layer( vl.markBar() .encode( vl.x().fieldT("date") .scale({domain: ["2020-02-01",Date()]}) .axis({grid: false, tickSize: 6, labelColor: '#929292' }), vl.y().sum("new_cases") .title("Daily Cases in Blue"), // vl.tooltip(['new_cases']) ), vl.markLine() .encode( vl.x().fieldT("date") .title(null), vl.y().sum("new_deaths") .title(null), vl.color({ value: 'darkred' }) ), vl.markArea({opacity: 0.3}) .encode( vl.x().fieldT("date") .title(null), vl.y().sum("new_deaths") .title("Daily Deaths in Red"), vl.color({ value: 'darkred' }) ) ) .width(width*0.85) .height(width*0.6) .resolve({scale:{y:'independent'}}) .render()32:T78c,viewof mortality_chart = { var location if(selectedCountry == 'United Kingdom') { location = UK_Region} else {location = selectedCountry} if(mortalityCodes.filter(d => d.country == location)[0] === undefined) { return "No mortality data available for this country" } { var chart = vl.data(mortalityByCountry(location).data2016.filter(d => d.Sex == mortalitySex) ) .title(location + " Excess Mortality") .layer( vl.markLine() .encode( vl.x().fieldQ("Week") .axis({grid: false, tickSize: 6, labelColor: '#929292' }), vl.y().fieldQ(mortalityAge), vl.color({ value: 'grey' }) ), vl.markLine() .data(mortalityByCountry(location).data2017.filter(d => d.Sex == mortalitySex)) .encode( vl.x().fieldQ("Week") .axis({grid: false, tickSize: 6, labelColor: '#929292' }), vl.y().fieldQ(mortalityAge), vl.color({ value: 'grey' }) ), vl.markLine() .data(mortalityByCountry(location).data2018.filter(d => d.Sex == mortalitySex)) .encode( vl.x().fieldQ("Week") .axis({grid: false, tickSize: 6, labelColor: '#929292' }), vl.y().fieldQ(mortalityAge), vl.color({ value: 'grey' }) ), vl.markLine() .data(mortalityByCountry(location).data2019.filter(d => d.Sex == mortalitySex)) .encode( vl.x().fieldQ("Week") .axis({grid: false, tickSize: 6, labelColor: '#929292' }), vl.y().fieldQ(mortalityAge), vl.color({ value: 'grey' }) ), vl.markLine() .data(mortalityByCountry(location).data2020.filter(d => d.Sex == mortalitySex)) .encode( vl.x().fieldQ("Week") .axis({grid: false, tickSize: 6, labelColor: '#929292' }), vl.y().fieldQ(mortalityAge), vl.color({ value: 'red' }) ), vl.markRule({opacity:0.5, color:'green'}) .data(mortalityWeek) .encode( vl.x().fieldQ('current') ) ) .width(width*0.9) .height(width*0.6) .render() return chart } }33:T433,viewof ICU_chart = { if(icuItalyRegionalData .filter(d => d.country === selectedCountry).length > 0) { const brush = vl.selectInterval().encodings('x'); const x = vl.x().fieldT('date').title(null); const base = vl["mark" + curveType]() .encode( vl.x().fieldT("date") .scale({domain: ["2020-02-01",Date()]}) .axis({grid: false, tickSize: 6, labelColor: '#929292' }) .title(null), vl.y().fieldQ("percentageBedsOccupied").scale({type: country_dist_type}) .title(null), vl.color().fieldN("region"), vl.tooltip(['region']) ) .height(width*0.6) .width(width*0.8); return vl.data(icuItalyRegionalData .filter(d => d.country === selectedCountry && +d["percentageBedsOccupied"] > 0)) .title("ICU Occupation by Region " + " (" + country_dist_type + " scale) - zoom using the lower chart") .vconcat( base.encode(x.scale({domain: brush})), base.select(brush).height(width*0.1).width(width*0.8) ) .render() } return "No ICU data available for this country" }34:T404,viewof ICU_chart_country = { if(icuItalyRegionalData .filter(d => d.country === selectedCountry).length > 0) { const brush = vl.selectInterval().encodings('x'); const x = vl.x().fieldT('date').title(null); const base = vl["mark" + curveType]() .encode( vl.x().fieldT("date") .scale({domain: ["2020-02-01",Date()]}) .axis({grid: false, tickSize: 6, labelColor: '#929292' }) .title(null), vl.y().mean("percentageBedsOccupied").scale({type: country_dist_type}) .title(null), ) .height(width*0.6) .width(width*0.8); return vl.data(icuItalyRegionalData .filter(d => d.country === selectedCountry && +d["percentageBedsOccupied"] > 0)) .title("Mean ICU Occupation by Country " + " (" + country_dist_type + " scale) - zoom using the lower chart") .vconcat( base.encode(x.scale({domain: brush})), base.select(brush).height(width*0.1).width(width*0.8) ) .render() } return "No ICU data available for this country" }35:T4fe,viewof regional_chart = { if(final_regional_data_global .filter(d => d.country === selectedCountry) .length === 0) { return "No regional data available for this country" } { var chart = vl.data(final_regional_data_global .filter(d => d.region === selectedRegion) .filter(d => +d["new_cases"] > 0) .filter(d => +d["new_deaths"] > 0) ) .title(selectedRegion + " Daily Cases & Deaths") .layer( vl.markBar() .encode( vl.x().fieldT("date") .scale({domain: ["2020-02-01",Date()]}) .axis({grid: false, tickSize: 6, labelColor: '#929292' }), vl.y().fieldQ("new_cases") .title("Daily Cases in Blue"), vl.tooltip(['new_cases']) ), vl.markLine() .encode( vl.x().fieldT("date") .title(null), vl.y().fieldQ("new_deaths") .title(null), vl.color({ value: 'darkRed' }) ), vl.markArea({opacity: 0.2}) .encode( vl.x().fieldT("date") .title(null), vl.y().fieldQ("new_deaths") .title("Daily Deaths in Red"), vl.color({ value: 'darkRed' }), vl.tooltip(['new_deaths']) ) ) .width(width*0.85) .height(width*0.6) .resolve({scale:{y:'independent'}}) .render() return chart } }36:T43c,viewof covidMap = { // You'll often see Leaflet examples initializing a map like L.map('map'), // which tells the library to look for a div with the id 'map' on the page. // In Observable, we instead create a div from scratch in this cell, so it's // completely self-contained. let container = DOM.element('div', { style: `width:${width}px;height:${width}px`}); // Note that I'm yielding the container pretty early here: this allows the // div to be placed on the page. This is important, because Leaflet uses // the div's .offsetWidth and .offsetHeight to size the map. If I were // to only return the container at the end of this method, Leaflet might // get the wrong idea about the map's size. yield container; // Now we create a map object and add a layer to it. let map = L.map(container).setView([50,12], 5); let osmLayer = L.tileLayer(map_styles[map_styling].url, { attribution: map_styles[map_styling].attribution }).addTo(map); let covidLayer = L.heatLayer(covid_heatmap, {minOpacity :0.5}, {raduis:3}, {blur:10}).addTo(map); } 37:T672,northEasternUSData = { var matrix = new Array; var ma = movingAverage(final_regional_data_us.map(d => +d.new_deaths), N) var k = 0; for (var i = 0; i +d.new_deaths), N) var k = 0; for (var i = 0; i +d.new_deaths), N) var k = 0; for (var i = 0; i +d.new_deaths), N) var k = 0; for (var i = 0; i d.continent === "Europe"); var ma = movingAverage(finalData.map(d => +d.new_deaths), N) var matrix = new Array; var k = 0; for (var i = 0; i d.continent === 'South America'); var ma = movingAverage(finalData.map(d => +d.new_deaths), N) var matrix = new Array; var k = 0; for (var i = 0; i d.continent === 'Asia' && d.location !