28:null 29:["$","title",null,{"children":"LIVE: Advanced Summary, A/B Comparison - Collapsible Tree | João Ricardo Mendes | Observable"}] 2e:T8ac,dynamicStyling = html``2f:T1296,chart = { const container = d3.create("div") .style("min-height", "200px"); const root = d3.hierarchy(dataTree); root.x0 = dy / 2; root.y0 = 0; root.descendants().forEach((d, i) => { d.id = i; d._children = d.children; //if (d.depth && d.data.name.length !== 7) d.children = null; }); const svg = container .append("svg") .attr("viewBox", [-margin.left, -margin.top, width, dx]) .style("font", "12px Lato") .style("user-select", "none"); const blurb = container .append("div") .attr("id", "blurb") .html(`

${kpiModules[0]}

${dataMockDisplayr[kpiModules[0]].blurb}

`) const gLink = svg.append("g") .attr("fill", "none") .attr("stroke", "#555") .attr("stroke-opacity", 0.4) .attr("stroke-width", 1.5); const gNode = svg.append("g") .attr("cursor", "pointer") .attr("pointer-events", "all"); function update(source) { const duration = d3.event && d3.event.altKey ? 2500 : 250; const nodes = root.descendants().reverse(); const links = root.links(); // Compute the new tree layout. tree(root); let left = root; let right = root; root.eachBefore(node => { if (node.x < left.x) left = node; if (node.x > right.x) right = node; }); const height = right.x - left.x + margin.top + margin.bottom; const transition = svg.transition() .duration(duration) .attr("viewBox", [-margin.left, left.x - margin.top, width, height]) .tween("resize", window.ResizeObserver ? null : () => () => svg.dispatch("toggle")); // Update the nodes… const node = gNode.selectAll("g") .data(nodes, d => d.id); // Enter any new nodes at the parent's previous position. const nodeEnter = node.enter().append("g") .attr("transform", d => `translate(${source.y0},${source.x0})`) .attr("fill-opacity", 0) .attr("stroke-opacity", 0) .classed("selectable", /*d => d._children ? false:*/ true) .attr("id", d => `_${d.id}`) //.attr("class", d => stringToClassName(d.data.name)) .on("click", (event, d) => { //console.log(d.id, d3.select(`#_${d.id}`).classed("selected"), !d3.select(`#_${d.id}`).classed("selected")) d.children = d.children ? null : d._children; d3.selectAll(".selectable").classed("selected", false) //single select d3.select(`#_${d.id}`).classed("selected", /*d => d._children ? false:*/!d3.select(`#_${d.id}`).classed("selected")) //update(d); blurb.html(`

${d.data.name}

${dataMockDisplayr[d.data.name]["blurb"]}

`) sessionStorage.setItem("clickedNodeId", `_${d.id}`); sessionStorage.setItem("clickedNodeName", d.data.name); set(viewof kpi, d.data.name) }); nodeEnter.append("circle") .attr("r", 5) .attr("fill", d => d._children ? "#999":"#555" ) .attr("stroke-width", 10); nodeEnter.append("text") .attr("dy", "0.31em") .attr("x", d => d._children ? -6 : 6) .attr("text-anchor", d => d._children ? "end" : "start") .text(d => d.data.name) .clone(true).lower() .attr("stroke-linejoin", "round") .attr("stroke-width", 3) .attr("stroke", "white"); // Transition nodes to their new position. const nodeUpdate = node.merge(nodeEnter).transition(transition) .attr("transform", d => `translate(${d.y},${d.x})`) .attr("fill-opacity", 1) .attr("stroke-opacity", 1); // Transition exiting nodes to the parent's new position. const nodeExit = node.exit().transition(transition).remove() .attr("transform", d => `translate(${source.y},${source.x})`) .attr("fill-opacity", 0) .attr("stroke-opacity", 0); // Update the links… const link = gLink.selectAll("path") .data(links, d => d.target.id); // Enter any new links at the parent's previous position. const linkEnter = link.enter().append("path") .attr("d", d => { const o = {x: source.x0, y: source.y0}; return diagonal({source: o, target: o}); }); // Transition links to their new position. link.merge(linkEnter).transition(transition) .attr("d", diagonal); // Transition