43:["$","title",null,{"children":"Popular Google Search Queries in the United Kingdom (UK) and Beyond | Caroline Cullinan | Observable"}] 4b:T961,html` This Observable notebook illustrates Google Search Trends relevant to the Google Search Topics of jobs, learning, finance, research, support, gaming, real estate, virtual, leisure, mental health, sustainability, and used. Popularity of the listed Google Search Topics has been explored for both pre-pandemic (i.e., 2017, 2018, 2019) and pandemic (i.e., 2020 and 2021) annual timeframes. Data has been visualised at both the United Kingdom (UK) scale and the global scale.

Section 1.0 of this Observable notebook outlines visualisations for Google Search Topics at both the UK and global scales. Visualisations in this section were created using an abridged list of keywords where UK-specific brands were omitted. As such, visualisations in this section can be used to compare the popularity of different Google Search queries in the UK vs. the rest of the world. Contrastingly, Section 2.0 of this Observable notebook outlines visualisations for Google Search Topics at the UK-level only. The dataset used to create the visualisations in this section has incorporated UK-specific brands. Thus, Section 2.0 visualisations should be used in instances where interest lies in how Google Search Topics compare across the UK (only). Please do not use Section 1.0 world visualisations in comparison to Section 2.0 UK visualisations. Instead, please use Section 1.0 world visualisations in comparison to Section 1.0 UK visualisations. Section 2.0 UK visualisations should only be used stand-alone as a means to capture a more comprehensive review of Google Trends across Search Topics within the UK only.

Please see the Appendix for a complete explanation of data sources, data metrics, and methods. Links to download the datasets used in this analysis can also be found in the Appendix.

Many alternative visualisation options have been made available for each figure. For each figure, please be sure to interact with the buttons located above each chart. In doing so, you can modify visualisation elements, and for some of the figures you can also switch between different metrics.

`4c:T7c5,{ const focusYears = [2020, 2021] const plot = PlotW.plot({ grid: true, marginLeft: 100, inset: 10, width: 1050, height: 740, facet: { data: data_UK_agg, x: "topic", columns:3 }, y : { domain: [5,yScaleLimit], label: ` ${yLabel}`, }, x: { ticks: 10, tickFormat: d3.timeFormat("%b") // domain:["January", "February", "March"] }, color: { type: "ordinal", domain: [2017, 2018, 2019, 2020, 2021], range: ["#E5B541", "#695B78","#096E76","#70C8F2", "#E02685"] //"#70C8F2", "#096E76", "#E02685", "#695B78", "#E5B541", // scheme: "category10", // legend: "ramp" }, marks: [ // PlotW.frame(), PlotW.line(data_UK_agg, { x: d=>d["dateMonth"], y: d=>d["value"], // z: "year", stroke: "year", strokeOpacity: d => focusYears.includes(d.year)? 1:lineOpacity, // stroke: d=> focusYears.includes(d.year)? d["year"]:"#ccc", // curve: "catmull-rom" curve: "basis" }), PlotW.text(data_UK_agg, PlotW.selectFirst({ text: d=> `${d.topic}`, x: new Date("2001-05-31T23:00Z"), y: yScale100==="Yes"? 100:85 })) ] }) const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize").style("fill", "black") const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey").style("stroke-width", "0.5px") const ticksPath = d3.select(plot).selectAll(".tick").selectAll("path").style("stroke-width", "0.5px") return html`

