2e:null 2f:["$","title",null,{"children":"Analyse van de COVID cijfers in België | Florian Dierickx | Observable"}] 34:T2a9d,{ // the bars of the top graph const bars = vl.layer( [vl.markBar() .encode( vl.x().fieldT("DATE"), // one bar per day along the x-axis vl.y().sum("CASES"), // for each day, sum the number of new cases vl.opacity().value(0.2), // make the bars less prominent vl.color() .condition([ // last 3 in gray { test: "dayofyear(now()) - dayofyear(datum.DATE) < 4", value: "gray" } ]) .value("steelblue"), // others in green vl.tooltip([ // add a tooltip to the bars {field: "DATE", type: "temporal", title: 'Datum'}, {field: "CASES", aggregate: "sum", title: "Nieuwe gevallen" } ]) ) .width(800)], // &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&& // Indication of major restrictive measures in Belgium (red) and/or regions (blue) // Source: https://nl.wikipedia.org/wiki/Maatregelen_coronacrisis_in_Belgi%C3%AB // &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&& // 2020-03-17 lockdown belgium [vl.markRule() .data({ values: [{DATE_LOCKDOWN: "2020-03-17", label: "Lockdown Belgium [17/3]"}]}) .encode( vl.x().fieldT("DATE_LOCKDOWN"), vl.color().value("red"), vl.size().value("1"), )], [vl.markText() .data({ values: [{DATE_LOCKDOWN: "2020-03-20", label: "Lockdown Belgium [17/3]"}]}) .encode( vl.x().fieldT("DATE_LOCKDOWN"), vl.color().value("red"), vl.size().value("12"), vl.text().value(["Start lockdown in Belgium"]), // edits to change angle -> to find how to include offsets (x and Y direction) vl.angle().value("-90"), )], // 2020-07-29 nightclock antwerp [vl.markRule() .data({ values: [{DATE_START_NIGHTCLOCK_ANTWERP: "2020-07-29", label: "Start nightclock in Antwerp"}]}) .encode( vl.x().fieldT("DATE_START_NIGHTCLOCK_ANTWERP"), vl.color().value("blue"), vl.size().value("1"), )], [vl.markText() .data({values: [{DATE_START_NIGHTCLOCK_ANTWERP_TEXT: "2020-07-31", label: "Start nightclock in Antwerp"}]}) .encode( vl.x().fieldT("DATE_START_NIGHTCLOCK_ANTWERP_TEXT"), vl.color().value("blue"), vl.size().value("12"), vl.text().value(["Start nightclock in Antwerp [23:30 - 06:00]"]), // edits to change angle vl.angle().value("-90"), )], // 2020-10-16 night clock Belgium [vl.markRule() .data({ values: [{DATE_START_NIGHTCLOCK_BELGIUM: "2020-10-16", label: "Start nightclock in Belgium"}]}) .encode( vl.x().fieldT("DATE_START_NIGHTCLOCK_BELGIUM"), vl.color().value("red"), vl.size().value("1"), )], [vl.markText() .data({values: [{DATE_START_NIGHTCLOCK_BELGIUM_TEXT: "2020-10-19", label: "Start nightclock in Belgium"}]}) .encode( vl.x().fieldT("DATE_START_NIGHTCLOCK_BELGIUM_TEXT"), vl.color().value("red"), vl.size().value("12"), vl.text().value(["Start nightclock in Belgium [00:00 - 05:00]"]), // edits to change angle -> to find how to include offsets (x and Y direction) vl.angle().value("-90"), )], // 2020-11-02 2nd lockdown belgium [vl.markRule() .data({ values: [{DATE_LOCKDOWN: "2020-11-02", label: "2nd Lockdown Belgium [2/10]"}]}) .encode( vl.x().fieldT("DATE_LOCKDOWN"), vl.color().value("red"), vl.size().value("1"), )], [vl.markText() .data({ values: [{DATE_LOCKDOWN: "2020-10-31", label: "2nd Lockdown Belgium [2/10]"}]}) .encode( vl.x().fieldT("DATE_LOCKDOWN"), vl.color().value("red"), vl.size().value("12"), vl.text().value(["Start second lockdown in Belgium"]), // edits to change angle -> to find how to include offsets (x and Y direction) vl.angle().value("-90"), )], ) // the average line on the top graph const average = vl.markLine({interpolate: "monotone"}) // draw a slightly smoother line // first calculate some secondary data points .transform( // calculate a rolling average with a window of the 6 days and today vl.groupby("PROVINCE").window(vl.mean("CASES").as("AVG_CASES")).frame([-6,0]), // sum the provinces for each day vl.groupby(["DATE", "INCOMPLETE"]).aggregate(vl.sum("AVG_CASES").as("AVG_CASES_BY_DATE")) ) .encode( vl.x() .fieldT("DATE") .title(null), // disable the title on the x-axis vl.y().sum("AVG_CASES_BY_DATE").title('Nieuwe gevallen per dag'), vl.tooltip().sum("AVG_CASES_BY_DATE") ); // the bottom graph const diff = vl.layer( [vl.markBar() // first calculate some secondary data points .transform( // rolling average, same as above vl.groupby("PROVINCE").window(vl.mean("CASES").as("AVG_CASES")).frame([-6,0]), vl.groupby("DATE").aggregate(vl.sum("AVG_CASES").as("AVG_CASES_BY_DATE")), // for each day, add a value containing the value of the previous day vl.window(vl.lag("AVG_CASES_BY_DATE").as("PREV_AVG_CASES_BY_DATE")).frame([-1,0]), // calculate the percentual difference between 2 days vl.calculate('datum.PREV_AVG_CASES_BY_DATE == null ? 0 : (datum.AVG_CASES_BY_DATE / (datum.PREV_AVG_CASES_BY_DATE)) - 1').as('DIFF') ) .encode( // draw the bars as before vl.x().fieldT("DATE").title(null), vl.y().sum("DIFF") .title('Verschil dag ervoor') .axis({format: ".0%"}), // format the values as percentage // color the bars according to a condition vl.color() .condition([ // last 3 in gray { test: "dayofyear(now()) - dayofyear(datum.DATE) < 4", value: "lightgray" }, // positive in orange { test: "datum.DIFF > 0", value: "orange" } ]) .value("green"), // others in green vl.tooltip([ {field: "DATE", type: "temporal", title: 'Datum'}, {field: "DIFF", format: ".2%", title: 'Verschil'} ]) ) .width(800) .height(150)], // &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&& // Indication of major restrictive measures in Belgium (red) and/or regions (blue) // Source: https://nl.wikipedia.org/wiki/Maatregelen_coronacrisis_in_Belgi%C3%AB // &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&& // 2020-03-17 lockdown belgium [vl.markRule() .data({ values: [{DATE_LOCKDOWN: "2020-03-17", label: "Lockdown Belgium [17/3]"}]}) .encode( vl.x().fieldT("DATE_LOCKDOWN"), vl.color().value("red"), vl.size().value("1"), )], // 2020-07-29 nightclock antwerp [vl.markRule() .data({ values: [{DATE_START_NIGHTCLOCK_ANTWERP: "2020-07-29", label: "Start nightclock