exiting nodes to the parent's new position. link.exit().transition(transition).remove() .attr("d", d => { const o = {x: source.x, y: source.y}; return diagonal({source: o, target: o}); }); // Stash the old positions for transition. root.eachBefore(d => { d.x0 = d.x; d.y0 = d.y; }); } update(root); return container.node(); } 30:Tc38,kpiTable = { try { const format = formats[kpiType] const kpiRows = kpiData["period.kpis"]["item"].map(o => o._row) const data = kpiRows.map((d,i) => { if(comparisonType == "period") { return { kpi: d, rank: kpiData["period.ranks"]["item"].map(o => o.x)[i], period : kpiData["period.kpis"]["item"].map(o => o.x)[i], delta : kpiData["time"]["item"].map(o => o[selectedPeriod] - o[comparisonPeriod])[i], significance : kpiData["period.sigs"].map(o => o.x <= -1*sigThreshold? "▼": o.x >= sigThreshold? "▲": "")[i], demogs: "", "time-series": "" } } else if(comparisonType == "item") { return { kpi: d, rank: kpiData["period.ranks"]["item"].map((o, ind) => -1*(o.x - kpiData["period.ranks"]["vs"][ind].x))[i], period : kpiData["period.kpis"]["item"].map(o => o.x)[i], delta : kpiData["time"]["item"].map((o, ind) => o[selectedPeriod] - kpiData["time"]["vs"][ind][selectedPeriod])[i], significance : kpiData["period.sigs"].map((o, ind) => o.x <= -1*sigThreshold? "▼": o.x >= sigThreshold? "▲": "")[i], demogs: "", "time-series": "" } } }) return Inputs.table(data, { width: { kpi: "18%", rank: "9%", period: "13%", delta: "13%", significance: "5%", demogs: "12%", "time-series": "32%" }, format: { rank: rank(), period: sparkBar(100, format["period"], format["divider"]["period"]), delta: sparkBar(100, format["delta"], format["divider"]["delta"], "bi-directional"), demogs: (comparisonType == "period") && (kpiType != "Average")? (e,i) => SmallStack(standardiseDemogsInRows(kpiRows[i]), format["period"]): (e,i) => SmallCol(standardiseDemogsInRows(kpiRows[i]), format["period"]), "time-series": comparisonType == "period"? (e,i) => sparkArea(kpiRows[i], format, { x: standardiseDatesInRows(kpiRows[i]).map(o => parseDate(o.date)), y: standardiseDatesInRows(kpiRows[i]).map(o => Math.max(0, o.value == null? 0: o.value)), sparkXdomain }): (e,i) => sparkLine(kpiRows[i], format, { x: standardiseDatesInRows(kpiRows[i]).map(o => parseDate(o.date)), y: standardiseDatesInRows(kpiRows[i]).map(o => o.value), sparkXdomain }) }, header: { kpi: `KPI`, rank: `${comparisonType == "period"? "Rank": "Rank delta"}`, period: `${item}`, delta: `${comparisonType == "period"? "v "+ comparisonPeriod: "v " + vs} pp.`, significance: `Sig.`, demogs: `${comparisonType == "period"? selectedPeriod + " by " + demogs: "Delta by " + demogs}`, "time-series": htl.html`${comparisonType == "period"?"Over time:": "Delta over time"}${dateMappedToStrings[date]} (v ${dateMappedToStrings[ comparisonDate ]} pp.)` } }) } catch(err) { return html` insufficient sample size `} }31:T5637,dataMockDisplayr = ({"Brand Tracking":{"data":[false],"blurb":["Click on the tree nodes to start exploring your data."]},"Brand Funnel KPIs":{"type":["Column %"],"data":[true],"blurb":["Consumers aided awareness, consideration and preference for your brand."],"period.kpis":{"item":[{"x":86.2841,"_row":"Awareness"},{"x":45.5568,"_row":"Consideration"},{"x":9.5333,"_row":"Preference"}],"vs":{}},"period.ranks":{"item":[{"x":4,"_row":"Awareness"},{"x":4,"_row":"Consideration"},{"x":4,"_row":"Preference"}],"vs":{}},"period.sigs":[{"x":0.3645,"_row":"Awareness"},{"x":-1.7675,"_row":"Consideration"},{"x":-0.1108,"_row":"Preference"}],"period.demogs":{"item":[{"Male":47.6392,"Female":52.3608,"_row":"Awareness"},{"Male":48.946,"Female":51.054,"_row":"Consideration"},{"Male":54.2771,"Female":45.7229,"_row":"Preference"}],"vs":{}},"time":{"item":[{"2021-Q2":86.6931,"2021-Q3":86.0919,"2021-Q4":85.6389,"2022-Q1":86.2841,"NET":86.1702,"_row":"Awareness"},{"2021-Q2":54.4978,"2021-Q3":56.9079,"2021-Q4":50.0559,"2022-Q1":45.5568,"NET":51.7759,"_row":"Consideration"},{"2021-Q2":9.1033,"2021-Q3":10.0147,"2021-Q4":9.5779,"2022-Q1":9.5333,"NET":9.5631,"_row":"Preference"}],"vs":{}}},"Brand