2b:null 2c:["$","title",null,{"children":"Neugründungsstatistik - wird abgelöst | Office for Statistics and Data of the Canton of Zurich | Observable"}] 31:T1240,viewof plot_neugruendungen_total = { // The selection const initDate = d3.timeFormat("%Y-%m-%d")(new Date()); // The selection const hover = vl .selectSingle("hover") .on("mouseover") .encodings("x") .nearest(true) .clear("moouseout") .init({ x: { year: 2022, month: +d3.timeFormat("%m")(new Date(initDate)), date: +d3.timeFormat("%d")(new Date(initDate)) - 8 } }); // predicate to test if a point is hover-selected // return false if the selection is empty const isHovered = hover.empty(false); // The line and point marks. Notice how we filter the points on hover const lineAndPoint = vl .layer( vl.markLine(), vl .markPoint(vl.opacity().if(isHovered, vl.value(1)).value(0), { size: 60, filled: true }) .transform(vl.filter(hover)) ) .encode( vl.y().fieldQ("total").scale({ domain: y_domain }), vl .color() .field("year") .scale({ domain: years, range: [ "#009EE0", //2018, 2019 - Pre-Corona years "#00407C", //2018, 2019 - Pre-Corona years "#9572D5", //2020, 2021 - Corona years "#54268E", //2020, 2021 - Corona years "#B01657", // previous year, 1st post-Corona year "#DC7700" // current year, 2nd post-Corona year // "#D40053" ] }) ); // The rule helps as a proxy for the hover. We draw rules all over the chart // so we can easily find the nearest one. We then hide them using opacity 0 const rule = vl .markRule({ strokeWidth: 0.5, tooltip: true }) // We pivot the data so we can show all the stock prices at once .data( tidy( rolled_AlleBranchen.filter( (d) => d.location == neugruendungen_selectGebiet ), select(["-location", "-industry", "-total"]), pivotWider({ namesFrom: "year", valuesFrom: "tooltipValue", valuesFill: "-" }), groupBy(["xTime", "tooltipDate"]) ) ) // .transform(vl.pivot("year").value("total").groupby(["xTime"])) .encode( vl.opacity().value(0).if(hover, vl.value(1)).value(0), vl.tooltip([ { field: "tooltipDate", title: "Tag" }, yearsNachGebiet[0], yearsNachGebiet[1], yearsNachGebiet[2], yearsNachGebiet[3], yearsNachGebiet[4], yearsNachGebiet[5] ]) ) .select(hover); return ( vl .layer(lineAndPoint, rule) .encode(vl.x().fieldT("xTime")) .data( rolled_AlleBranchen.filter( (d) => d.location == neugruendungen_selectGebiet ) ) .width(plotWidth(1)) // specifying responsive width .height(500) // .title({ // text: "Anzahl Neugründungen, " + neugruendungen_selectGebiet, // subtitle: "Kumulierte Tageswerte" // }) .autosize({ resize: true, type: "fit" }) .config({ locale: { number: localeNumber, time: localeTime }, view: { stroke: null }, // scale: { bandPaddingInner: 0.2, bandPaddingOuter: 0.2, stroke: 2 }, background: "#ffffff", axis: { labelFont: "Arial Black", labelFontSize: 12, labelColor: "black", labelPadding: 7, titleFont: "Arial Black", titleFontSize: 13, titleFontColor: "black", domainColor: "#666666", tickColor: "#666666" }, axisX: { tickCount: plotWidth(1) / 120, format: "%b", title: null, grid: false }, axisY: { title: null, tickCount: height / 120, labelLimit: 300, gridColor: "#cccccc" }, title: { font: "Arial Black", fontSize: 16, offset: 15, anchor: "start", lineHeight: 1.5, padding: 50, subtitleFont: "Arial", subtitleFontSize: 14 }, header: { labelOrient: "top", labelPadding: 5, lineHeight: 3, labelFontSize: 13, labelFont: "Arial Black", labelColor: "black" }, legend: { orient: "top", symbolSize: 250, labelFontSize: 14, labelFont: "Arial", labelColor: "black", labelLimit: 300, columns: 6, // responsive legend width rowPadding: 10, title: null }, text: { font: "Arial", fontSize: 16, color: "black" } }) .render() ); }32:T8d4,viewof plotByIndustries = { const all_plots = []; for (var i = 0; i < selectedIndustries.length; i++) { var plot_i = draw_plotComparison(i); all_plots.push(plot_i); } return ( vl .vconcat(all_plots) // .title({ // text: "Anzahl Konkurseröffnungen nach