in Antwerp"}]}) .encode( vl.x().fieldT("DATE_START_NIGHTCLOCK_ANTWERP"), vl.color().value("blue"), vl.size().value("1"), )], // 2020-10-16 night clock Belgium [vl.markRule() .data({ values: [{DATE_START_NIGHTCLOCK_BELGIUM: "2020-10-16", label: "Start nightclock in Belgium"}]}) .encode( vl.x().fieldT("DATE_START_NIGHTCLOCK_BELGIUM"), vl.color().value("red"), vl.size().value("1"), )], // 2020-11-02 2nd lockdown belgium [vl.markRule() .data({ values: [{DATE_LOCKDOWN: "2020-11-02", label: "2nd Lockdown Belgium [2/10]"}]}) .encode( vl.x().fieldT("DATE_LOCKDOWN"), vl.color().value("red"), vl.size().value("1"), )], ) // the bottom graph const diff2 = vl.layer( [vl.markBar() // first calculate some secondary data points .transform( // rolling average, same as above vl.groupby("PROVINCE").window(vl.mean("CASES").as("AVG_CASES")).frame([-6,0]), vl.groupby("DATE").aggregate(vl.sum("AVG_CASES").as("AVG_CASES_BY_DATE")), // for each day, add a value containing the value of the previous day vl.window(vl.lag("AVG_CASES_BY_DATE", 7).as("PREV_AVG_CASES_BY_DATE")).frame([-1,0]), // calculate the percentual difference between 2 days vl.calculate('datum.PREV_AVG_CASES_BY_DATE == null ? 0 : (datum.AVG_CASES_BY_DATE / (datum.PREV_AVG_CASES_BY_DATE)) - 1').as('DIFF') ) .encode( // draw the bars as before vl.x().fieldT("DATE").title(null), vl.y().sum("DIFF") .title('Verschil week ervoor') .axis({format: ".0%"}), // format the values as percentage // color the bars according to a condition vl.color() .condition([ // last 3 in gray { test: "dayofyear(now()) - dayofyear(datum.DATE) < 4", value: "lightgray" }, // positive in orange { test: "datum.DIFF > 0", value: "orange" } ]) .value("green"), // others in green vl.tooltip([ {field: "DATE", type: "temporal", title: 'Datum'}, {field: "DIFF", format: ".2%", title: 'Verschil'} ]) ) .width(800) .height(150)], // &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&& // Indication of major restrictive measures in Belgium (red) and/or regions (blue) // Source: https://nl.wikipedia.org/wiki/Maatregelen_coronacrisis_in_Belgi%C3%AB // &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&& // 2020-03-17 lockdown belgium [vl.markRule() .data({ values: [{DATE_LOCKDOWN: "2020-03-17", label: "Lockdown Belgium [17/3]"}]}) .encode( vl.x().fieldT("DATE_LOCKDOWN"), vl.color().value("red"), vl.size().value("1"), )], // 2020-07-29 nightclock antwerp [vl.markRule() .data({ values: [{DATE_START_NIGHTCLOCK_ANTWERP: "2020-07-29", label: "Start nightclock in Antwerp"}]}) .encode( vl.x().fieldT("DATE_START_NIGHTCLOCK_ANTWERP"), vl.color().value("blue"), vl.size().value("1"), )], // 2020-10-16 night clock Belgium [vl.markRule() .data({ values: [{DATE_START_NIGHTCLOCK_BELGIUM: "2020-10-16", label: "Start nightclock in Belgium"}]}) .encode( vl.x().fieldT("DATE_START_NIGHTCLOCK_BELGIUM"), vl.color().value("red"), vl.size().value("1"), )], // 2020-11-02 2nd lockdown belgium [vl.markRule() .data({ values: [{DATE_LOCKDOWN: "2020-11-02", label: "2nd Lockdown Belgium [2/10]"}]}) .encode( vl.x().fieldT("DATE_LOCKDOWN"), vl.color().value("red"), vl.size().value("1"), )], ) // concat the graphs and render return vl.vconcat(vl.layer(bars, average), diff, diff2) .title(`Evolutie geregistreerde besmettingen in ${province_2}`) .data(province_grouped_cases.filter(d => new Date(d.DATE) <= new Date(d4)).filter(d => province_2 == "België" ? true : d.PROVINCE == province_2)) .render() }35:T1272,{ // the bars of the top graph const bars = vl.markBar() .encode( vl.x().fieldT("DATE"), // one bar per day along the x-axis vl.y().sum("CASES"), // for each day, sum the number of new cases vl.opacity().value(0.2), // make the bars less prominent vl.color() .condition([ // last 3 in gray { test: "dayofyear(now()) - dayofyear(datum.DATE) < 4", value: "gray" } ]) .value("steelblue"), // others in green vl.tooltip([ // add a tooltip to the bars {field: "DATE", type: "temporal", title: 'Datum'}, {field: "CASES", aggregate: "sum", title: "Nieuwe gevallen" } ]) ) .width(700); // the average line on the top graph const average = vl.markLine({interpolate: "monotone"}) // draw a slightly smoother line // first calculate some secondary data points .transform( // calculate a rolling average with a window of the 6 days and today vl.groupby("PROVINCE").window(vl.mean("CASES").as("AVG_CASES")).frame([-6,0]), // sum the provinces for each day vl.groupby(["DATE", "INCOMPLETE"]).aggregate(vl.sum("AVG_CASES").as("AVG_CASES_BY_DATE")) ) .encode( vl.x() .fieldT("DATE") .title(null), // disable the title on the x-axis vl.y().sum("AVG_CASES_BY_DATE").title('Nieuwe gevallen per dag'), vl.tooltip().sum("AVG_CASES_BY_DATE") ); // the bottom graph const diff = vl.markBar() // first calculate some secondary data points .transform( // rolling average, same as above vl.groupby("PROVINCE").window(vl.mean("CASES").as("AVG_CASES")).frame([-6,0]), vl.groupby("DATE").aggregate(vl.sum("AVG_CASES").as("AVG_CASES_BY_DATE")), // for each day, add a value containing the value of the previous day vl.window(vl.lag("AVG_CASES_BY_DATE").as("PREV_AVG_CASES_BY_DATE")).frame([-1,0]), // calculate the percentual difference between 2 days vl.calculate('datum.PREV_AVG_CASES_BY_DATE == null ? 0 : (datum.AVG_CASES_BY_DATE / (datum.PREV_AVG_CASES_BY_DATE)) - 1').as('DIFF') ) .encode( // draw the bars as before vl.x().fieldT("DATE").title(null), vl.y().sum("DIFF") .title('Verschil dag ervoor') .axis({format: ".0%"}), // format the values as percentage // color the bars according to a condition vl.color() .condition([ // last 3 in gray { test: "dayofyear(now()) - dayofyear(datum.DATE) < 4", value: "lightgray" }, // positive in orange { test: "datum.DIFF > 0", value: "orange" } ]) .value("green"), // others in green vl.tooltip([ {field: "DATE", type: "temporal", title: 'Datum'}, {field: "DIFF", format: ".2%", title: 'Verschil'} ]) ) .width(700) .height(150); // the bottom graph const diff2 = vl.markBar() // first calculate some secondary data points .transform( // rolling average, same as above vl.groupby("PROVINCE").window(vl.mean("CASES").as("AVG_CASES")).frame([-6,0]), vl.groupby("DATE").aggregate(vl.sum("AVG_CASES").as("AVG_CASES_BY_DATE")), // for each day, add a value containing the value of the previous day vl.window(vl.lag("AVG_CASES_BY_DATE", 7).as("PREV_AVG_CASES_BY_DATE")).frame([-1,0]), // calculate the percentual difference between 2 days vl.calculate('datum.PREV_AVG_CASES_BY_DATE == null ? 