== 'China'); var ma = movingAverage(finalData.map(d => +d.new_deaths), N); var matrix = new Array; var k = 0; for (var i = 0; i d.Region == italyRegionalData[i].Region)[0]["Population"], iCUBedsBeforeCovid: ICU_Italy.filter(d => d.Region == italyRegionalData[i].Region)[0]["ICU Beds pre-covid"], additionalBedsPlanned: ICU_Italy.filter(d => d.Region == italyRegionalData[i].Region)[0]["Additional beds planned"], bedsNow: ICU_Italy.filter(d => d.Region == italyRegionalData[i].Region)[0]["Beds now"], percentageBedsPer100k: ICU_Italy.filter(d => d.Region == italyRegionalData[i].Region)[0]["%beds per 100k"], percentageBedsOccupied: (italyRegionalData[i].IntensiveCare)/(ICU_Italy.filter(d => d.Region == italyRegionalData[i].Region)[0]["Beds now"])*100, } } return matrix } 3f:T501,mortalityCodes = [ {country:'Austria',code:'AUT'}, {country:'Belgium',code:'BEL'}, {country:'Bulgaria',code:'BGR'}, {country:'Chile',code:'CHL'}, {country:'Croatia',code:'HRV'}, {country:'Czech Republic',code:'CZE'}, {country:'Denmark',code:'DNK'}, {country:'England and Wales',code:'GBRTENW'}, {country:'Estonia',code:'EST'}, {country:'Finland',code:'FIN'}, {country:'France',code:'FRATNP'}, {country:'Germany',code:'DEUTNP'}, {country:'Greece',code:'GRC'}, {country:'Hungary',code:'HUN'}, {country:'Iceland',code:'ISL'}, {country:'Israel',code:'ISR'}, {country:'Italy',code:'ITA'}, {country:'Latvia',code:'LVA'}, {country:'Lithuania',code:'LTU'}, {country:'Luxembourg',code:'LUX'}, {country:'Netherlands',code:'NLD'}, {country:'New Zealand',code:'NZL_NP'}, {country:'Northern Ireland',code:'GBR_NIR'}, {country:'Norway',code:'NOR'}, {country:'Poland',code:'POL'}, {country:'Portugal',code:'PRT'}, {country:'Republic of Korea',code:'KOR'}, {country:'Russia',code:'RUS'}, {country:'Scotland',code:'GBR_SCO'}, {country:'Slovenia',code:'SVN'}, {country:'Slovakia',code:'SVK'}, {country:'Spain',code:'ESP'}, {country:'Sweden',code:'SWE'}, {country:'Switzerland',code:'CHE'}, {country:'United States',code:'USA'}] 40:T58f,function getDataBy({ dataKey, date, selectedContinents, order, minimumPopulation }) { return ( final_world_data .filter(d => d) .filter(d => d.iso_code !== 'OWID_WRL') // Filter out countries with populations under 1 million .filter(d => d.population > parseInt(minimumPopulation)) .filter(d => { return d.date === getIsoDate(date); }) .filter(d => d[dataKey]) .filter(d => { const country = countries.find(c => c.iso3 === d.iso_code); const continent = continents.find((c, i) => { if (!country) { return false; } return c.id === country.continentCode; }); if (!continent) { return false; } return selectedContinents.includes(continent.id); }) .map(d => { const country = countries.find(c => c.iso3 === d.iso_code); const continent = continents.find(c => c.id === country.continentCode); const name = country.shortName || country.name; return { name, id: country.iso3, value: d[dataKey], fill: continent.fill, colour: continent.colour || 'white' }; }) .filter(d => d.value !== "0.0") .sort(function(a, b) { const mod = order === 'desc' ? -1 : 1; return mod * (a.value - b.value); }) ); }41:[] 2f:{"id":"3070e772c5e3eb4c","slug":"covid-charts","title":"Covid Charts","thumbnail":"5e7c53c1d2066c602d8c44573b5bc318201e97ce097bfdf074ae709493439998","access_level":"public","create_time":"$D2020-05-19T08:04:52.013Z","update_time":"$D2021-11-01T14:24:24.491Z","view_time":null,"first_public_time":"$D2020-05-19T21:02:23.864Z","fork":false,"stars":2,"starred":false,"owner":{"type":"team","id":"bcbd34b126c1c773","login":"alastairorchard","name":"","avatar_url":"https://avatars-next.observableusercontent.com/323fd2061169ea2bd69b7ab108d92e1d575c6ee8e03abf6a96428e2dd691eef9","tier":"starter_2024"},"version":6216,"publish_version":6216,"role":null,"authored":false,"authors":[{"type":"individual","id":"4be011186b558e8a","login":"alastairorchard","name":"","avatar_url":"https://avatars-next.observableusercontent.com/323fd2061169ea2bd69b7ab108d92e1d575c6ee8e03abf6a96428e2dd691eef9","tier":null}],"members":[{"type":"individual","id":"4be011186b558e8a","login":"alastairorchard","name":"","avatar_url":"https://avatars-next.observableusercontent.com/323fd2061169ea2bd69b7ab108d92e1d575c6ee8e03abf6a96428e2dd691eef9","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":10,"collection":null,"type":"notebook","body":{"title":"Covid Charts","cells":[{"id":0,"value":"md`# Covid Charts`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":923,"value":"md`\nI'm building an automated notebook to replace an excel spreadsheet that is laborious to maintain, but it's become too large so I'm splitting it up. This is the [original notebook](https://observablehq.com/@alastairorchard/covid-charts). Other views are available at the following links :`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5872,"value":"md`\n- [Global](https://observablehq.com/@alastairorchard/covid-charts-global)\n- [Countries & Regions](https://observablehq.com/@alastairorchard/covid-charts-countries-regions)\n- [Italy](https://observablehq.com/@alastairorchard/covid-charts-italy)\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2123,"value":"md `Shout out to my bro @dunstan and his colleagues on the Observable team, as well as to all the talented folks from whom I imported code!`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5865,"value":"toc('h1, h2, h3, h4', 'Table of Contents')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5916,"value":"md`## Global Bubble Chart`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4612,"value":"bubbleChartEnd = {\n const svg = d3\n .create(\"svg\")\n .attr('id', 'bubble-chart')\n .attr(\"viewBox\", [0, 0, width, bubbleHeight])\n .attr(\"font-size\", 12)\n .attr(\"font-weight\", 'bold')\n .attr(\"font-family\", \"sans-serif\")\n .attr(\"text-anchor\", \"middle\");\n\n renderBubbleChart(svg, data);\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4966,"value":"md`\nThese radio-buttons change the data displayed by all the charts in the workbook :`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4624,"value":"viewof category = radio({\n title: 'Data Type',\n options: categories.map(c => ({\n label: c.title,\n value: c.key\n })),\n value: 'cases'\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4628,"value":"viewof dataKey = radio({\n title: 'Data View',\n // description: 'Please select your preferred contact method',\n options: dataSources\n .filter(c => c.category === category)\n .map(c => ({\n label: c.title,\n value: c.key\n })),\n value: `total_${category}`\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4728,"value":"viewof minimumPopulation = radio({\n title: 'Population',\n options: [\n { label: 'All countries', value: 0 },\n { label: 'At least 1 million', value: 1000000 },\n { label: 'At least 10 million', value: 10000000 }\n ],\n value: 1000000\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5913,"value":"md`## Situation Table`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4971,"value":"md`\nYou can use this filter to narrow the data set used by all the charts :`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4307,"value":"viewof table_start = text({\n title: \"Filter Results from\",\n min: 1,\n max: andon.length - 2,\n step:1,\n value: 1\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4324,"value":"viewof table_end = text({\n title: \"...to\",\n // description: \"use these values to filter the data set\",\n value: 5,\n min: 1,\n max: andon.length - 1,\n step:1,\n value: 10\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4355,"value":"viewof table_size = radio({\n options: [\n { label: 'Compact', value: 'compact' },\n { label: 'Extended', value: 'extended' },\n ],\n value: 'compact',\n})\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3989,"value":"viewof dashboard = {\n if ((table_end - table_start) > 1) {\n if (table_size === \"compact\") {\n var tablet = table(compact_andon_ordered.slice(table_start-1,table_end))\n}\n else\n{\n var tablet = table(andon_ordered.slice(table_start-1,table_end))\n}\n return tablet\n }\n else\n {\n return \"invalid range\"\n }\n \n }","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5910,"value":"md`## Manual Override`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5134,"value":"md`\nType in a comma separated list of countries like the example below to override the countries shown in the charts`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5136,"value":"md`\nExample : Italy,France,Spain,Germany,United Kingdom,United States`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5105,"value":"viewof manualList = text({\n title: \"Country List Manual Override\",\n value: \"\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4538,"value":"chart = {\n const svg = d3.select(DOM.svg(width, height));\n\n svg.append(\"g\")\n .call(xAxis);\n\n svg.append(\"g\")\n .call(yAxis);\n \n const group = svg.append(\"g\")\n .selectAll(\"g\")\n .data(joy_data_to_chart)\n .join(\"g\")\n .attr(\"transform\", d => `translate(0,${y(d.location) + 1})`);\n\n group.append(\"path\")\n .attr(\"fill\", \"#ddd\")\n .attr(\"opacity\", \"75%\")\n .attr(\"d\", d => area(d.values));\n\n group.append(\"path\")\n .attr(\"fill\", \"none\")\n .attr(\"stroke\", \"black\")\n .attr(\"d\", d => line(d.values));\n\n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5907,"value":"md`## Situation Charts`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5955,"value":"viewof country_scale_type = radio({\n title: 'Scale',\n options: [\n { label: 'Shared', value: 'shared' },\n { label: 'Independent', value: 'independent' },\n ],\n value: 'independent',\n})\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3678,"value":"viewof country_chart_matrix = vl.markLine()\n .data(data_to_chart)\n.title(null)\n .encode(\n vl.x().fieldT(\"date\")\n .title(null),\n vl.y().fieldQ(vl.repeat(\"repeat\")),\n // .title(null),\n vl.color().fieldN(\"location\") \n .legend(null),\n \n vl.row().fieldN(\"location\")\n .title(null)\n\n )\n .width((width / 8)-10)\n .height(100)\n .resolve({\n axis: { x: 'independent' },\n scale: { y: country_scale_type }})\n .repeat([\"new_cases\", \"total_cases\", \"new_deaths\", \"total_deaths\"])\n .columns(4)\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4250,"value":"viewof country_dist_type = radio({\n options: [\n { label: 'Linear', value: 'linear' },\n { label: 'Log', value: 'log' },\n ],\n value: 'linear',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5337,"value":"viewof curveType = radio({\n options: [\n { label: 'Area', value: 'Area' },\n { label: 'Line', value: 'Line' },\n ],\n value: 'Line',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4921,"value":"country_chart = {\n const brush = vl.selectInterval().encodings('x');\n const x = vl.x().fieldT('date').title(null);\n \n const base = vl[\"mark\" + curveType]()\n.encode(\n vl.x().fieldT(\"date\")\n .scale({domain: [\"2020-02-01\",Date()]})\n .axis({grid: false, tickSize: 6, labelColor: '#929292' })\n .title(null),\n vl.y().sum(dataKey).scale({type: country_dist_type})\n .title(null),\n vl.color().fieldN(\"location\"),\n vl.tooltip(['location'])\n )\n .height(width*0.6)\n .width(width*0.8);\n \n return vl.data(data_to_chart.filter(d => +d[dataKey] > 0 )) \n .title(dataKey + \" by Country \" + \" (\" + country_dist_type + \" scale) - zoom using the lower chart\")\n \n .vconcat(\n base.encode(x.scale({domain: brush})),\n base.select(brush).height(width*0.1).width(width*0.8)\n )\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5904,"value":"md`## Mortality`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3352,"value":"cases_to_death_ratio_chart = {\n const brush = vl.selectInterval().encodings('x');\n const x = vl.x().fieldT('date').title(null);\n \n const base = vl[\"mark\" + curveType]()\n.encode(\n vl.x().fieldT(\"date\")\n .scale({domain: [\"2020-02-01\",Date()]})\n .axis({grid: false, tickSize: 6, labelColor: '#929292' })\n .title(null),\n vl.y().fieldQ(\"total_ratio\")\n .title(null),\n vl.color().fieldN(\"location\"),\n vl.tooltip(['location'])\n )\n .height(width*0.6)\n .width(width*0.8);\n \n return vl.data(ratio_deaths_to_cases.filter(d => +d[\"total_ratio\"] > 0))\n .title(\"Total % Mortality (linear scale) - zoom using the lower chart\")\n \n .vconcat(\n base.encode(x.scale({domain: brush})),\n base.select(brush).height(width*0.1).width(width*0.8)\n )\n .render()\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5901,"value":"md`## Testing`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5187,"value":"tests_to_cases_ratio_chart = {\n const brush = vl.selectInterval().encodings('x');\n const x = vl.x().fieldT('date').title(null);\n \n const base = vl[\"mark\" + curveType]()\n.encode(\n vl.x().fieldT(\"date\")\n .scale({domain: [\"2020-02-01\",Date()]})\n .axis({grid: false, tickSize: 6, labelColor: '#929292' })\n .title(null),\n vl.y().fieldQ(\"new_ratio\")\n .title(null),\n vl.color().fieldN(\"location\"),\n vl.tooltip(['location'])\n )\n .height(width*0.6)\n .width(width*0.8);\n \n return vl.data(ratio_tests_to_cases.filter(d => +d[\"new_ratio\"] > 0))\n .title(\"Percentage of Tests Positive (linear scale) - zoom using the lower chart\")\n \n .vconcat(\n base.encode(x.scale({domain: brush})),\n base.select(brush).height(width*0.1).width(width*0.8)\n )\n .render()\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5898,"value":"md`## Country Chart`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2919,"value":"md`You can look at the specific data from any country by typing in the box below :`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2861,"value":"viewof selectedCountry = autoSelect({\n options: Array.from(new Set(final_world_data.map(d => d.location))).sort(),\n placeholder: \"Search for a country . . .