Branchen, Kanton Zürich", // subtitle: `Kumulierte Tageswerte (${yearsAll[1]}, ${yearsAll[0]}) und gleitender Durchschnitt` // }) .autosize({ resize: true, type: "fit" }) .config({ color: color, locale: { number: localeNumber, time: localeTime }, view: { stroke: null }, // scale: { bandPaddingInner: 0.2, bandPaddingOuter: 0.2, stroke: 2 }, background: "#ffffff", concat: { spacing: 50 }, axis: { labelFont: "Arial Black", labelFontSize: 12, labelColor: "black", labelPadding: 7, titleFont: "Arial Black", titleFontSize: 13, titleFontColor: "black", domainColor: "#666666", tickColor: "#666666" }, axisX: { tickCount: plotWidth(1) / 120, format: "%b", title: null, grid: false }, axisY: { title: null, tickCount: height / 120, labelLimit: 300, gridColor: "#cccccc" }, title: { font: "Arial Black", fontSize: 16, offset: 15, anchor: "start", lineHeight: 5, padding: 40, subtitleFont: "Arial", subtitleFontSize: 14 }, header: { labelOrient: "top", labelPadding: 5, lineHeight: 3, labelFontSize: 13, labelFont: "Arial Black", labelColor: "black" }, legend: { orient: "top", symbolSize: 200, labelFontSize: 14, labelFont: "Arial", labelColor: "black", labelLimit: 300, columns: nCol_Legend, // responsive legend width rowPadding: 10, title: null }, text: { align: "center", fill: "black", fontSize: 12, dx: 0 } }) .render() ); }33:T69a,// Compute the moving average for the pre-Corona years (2017, 2018, 2019) for all selected industries. selectedIndustries_movingAvg_comparisonA = { const dataset = []; for (var index = 0; index < selectedIndustries.length; ++index) { let returnedData = tidy( importedData, filter( (d) => d.industry == selectedIndustries[index] && // walk through selected industries d.location == "ZH" // limited to Kanton Zürich ), groupBy( ["year", "industry"], mutateWithSummary({ cumsum: cumsum("value") }) ), // compute the cumsum by year filter( (d) => d.year == yearsAll[6] || d.year == yearsAll[5] || d.year == yearsAll[4] ), // Comparison period of 4 years, excluding the most recent 2 years groupBy( ["xTime", "industry"], // group on the shared x scale value summarize({ min: min("cumsum"), // compute aggregated data with low, high and center values mean: mean("cumsum"), max: max("cumsum") }) ), mutateWithSummary({ movingAvg: roll(7, mean("mean"), { partial: true, align: "left" }) // rolling 7 day average with current row as the first item }), mutate({ year: (d) => `∅ ${yearsAll[6]}-${yearsAll[4]}` }), mutate({ value: (d) => d.movingAvg, tooltipDate: (d) => formatDate(d.xTime), tooltipValue: (d) => d.value.toFixed(1) }), select([ "year", "xTime", "tooltipDate", "industry", "value", "tooltipValue" ]) ); dataset.push(returnedData); } return dataset; }34:T648,// Compute the moving average for the Corona years (2020-2021) for all selected industries. selectedIndustries_movingAvg_comparisonB = { const dataset = []; for (var index = 0; index < selectedIndustries.length; ++index) { let returnedData = tidy( importedData, filter( (d) => d.industry == selectedIndustries[index] && // walk through selected industries d.location == "ZH" // limited to Kanton Zürich ), groupBy( ["year", "industry"], mutateWithSummary({ cumsum: cumsum("value") }) ), // compute the cumsum by year filter((d) => d.year == yearsAll[2] || d.year == yearsAll[3]), // Comparison period of 4 years, excluding the most recent 2 years groupBy( ["xTime", "industry"], // group on the shared x scale value summarize({ min: min("cumsum"), // compute aggregated data with low, high and center values mean: mean("cumsum"), max: max("cumsum") }) ), mutateWithSummary({ movingAvg: roll(7, mean("mean"), { partial: true, align: "left" }) // rolling 7 day average with current row as the first item }), mutate({ year: (d) => `∅ ${yearsAll[3]}-${yearsAll[2]}` }), mutate({ value: (d) => d.movingAvg, tooltipDate: (d) => formatDate(d.xTime), tooltipValue: (d) => d.value.toFixed(1) }), select([ "year", "xTime", "tooltipDate", "industry", "value", "tooltipValue" ]) ); dataset.push(returnedData); } return dataset; }35:T1afc,// function to draw plots based on index for select industry function draw_plot(index_selectedIndustry) { // const annotation_data = [ // { // x: `2022-07-01T23:00Z`, // x2: `2022-07-01T23:00Z`, // y2: selectedIndustries_movingAvg_comparisonA[ // index_selectedIndustry // ][182].min, // y: // selectedIndustries_movingAvg_comparisonA[ // index_selectedIndustry // ][182].min * 0.6, // label_top: "Bandbreite der Konkurse", // labels: `zwischen ${d3.min(years)}-${d3.max(years) - 2}` // } // ]; let plot = vl .layer( // Adding the annotation for the band width // vl // .markRule({ color: "#000", strokeWidth: 1 }) // .data({ // values: annotation_data // }) // .encode( // vl.x().fieldT("x"), // vl.x2().fieldT("x2"), // vl.y({ type: "quantitative", field: "y" }), // vl.y2({ type: "quantitative", field: "y2" }), // vl.text({ type: "nominal", field: "labels" }) // ), // vl // .markText({ align: "left", dx: 5, dy: -20 }) // .data({ // values: annotation_data // }) // .encode( // vl.x().fieldT("x"), // vl.x2().fieldT("x2"), // vl.y({ type: "quantitative", field: "y" }), // vl.y2({ type: "quantitative", field: "y2" }), // vl.text({ type: "nominal", field: "label_top" }) // ), // vl // .markText({ align: "left", dx: 5, dy: -5 }) // .data({ // values: annotation_data // }) // .encode( // vl.x().fieldT("x"), // vl.x2().fieldT("x2"), // vl.y({ type: "quantitative", field: "y" }), // vl.y2({ type: "quantitative", field: "y2" }), // vl.text({ type: "nominal", field: "labels" }) // ), // End of annotations vl .markLine({ strokeWidth: 3 }) //{ color: { value: "#333" } } .data(selectedIndustries_movingAvg_comparisonB[index_selectedIndustry]) .encode( vl.x().fieldT("xTime"), vl.y().fieldQ("value"), vl .strokeDash({ strokeWidth: 3 }) .field("year") .scale({ domain: [ yearsAll[0], yearsAll[1], `∅ ${yearsAll[3]}-${yearsAll[2]}`, `∅ ${yearsAll[6]}-${yearsAll[4]}` ], range: [ [1, 0], // current year, 2nd post-Corona year [1, 0], // previous year, 1st post-Corona year [4, 4], // Avg. Corona years [8, 8] // Avg. pre-Corona years ] }), vl .color() .field("year") .scale({ domain: [ yearsAll[0], yearsAll[1], `∅ ${yearsAll[3]}-${yearsAll[2]}`, `∅ ${yearsAll[6]}-${yearsAll[4]}` ], range: [ "#DC7700", // current year, 2nd post-Corona year "#B01657", // previous year, 1st post-Corona year "#7F3DA7", // Avg. Corona years "#0070B4" // Avg. pre-Corona years ] }), vl.tooltip(["year", "xTime", "value"]) ), vl .markLine({ strokeWidth: 3 }) //{ color: { value: "#333" } } .data(selectedIndustries_movingAvg_comparisonA[index_selectedIndustry]) .encode( vl.x().fieldT("xTime"), vl.y().fieldQ("value"), vl .strokeDash({ strokeWidth: 3 }) .field("year") .scale({ domain: [ yearsAll[0], yearsAll[1], `∅ ${yearsAll[3]}-${yearsAll[2]}`, `∅ ${yearsAll[6]}-${yearsAll[4]}` ], range: [ [1, 0], // current year, 2nd post-Corona year [1, 0], // previous year, 1st post-Corona year [4, 4], // Avg. Corona years [8, 8] // Avg. pre-Corona years ] }), vl .color() .field("year") .scale({ domain: [ yearsAll[0], yearsAll[1], `∅ ${yearsAll[3]}-${yearsAll[2]}`, `∅ ${yearsAll[6]}-${yearsAll[4]}` ], range: [ "#DC7700", // current year, 2nd post-Corona year "#B01657", // previous year, 1st post-Corona year "#7F3DA7", // Avg. Corona years "#0070B4" // Avg. pre-Corona years ] }), vl.tooltip(["year", "xTime", "value"]) ), // vl // .markErrorband({ // band: { color: "#ccc", opacity: 0.25 }, // tooltip: false // }) // .data( // selectedIndustries_movingAvg_comparisonA[index_selectedIndustry] // ) // .encode( // vl.y().fieldQ("min").scale({ zero: false }).title(null), // vl.y2().fieldQ("max").title(null), // vl.x().fieldT("xTime") // ), vl .markLine({ strokeWidth: 3 }) .data(selectedIndustries_cumSum_2recentYears[index_selectedIndustry]) .encode( vl.x().fieldT("xTime"), vl .y() .fieldQ("value") .scale({ domain: y_domain_industries[index_selectedIndustry] }), vl .color() .field("year") .scale({ domain: [ yearsAll[0], yearsAll[1], `∅ ${yearsAll[3]}-${yearsAll[2]}`, `∅ ${yearsAll[6]}-${yearsAll[4]}` ], range: [ "#DC7700", // current year, 2nd post-Corona year "#B01657", // previous year, 1st post-Corona year "#7F3DA7", // Avg. Corona years "#0070B4" // Avg. pre-Corona years ] }), vl.tooltip(["year", "xTime", "value"]) ), vl .markText({ font: "Arial Black" }) .data({ values: [{}] }) .encode( // vl.x({ // datum: "2022-01-01T23:00Z", // type: "temporal" // }), vl.y({ datum: y_domain_industries[index_selectedIndustry][1] * 0.05, // y_domain_industries[index_selectedIndustry][0], type: "quantitative" }), // .scale({ domain: y_domain_industries[index_selectedIndustry] }), vl.text({ datum: selectedIndustries[index_selectedIndustry], type: "nominal" }) ) ) .width(plotWidth(1)) .height(height); return plot; }36:Teed,function draw_plotComparison(index_selectedIndustry) { const initDate = d3.timeFormat("%Y-%m-%d")(new Date()); // The selection const hover = vl .selectSingle("hover") .on("mouseover") .encodings("x") .nearest(true) .clear("moouseout") .init({ x: { year: 2022, month: +d3.timeFormat("%m")(new Date(initDate)), date: +d3.timeFormat("%d")(new Date(initDate)) - 8 } }); // predicate to test if a point is hover-selected // return false if the selection is empty const isHovered = hover.empty(false); // The line and point marks. Notice how we filter the points on hover const lineAndPoint = vl .layer( vl.markLine().encode( vl .strokeDash({ strokeWidth: 3 }) .field("year") .scale({ domain: [ yearsAll[0], yearsAll[1], `∅ ${yearsAll[3]}-${yearsAll[2]}`, `∅ ${yearsAll[6]}-${yearsAll[4]}` ], range: [ [1, 0], // current year, 2nd post-Corona year [1, 0], // previous year, 1st post-Corona year [4, 4], // Avg. Corona years [8, 8] // Avg. pre-Corona years ] }) ), vl .markPoint(vl.opacity().if(isHovered, vl.value(1)).value(0), { size: 40, filled: false, legend: null }) .transform(vl.filter(hover)) ) .encode( // vl.x().fieldT("xTime"), vl.y().fieldQ("value").scale(y_domain_industries[index_selectedIndustry]), vl .color({ strokeWidth: 3 }) .field("year") .scale({ domain: [ yearsAll[0], yearsAll[1], `∅ ${yearsAll[3]}-${yearsAll[2]}`, `∅ ${yearsAll[6]}-${yearsAll[4]}` ], range: [ "#DC7700", // current year, 2nd post-Corona year "#B01657", // previous year, 1st post-Corona year "#7F3DA7", // Avg. Corona years "#0070B4" // Avg. pre-Corona years ] }) ); // The rule helps as a proxy for the hover. We draw rules all over the chart // so we can easily find the nearest one. We then hide them using opacity 0 const rule = vl .markRule({ strokeWidth: 0.5, tooltip: true }) // We pivot the data so we can show all the values at once .data( tidy( selectedIndustries_comaprison.filter( (d) => d.industry == selectedIndustries[index_selectedIndustry] ), select(["-value"]), pivotWider({ namesFrom: "year", valuesFrom: "tooltipValue", valuesFill: "-" }), groupBy(["xTime", "tooltipDate", "industry"]) ) ) // .transform( // vl.pivot("year").value("tooltipValue").groupby(["xTime", "tooltipDate"]) // ) .encode( vl.opacity().value(0).if(hover, vl.value(1)).value(0), vl.tooltip([ yearsIndustries[0], yearsIndustries[1], yearsIndustries[2], yearsIndustries[3], // `∅ ${yearsAll[6]}-${yearsAll[4]}`, // `∅ ${yearsAll[3]}-${yearsAll[2]}`, // yearsAll[1], // yearsAll[0], { field: "tooltipDate", title: "Tag" } ]) ) .select(hover); let plot = vl .layer(lineAndPoint, rule) .encode(vl.x().fieldT("xTime")) .data( selectedIndustries_comaprison.filter( (d) => d.industry == selectedIndustries[index_selectedIndustry] ) ) .title({ // adding facette title text: selectedIndustries[index_selectedIndustry], anchor: "start", color: "black", fontSize: 14 }) .width(plotWidth(1)) // specifying responsive width .height(height); return plot; }37:T81b,plot_neugruendungen_industries = { const all_plots = []; for (var i = 0; i < selectedIndustries.length; i++) { var plot_i = draw_plot(i); all_plots.push(plot_i); } return vl .vconcat(all_plots) .title({ text: "Anzahl Neugründungen nach Branchen, Kanton Zürich", subtitle: `Kumulierte Tageswerte (${yearsAll[1]}, ${yearsAll[0]}) und gleitender Durchschnitt` }) .autosize({ resize: true, type: "fit" }) .config({ color: color, locale: { number: localeNumber, time: localeTime }, view: { stroke: null }, // scale: { bandPaddingInner: 0.2, bandPaddingOuter: 0.2, stroke: 2 }, background: "#ffffff", concat: { spacing: 50 }, axis: { labelFont: "Arial Black", labelFontSize: 12, labelColor: "black", labelPadding: 7, titleFont: "Arial Black", titleFontSize: 13, titleFontColor: "black", domainColor: "#666666", tickColor: "#666666" }, axisX: { tickCount: plotWidth(1) / 120, format: "%b", title: null, grid: false }, axisY: { title: null, tickCount: height / 120, labelLimit: 300, gridColor: "#cccccc" }, title: { font: "Arial Black", fontSize: 16, offset: 15, anchor: "start", lineHeight: 5, padding: 40, subtitleFont: "Arial", subtitleFontSize: 14 }, header: { labelOrient: "top", labelPadding: 5, lineHeight: 3, labelFontSize: 13, labelFont: "Arial Black", labelColor: "black" }, legend: { orient: "top", symbolSize: 600, labelFontSize: 14, labelFont: "Arial", labelColor: "black", labelLimit: 300, columns: 2, // responsive legend width rowPadding: 10, title: null }, text: { align: "center", fill: "black", fontSize: 12, dx: 0 } }) .render(); 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Anzahl Neugründungen, ${neugruendungen_selectGebiet}

Kumulierte Tageswerte

${viewof neugruendungen_selectGebiet}

${viewof plot_neugruendungen_total}

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":400,"value":"viewof neugruendungen_selectGebiet = Inputs.radio(\n [\"Schweiz\", \"Kanton Zürich\"],\n {\n value: \"Kanton Zürich\",\n label: \"Region wählen\"\n }\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1182,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1875,"value":"neugruendungenNachBranche = htl.html`

Anzahl Neugründungen nach Branche, Kanton Zürich

Kumulierte Tageswerte und gleitender Durchschnitt

${viewof plotByIndustries}

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1613,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":757,"value":"---\n## Parameters","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":196,"value":"margin = ({\n top: 30,\n right: 10,\n bottom: 30,\n left: 50\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":192,"value":"svgHeight = 450","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":194,"value":"plotWidth = function (nPlots) {\n if (nPlots == 1) {\n // Entspricht Mobile\n if (width < 415) {\n return width * 0.88;\n } else if (width >= 415 && width < 768) {\n return width * 0.855;\n // Entspricht Tablets\n } else if (width >= 768 && width <= 1248) {\n return width * 0.877;\n // Entspricht Laptops & Desktops\n } else {\n return 1128 * 0.9275;\n }\n } else {\n // Entspricht Mobile\n if (width < 768) {\n return (width * 1.4) / nPlots;\n // Entspricht Tablets\n } else if (width >= 768 && width <= 1248) {\n return (width * 1.6) / nPlots;\n // Entspricht Laptops & Desktops\n } else {\n return (1128 * 0.92) / nPlots;\n }\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":190,"value":"height = svgHeight - margin.top - margin.bottom","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":174,"value":"y_domain = d3.extent([\n 0,\n Math.round(\n d3.max(\n rolled_AlleBranchen.filter(\n (d) => d.location == neugruendungen_selectGebiet\n ),\n (d) => d.total\n ) * 1.1\n )\n])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":998,"value":"// setting a dynamic y axis domain based on the max extent by industry\ny_domain_industries = {\n const domain = [];\n for (var i = 0; i < selectedIndustries.length; i++) {\n var extent = (function () {\n if (\n