0 : (datum.AVG_CASES_BY_DATE / (datum.PREV_AVG_CASES_BY_DATE)) - 1').as('DIFF') ) .encode( // draw the bars as before vl.x().fieldT("DATE").title(null), vl.y().sum("DIFF") .title('Verschil week ervoor') .axis({format: ".0%"}), // format the values as percentage // color the bars according to a condition vl.color() .condition([ // last 3 in gray { test: "dayofyear(now()) - dayofyear(datum.DATE) < 4", value: "lightgray" }, // positive in orange { test: "datum.DIFF > 0", value: "orange" } ]) .value("green"), // others in green vl.tooltip([ {field: "DATE", type: "temporal", title: 'Datum'}, {field: "DIFF", format: ".2%", title: 'Verschil'} ]) ) .width(700) .height(150); // concat the graphs and render return vl.vconcat(vl.layer(bars, average), diff, diff2) .title(`Evolutie in ${province}`) .data(province_grouped_cases .filter(d => new Date(d.DATE) >= new Date("2020-06-02")) .filter(d => new Date(d.DATE) <= new Date(d4)) .filter(d => province == "België" ? true : d.PROVINCE == province) ) .render() }36:T82f,{ // here we use the facet feature of vega to create subplots of each of the values of a data field const dots = vl.markCircle() .encode( vl.x().fieldT("DATE").axis({tickCount: 10}), vl.y().sum("NORMALIZED_CASES"), vl.opacity().value(0.3), vl.tooltip().fieldT('DATE') ); const average = vl.markLine({interpolate: "monotone"}) .transform( //vl.groupby("PROVINCE").window(vl.mean("NORMALIZED_CASES").as("ROLLING")).frame([-3,3]) vl.groupby("PROVINCE").window(vl.mean("NORMALIZED_CASES").as("ROLLING")).frame([-6,0]) ) .encode( vl.x().fieldT("DATE").title(null), vl.y().sum("ROLLING").title('gevallen per dag per 100k inwoners') ); const rule = vl.markRule() .data({"values": [{"name": "20 per 100k inwoners", "ref": 20/14}, {"name": "120 per 100k inwoners", "ref": 120/14}, {"name": "400 per 100k inwoners", "ref": 400/14}]}) .encode( vl.y().fieldQ("ref"), vl.color().fieldN("name") .sort(["20 per 100k inwoners", "120 per 100k inwoners", "400 per 100k inwoners"]) .scale({range: ["orange", "red", "#66023C"]}) .legend({ orient: "none", title:"Incidentie laatste 14 dagen", legendX: 730, legendY: 720 }) ); return vl.layer(rule, dots, average) .width(150).height(250) .facet({field: "PROVINCE", title: null, sort: ["Antwerpen", "Limburg", "Oost-Vlaanderen", "Vlaams-Brabant", "West-Vlaanderen", "Henegouwen", "Luik", "Luxemburg", "Namen", "Waals-Brabant", "Brussel"]}).columns(5) .resolve({scale: {x: 'independent'}}) // plot an x-axis on each graph instead of only at the bottom .data(extended_province_grouped_cases .filter(d => d.PROVINCE) .filter(d => new Date(d.DATE) >= new Date("2020-07-01")) .filter(d => ![d3s, d2, d1].includes(d.DATE)) ) .title({text: "Aantal nieuwe besmettingen per dag per 100k inwoners per provincie", subtitle: "Opgepast, het 7-daags gemiddelde wordt getoond, de drempelwaarden werden omgerekend."}) .render() }37:T4a1,function heatmap(van, tot) { const squares = vl.markRect() .encode( vl.x().fieldN("AGEGROUP").title(null).axis({orient: "top"}), vl.y().fieldN("PROVINCE_CLEAN").title(null), vl.color().sum("DIFF").scale({domainMid: 0, scheme: 'redblue', reverse: true}).legend({title: null}) ); const text = vl.markText() .encode( vl.x().fieldN("AGEGROUP"), vl.y().fieldN("PROVINCE_CLEAN"), vl.text().sum("DIFF") ); const totalAge = vl.markText({fontStyle: "bold"}) .encode( vl.x().fieldN("AGEGROUP").title(null).axis(null), vl.text().sum("DIFF") ); const totalProvince = vl.markText({fontStyle: "bold", align: "right"}) .encode( vl.y().fieldN("PROVINCE_CLEAN").title(null).axis(null), vl.text().sum("DIFF") ); const totalTotal = vl.markText({fontStyle: "bold", align: "right"}) .encode( vl.text().sum("DIFF") ); return vl.hconcat(vl.vconcat(vl.layer(squares, text), totalAge), vl.vconcat(totalProvince, totalTotal)) .data(diff(van, tot)) .title(`Verschil in 7-daags totaal tussen ${van} en ${tot}`) .config({view: {stroke: null, step: 30}}) .render(); }38:T906,function heatmap2(van, tot) { const squares = vl.markRect() .encode( vl.x().fieldN("AGEGROUP").title(null).axis({orient: "top"}), vl.y().fieldN("PROVINCE_CLEAN").title(null), vl.color().sum("CASES").scale({scheme: 'greens'}).legend({title: null, orient: "bottom"}) ); const text = vl.markText() .encode( vl.x().fieldN("AGEGROUP"), vl.y().fieldN("PROVINCE_CLEAN"), vl.text().sum("CASES") ); const totalAge = vl.markText({fontStyle: "bold"}) .encode( vl.x().fieldN("AGEGROUP").title(null).axis(null), vl.text().sum("CASES") ); const totalProvince = vl.markText({fontStyle: "bold", align: "right"}) .encode( vl.y().fieldN("PROVINCE_CLEAN").title(null).axis(null), vl.text().sum("CASES") ); const totalTotal = vl.markText({fontStyle: "bold", align: "right"}) .encode( vl.text().sum("CASES") ); const squares2 = vl.markRect() .encode( vl.x().fieldN("AGEGROUP").title(null).axis({orient: "top"}), vl.y().fieldN("PROVINCE_CLEAN").title(null), vl.color().sum("DIFF").scale({domainMid: 0, scheme: 'redblue', reverse: true}).legend({title: null, orient: "bottom"}) ); const text2 = vl.markText() .encode( vl.x().fieldN("AGEGROUP"), vl.y().fieldN("PROVINCE_CLEAN"), vl.text().sum("DIFF") ); const totalAge2 = vl.markText({fontStyle: "bold"}) .encode( vl.x().fieldN("AGEGROUP").title(null).axis(null), vl.text().sum("DIFF") ); const totalProvince2 = vl.markText({fontStyle: "bold", align: "right"}) .encode( vl.y().fieldN("PROVINCE_CLEAN").title(null).axis(null), vl.text().sum("DIFF") ); const totalTotal2 = vl.markText({fontStyle: "bold", align: "right"}) .encode( vl.text().sum("DIFF") ); return vl.hconcat( vl.hconcat(vl.vconcat(vl.layer(squares, text), totalAge), vl.vconcat(totalProvince, totalTotal)), vl.hconcat(vl.vconcat(vl.layer(squares2, text2), totalAge2), vl.vconcat(totalProvince2, totalTotal2)) ) .data(diff(van, tot)) .title(`Aantal bevestigde gevallen tussen ${van} en ${tot} en het verschil t.o.v. de week voordien`) .resolve({scale: {color: 'independent'}}) .config({view: {stroke: null, step: 35}}) .render(); }39:T81b,function heatmap3(van, tot) { const squares3 = vl.markRect() .encode( vl.x().fieldN("AGEGROUP").title(null).axis({orient: "top"}), vl.y().fieldN("PROVINCE_CLEAN").title(null), vl.color().fieldQ("REL").scale({domainMid: 1, scheme: 'redblue', reverse: true, type: "log", domain: [1/9, 9]}).legend({title: null, orient: "bottom", values: [1/5, 1/3, 1/2, 1, 2, 3, 5]}) ).width(400).height(440); const text3 = vl.markText() .encode( vl.x().fieldN("AGEGROUP"), vl.y().fieldN("PROVINCE_CLEAN"), vl.text().sum("PDIFF").format(".0%") ); const totalAge3 = vl.markText({fontStyle: "bold"}) .transform( vl.groupby("AGEGROUP").aggregate(vl.sum("CASES").as("SUM_CASES"), vl.sum("DIFF").as("SUM_DIFF")), vl.calculate('(datum.SUM_CASES / (datum.SUM_CASES + (-1 * datum.SUM_DIFF))) - 1').as("PDIFF") ) .encode( vl.x().fieldN("AGEGROUP").title(null).axis(null), vl.text().fieldQ("PDIFF").format(".0%") ); const totalProvince3 = vl.markText({fontStyle: "bold", align: "right"}) .transform( vl.groupby("PROVINCE_CLEAN").aggregate(vl.sum("CASES").as("SUM_CASES"), vl.sum("DIFF").as("SUM_DIFF")), vl.calculate('(datum.SUM_CASES / (datum.SUM_CASES + (-1 * datum.SUM_DIFF))) - 1').as("PDIFF") ) .encode( vl.y().fieldN("PROVINCE_CLEAN").title(null).axis(null), vl.text().sum("PDIFF").format(".0%") ); const totalTotal3 = vl.markText({fontStyle: "bold", align: "right"}) .transform( vl.aggregate(vl.sum("CASES").as("SUM_CASES"), vl.sum("DIFF").as("SUM_DIFF")), vl.calculate('(datum.SUM_CASES / (datum.SUM_CASES + (-1 * datum.SUM_DIFF))) - 1').as("PDIFF") ) .encode( vl.text().sum("PDIFF").format(".0%") ); return vl.hconcat(vl.vconcat(vl.layer(squares3, text3), totalAge3), vl.vconcat(totalProvince3, totalTotal3)) .data(diff(van, tot)) .title(`Procentueel verschil tussen ${van} en ${tot}`) .resolve({scale: {color: 'independent'}}) .config({view: {stroke: null, step: 40}}) .render(); }3a:T484,june_young_old_data = { const result = []; Array.from(new Set(june_age_data.map(d => d.DATE))).forEach(date => { const y = june_age_data.filter(d => d.DATE === date && d.AGEGROUP === "0-9")[0].CASES + june_age_data.filter(d => d.DATE === date && d.AGEGROUP === "10-19")[0].CASES + june_age_data.filter(d => d.DATE === date && d.AGEGROUP === "20-29")[0].CASES + june_age_data.filter(d => d.DATE === date && d.AGEGROUP === "30-39")[0].CASES + june_age_data.filter(d => d.DATE === date && d.AGEGROUP === "40-49")[0].CASES + june_age_data.filter(d => d.DATE === date && d.AGEGROUP === "50-59")[0].CASES + june_age_data.filter(d => d.DATE === date && d.AGEGROUP === "60-69")[0].CASES; const o = june_age_data.filter(d => d.DATE === date && d.AGEGROUP === "70-79")[0].CASES + june_age_data.filter(d => d.DATE === date && d.AGEGROUP === "80-89")[0].CASES + june_age_data.filter(d => d.DATE === date && d.AGEGROUP === "90+")[0].CASES; result.push({DATE: date, CASES: y/7, AGE: "0-69"}); result.push({DATE: date, CASES: o/7, AGE: "70+"}); }); return result; }3b:T870,extended_province_grouped_cases = { const result = []; const grouped = d3.rollup(cases, v => d3.sum(v, d => d.CASES), d => d.DATE, d => d.PROVINCE); dates.forEach(date => { provinces.forEach(province => { result.push({ DATE: date, PROVINCE: province, CASES: grouped.get(date).get(province) || 0, NORMALIZED_CASES: 100000*(grouped.get(date).get(province) || 0) / population[province] }); }); const vl = (grouped.get(date).get("WestVlaanderen") || 0) + (grouped.get(date).get("OostVlaanderen") || 0) + (grouped.get(date).get("Antwerpen") || 0) + (grouped.get(date).get("Limburg") || 0) + (grouped.get(date).get("VlaamsBrabant") || 0); const wal = (grouped.get(date).get("Liège") || 0) + (grouped.get(date).get("BrabantWallon") || 0) + (grouped.get(date).get("Hainaut") || 0) + (grouped.get(date).get("Namur") || 0) + (grouped.get(date).get("Luxembourg") || 0); const bel = vl + wal + (grouped.get(date).get("Brussels") || 0)+ (grouped.get(date).get(undefined) || 0); result.push({DATE: date, PROVINCE: "Vlaanderen", CASES: vl, NORMALIZED_CASES: 100000*vl / population["Vlaanderen"]}); result.push({DATE: date, PROVINCE: "Wallonië", CASES: wal, NORMALIZED_CASES: 100000*wal / population["Wallonië"]}); result.push({DATE: date, PROVINCE: "België", CASES: bel, NORMALIZED_CASES: 100000*bel / population["België"]}); }); result.forEach(d => { if(d.PROVINCE === "WestVlaanderen") d.PROVINCE = "West-Vlaanderen"; if(d.PROVINCE === "OostVlaanderen") d.PROVINCE = "Oost-Vlaanderen"; if(d.PROVINCE === "VlaamsBrabant") d.PROVINCE = "Vlaams-Brabant"; if(d.PROVINCE === "Liège") d.PROVINCE = "Luik"; if(d.PROVINCE === "BrabantWallon") d.PROVINCE = "Waals-Brabant"; if(d.PROVINCE === "Hainaut") d.PROVINCE = "Henegouwen"; if(d.PROVINCE === "Namur") d.PROVINCE = "Namen"; if(d.PROVINCE === "Luxembourg") d.PROVINCE = "Luxemburg"; if(d.PROVINCE === "Brussels") d.PROVINCE = "Brussel"; }); return result; }3c:[] 32:{"id":"115f464bd1a02611","slug":"analyse-van-de-covid-cijfers-in-belgie","title":"Analyse van de COVID cijfers in België","thumbnail":"f1524756876452b168c69c79813b56f9994a3a4a14000f14bea9c899ca8b7572","access_level":"public","create_time":"$D2020-10-25T23:45:43.299Z","update_time":"$D2020-10-30T23:46:07.934Z","view_time":null,"first_public_time":"$D2020-10-26T00:03:36.091Z","has_unpublished_edits":null,"fork":{"id":"40cafeedda68be82","slug":"evolutie-positieve-testen","title":"Analyse van de COVID cijfers in België","owner":{"id":"1f20537af0cde79f","login":"bmesuere","name":"Bart Mesuere"}},"stars":0,"starred":false,"owner":{"type":"team","id":"23bcab3525eb4001","login":"floriandierickx","name":"Florian