Momentum KPIs":{"type":["Average"],"data":[true],"blurb":["Consumers perception of if your brand is on it's way up or down."],"period.kpis":{"item":[{"x":29.5152,"_row":"On its way up"},{"x":-12.4826,"_row":"On its way down"},{"x":17.0327,"_row":"Net"}],"vs":{}},"period.ranks":{"item":[{"x":5,"_row":"On its way up"},{"x":3,"_row":"On its way down"},{"x":5,"_row":"Net"}],"vs":{}},"period.sigs":[{"x":-0.9512,"_row":"On its way up"},{"x":0,"_row":"On its way down"},{"x":-0.9195,"_row":"Net"}],"period.demogs":{"item":[{"Male":37.8338,"Female":26.5455,"_row":"On its way up"},{"Male":-13.2894,"Female":-12.6443,"_row":"On its way down"},{"Male":24.5444,"Female":13.9012,"_row":"Net"}],"vs":{}},"time":{"item":[{"2021-Q2":30.0338,"2021-Q3":33.866,"2021-Q4":31.9232,"2022-Q1":29.5152,"NET":31.3618,"_row":"On its way up"},{"2021-Q2":-15.3893,"2021-Q3":-11.423,"2021-Q4":-12.9516,"2022-Q1":-12.4826,"NET":-13.0417,"_row":"On its way down"},{"2021-Q2":14.6446,"2021-Q3":22.443,"2021-Q4":18.9715,"2022-Q1":17.0327,"NET":18.3201,"_row":"Net"}],"vs":{}}},"Brand Salience KPIs":{"type":["Column %"],"data":[true],"blurb":["This shows how well known and present in consumers’ mind your brand is compared to competitors on all awareness indicators."],"period.kpis":{"item":[{"x":86.2841,"_row":"Aided Awareness"},{"x":38.0769,"_row":"Spontaneous Awareness"},{"x":7.2536,"_row":"Top of Mind Awareness"}],"vs":{}},"period.ranks":{"item":[{"x":4,"_row":"Aided Awareness"},{"x":4,"_row":"Spontaneous Awareness"},{"x":4,"_row":"Top of Mind Awareness"}],"vs":{}},"period.sigs":[{"x":0.3645,"_row":"Aided Awareness"},{"x":0.6089,"_row":"Spontaneous Awareness"},{"x":-1.0835,"_row":"Top of Mind Awareness"}],"period.demogs":{"item":[{"Male":47.6392,"Female":52.3608,"_row":"Aided Awareness"},{"Male":50.0515,"Female":49.9485,"_row":"Spontaneous Awareness"},{"Male":55.0748,"Female":44.9252,"_row":"Top of Mind Awareness"}],"vs":{}},"time":{"item":[{"2021-Q2":86.6931,"2021-Q3":86.0919,"2021-Q4":85.6389,"2022-Q1":86.2841,"NET":86.1702,"_row":"Aided Awareness"},{"2021-Q2":45.8724,"2021-Q3":41.9957,"2021-Q4":36.5763,"2022-Q1":38.0769,"NET":40.5914,"_row":"Spontaneous Awareness"},{"2021-Q2":9.5858,"2021-Q3":11.7615,"2021-Q4":8.6834,"2022-Q1":7.2536,"NET":9.3363,"_row":"Top of Mind Awareness"}],"vs":{}}},"Mental Availability KPIs":{"type":["Average"],"data":[true],"blurb":["Share of your brand’s associations with all mental cues. 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It reflects how easily your brand comes to mind at the moment of purchase."],"period.kpis":{"item":[{"x":12.9696,"_row":"Recent Brand Experience - Contact"}],"vs":{}},"period.ranks":{"item":[{"x":5,"_row":"Recent Brand Experience - Contact"}],"vs":{}},"period.sigs":[{"x":-0.1953,"_row":"Recent Brand Experience - Contact"}],"period.demogs":{"item":[{"Male":16.921,"Female":9.7476,"_row":"Recent Brand Experience - Contact"}],"vs":{}},"time":{"item":[{"2021-Q2":12.1471,"2021-Q3":12.332,"2021-Q4":13.3058,"2022-Q1":12.9696,"NET":12.6923,"_row":"Recent Brand Experience - Contact"}],"vs":{}}},"Touchpoint Exposure KPIs":{"type":["Column %"],"data":[true],"blurb":["This shows the recent exposure in the past 3 months to your brand across touchpoints. It is based on consumers aware of your brand."],"period.kpis":{"item":[{"x":19.0011,"_row":"TV/cinema ad"},{"x":18.3125,"_row":"Print ad"},{"x":20.3202,"_row":"Online ad"},{"x":7.6656,"_row":"Outdoor ad"},{"x":6.813,"_row":"Radio ad"},{"x":12.5618,"_row":"Sponsorship"},{"x":12.0223,"_row":"Leaflet"},{"x":11.0871,"_row":"Social