d3.max(selectedIndustries_movingAvg_comparisonB[i], (d) => d.max) >\n d3.max(selectedIndustries_cumSum_2recentYears[i], (d) => d.value)\n ) {\n return d3.extent([\n 0,\n Math.round(\n d3.max(selectedIndustries_movingAvg_comparisonB[i], (d) => d.max)\n )\n ]);\n } else {\n return d3.extent([\n 0,\n Math.round(\n d3.max(selectedIndustries_cumSum_2recentYears[i], (d) => d.value) *\n 1.05\n )\n ]);\n }\n })();\n domain.push(extent);\n }\n return domain;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":713,"value":"color = d3.scaleOrdinal(years, [\n \"#009EE0\", //2018, 2019 - Pre-Corona years\n \"#00407C\", //2018, 2019 - Pre-Corona years\n \"#9572D5\", //2020, 2021 - Corona years\n \"#54268E\", //2020, 2021 - Corona years\n \"#B01657\", // previous year, 1st post-Corona year\n \"#DC7700\" // current year, 2nd post-Corona year\n // \"#D40053\"\n])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1615,"value":"nCol_Legend = {\n if (width >= 1100) {\n return 4;\n } else if (width < 1100 && width >= 750) {\n return 2;\n } else {\n return 1;\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1583,"value":"formatDate = d3.timeFormat(\"%d.%m.\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"// Sets the locale for Vega-Lite axis, but not for text in tooltip (CMS does not recognise)\nlocaleNumber = await d3.json(\n \"https://cdn.jsdelivr.net/npm/d3-format@3/locale/de-CH.json\"\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":106,"value":"localeTime = await d3.json(\n \"https://raw.githubusercontent.com/d3/d3-time-format/main/locale/de-CH.json\"\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":826,"value":"selectedIndustries = [\n \"Gesundheits- und Sozialwesen\",\n \"Gastgewerbe/Beherbergung und Gastronomie\",\n \"Erbringung von sonstigen Dienstleistungen\"\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1282,"value":"[...new Set(d3.map(neugruendungen, (d) => d.br_abschnitt_red))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":764,"value":"---\n\n## Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":7,"value":"dataPath = \"https://www.web.statistik.zh.ch/ogd/data/KANTON_ZUERICH_ZEFIX_Neugruendungen.csv\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1291,"value":"importedData = d3 // .csv() // FileAttachment(\"zefix_neu (3).csv\") //\n .csv(dataPath, { typed: true })\n .then(function (data) {\n data.forEach(function (d) {\n d.date = d.date;\n d.year = d.date.slice(0, 4);\n d.jahr = +d.year;\n d.xTime = d3.timeParse(\"%Y-%m-%d\")(`2022-${d.date.slice(5)}`); // xTime is time used for x scale so different year's data can share the x scale\n d.value = +d.value; // Values to numeric\n d.location = d.location;\n d.industry = d.br_abschnitt_red;\n });\n return data;\n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1526,"value":"maxYear = d3.max(importedData, (d) => d.jahr)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1530,"value":"minYear = maxYear - 5","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1532,"value":"neugruendungen = tidy(\n importedData,\n filter((d) => d.jahr >= minYear)\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":526,"value":"years = [...new Set(d3.map(neugruendungen, (d) => d.year))].reverse()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":337,"value":"rolled_AlleBranchen = tidy(\n neugruendungen,\n filter(\n (d) =>\n d.year >= d3.max(years) - 5 &&\n d.year <= d3.max(years) &&\n d.industry == \"Alle Branchen\"\n ),\n groupBy([\"location\", \"year\"], mutateWithSummary({ total: cumsum(\"value\") })),\n mutate({\n location: (d) => (d.location == \"CH\" ? \"Schweiz\" : \"Kanton Zürich\")\n }),\n mutate({\n // tag: (d) => d3.timeFormat(\"%d.%m.