Dierickx","avatar_url":"https://avatars-next.observableusercontent.com/a783d31aeadf2d2231993b6469f92f6cea4e568911e94b9e7a1735d8431298e8","tier":"starter_2024"},"version":1403,"publish_version":1403,"role":null,"authored":false,"authors":[{"type":"individual","id":"095be1994b643ecb","login":"floriandierickx","name":"Florian Dierickx","avatar_url":"https://avatars-next.observableusercontent.com/a783d31aeadf2d2231993b6469f92f6cea4e568911e94b9e7a1735d8431298e8","tier":null}],"members":[{"type":"individual","id":"095be1994b643ecb","login":"floriandierickx","name":"Florian Dierickx","avatar_url":"https://avatars-next.observableusercontent.com/a783d31aeadf2d2231993b6469f92f6cea4e568911e94b9e7a1735d8431298e8","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Analyse van de COVID cijfers in België","cells":[{"id":0,"value":"md`# Analyse van de COVID cijfers in België\n\n> In een vorige versie van deze notebook stond en analyse naar aanleiding van enkele berichten in de media op 27 juni. Deze historische versie is te bekijken op [https://observablehq.com/@bmesuere/analyse-van-de-data-nav-berichten-in-de-media-over-een-stijgi?collection=@bmesuere/covid](https://observablehq.com/@bmesuere/analyse-van-de-data-nav-berichten-in-de-media-over-een-stijgi?collection=@bmesuere/covid). De notebook hieronder is aangepast om te focussen op meer recente inzichten.\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1073,"value":"md`**Overzicht**\n* [Algemene trend](#algemene_trend)\n* [Trend laatste maanden](#laatste_maanden)\n* [Per provincie](#per_provincie)\n* [Per leeftijd](#per_leeftijd)\n* [Jong vs. Oud](#jong_oud)\n* [Vergelijking vorige week](#vorige_week)\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":331,"value":"algemene_trend = md`

\n## Algemene trend\n\nHieronder zien we een aantal grafieken die het aantal positieve gevallen weergeeft. Op de bovenste grafiek zien we telkens per dag een balkje dat het aantal positieve testen weergeeft dat die dag werd afgenomen. De cijfers van de laatste 2-3 dagen zijn dus niet niet volledig aangezien de resultaten daarvan nog niet allemaal gekend zijn. De volle lijn geeft het 7-daags gemiddelde weer.\n\nDaaronder zien we 2 staafdiagrammen die de evolutie van het 7-daags gemiddelde weergeven. De eerste vergelijkt met de dag ervoor, de onderste met een week geleden. Dit laatste cijfer wordt tegenwoordig gerapporteerd door de pers.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1346,"value":"viewof province_2 = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":308,"value":"laatste_maanden = md`

\n## De laatste maanden\nOm een duidelijker beeld te krijgen, kunnen we inzoomen op de laatste paar weken. Je kan ook filteren op 1 provincie:`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1011,"value":"viewof province = html``","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":311,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":82,"value":"per_provincie = md`

\n## Per provincie\nWe kunnen dezelfde grafiek ook opstellen per provincie.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":79,"value":"{\n // here we use the facet feature of vega to create subplots of each of the values of a data field\n const dots = vl.markCircle()\n .encode(\n vl.x().fieldT(\"DATE\"),\n vl.y().sum(\"CASES\"),\n vl.tooltip().sum(\"CASES\"),\n vl.opacity().value(0.3)\n );\n \n const average = vl.markLine({interpolate: \"monotone\"})\n .transform(\n vl.groupby(\"PROVINCE\").window(vl.mean(\"CASES\").as(\"ROLLING\")).frame([-3,3])\n )\n .encode(\n vl.x().fieldT(\"DATE\").title(null),\n vl.y().sum(\"ROLLING\").title('Nieuwe gevallen per dag')\n );\n \n return vl.layer(dots, average)\n .width(150).height(150)\n .facet(vl.field('PROVINCE')).columns(4) // create a graph for each province, limit to 4 columns\n .resolve({scale: {x: 'independent'}}) // plot an x-axis on each graph instead of only at the bottom\n .data(province_grouped_cases)\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":342,"value":"md`Of als we inzoomen op de periode sinds 1 juni:`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":339,"value":"{\n // here we use the facet feature of vega to create subplots of each of the values of a data field\n const dots = vl.markCircle()\n .encode(\n vl.x().fieldT(\"DATE\"),\n vl.y().sum(\"CASES\"),\n vl.opacity().value(0.3)\n );\n \n const average = vl.markLine({interpolate: \"monotone\"})\n .transform(\n //vl.groupby(\"PROVINCE\").window(vl.mean(\"CASES\").as(\"ROLLING\")).frame([-3,3])\n vl.groupby(\"PROVINCE\").window(vl.mean(\"CASES\").as(\"ROLLING\")).frame([-6,0])\n )\n .encode(\n vl.x().fieldT(\"DATE\").title(null),\n vl.y().sum(\"ROLLING\").title('Nieuwe gevallen per dag')\n );\n \n return vl.layer(dots, average)\n .width(150).height(150)\n .facet(vl.field('PROVINCE')).columns(4) // create a graph for each province, limit to 4 columns\n .resolve({scale: {x: 'independent', y: 'independent'}}) // plot an x-axis on each graph instead of only at the bottom\n .data(province_grouped_cases.filter(d => new Date(d.DATE) >= new Date(\"2020-06-01\")))\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":784,"value":"md`1 provincie uitgelicht:`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1034,"value":"viewof province_detail = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":549,"value":"{\n // here we use the facet feature of vega to create subplots of each of the values of a data field\n const dots = vl.markCircle()\n .encode(\n vl.x().fieldT(\"DATE\"),\n vl.y().sum(\"CASES\"),\n vl.opacity().value(0.3),\n vl.tooltip().fieldT(\"DATE\")\n );\n \n const average = vl.markLine({interpolate: \"monotone\"})\n .transform(\n vl.groupby(\"PROVINCE\").window(vl.mean(\"CASES\").as(\"ROLLING\")).frame([-6,0])\n )\n .encode(\n vl.x().fieldT(\"DATE\").title(null),\n vl.y().sum(\"ROLLING\").title('Nieuwe gevallen per dag')\n );\n \n return