media"},{"x":18.3524,"_row":"Online search"},{"x":19.7512,"_row":"Customers product review"},{"x":24.6192,"_row":"Experts product review"},{"x":29.4062,"_row":"Word of mouth"},{"x":20.1329,"_row":"News article"},{"x":14.2877,"_row":"Store staff"},{"x":21.9654,"_row":"Brand's website"},{"x":22.8376,"_row":"In-store"},{"x":22.477,"_row":"Retailer's website"}],"vs":{}},"period.ranks":{"item":[{"x":4,"_row":"TV/cinema ad"},{"x":4,"_row":"Print ad"},{"x":5,"_row":"Online ad"},{"x":10,"_row":"Outdoor ad"},{"x":9,"_row":"Radio ad"},{"x":5,"_row":"Sponsorship"},{"x":8,"_row":"Leaflet"},{"x":8,"_row":"Social media"},{"x":5,"_row":"Online search"},{"x":5,"_row":"Customers product review"},{"x":4,"_row":"Experts product review"},{"x":4,"_row":"Word of mouth"},{"x":3,"_row":"News article"},{"x":6,"_row":"Store staff"},{"x":5,"_row":"Brand's website"},{"x":5,"_row":"In-store"},{"x":7,"_row":"Retailer's website"}],"vs":{}},"period.sigs":[{"x":-0.665,"_row":"Brand I trust"},{"x":0.5738,"_row":"Easy to identify"},{"x":1.6705,"_row":"Endorsed"},{"x":-0.3236,"_row":"Expert in the category"},{"x":-0.0662,"_row":"Has something unique"},{"x":-0.3176,"_row":"Meets my needs"},{"x":0.8386,"_row":"Modern"},{"x":-0.3448,"_row":"Promotes society changes"},{"x":-0.7081,"_row":"Proud to own"},{"x":0.1038,"_row":"Sets standards"},{"x":-0.4051,"_row":"Talk positively about"},{"x":-0.4668,"_row":"Values I identify with"}],"period.demogs":{"item":[{"Male":"NaN","Female":"NaN","_row":"TV/cinema ad"},{"Male":"NaN","Female":"NaN","_row":"Print ad"},{"Male":"NaN","Female":"NaN","_row":"Online ad"},{"Male":"NaN","Female":"NaN","_row":"Outdoor ad"},{"Male":"NaN","Female":"NaN","_row":"Radio ad"},{"Male":"NaN","Female":"NaN","_row":"Sponsorship"},{"Male":"NaN","Female":"NaN","_row":"Leaflet"},{"Male":"NaN","Female":"NaN","_row":"Social media"},{"Male":"NaN","Female":"NaN","_row":"Online search"},{"Male":"NaN","Female":"NaN","_row":"Customers product review"},{"Male":"NaN","Female":"NaN","_row":"Experts product review"},{"Male":"NaN","Female":"NaN","_row":"Word of mouth"},{"Male":"NaN","Female":"NaN","_row":"News article"},{"Male":"NaN","Female":"NaN","_row":"Store staff"},{"Male":"NaN","Female":"NaN","_row":"Brand's website"},{"Male":"NaN","Female":"NaN","_row":"In-store"},{"Male":"NaN","Female":"NaN","_row":"Retailer's website"}],"vs":{}},"time":{"item":[{"2021-Q2":11.7016,"2021-Q3":6.8723,"2021-Q4":13.3607,"2022-Q1":19.0011,"NET":12.826,"_row":"TV/cinema ad"},{"2021-Q2":8.3058,"2021-Q3":12.1348,"2021-Q4":20.468,"2022-Q1":18.3125,"NET":15.1475,"_row":"Print ad"},{"2021-Q2":8.3864,"2021-Q3":14.7837,"2021-Q4":15.4584,"2022-Q1":20.3202,"NET":15.0079,"_row":"Online ad"},{"2021-Q2":4.9123,"2021-Q3":14.6484,"2021-Q4":13.6906,"2022-Q1":7.6656,"NET":10.4497,"_row":"Outdoor ad"},{"2021-Q2":13.9834,"2021-Q3":10.8716,"2021-Q4":13.1583,"2022-Q1":6.813,"NET":11.1113,"_row":"Radio ad"},{"2021-Q2":6.0643,"2021-Q3":13.175,"2021-Q4":14.7905,"2022-Q1":12.5618,"NET":11.8979,"_row":"Sponsorship"},{"2021-Q2":12.9195,"2021-Q3":20.4996,"2021-Q4":16.5084,"2022-Q1":12.0223,"NET":15.5667,"_row":"Leaflet"},{"2021-Q2":10.3269,"2021-Q3":11.1729,"2021-Q4":13.2348,"2022-Q1":11.0871,"NET":11.5208,"_row":"Social media"},{"2021-Q2":13.9089,"2021-Q3":28.185,"2021-Q4":31.0463,"2022-Q1":18.3524,"NET":23.282,"_row":"Online search"},{"2021-Q2":12.7419,"2021-Q3":19.0518,"2021-Q4":21.0585,"2022-Q1":19.7512,"NET":18.3961,"_row":"Customers product review"},{"2021-Q2":9.4552,"2021-Q3":12.0895,"2021-Q4":21.197,"2022-Q1":24.6192,"NET":17.2228,"_row":"Experts product review"},{"2021-Q2":19.0323,"2021-Q3":28.4476,"2021-Q4":25.563,"2022-Q1":29.4062,"NET":25.8649,"_row":"Word of mouth"},{"2021-Q2":13.4377,"2021-Q3":9.0231,"2021-Q4":10.4973,"2022-Q1":20.1329,"NET":13.2719,"_row":"News article"},{"2021-Q2":5.8026,"2021-Q3":13.1393,"2021-Q4":23.4688,"2022-Q1":14.2877,"NET":14.6159,"_row":"Store