\",\n value: 'Italy',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3298,"value":"viewof country_dash = printTable(country_KPIs, [\n {field: 'Country', title: 'Country'},\n {field: 'Total_Cases', title: 'Total Cases'},\n {field: 'Total_Deaths', title: 'Total Deaths'},\n])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2933,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5974,"value":"md`## Excess Mortality`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5987,"value":"md `The red line indicates 2020 mortality of all types. The grey lines are 2016-19. Think that Covid is fake? Think that deaths are being wrongly attributed to Covid? This chart is the best way to see the effect of the pandemic on the overall mortality of a population`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6125,"value":"md`If you have chosen the UK then you need to specify which countries to show`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6035,"value":"viewof UK_Region = radio({\n\n options:\n [\n \n { label: 'England & Wales', value: 'England and Wales' },\n { label: 'Scotland', value: 'Scotland' },\n { label: 'Northern Ireland', value: 'Northern Ireland' }\n\n ],\n value: 'England and Wales',\n \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5990,"value":"md`Choose between age range and sex to filter mortality rates`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5978,"value":"viewof mortalityAge = radio({\n options: [\n { label: '0-14', value: 'D0_14' },\n { label: '15-64', value: 'D15_64' },\n { label: '65-74', value: 'D65_74' },\n { label: '75-84', value: 'D75_84' },\n { label: '85+', value: 'D85p' },\n { label: 'All', value: 'DTotal' },\n ],\n value: 'DTotal',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5981,"value":"viewof mortalitySex = radio({\n options: [\n { label: 'Male', value: 'm' },\n { label: 'Female', value: 'f' },\n { label: 'All', value: 'b' },\n ],\n value: 'b',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5984,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5895,"value":"md`## Regional Analysis`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4853,"value":"regions_chart = {\n const brush = vl.selectInterval().encodings('x');\n const x = vl.x().fieldT('date').title(null);\n \n const base = vl[\"mark\" + curveType]()\n.encode(\n vl.x().fieldT(\"date\")\n .scale({domain: [\"2020-02-01\",Date()]})\n .axis({grid: false, tickSize: 6, labelColor: '#929292' })\n .title(null),\n vl.y().sum(dataKey).scale({type: country_dist_type})\n .title(null),\n vl.color().fieldN(\"region\"),\n vl.tooltip(['region'])\n )\n .height(width*0.6)\n .width(width*0.8);\n \n return vl.data(final_regional_data_global\n .filter(d => d.country === selectedCountry && +d[dataKey] > 0))\n .title(dataKey + \" by Region \" + \" (\" + country_dist_type + \" scale) - zoom using the lower chart\")\n \n .vconcat(\n base.encode(x.scale({domain: brush})),\n base.select(brush).height(width*0.1).width(width*0.8)\n )\n .render()\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6151,"value":"md`Number of ICU Beds Remaining`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6153,"value":"icuBedsRemaining","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6144,"value":"viewof ICU_status_chart = {\n\n var chart = vl.data(latestICUData)\n \n .title(selectedCountry + \" Regional ICU Status\")\n .layer(\n vl.markBar() \n .encode(\n vl.x().fieldN('region'),\n vl.y().fieldQ('percentageBedsOccupied'),\n\n ),\n vl.markRule({opacity:0.5, color:'red'})\n.data(ICU_threshold)\n .encode(\n vl.y().fieldQ('threshold')\n )\n )\n \n .width(width*0.9)\n .height(width*0.6)\n .render()\nreturn chart\n}\n\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6148,"value":"viewof ICU_beds_chart = {\n\n var chart = vl.data(latestICUData)\n \n .title(selectedCountry + \" Regional ICU Beds\")\n .layer(\n vl.markBar() \n .encode(\n vl.x().fieldN('region'),\n vl.y().fieldQ('intensiveCare'),\n\n ),\n vl.markBar({opacity:0.3, color: 'green'}) \n .encode(\n vl.x().fieldN('region'),\n vl.y().fieldQ('bedsNow'),\n )\n )\n \n .width(width*0.9)\n .height(width*0.6)\n .render()\nreturn chart\n}\n\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5820,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6021,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":937,"value":"md` Next I grab the output of a Jupyter notebook I use to calculate the realtime Rt values for each region. I'm trying to convert this to Observable, but it's pretty hard. I only have data for Italy, UK and US, and I update them every few days...`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4437,"value":"viewof rt_chart = {\n if(final_rt_data_global.filter(d => d.country === selectedCountry).length > 0) {\n\nvar chart = vl.data(final_rt_data_global.filter(d => d.country === selectedCountry))\n\n .title(selectedCountry + \" Rt by Region\")\n .layer(\n\nvl.markLine({ opacity: 0.8 }) \n \n\n .encode(\n \n vl.x().fieldT(\"date\")\n// .scale({domain: [\"2020-07-01\",Date()]})\n .axis({grid: false, tickSize: 6, labelColor: '#929292' })\n .title(null),\n vl.y().fieldQ(\"Rt\")\n .axis({title: `Rt`}),\n vl.color().fieldN(\"region\"),\n vl.tooltip(['Rt', 'region'])\n \n ),\n\n vl.markRule({opacity:0.5, color:'green'})\n.data(rt_threshold)\n .encode(\n vl.y().fieldQ('threshold')\n )\n \n .width(width*0.75)\n .height(400)\n )\n .render()\nreturn chart\n }\nreturn \"No Rt data available for this country\"\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1295,"value":"md`We can also average this all out for the country - even if Rt values are best monitored regionally...