vl.layer(dots, average)\n .title (province_detail)\n //.width(150).height(150)\n .data(extended_province_grouped_cases\n .filter(d => d.PROVINCE === province_detail)\n .filter(d => new Date(d.DATE) >= new Date(\"2020-06-01\"))\n .filter(d => ![d3s, d2, d1].includes(d.DATE)))\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1258,"value":"md`1 leeftijdscategorie uitgelicht`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1255,"value":"viewof agegroup_detail = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1215,"value":"{\n // here we use the facet feature of vega to create subplots of each of the values of a data field\n const dots = vl.markCircle()\n .encode(\n vl.x().fieldT(\"DATE\"),\n vl.y().sum(\"CASES\"),\n vl.opacity().value(0.3),\n vl.tooltip().fieldT(\"DATE\")\n );\n \n const average = vl.markLine({interpolate: \"monotone\"})\n .transform(\n vl.groupby(\"AGEGROUP\").window(vl.mean(\"CASES\").as(\"ROLLING\")).frame([-6,0])\n )\n .encode(\n vl.x().fieldT(\"DATE\").title(null),\n vl.y().sum(\"ROLLING\").title('Nieuwe gevallen per dag')\n );\n \n return vl.layer(dots, average)\n .title (agegroup_detail)\n //.width(150).height(150)\n .data(age_grouped_cases\n .filter(d => d.AGEGROUP == agegroup_detail)\n .filter(d => new Date(d.DATE) >= new Date(\"2020-06-01\"))\n .filter(d => ![d3s, d2, d1].includes(d.DATE)))\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1308,"value":"{\n // here we use the facet feature of vega to create subplots of each of the values of a data field\n const dots = vl.markCircle()\n .encode(\n vl.x().fieldT(\"DATE\"),\n vl.y().sum(\"CASES\"),\n vl.opacity().value(0.3),\n vl.tooltip().fieldT(\"DATE\")\n );\n \n const average = vl.markLine({interpolate: \"monotone\"})\n .transform(\n vl.groupby(\"AGEGROUP\").window(vl.mean(\"CASES\").as(\"ROLLING\")).frame([-6,0])\n )\n .encode(\n vl.x().fieldT(\"DATE\").title(null),\n vl.y().sum(\"ROLLING\").title('Nieuwe gevallen per dag'),\n vl.color().fieldN(\"AGEGROUP\").title(\"Leeftijdscategorie\")\n );\n \n return vl.layer(average)\n .title (\"Evolutie aantal gevallen 0-29 jaar\")\n //.width(150).height(150)\n .data(age_grouped_cases\n .filter(d => d.AGEGROUP == \"0-9\" || d.AGEGROUP == \"10-19\" || d.AGEGROUP == \"20-29\")\n .filter(d => new Date(d.DATE) >= new Date(\"2020-06-01\"))\n .filter(d => ![d3s, d2, d1].includes(d.DATE)))\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":574,"value":"md`We kunnen ook normaliseren voor het aantal inwoners. De oranje lijn geeft de drempel van 60 besmettingen per 100000 inwoners over een periode van 2 weken weer.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":710,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":87,"value":"md`## Lopend totaal\nIn plaats van het lopend gemiddelde kunnen we ook naar het lopend totaal over de laatste 7 dagen kijken, dit toont exact dezelfde trend als de volle lijn in de grafieken hierboven. Merk op dat de resultaten van de laatste 3 dagen nog niet compleet zijn.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":795,"value":"{\n // here we use the facet feature of vega to create subplots of each of the values of a data field\n const dots = vl.markLine()\n .encode(\n vl.x().fieldT(\"DATE\").title(null),\n vl.y().sum(\"CASES\").title('Nieuwe gevallen laatste week'),\n vl.tooltip(vl.sum(\"CASES\"))\n );\n \n return vl.layer(dots)\n .width(150).height(150)\n .facet(vl.field('PROVINCE')).columns(4) // create a graph for each province, limit to 4 columns\n .resolve({scale: {x: 'independent'}}) // plot an x-axis on each graph instead of only at the bottom\n .data(june_province_data)\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":141,"value":"md`We kunnen op dezelde manier naar de data kijken per leeftijdsgroep.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":144,"value":"{\n // here we use the facet feature of vega to create subplots of each of the values of a data field\n const dots = vl.markLine()\n .encode(\n vl.x().fieldT(\"DATE\").title(null),\n vl.y().sum(\"CASES\").title('Som nieuwe gevallen per 7 dagen'),\n vl.tooltip(vl.sum(\"CASES\"))\n );\n \n return vl.layer(dots)\n .width(150).height(150)\n .facet(vl.field('AGEGROUP')).columns(4) // create a graph for each province, limit to 4 columns\n .resolve({scale: {x: 'independent'}}) // plot an x-axis on each graph instead of only at the bottom\n .data(june_age_data\n .filter(d => ![d3s, d2, d1].includes(d.DATE)))\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":184,"value":"md`We kunnen ook beide parameters apart plotten, we krijgen dan deze gigantische grafiek:`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":174,"value":"{\n // here we use the facet feature of vega to create subplots of each of the values of a data field\n const dots = vl.markLine()\n .encode(\n vl.x().fieldT(\"DATE\").title(null),\n vl.y().sum(\"CASES\").title(null),\n vl.tooltip(vl.sum(\"CASES\"))\n );\n \n return vl.layer(dots)\n .width(60).height(60)\n .facet({column: vl.field('AGEGROUP'), row: vl.field('PROVINCE')})\n .resolve({scale: {x: 'independent'}}) // plot an x-axis on each graph instead of only at the bottom\n .data(june_data\n .filter(d => ![d3s, d2, d1].includes(d.DATE)))\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1082,"value":"per_leeftijd = md`

\n## Per leeftijd`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1058,"value":"viewof province_age = html``","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":818,"value":"{\n // here we use the facet feature of vega to create subplots of each of the values of a data field\n const dots = vl.markLine()\n .encode(\n vl.x().fieldT(\"DATE\").title(null),\n vl.y().sum(\"CASES\").title(null)/*.scale({type: \"log\", domainMin: 1, clamp: true})*/,\n vl.color().fieldN(\"AGEGROUP\").title(null),\n vl.tooltip(vl.sum(\"CASES\"))\n );\n \n return vl.layer(dots)\n .width(500).height(500)\n .title(\"7-daags gemiddelde van aantal nieuwe gevallen per dag in \" + province_age)\n //.facet(vl.field('AGEGROUP')).columns(4)\n .resolve({scale: {x: 'independent'}}) // plot an x-axis on each graph instead of only at the bottom\n .data(june_data.filter(d => d.PROVINCE == province_age)\n .filter(d => ![d3s, d2, d1].includes(d.DATE)))\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":907,"value":"jong_oud = md`