staff"},{"2021-Q2":14.3807,"2021-Q3":16.5343,"2021-Q4":26.4444,"2022-Q1":21.9654,"NET":20.143,"_row":"Brand's website"},{"2021-Q2":12.8379,"2021-Q3":32.7936,"2021-Q4":30.5247,"2022-Q1":22.8376,"NET":25.2379,"_row":"In-store"},{"2021-Q2":17.8194,"2021-Q3":26.0674,"2021-Q4":37.497,"2022-Q1":22.477,"NET":26.4092,"_row":"Retailer's website"}],"vs":{}}},"Brand Consistency KPIs":{"type":["Column %"],"data":[true],"blurb":["How consistent is consumers' experience with your brand."],"period.kpis":{"item":[{"x":0,"_row":"Not at all"},{"x":6.9403,"_row":"Very little"},{"x":38.5391,"_row":"Somewhat"},{"x":34.4953,"_row":"A lot"},{"x":20.0253,"_row":"Completely"},{"x":100,"_row":"NET"}],"vs":{}},"period.ranks":{"item":[{"x":4,"_row":"Not at all"},{"x":2,"_row":"Very little"},{"x":2,"_row":"Somewhat"},{"x":5,"_row":"A lot"},{"x":4,"_row":"Completely"}],"vs":{}},"period.sigs":[{"x":0,"_row":"Not at all"},{"x":1.0819,"_row":"Very little"},{"x":-0.5548,"_row":"Somewhat"},{"x":0.5183,"_row":"A lot"},{"x":-0.6669,"_row":"Completely"},{"x":-0.0704,"_row":"NET"}],"period.demogs":{"item":[{"Male":0,"Female":0,"_row":"Not at all"},{"Male":4.1825,"Female":0,"_row":"Very little"},{"Male":41.3226,"Female":44.1695,"_row":"Somewhat"},{"Male":29.4103,"Female":33.9093,"_row":"A lot"},{"Male":25.0846,"Female":21.9212,"_row":"Completely"},{"Male":100,"Female":100,"_row":"NET"}],"vs":{}},"time":{"item":[{"2021-Q2":6.6647,"2021-Q3":2.2323,"2021-Q4":2.6383,"2022-Q1":6.9403,"NET":4.5595,"_row":"Very little"},{"2021-Q2":36.078,"2021-Q3":29.4419,"2021-Q4":42.3737,"2022-Q1":38.5391,"NET":36.7313,"_row":"Somewhat"},{"2021-Q2":31.556,"2021-Q3":44.2826,"2021-Q4":31.0713,"2022-Q1":34.4953,"NET":35.3274,"_row":"A lot"},{"2021-Q2":25.7013,"2021-Q3":24.0433,"2021-Q4":23.9167,"2022-Q1":20.0253,"NET":23.3818,"_row":"Completely"},{"2021-Q2":100,"2021-Q3":100,"2021-Q4":100,"2022-Q1":100,"NET":100,"_row":"NET"}],"vs":{}}},"Net Promoter Score KPIs":{"type":["Average"],"data":[true],"blurb":["Consumers' willingness to recommend your brand, measured by: promoters (8 to 10 out of 10) - detractors (6 out of 10 or below)."],"period.kpis":{"item":[{"x":35.1588,"_row":"Net Promoter Score"}],"vs":{}},"period.ranks":{"item":[{"x":4,"_row":"Net Promoter Score"}],"vs":{}},"period.sigs":[{"x":0.1993,"_row":"Net Promoter Score"}],"period.demogs":{"item":[{"Male":34.8591,"Female":31.6778,"_row":"Net Promoter Score"}],"vs":{}},"time":{"item":[{"2021-Q2":33.3097,"2021-Q3":10.725,"2021-Q4":33.5394,"2022-Q1":35.1588,"NET":26.1401,"_row":"Net Promoter Score"}],"vs":{}}},"configs":{"kpi":["Column %"],"periodicity":["Quarter"],"period":["2022-Q1"],"periods":["2021-Q2","2021-Q3","2021-Q4","2022-Q1"],"comparison.period":["2021-Q4"],"period.comparison.vs":[1],"comparison.type":["period"],"demogs":{"demog":["Gender"],"demog.options":["Male","Female"]},"item":["Audio-Technica "],"vs":["Comparison period"],"sig.threshold":["1.65"],"tree.source":["Brand Tracking*n*n *tab*Brand Performance*n*n *tab**tab**tab*Brand Momentum KPIs*n*n *tab**tab**tab*Brand Funnel KPIs*n*n *tab*Brand Diagnostics*n*n *tab**tab*Mental Availability KPIs*n*n *tab**tab**tab*Brand Salience KPIs*n*n *tab**tab**tab*Mental Availability KPIs*n*n *tab**tab*Brand Attachment*n*n *tab**tab**tab*Brand Attachment KPIs*n*n *tab**tab*Brand Knowledge KPIs*n*n *tab**tab**tab*Brand Imagery KPIs*n*n *tab**tab**tab*Product Imagery KPIs*n*n *tab**tab*Brand Experience*n*n *tab**tab**tab*Recent Brand Experience KPIs*n*n *tab**tab**tab*Touchpoint Exposure KPIs*n*n *tab**tab**tab*Brand Consistency KPIs*n*n *tab**tab**tab*Net Promoter Score KPIs*n*n "]}})32:T50f,kpiTableDataTest = { const format = formats[kpiType] const kpiRows = kpiData["period.kpis"]["item"].map(o => o._row) const data = kpiRows.map((d,i) => { if(comparisonType == "period") { return { kpi: d, rank: kpiData["period.ranks"]["item"].map(o => o.x)[i], period : kpiData["period.kpis"]["item"].map(o => o.x)[i], delta : kpiData["time"]["item"].map(o => o[selectedPeriod] - o[comparisonPeriod])[i], significance : kpiData["period.sigs"].map(o => o.x <= -1*sigThreshold? "▼": o.x >= sigThreshold? "▲": "")[i], demogs: "", "time-series": "" } } else if(comparisonType == "item") { return { kpi: d, rank: kpiData["period.ranks"]["item"].map((o, ind) => o.x - kpiData["period.ranks"]["vs"][ind].x)[i], period : kpiData["period.kpis"]["item"].map(o => o.x)[i], delta : kpiData["time"]["item"].map((o, ind) => o[selectedPeriod] - kpiData["time"]["vs"][ind][selectedPeriod])[i], significance : kpiData["period.sigs"].map((o, ind) => o.x <= -1*sigThreshold? "▼": o.x >= sigThreshold? "▲": "")[i], demogs: standardiseDemogsInRows(kpiRows[i]), "time-series": standardiseDatesInRows(kpiRows[i]) } } }) return data }33:Ta86,//Tweaked from https://observablehq.com/@mbostock/covid-cases-by-state function sparkArea(kpi, format, { x: X, y: Y, xdomain = d3.extent(X), ydomain = (comparisonType == "period") & (kpiType != "Average")? [0,Math.max(60,d3.extent(Y)[1])]: (kpiType == "Average")?[Math.min(-100,d3.extent(Y)[0]),Math.max(100,d3.extent(Y)[1])]: [Math.min(-35,d3.extent(Y)[0]),Math.max(35,d3.extent(Y)[1])], width = 200, height = barHeightStandard }) { const kpiMapped = d3.group(standardiseDatesInRows(kpi), o => o.date) const x = d3.scaleUtc(xdomain, [0, width]); const y = d3.scaleLinear(ydomain, [height, 0]); const labelSpace = 55 const area = d3.area() .x((d, i) => x(X[i])) .y1((d, i) => y(Y[i])) .y0(height) .defined((d, i) => { return !isNaN(X[i]) && !isNaN(Y[i]) }); let el = d3.create("svg") el .attr("width", width + labelSpace) .attr("height", height) .style("vertical-align", "middle") .call(g => g.append("path") .attr("fill", comparisonType == "period"? "#e1e1e1": "transparent") .attr("d", area(standardiseDatesInRows(kpi)))) .call(g => g.append("path") .attr("fill", "none") .attr("stroke", "black") .attr("d", area.lineY1()(standardiseDatesInRows(kpi)))) .call(g => g.append("line") .attr("x1",x(X[dates.indexOf(date)])) .attr("y1", y(ydomain[1])) .attr("x2",x(X[dates.indexOf(date)])) .attr("y2", y(ydomain[0])) .attr("stroke", "#000") ) .call(g => g.append("line") .attr("x1",x(X[dates.indexOf(date) - comparison.shift])) .attr("y1", y(ydomain[1])) .attr("x2",x(X[dates.indexOf(date) - comparison.shift])) .attr("y2", y(ydomain[0])) .attr("stroke", "black") ) .call(g => g.append("rect") .attr("x", x(X[dates.indexOf(date) - comparison.shift])) .attr("y", y(ydomain[1])) .attr("width", x(X[dates.indexOf(date)]) - x(X[dates.indexOf(date) - comparison.shift])) .attr("height", y(ydomain[0]) - y(ydomain[1])) .attr("fill", "#CF4800") .attr("fill-opacity", 0.4) .attr("stroke-width", 0) ) el.append("text") .attr("x", width + 5) .attr("y", height/1.5) .text((o) => { let deltas = ((typeof kpiMapped.get(date) != "undefined") && (typeof kpiMapped.get(comparisonDate) != "undefined"))? kpiMapped.get(date)[0].value - kpiMapped.get(comparisonDate)[0].value : "" return `(${d3.format(format["delta"])(deltas/format["divider"]["delta"])})` }) .attr("fill", "black") return el .node() ; }34:Tafa,//Tweaked from https://observablehq.com/@mbostock/covid-cases-by-state function sparkLine(kpi, format, { x: X, y: Y, xdomain = d3.extent(X), ydomain = kpiType == "Average"? [Math.min(-100,d3.extent(Y)[0]),Math.max(100,d3.extent(Y)[1])]: [Math.min(-35,d3.extent(Y)[0]),Math.max(35,d3.extent(Y)[1])], width = 200, height = barHeightStandard }) { const kpiMapped = d3.group(standardiseDatesInRows(kpi), o => o.date) const x = d3.scaleUtc(xdomain, [0, width]); const y = d3.scaleLinear(ydomain, [height, 0]); const labelSpace = 55 const area = d3.area() .x((d, i) => x(X[i])) .y1((d, i) => y(Y[i])) .y0(height) .defined((d, i) => { return !isNaN(X[i]) && !isNaN(Y[i]) }); let el = d3.create("svg") el .attr("width", width + labelSpace) .attr("height", height) .style("vertical-align", "middle") .call(g => g.append("path") .attr("fill", "transparent") .attr("d", area(standardiseDatesInRows(kpi)))) .call(g => g.append("path") .attr("fill", "none") .attr("stroke", "black") .attr("d", area.lineY1()(standardiseDatesInRows(kpi)))) .call(g => g.append("line") .attr("x1",x(X[dates.indexOf(date)])) .attr("y1", y(ydomain[1])) .attr("x2",x(X[dates.indexOf(date)])) .attr("y2", y(ydomain[0])) .attr("stroke", "#000") ) .call(g => g.append("line") .attr("x1",x(X[dates.indexOf(date) - comparison.shift])) .attr("y1", y(ydomain[1])) .attr("x2",x(X[dates.indexOf(date) - comparison.shift])) .attr("y2", y(ydomain[0])) .attr("stroke", "black") ) .call(g => g.append("line") .attr("x1",x(X[dates.length -1])) .attr("y1", y(0)) .attr("x2",x(X[0])) .attr("y2", y(0)) .attr("stroke", "grey") .attr("stroke-width", "0.3px") ) .call(g => g.append("rect") .attr("x", x(X[dates.indexOf(date) - comparison.shift])) .attr("y", y(ydomain[1])) .attr("width", x(X[dates.indexOf(date)]) - x(X[dates.indexOf(date) - comparison.shift])) .attr("height", y(ydomain[0]) - y(ydomain[1])) .attr("fill", "#CF4800") .attr("fill-opacity", 0.4) .attr("stroke-width", 0) ) el.append("text") .attr("x", width + 5) .attr("y", height/1.5) .text((o) => { let deltas = ((typeof kpiMapped.get(date) != "undefined") && (typeof kpiMapped.get(comparisonDate) != "undefined"))? kpiMapped.get(date)[0].value - kpiMapped.get(comparisonDate)[0].value : "" return `(${d3.format(format["delta"])(deltas/format["divider"]["delta"])})` }) .attr("fill", "black") return el .node() ; }35:T64f,//adaptation of @observablehq/summary-table {SmallStack} SmallCol = (categoryData, format, maxCategories = 100) => { // Get a col chart for the demogs let chartData = categoryData; let allDataNaN = categoryData.map(o => o.count).every(o => isNaN(o)) let categories = 0; if (chartData.length > maxCategories) { chartData = categoryData.filter((d, i) => i < maxCategories); const total = d3.sum(categoryData, (d) => d.count); const otherCount = total - d3.sum(chartData, (d) => d.count); let other = {}; other[demogs] = "Other categories..."; other.count = otherCount; other.pct = other.count / total; chartData.push(other); } return addTooltips( Plot.barY(chartData, { x: demogs, fill: demogs, y: "count", title: (d) => { return d[demogs] + "\n" + d3.format(format)(d.pct) } }).plot({ color: { scheme: "blues" }, marks: [ /*Plot.text([0, 0], { x: 0, dy: 0 }),*/ Plot.ruleY([0], {stroke: "grey"}) ], style: { paddingTop: "25px", paddingBottom: "0px", textAnchor: "start", overflow: "visible", backgroundColor: "transparent" }, x: { axis: null }, color: { domain: chartData.map((d) => d[demogs]), scheme: "blues", reverse: true }, height: allDataNaN? 0: barHeightStandard, width: 100, y: { axis: null, range: [barHeightStandard, 0], domain: kpiType == "Average"? [-100,100]: [-0.35,0.35] } }), { fill: "darkblue" } ); }36:T675,SmallStack = (categoryData, format, maxCategories = 100) => { // Get a horizontal stacked bar const label = categoryData.length === 1 ? " category" : " categories"; let chartData = categoryData; let allDataNaN = categoryData.map(o => o.count).every(o => isNaN(o)) let categories = 0; if (chartData.length > maxCategories) { chartData = categoryData.filter((d, i) => i < maxCategories); const total = d3.sum(categoryData, (d) => d.count); const otherCount = total - d3.sum(chartData, (d) => d.count); let other = {}; other[demogs] = "Other categories..."; other.count = otherCount; other.pct = other.count / total; chartData.push(other); } return addTooltips( Plot.barX(chartData, { x: "count", fill: demogs, y: 0, title: (d) => d[demogs] + "\n" + d3.format(format)(d.pct) }).plot({ color: { scheme: "blues" }, marks: [ /*Plot.text([0, 0], { x: 0, dy: 100, text: (d) => d3.format(",.0f")(categoryData.length) + `${label}` })*/ //Plot.ruleX([0]) ], style: { paddingTop: "12px", paddingBottom: "3px !important", textAnchor: "start", overflow: "visible", backgroundColor: "transparent", fontSize: "10px !important" }, x: { axis: null }, color: { domain: chartData.map((d) => d[demogs]), scheme: "blues", reverse: true }, height: allDataNaN? 