`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1298,"value":"viewof mean_rt_chart \n \n = {\n if(final_rt_data_global.filter(d => d.country === selectedCountry).length > 0) {\n\nvar chart = vl.data(final_rt_data_global.filter(d => d.country === selectedCountry))\n .title(selectedCountry + \" Mean Rt\")\n\n.layer(\n\n vl.markLine({point:{filled: false,fill: \"white\"}}) \n\n .encode(\n vl.x().fieldT(\"date\")\n .axis({title: null})\n .axis({grid: false, tickSize: 6, labelColor: '#929292' }),\n vl.y().mean(\"Rt\")\n .axis({title: `Rt`})\n \n ),\n vl.markArea({opacity: 0.2}) \n .encode(\n vl.x().fieldT(\"date\")\n .axis({title: null}),\n vl.y().mean('Low_90'),\n vl.y2().mean('High_90')\n// .axis({title: `Rt`})\n ),\n\n vl.markRule({opacity:0.5, color:'green'})\n.data(rt_threshold)\n .encode(\n vl.y().fieldQ('threshold')\n )\n \n .width(width*0.9)\n .height(400)\n )\n .render()\nreturn chart\n }\nreturn \"No Rt data available for this country\"\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2982,"value":"md`I then split all the data into separate graphs for each region. You can change the scale using the radio buttons...`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2977,"value":"viewof region_scale_type = radio({\n title: 'Scale',\n options: [\n { label: 'Shared', value: 'shared' },\n { label: 'Independent', value: 'independent' },\n ],\n value: 'independent',\n})\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3136,"value":"viewof regional_chart_matrix = vl.markLine()\n .data(final_regional_data_global\n .filter(d => d.country === selectedCountry))\n.title(null)\n .encode(\n vl.x().fieldT(\"date\")\n .title(null),\n vl.y().fieldQ(vl.repeat(\"repeat\")),\n // .title(null),\n vl.color().fieldN(\"region\") \n .legend(null),\n \n vl.row().fieldN(\"region\")\n .title(null)\n\n )\n .width((width / 8)-10)\n .height(100)\n .resolve({\n axis: { x: 'independent' },\n scale: { y: region_scale_type }})\n .repeat([\"new_cases\", \"total_cases\", \"new_deaths\", \"total_deaths\"])\n .columns(4)\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5892,"value":"md`## Region Chart`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2963,"value":"md`We can show a specific graph for any region from the selected country`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3764,"value":"viewof selectedRegion = \n{\n if (final_regional_data_global.filter(d => d.country === selectedCountry).length === 0) {\n return select({options: [\"No Regions Available\"], value:\"No Regions Available\"})\n }\n else {\n return select({\n title: 'Select Region',\n options: Array.from(new Set((final_regional_data_global.filter(d => d.country === selectedCountry)).map(d => d.region))).sort(),\n placeholder: \"Search for a region . . .\",\n value: '',\n})\n }\n}\n\n ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3307,"value":"viewof regional_dash = {\nif (final_regional_data_global.filter(d => d.country === selectedCountry).length === 0) {\nreturn \"No regional data available for this country\"\n}\n\n var table = printTable(regional_KPIs, [\n {field: 'Region', title: 'Region'},\n {field: 'Total_Cases', title: 'Total Cases'},\n {field: 'Total_Deaths', title: 'Total Deaths'},\n])\n return table\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3728,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3021,"value":" viewof regional_rt_chart = {\n if(final_rt_data_global.filter(d => d.country === selectedCountry).length === 0) {\n \n return \"No Rt data available for this region\" }\n {\nvar chart = vl.data(final_rt_data_global.filter(d => d.region === selectedRegion))\n .layer(\n \n vl.markLine({point:{filled: false,fill: \"white\"}}) \n .title(selectedRegion + \" Realtime Rt\")\n .encode(\n vl.x().fieldT(\"date\")\n .axis({title: null})\n .axis({grid: false, tickSize: 6, labelColor: '#929292' }),\n vl.y().fieldQ(\"Rt\")\n .axis({title: `Rt`})\n ),\n vl.markArea({opacity: 0.2}) \n .encode(\n vl.x().fieldT(\"date\")\n .axis({title: null}),\n vl.y().fieldQ('Low_90'),\n vl.y2().fieldQ('High_90')\n// .axis({title: `Rt`})\n ),\n\n vl.markRule({opacity:0.5, color:'green'})\n .data(rt_threshold)\n .encode(\n vl.y().fieldQ('threshold')\n )\n \n\n .width(width*0.9)\n .height(400)\n \n )\n .render()\nreturn chart\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5889,"value":"md`## Case Map`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4689,"value":"viewof map_styling = select({\n title: 'Choose a map styling',\n options: [\n {value: 'cartoLight', label: 'cartoLight'},\n {value: 'stamen', label: 'Stamen (no street names)'},\n {value: 'openStreetMap', label: 'OpenStreetMap (with street names)'},\n ],\n value: 'cartoLight'\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4686,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4695,"value":"md`Coordinates are only available for European countries and their overseas territories...`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5886,"value":"md`## Regional Comparison`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5616,"value":"viewof N = {\n const form = html``;\n form.i.oninput = () => form.value = form.i.valueAsNumber;\n form.i.oninput();\n return form;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5519,"value":"temperate_zone_chart = {\n const brush = vl.selectInterval().encodings('x');\n const x = vl.x().fieldT('date').title(null);\n \n const base = vl.markArea()\n.encode(\n vl.x().fieldT(\"date\")\n .scale({domain: [\"2020-02-01\",Date()]})\n .axis({grid: false, tickSize: 6, labelColor: '#929292' })\n .title(null),\n vl.y().mean('new_deaths')\n .title(null),\n vl.color().fieldN(\"location\"),\n vl.tooltip(['location'])\n )\n .height(width*0.6)\n .width(width*0.8);\n \n return vl.data(temperateZoneData.filter(d => +d.new_deaths > 0)) \n .title(\"Deaths per Temperate Zone - zoom using the lower chart\")\n \n .vconcat(\n base.encode(x.scale({domain: brush})),\n base.select(brush).height(width*0.1).width(width*0.8)\n )\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5529,"value":"tropical_zone_chart2 = {\n const brush = vl.selectInterval().encodings('x');\n const x = vl.x().fieldT('date').title(null);\n \n const base = vl.markArea()\n.encode(\n vl.x().fieldT(\"date\")\n .scale({domain: [\"2020-02-01\",Date()]})\n .axis({grid: false, tickSize: 6, labelColor: '#929292' })\n .title(null),\n vl.y().mean('new_deaths')\n .title(null),\n vl.color().fieldN(\"location\"),\n vl.tooltip(['location'])\n )\n .height(width*0.6)\n .width(width*0.8);\n \n return vl.data(tropicalZoneData.filter(d => +d.new_deaths > 0 && +d.new_deaths < 1000)) \n .title(\"Deaths per Tropical Zone - zoom using the lower chart\")\n \n .vconcat(\n base.encode(x.scale({domain: brush})),\n base.select(brush).height(width*0.1).width(width*0.8)\n )\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5322,"value":"md`\nI've recently added this fantastic streamgraph from @attharmirza because it so beautifully shows how the pandemic is playing out across the globe. It all ends when the chart shrinks back to a point. I'll link it up to the other controls on the page when I have a chance, but for now it's just ripped straight from his notebook`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5327,"value":"{\n let colors = \"\";\n\n for (let i = 0; i < regions.length; i++) {\n i < regions.length - 1\n ? (colors =\n colors +\n `✦ ${\n regions[i]\n }, `)\n : (colors =\n colors +\n ` and ✦ ${\n regions[i]\n }`);\n }\n\n return md`The chart is arranged into **regions** from West to East, with the U.S. pulled out to make trends easier to see. Here's the color scheme: ${colors}.