\n## Verdeling tussen jong en oud`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":894,"value":"{\n // here we use the facet feature of vega to create subplots of each of the values of a data field\n const dots = vl.markLine()\n .encode(\n vl.x().fieldT(\"DATE\").title(null),\n vl.y().sum(\"CASES\").title(null).scale({type: \"log\"}),\n vl.color().fieldN(\"AGE\").title(\"Leeftijd\").legend({orient: \"bottom\"}),\n vl.tooltip(vl.sum(\"CASES\"))\n );\n \n return vl.vconcat(dots)\n .title(\"Gemiddeld aantal bevestigde besmettingen per dag, log-plot\")\n //.facet(vl.field('AGEGROUP')).columns(4)\n .resolve({scale: {x: 'independent'}}) // plot an x-axis on each graph instead of only at the bottom\n .data(june_young_old_data.filter(d => ![d3s, d2, d1].includes(d.DATE)))\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":222,"value":"vorige_week = md`

\n## Vergelijking met vorige week en maand\nAls we enkel het lopende totaal van 2 dagen willen vergelijken, dan is een heatmap een betere voorstelling. We kunnen bv gisteren met vandaag vergelijken:`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":240,"value":"heatmap(day_before, d4)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":191,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":267,"value":"md`Sciensano vergelijkt telkens de cijfers met de week voordien:`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":613,"value":"heatmap2(week_before, d4)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":635,"value":"md`Gevallen zonder leeftijd of woonplaats werden niet opgenomen in de grafiek.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1120,"value":"heatmap3(week_before, d4)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":610,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1117,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":203,"value":"function diff (van, tot) {\n const result = [];\n const grouped = d3.rollup(june_data, v => v, d => d.PROVINCE, d => d.AGEGROUP, d => d.DATE);\n provinces.forEach(province => {\n agegroups.forEach(group => {\n const c = grouped.get(province) ? grouped.get(province).get(group) : null;\n if (c) {\n const p = (c.get(tot)[0].CASES / c.get(van)[0].CASES) -1;\n const rel = c.get(tot)[0].CASES / c.get(van)[0].CASES;\n const d = c.get(tot)[0].CASES - c.get(van)[0].CASES;\n const e = c.get(tot)[0].CASES;\n result.push({DATE: tot, PROVINCE: province, PROVINCE_CLEAN: provinceClean[province], AGEGROUP: group, DIFF: d, CASES: e, PDIFF: p, REL: rel});\n }\n });\n });\n return result; \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":518,"value":"today = d.timeFormat(\"%Y-%m-%d\")(d.timeHour.offset(Date.now(), -4))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":750,"value":"d1 = d.timeFormat(\"%Y-%m-%d\")(d.timeDay.offset(Date.parse(today), -1))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":743,"value":"d2 = d.timeFormat(\"%Y-%m-%d\")(d.timeDay.offset(Date.parse(today), -2))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":742,"value":"d3s = d.timeFormat(\"%Y-%m-%d\")(d.timeDay.offset(Date.parse(today), -3))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":525,"value":"d4 = d.timeFormat(\"%Y-%m-%d\")(d.timeDay.offset(Date.parse(today), -4))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":532,"value":"day_before = d.timeFormat(\"%Y-%m-%d\")(d.timeDay.offset(Date.parse(d4), -1))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":535,"value":"week_before = d.timeFormat(\"%Y-%m-%d\")(d.timeDay.offset(Date.parse(d4), -7))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":538,"value":"two_weeks_before = d.timeFormat(\"%Y-%m-%d\")(d.timeDay.offset(Date.parse(d4), -14))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":542,"value":"month_before = d.timeFormat(\"%Y-%m-%d\")(d.timeDay.offset(Date.parse(d4), -30))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":888,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":90,"value":"june_province_data = {\n const result = [];\n const june_cases = province_grouped_cases.filter(l => l.DATE && (l.DATE.startsWith('2020-06') || l.DATE.startsWith('2020-07') || l.DATE.startsWith('2020-08')|| l.DATE.startsWith('2020-09')|| l.DATE.startsWith('2020-10')));\n const by_province = new Map(d3.groups(june_cases, d => d.PROVINCE))\n provinces.forEach(province => {\n const c = by_province.get(province);\n for (let i = 6; i < c.length; i++) {\n const sum = c[i-6].CASES + c[i-5].CASES + c[i-4].CASES + c[i-3].CASES + c[i-2].CASES + c[i-1].CASES + c[i-0].CASES;\n result.push({DATE: c[i].DATE, PROVINCE: province, CASES: sum});\n }\n });\n return result; \n \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":139,"value":"june_age_data = {\n const result = [];\n const june_cases = age_grouped_cases.filter(l => l.DATE && (l.DATE.startsWith('2020-06') || l.DATE.startsWith('2020-07') || l.DATE.startsWith('2020-08')|| l.DATE.startsWith('2020-09')|| l.DATE.startsWith('2020-10')));\n const by_group = new Map(d3.groups(june_cases, d => d.AGEGROUP))\n agegroups.forEach(group => {\n const c = by_group.get(group);\n for (let i = 6; i < c.length; i++) {\n const sum = c[i-6].CASES + c[i-5].CASES + c[i-4].CASES + c[i-3].CASES + c[i-2].CASES + c[i-1].CASES + c[i-0].CASES;\n result.push({DATE: c[i].DATE, AGEGROUP: group, CASES: sum});\n }\n });\n return result; \n \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":161,"value":"june_data = {\n const result = [];\n const june_cases = grouped_cases.filter(l => l.DATE && (l.DATE.startsWith('2020-06') || l.DATE.startsWith('2020-07') || l.DATE.startsWith('2020-08')|| l.DATE.startsWith('2020-09')|| l.DATE.startsWith('2020-10')));\n const grouped = d3.rollup(june_cases, v => v, d => d.PROVINCE, d => d.AGEGROUP);\n provinces.forEach(province => {\n agegroups.forEach(group => {\n const c = grouped.get(province).get(group);\n for (let i = 6; i < c.length; i++) {\n const sum = c[i-6].CASES + c[i-5].CASES + c[i-4].CASES + c[i-3].CASES + c[i-2].CASES + c[i-1].CASES + c[i-0].CASES;\n result.push({DATE: c[i].DATE, PROVINCE: province, AGEGROUP: group, CASES: sum});\n }\n });\n });\n return result.filter(r => r.PROVINCE !