0:barHeightStandard, width: 205, y: { axis: null, range: [barHeightStandard, 0] } }), { fill: "darkblue" } ); }37:T481,demogsGroupByRow = { const format = formats[kpiType] const output = d3.group(kpiData["period.demogs"]["item"].map( (o, ind) => { const z = [] const keysToDemog = Object.keys(o).filter(o => o != "_row") const values = Object.values(o) const row = values[values.length - 1] const v = kpiData["period.demogs"]["vs"][ind] const vKeysToDemog = typeof v != "undefined"? Object.keys(v).filter(v => v != "_row"): null const vValues = typeof v != "undefined"? Object.values(v): null keysToDemog.forEach((k,i) => { const x = {} x[demogs] = k x["count"] = values[i]/format["divider"]["period"] x["pct"] = values[i]/format["divider"]["period"] x["id"] = row x["type"] = "item" z.push(x) if((typeof v != "undefined") && (k == vKeysToDemog[i])) { const y = {} y[demogs] = k y["count"] = vValues[i]/format["divider"]["period"] y["pct"] = vValues[i]/format["divider"]["period"] y["id"] = row y["type"] = "vs" z.push(y) } }) return z }).flat(), o => o.type , o => o.id) return output }38:T46a,standardiseDemogsInRows = function(rowFromMap) { return demogOptions.map( (o,i) => { const output = {} if( ((comparisonType == "period") && (typeof demogsGroupByRow.get("item").get(rowFromMap) == "undefined")) || ((comparisonType == "item") && ((typeof demogsGroupByRow.get("item").get(rowFromMap) == "undefined") || (typeof demogsGroupByRow.get("vs").get(rowFromMap) == "undefined")) ) ) { output[demogs] = o output["count"] = null output["pct"] = null output["id"] = rowFromMap } else { output[demogs] = o output["count"] = comparisonType == "period"? demogsGroupByRow.get("item").get(rowFromMap)[i]["count"]: demogsGroupByRow.get("item").get(rowFromMap)[i]["count"] - demogsGroupByRow.get("vs").get(rowFromMap)[i]["count"] output["pct"] = comparisonType == "period"? demogsGroupByRow.get("item").get(rowFromMap)[i]["pct"]: demogsGroupByRow.get("item").get(rowFromMap)[i]["pct"] - demogsGroupByRow.get("vs").get(rowFromMap)[i]["pct"] output["id"] = rowFromMap } return output }) }39:T60a,toParseableDate = function(value){ const periods_object_index = { Month: { split: "-", yearPart: 0, toMapPart: 1, mapping: Object.fromEntries( ["01","02","03","04","05","06","07","08","09","10","11","12"].map(o => { return [o,o] }) ) }, Quarter: {split: "-", yearPart: 0, toMapPart: 1, mapping: Object.fromEntries( ["Q1","Q2","Q3","Q4"].map(o => { return o == "Q1"? [o, "01"]: o == "Q2"? [o, "04"]: o == "Q3"? [o, "07"]: o == "Q4"? [o, "10"]: null }) ) }, Half: {split: "-", yearPart: 0, toMapPart: 1, mapping: Object.fromEntries( ["H1","H2"].map(o => { const output = {} output[o] = o == "H1"? [o, "01"]: o == "H2"? [o, "07"]: null return output }) ) }, Year: {split: "Y", yearPart: 0, toMapPart: null} } const splitValue = value.split(periods_object_index[periodicity]["split"]) //datecheck if you need it //Object.prototype.toString.call(date) === '[object Date]' const year = Number(splitValue[periods_object_index[periodicity].yearPart]) const month = periods_object_index[periodicity].mapping [ splitValue[periods_object_index[periodicity].toMapPart] ] const day = "01" return typeof month != "undefined"? `${year}-${month}-${day}`: null }3a:[] 2c:{"id":"866628ae78a0785c","slug":null,"title":"LIVE: Advanced Summary, A/B Comparison - Collapsible 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