`;\n \n }\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5332,"value":"streamgraph","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5876,"value":"md`# Appendix`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1646,"value":"md`## Data Handling`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1650,"value":"md`Here is where I try to clean up the various csv files that I suck into the notebook from repositories around the net. First the source files :`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":814,"value":"rt_data_italy = d3.csv(\"https://gist.githubusercontent.com/alastairorchard/e32407d6468eb92d1750c410c1ad0aec/raw/f113e2e3b0c27e545712a6c40bbf8f253bc515f6/Allregions_Italy_Rt_2020-09-09.csv\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2569,"value":"//rt_data_uk =d3.csv(\"https://gist.githubusercontent.com/alastairorchard/4ed5eb2fc32c6ef55a426dd3957e1c85/raw/037d71538e7a1d1e76b4b787ca7ebc5f0643a159/Allregions_UK_Rt_2020-09-09.csv\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2571,"value":"rt_data_us = d3.csv(\"https://gist.githubusercontent.com/alastairorchard/e9613254e9fa81203096cb0bafecd178/raw/26eef3511b6ca6c7ddbb8f5b226550c9e4aea4ad/Allregions_US_Rt_2020-09-09.csv\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1726,"value":"state_data_us = d3.csv(\"https://api.covidtracking.com/v1/states/daily.csv\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2137,"value":"regional_data_eu = d3.csv(\"https://raw.githubusercontent.com/ec-jrc/COVID-19/master/data-by-region/jrc-covid-19-all-days-by-regions.csv\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3536,"value":"world_data = d3.csv('https://covid.ourworldindata.org/data/owid-covid-data.csv')\n/*, ({date, iso_code, location, continent, new_cases, total_cases, new_deaths, total_deaths, new_cases_per_million, total_cases_per_million, new_deaths_per_million, total_deaths_per_million}) => ({\n iso_code: iso_code,\n continent: continent,\n country: location,\n date: date,\n new_cases: new_cases,\n total_cases: total_cases,\n new_deaths: new_deaths,\n total_deaths: total_deaths,\n new_cases_per_million: new_cases_per_million, \n total_cases_per_million: total_cases_per_million, \n new_deaths_per_million: new_deaths_per_million, \n total_deaths_per_million: total_deaths_per_million\n }))\n */","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1663,"value":"md`Then the filtered ones :`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2374,"value":"filtered_regional_data_eu = Array.from(d3.group(regional_data_eu, d => d.Region))\n .map(([, rows]) => process_data(rows)).flat()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2057,"value":"md`...and finalized :`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1731,"value":"final_regional_data_us = {\n var matrix = new Array;\n for (var i = 0; i<(state_data_us.length);i++)\n {\n matrix[i] = {country: 'United States',\n date: state_data_us[i].date.slice(0,4)+\"-\"+state_data_us[i].date.slice(4,6)+\"-\"+state_data_us[i].date.slice(6,8),\n region: state_data_us[i]['state'],\n lat:\"\",\n long:\"\",\n new_cases: state_data_us[i]['positiveIncrease'],\n total_cases: state_data_us[i]['positive'],\n new_deaths: state_data_us[i]['deathIncrease'],\n total_deaths: state_data_us[i]['death']\n }\n }\n return matrix\n }","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2376,"value":"final_regional_data_eu = filtered_regional_data_eu.filter(d => d.region !==\"NOT SPECIFIED\" && d.date > \"1973-01-01\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1854,"value":"final_rt_data_italy = {\n var matrix = new Array;\n for (var i = 0; i<(rt_data_italy.length);i++)\n {\n matrix[i] = {country: 'Italy',\n date: rt_data_italy[i].data.split(\" \")[0],\n region: rt_data_italy[i]['denominazione_regione'],\n Rt: rt_data_italy[i]['ML'],\n Low_90: rt_data_italy[i]['Low_90'],\n High_90: rt_data_italy[i]['High_90'],\n Low_50: rt_data_italy[i]['Low_50'],\n High_50: rt_data_italy[i]['High_50'],\n }\n }\n return matrix\n }\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2608,"value":"/*final_rt_data_uk = {\n var matrix = new Array;\n for (var i = 0; i<(rt_data_uk.length);i++)\n {\n matrix[i] = {country: 'United Kingdom',\n date: rt_data_uk[i]['Specimen date'],\n region: rt_data_uk[i]['Area name'],\n Rt: rt_data_uk[i]['ML'],\n Low_90: rt_data_uk[i]['Low_90'],\n High_90: rt_data_uk[i]['High_90'],\n Low_50: rt_data_uk[i]['Low_50'],\n High_50: rt_data_uk[i]['High_50'],\n }\n }\n return matrix\n }\n*/","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2723,"value":"final_rt_data_us = {\n var matrix = new Array;\n for (var i = 0; i<(rt_data_us.length);i++)\n {\n matrix[i] = {country: 'United States',\n date: rt_data_us[i]['date'],\n region: rt_data_us[i]['state'],\n Rt: rt_data_us[i]['ML'],\n Low_90: rt_data_us[i]['Low_90'],\n High_90: rt_data_us[i]['High_90'],\n Low_50: rt_data_us[i]['Low_50'],\n High_50: rt_data_us[i]['High_50'],\n }\n }\n return matrix\n }\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4421,"value":"rt_threshold = [{date: \"2020-02-01\", threshold: 1},{date: Date(), threshold: 1}]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3353,"value":"ratio_deaths_to_cases = {\n var matrix = new Array;\n for (var i = 0; i<(data_to_chart.length);i++)\n {\n matrix[i] = {location: data_to_chart[i].location,\n date: data_to_chart[i].date,\n region: data_to_chart[i].region,\n lat:data_to_chart[i].lat,\n long:data_to_chart[i].long,\n new_ratio: ((data_to_chart[i].new_deaths)/(data_to_chart[i].new_cases))*100,\n total_ratio: ((data_to_chart[i].total_deaths)/(data_to_chart[i].total_cases))*100\n }\n }\n return matrix\n }","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5190,"value":"ratio_tests_to_cases = {\n\n \n \n var matrix = new Array;\n for (var i = 0; i<(data_to_chart.length);i++)\n {\n if (data_to_chart[i].new_tests == 0) {\n var NR = NaN}\n else\n {\n var NR = ((data_to_chart[i].new_cases)/(data_to_chart[i].new_tests))*100\n }\n if (data_to_chart[i].total_tests == 0) {\n var TR = NaN}\n else\n {\n var TR = ((data_to_chart[i].total_cases)/(data_to_chart[i].total_tests))*100\n }\n matrix[i] = {location: data_to_chart[i].location,\n date: data_to_chart[i].date,\n region: data_to_chart[i].region,\n lat:data_to_chart[i].lat,\n long:data_to_chart[i].long,\n new_ratio: NR,\n total_ratio: TR\n }\n }\n return matrix\n }","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3303,"value":"regional_KPIs = \n[\n {Region: selectedRegion, Total_Cases: d3.sum(final_regional_data_global.filter(d => d.region === selectedRegion), d => d.new_cases), Total_Deaths: d3.sum(final_regional_data_global.filter(d => d.region === selectedRegion), d => d.new_deaths)\n }\n ]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3810,"value":"country_KPIs = \n[\n {Country: selectedCountry, Total_Cases: d3.sum(final_world_data.filter(d => d.location === selectedCountry), d => d.new_cases), Total_Deaths: d3.sum(final_world_data.filter(d => d.location === selectedCountry), d => d.new_deaths)\n }\n ]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1322,"value":"covid_heatmap = {\n var matrix = new Array;\n for (var i = 0; i<(final_regional_data_global.length);i++)\n {\n matrix[i] = [final_regional_data_global[i].lat, final_regional_data_global[i].long, final_regional_data_global[i].new_cases*0.1]}\n \n return matrix\n }","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2908,"value":"md`Finally, all the charts in the notebook point to these combined arrays :`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3002,"value":"final_rt_data_global = d3.merge([final_rt_data_italy,final_rt_data_us/*,final_rt_data_uk*/])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2318,"value":"final_regional_data_global = d3.merge([final_regional_data_eu,final_regional_data_us])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":3559,"value":"final_world_data = world_data.filter(d => d.date > \"2020-02-01\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4035,"value":"grouped_world_data = Array.from(d3.group(final_world_data, d => d.location))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5350,"value":"md`## Zone Comparisons`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5353,"value":"final_regional_data_us","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5360,"value":"northEasternUS = ['NY','MA','CT','OH','MI','PA','VT','WI','NH','ME']","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5362,"value":"southernUS = ['FL','TX','AZ','NM','LA','SA','GA','SC','MO']","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5364,"value":"westernUS = ['CA','OR','WA']","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6194,"value":"swUS = ['FL','TX','AZ','NM','LA','SA','GA','SC','MO','CA','OR','WA']","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5369,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5371,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6205,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5374,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5418,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5421,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5552,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5397,"value":"temperateZoneData = d3.merge([northEasternUSData,europeData])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5538,"value":"tropicalZoneData = d3.merge([swUSData,southAmericaData,asiaData])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4783,"value":"md`## Dashboard`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4116,"value":"country_list = {\n if(manualList.length === 0) {\nreturn andon_country_list}\n {\n return d3.csvParse(manualList).columns}\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5119,"value":"andon_country_list = andon_ordered.slice(table_start-1,table_end).map(d => d.Country)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4029,"value":"andon = {\n var matrix = new Array;\n for (var i = 0; i d.CountryName == \"Italy\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5770,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6165,"value":"icuBedsRemaining = (d3.rollup((icuItalyRegionalData.slice(icuItalyRegionalData.length-ICU_Italy.length,icuItalyRegionalData.length)), v => d3.sum(v, d => d.bedsNow)))-(d3.rollup((icuItalyRegionalData.slice(icuItalyRegionalData.length-ICU_Italy.length,icuItalyRegionalData.length)), v => d3.sum(v, d => d.intensiveCare)))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6163,"value":"ICU_threshold = [{threshold: 30}]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6160,"value":"latestICUData = (icuItalyRegionalData.slice(icuItalyRegionalData.length-ICU_Italy.length,icuItalyRegionalData.length))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5961,"value":"md`## Excess Mortality`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5968,"value":"mortalityData = d3.csv(\"https://gist.githubusercontent.com/alastairorchard/21e074304af76d77c527407c54bec9a4/raw/f4044077c3001fa87824eca1bfa932bc0a02e4ba/Mortality\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5964,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5959,"value":"mortalityByCountry = (country)=>{\n \n \n const location = mortalityData.filter(c => c.CountryCode == mortalityCodes.filter(d => d.country == country)[0].code);\n let data2016 = location.filter(d => (d.Year === '2016')) \n let data2017 = location.filter(d=> (d.Year === '2017'))\n let data2018 = location.filter(d=> (d.Year === '2018'))\n let data2019 = location.filter(d=> (d.Year === '2019'))\n let data2020 = location.filter(d=> (d.Year === '2020'))\n \n \n return(\n {\n data2016:data2016,\n data2017:data2017,\n data2018:data2018,\n data2019:data2019,\n data2020:data2020})\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6114,"value":"mortalityWeek = [\n {current :weekN(today)}]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4648,"value":"md`## Bubble Chart`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4643,"value":"data = getDataBy({\n dataKey,\n date,\n selectedContinents,\n minimumPopulation,\n order,\n country_list\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4721,"value":"mostRecentCovidData = final_world_data.filter(d => d.date === getIsoDate(endDate))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4723,"value":"$40","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4657,"value":"import {renderBubbleChart, order, date, endDate, getIsoDate, countries, continents, selectedContinents, categories, dataSources} from '@unkleho/covid-19-bubble-chart-with-d3-render'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4652,"value":"bubbleHeight = {\n if (width > 450) {\n // console.log(screen);\n return 450;\n }\n\n return