== undefined && r.AGEGROUP !== undefined); \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"cases = (await fetch('https://epistat.sciensano.be/Data/COVID19BE_CASES_AGESEX.json')).json()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"province_grouped_cases = {\n let result = [];\n const grouped = d3.rollup(cases, v => d3.sum(v, d => d.CASES), d => d.DATE, d => d.PROVINCE);\n dates.forEach(date => {\n provinces.forEach(province => {\n result.push({DATE: date, PROVINCE: province, CASES: grouped.get(date).get(province) || 0});\n });\n });\n result = result.map(d => {\n d.INCOMPLETE = false;\n if ([d3s, d2, d1].includes(d.DATE)) d.INCOMPLETE = true;\n return d;\n });\n return result;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":720,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":134,"value":"age_grouped_cases = {\n const result = [];\n const grouped = d3.rollup(cases, v => d3.sum(v, d => d.CASES), d => d.DATE, d => d.AGEGROUP);\n dates.forEach(date => {\n agegroups.forEach(group => {\n result.push({DATE: date, AGEGROUP: group, CASES: grouped.get(date).get(group) || 0});\n });\n });\n return result;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":156,"value":"grouped_cases = {\n const result = [];\n const grouped = d3.rollup(cases, v => d3.sum(v, d => d.CASES), d => d.DATE, d => d.PROVINCE, d => d.AGEGROUP);\n dates.forEach(date => {\n provinces.forEach(province => {\n agegroups.forEach(group => {\n result.push({DATE: date, PROVINCE: province, AGEGROUP: group, CASES: grouped.get(date).get(province) ? grouped.get(date).get(province).get(group) || 0 : 0});\n });\n });\n });\n return result;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":46,"value":"dates = Array.from(new Set(cases.map(c => c.DATE)))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":49,"value":"provinces = Array.from(new Set(cases.map(c => c.PROVINCE)))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1039,"value":"cleanProvinces = Array.from(new Set(extended_province_grouped_cases.map(d => d.PROVINCE)))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1044,"value":"provinceClean = {\n return {\n Antwerpen: \"Antwerpen\",\n Brussels: \"Brussel\",\n Liège: \"Luik\",\n Limburg: \"Limburg\",\n OostVlaanderen: \"Oost-Vlaanderen\",\n VlaamsBrabant: \"Vlaams-Brabant\",\n BrabantWallon: \"Waals-Brabant\",\n WestVlaanderen: \"West-Vlaanderen\",\n Hainaut: \"Henegouwen\",\n Namur: \"Namen\",\n Luxembourg: \"Luxemburg\"\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":129,"value":"agegroups = Array.from(new Set(cases.map(c => c.AGEGROUP))).sort()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":569,"value":"population = {\n return {\n Antwerpen: 1869730,\n Brussels: 1218255,\n Liège: 1109800,\n Limburg: 877370,\n OostVlaanderen: 1525255,\n VlaamsBrabant: 1155843,\n WestVlaanderen: 1200945,\n BrabantWallon: 406019,\n Hainaut: 1346840,\n Namur: 495832,\n Luxembourg: 286752,\n Vlaanderen: 6629143,\n Wallonië: 3645243,\n België: 11492641,\n };\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"d3 = require(\"d3-array@^2.2\")\nd3 = require(\"d3@6\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":520,"value":"d = require(\"d3@5\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1387,"value":"tests_raw = (await(await fetch(\"https://epistat.sciensano.be/Data/COVID19BE_tests.json\")).json())\n .filter(d => d.DATE != \"2020-10-27\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1389,"value":"tests_by_day = d3.rollup(tests, v => {return {TESTS_ALL: v[0].TESTS_ALL, TESTS_ALL_POS: v[0].TESTS_ALL_POS}}, d => d.DATE)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1392,"value":"tests = {\n const results = [];\n d3.rollup((await (await fetch(\"https://epistat.sciensano.be/Data/COVID19BE_tests.json\")).json()), \n v => {return {TESTS_ALL : d3.sum(v, d => d.TESTS_ALL), TESTS_ALL_POS : d3.sum(v, d => d.TESTS_ALL_POS)}}, d => d.DATE)\n .forEach((tests, date) => {results.push({DATE: date, TESTS_ALL: tests.TESTS_ALL, TESTS_ALL_POS: tests.TESTS_ALL_POS})\n })\n return results;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1396,"value":"cases_by_day = d3.rollup(cases, v => d3.sum(v, d => d.CASES), d => d.DATE)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1394,"value":"test_positivity_rate = dates.map(d => {\n return {\n DATE: d,\n TESTS: tests_by_day.get(d).TESTS_ALL,\n TESTS_POS: tests_by_day.get(d).TESTS_ALL_POS,\n CASES: cases_by_day.get(d),\n RATE: tests_by_day.get(d).TESTS_ALL_POS / tests_by_day.get(d).TESTS_ALL\n };\n}).slice(0, -2)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1383,"value":"printTable(tests_raw\n .map(d => {return {DATE: d.DATE, DAY: d3.timeFormat(\"%a\")(new Date(d.DATE)), PROVINCE: d.PROVINCE, TESTS: d.TESTS_ALL, TESTS_POS: d.TESTS_ALL_POS, RATIO: d3.format(\".1%\")(d.TESTS_ALL_POS / d.TESTS_ALL)}})\n .filter(d => province_rate == \"België\" ? d.PROVINCE : d.PROVINCE == province_rate)\n .slice(province_rate == \"België\" ? -22 : -14)\n .slice(0, province_rate == \"België\" ? 11 : 14))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"import {vl} from '@vega/vega-lite-api'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1402,"value":"d3 = require(\"d3@6\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1385,"value":"import {printTable} from '@uwdata/data-utilities'","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W3c","stdlib":"1","titling":"auto"}} 33:"$32:body"