\")(new Date(d.xTime)),\n tooltipDate: (d) => formatDate(d.xTime),\n tooltipValue: (d) => d.total.toLocaleString(\"de-CH\", { thousands: \"'\" })\n }),\n select([\n \"year\",\n \"xTime\",\n // \"tag\",\n \"tooltipDate\",\n \"location\",\n \"industry\",\n \"total\",\n \"tooltipValue\"\n ])\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1866,"value":"yearsNachGebiet = [...new Set(d3.map(rolled_AlleBranchen, (d) => d.year))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1540,"value":"yearsAll = [...new Set(d3.map(importedData, (d) => d.year))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":854,"value":"// Computing the cumulative sum for all selected industries for the last 2 years\nselectedIndustries_cumSum_2recentYears = {\n const dataset = [];\n for (var index = 0; index < selectedIndustries.length; ++index) {\n let returnedData = tidy(\n importedData,\n filter(\n (d) =>\n d.industry == selectedIndustries[index] && // walk through selected industries\n d.location == \"ZH\" && // limited to Kanton Zürich\n (d.year == yearsAll[0] || d.year == yearsAll[1]) // compute values only for the 2 most recent years\n ),\n groupBy([\"year\"], mutateWithSummary({ cumsum: cumsum(\"value\") })), // compute the cumsum by year\n mutate({\n value: (d) => d.cumsum,\n tooltipDate: (d) => formatDate(d.xTime),\n tooltipValue: (d) => d.value\n }),\n select([\n \"year\",\n \"xTime\",\n \"tooltipDate\",\n \"industry\",\n \"value\",\n \"tooltipValue\"\n ])\n );\n dataset.push(returnedData);\n }\n return dataset;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":918,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1546,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1697,"value":"selectedIndustries_comaprison = [\n selectedIndustries_cumSum_2recentYears.flat(),\n selectedIndustries_movingAvg_comparisonA.flat(),\n selectedIndustries_movingAvg_comparisonB.flat()\n].flat()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1826,"value":"yearsIndustries = [\n ...new Set(d3.map(selectedIndustries_comaprison, (d) => d.year))\n]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":23,"value":"---\n## Functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":929,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1611,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":163,"value":"function wrangleData(data) {\n const groupedByYear = Array.from(\n d3.rollup(\n data,\n (v) => {\n if (v.length === 1) {\n return v[0];\n } else {\n return Object.assign({}, v[0], {\n value: d3.sum(v, (d) => d.value),\n location: \"CH\"\n });\n }\n },\n (d) => d.year,\n (d) => d.date\n ),\n ([key, values]) => ({\n key,\n values: Array.from(values.values())\n })\n );\n groupedByYear.forEach((d) =>\n d.values.forEach((e, i) => {\n if (i === 0) {\n e.total = e.value;\n } else {\n e.total = e.value + d.values[i - 1].total;\n }\n })\n );\n return groupedByYear;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":166,"value":"// displayData = wrangleData(konkurse)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":14,"value":"---\n## Imports & dependencies","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":42,"value":"import { vl } from \"@vega/vega-lite-api\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1083,"value":"// import { vl } from \"@vega/vega-tooltip\" ","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":84,"value":"import {\n tidy,\n filter,\n select,\n groupBy,\n summarize,\n mutateWithSummary,\n cumsum,\n sort,\n desc,\n asc,\n min,\n max,\n mutate,\n roll,\n mean,\n pivotWider,\n rename,\n when\n} from \"@pbeshai/tidyjs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":289,"value":"d3 = require(\"d3@6\", \"d3-array@^3\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1881,"value":"cssStyles = html`\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1884,"value":"// css = html`\n// \n// \n// \n// `","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1886,"value":"// stylesheet = html`\n// `","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":945,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W38","stdlib":"1","titling":"auto"}} 30:"$2f:body"