width}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4567,"value":"md`## Joy Plot`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4555,"value":"height = ((joy_data_to_chart.length * 25) + 100)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4549,"value":"yAxis = g => g\n .attr(\"transform\", `translate(${margin.left},0)`)\n .call(d3.axisLeft(y).tickSize(0).tickPadding(4))\n .call(g => g.select(\".domain\").remove())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4584,"value":"xAxis = g => g\n .attr(\"transform\", `translate(0,${height - margin.bottom})`)\n .call(d3.axisBottom(x)\n .ticks(width / 80)\n .tickSizeOuter(0))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4529,"value":"y = d3.scalePoint()\n .domain(joy_data_to_chart.map(d => d.location))\n .range([margin.top, height - margin.bottom])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4589,"value":"x = d3.scaleTime()\n .domain(d3.extent(joy_data.dates))\n .range([margin.left, width - margin.right])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4509,"value":"joy_data = {\n const data = final_world_data.filter(d => d.location !== \"World\");\n const dates = Array.from(d3.group(data, d => d.date).keys()).sort(d3.ascending);\n return {\n dates: dates,//.map(d => new Date(d)),\n series: d3.groups(data, d => d.location).map(([location, values]) => {\n const value = new Map(values.map(d => [d.date, d.new_cases]));\n return {location, values: dates.map(d => value.get(d))};\n })\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4574,"value":"joy_data_to_chart = {\n var matrix = new Array;\n var k = 0;\n for (var i = 0; i {\n \n d.setUTCDate(d.getUTCDate() + 4 - (d.getUTCDay()||7));\n \n const yearStart = new Date(Date.UTC(d.getUTCFullYear(),0,1));\n \n const weekNo = Math.ceil(( ( (d - yearStart) / 86400000) + 1)/7);\n \n return weekNo;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6109,"value":"today =new Date() ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5609,"value":"function movingAverage(values, N) {\n let i = 0;\n let sum = 0;\n const means = new Float64Array(values.length).fill(NaN);\n for (let n = Math.min(N - 1, values.length); i < n; ++i) {\n sum += values[i];\n }\n for (let n = values.length; i < n; ++i) {\n sum += values[i];\n means[i] = sum / N;\n sum -= values[i - N + 1];\n }\n return means;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2149,"value":"process_data = data => {\n const output = data.map((d, i) => ({\n country: d.CountryName,\n date: d.Date,\n region: d.Region,\n lat: d.lat,\n long: d.lon,\n new_cases: Math.max(d.CumulativePositive - (data[i - 1] ? data[i - 1].CumulativePositive : 0), 0),\n total_cases: d.CumulativePositive,\n new_deaths: Math.max(d.CumulativeDeceased - (data[i - 1] ? data[i - 1].CumulativeDeceased : 0), 0),\n total_deaths: d.CumulativeDeceased,\n }));\n\n// Object.assign(output, data[0], {\n \n ['Date', 'CumulativePositive', 'CumulativeDeceased'].forEach(key => delete output[key]);\n\n return output;\n};","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2750,"value":"firstBy = thenBy.firstBy","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2747,"value":"thenBy = require('https://bundle.run/thenby@1.3.4')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6,"value":"d3 = require(\"d3@5\", \"d3-array@2\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":12,"value":"//import { vl } from \"@vega/vega-lite-api\"\nvl = {\n const [vega, vegalite, api, tooltip] = await Promise.all([\n 'vega@5.15',\n 'vega-lite@4.13.1',\n 'vega-lite-api@0.12.0',\n 'vega-tooltip@0.23.2'\n ].map(module => require(module)));\n\n const options = {\n config: {\n // vega-lite default configuration\n config: {\n view: {continuousWidth: 400, continuousHeight: 300},\n mark: {tooltip: null}\n }\n },\n init: view => {\n // initialize tooltip handler\n view.tooltip(new tooltip.Handler().call);\n // enable horizontal scrolling for large plots\n if (view.container()) view.container().style['overflow-x'] = 'auto';\n },\n view: {\n // view constructor options\n loader: vega.loader({baseURL: 'https://cdn.jsdelivr.net/npm/vega-datasets@1/'}),\n renderer: 'canvas'\n }\n };\n \n return api.register(vega, vegalite, options);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5926,"value":"md`## Imports`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6199,"value":"d3Array = require(\"d3-array\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4507,"value":"import {line, margin, area} from '@d3/ridgeline-plot'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4369,"value":"import {table, defaultHumanize} from '@awendland/fancy-table'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":50,"value":"import {filter, filteri, take, taker} from \"@mbostock/manipulating-flat-arrays\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":575,"value":"import {thumbnail} from \"@observablehq/vega-lite-chart-types\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":701,"value":"import { radio, select, autoSelect, worldMapCoordinates, slider, text } from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":2629,"value":"import {printTable} from '@uwdata/data-utilities'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":4283,"value":"import {rangeSlider} from '@mootari/range-slider'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5286,"value":"import {streamgraph,gray,regions,color,codesHighlight,crosswalk} from '@attharmirza/the-coronavirus-pandemic-ends-when-this-chart-shrinks-agai'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1316,"value":"L = {\n const L = await require('leaflet');\n if (!L._css) {\n document.head.appendChild(L._css = html`\n \n \n `);\n }\n await require('leaflet.markercluster@1.4.1').catch(() => L.markerClusterGroup);\n await require('leaflet.heat').catch(() => L.heatLayer);\n return L;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1434,"value":"leafletCSS = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":1395,"value":"map_styles = ({\n 'stamen': {\n url: 'https://stamen-tiles-{s}.a.ssl.fastly.net/watercolor/{z}/{x}/{y}.png', \n attribution: 'Map tiles by Stamen Design, CC BY 3.0 — Map data © OpenStreetMap'\n },\n 'openStreetMap': {\n url: 'https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png',\n attribution: '© OpenStreetMap'\n },\n 'cartoLight': {\n url: 'https://cartodb-basemaps-{s}.global.ssl.fastly.net/light_all/{z}/{x}/{y}{r}.png',\n attribution: '© OpenStreetMap | © CartoDB'\n },\n \n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":5867,"value":"import {toc} from \"@tomshanley/indented-toc\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W41","stdlib":"1","titling":"auto"}} 30:"$2f:body"