${plot}

` }4d:T77b,{ const focusYears = [2020, 2021] const plot = PlotW.plot({ grid: true, marginLeft: 100, inset: 10, width: 1050, height: 740, facet: { data: data_UK_agg, x: "topic", columns:3 }, x: { ticks: 10, tickFormat: d3.timeFormat("%b") // domain:["January", "February", "March"] }, y : { domain: [5,yScaleLimit_2], label: ` ${yLabel}`, }, color: { type: "ordinal", domain: [2017, 2018, 2019, 2020, 2021], range: ["#ccc", "#ccc","#ccc","#70C8F2", "#E02685"] // scheme: "category10", // legend: "ramp" }, marks: [ // PlotW.frame(), PlotW.line(data_UK_agg, { x: d=>d["dateMonth"], y: d=>d["value"], // z: "year", stroke: "year", strokeWidth: d => focusYears.includes(d.year)? 2:1.2, // stroke: d=> focusYears.includes(d.year)? d["year"]:"#ccc", // curve: "catmull-rom" curve: "basis" }), PlotW.text(data_UK_agg, PlotW.selectFirst({ text: d=> `${d.topic}`, x: new Date("2001-05-31T23:00Z"), y: yScale100_2==="Yes"? 100:85})) ] }) const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize").style("fill", "black") const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey").style("stroke-width", "0.5px") const ticksPath = d3.select(plot).selectAll(".tick").selectAll("path").style("stroke-width", "0.5px") return html`

${plot}

` }4e:T830,{ const focusYears = [2020, 2021] const plot = PlotW.plot({ grid: true, marginLeft: 100, inset: 10, width: 1050, height: 740, facet: { data: data_world_agg, x: "topic", columns:3 }, y : { domain: [5,yScaleLimit_3], tickFormat: (f => x => f((x)))(d3.format("d")), label: ` ${yLabel}`, }, x: { ticks: 10, tickFormat: d3.timeFormat("%b") // domain:["January", "February", "March"] }, color: { type: "ordinal", domain: [2017, 2018, 2019, 2020, 2021], range: ["#E5B541", "#695B78","#096E76","#70C8F2", "#E02685"] //"#70C8F2", "#096E76", "#E02685", "#695B78", "#E5B541", // scheme: "category10", // legend: "ramp" }, marks: [ // PlotW.frame(), PlotW.line(data_world_agg, { x: d=>d["dateMonth"], y: d=>d["value"], k:7, // z: "year", stroke: "year", //strokeWidth: d => focusYears.includes(d.year)? 2:1.2, strokeOpacity: d => focusYears.includes(d.year)? 1:lineOpacity2, curve: "basis", // curve: "catmull-rom" } ), PlotW.text(data_world_agg, PlotW.selectFirst({ text: d=> `${d.topic}`, x: new Date("2001-05-31T23:00Z"), y: yScale100_3==="Yes"?100:85 })) ] }) const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize").style("fill", "black") const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey").style("stroke-width", "0.5px") const ticksPath = d3.select(plot).selectAll(".tick").selectAll("path").style("stroke-width", "0.5px") return html`

${plot}

` }4f:T79e,{ const focusYears = [2020, 2021] const plot = PlotW.plot({ grid: true, marginLeft: 100, inset: 10, width: 1050, height: 740, facet: { data: data_world_agg, x: "topic", columns:3 }, y : { domain: [5,yScaleLimit_4], tickFormat: (f => x => f((x)))(d3.format("d")), label: ` ${yLabel}`, }, x: { ticks: 10, tickFormat: d3.timeFormat("%b") // domain:["January", "February", "March"] }, color: { type: "ordinal", domain: [2017, 2018, 2019, 2020, 2021], range: ["#ccc", "#ccc","#ccc","#70C8F2", "#E02685"] // scheme: "category10", // legend: "ramp" }, marks: [ // PlotW.frame(), PlotW.line(data_world_agg, { x: d=>d["dateMonth"], y: d=>d["value"], k:7, // z: "year", stroke: "year", strokeWidth: d => focusYears.includes(d.year)? 2:1.2, curve: "basis", // curve: "catmull-rom" } ), PlotW.text(data_world_agg, PlotW.selectFirst({ text: d=> `${d.topic}`, x: new Date("2001-05-31T23:00Z"), y: yScale100_4==="Yes"?100:85})) ] }) const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize").style("fill", "black") const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey").style("stroke-width", "0.5px") const ticksPath = d3.select(plot).selectAll(".tick").selectAll("path").style("stroke-width", "0.5px") return html`

${plot}

` }50:T10b1,{ const focusYears = [2020, 2021] const plot = normalized_5==="No"? PlotW.plot({ grid: true, marginLeft: 30, marginRight: 30, inset: 10, width: 1050, height: 350, facet: { data: data_merged, x: "topic", columns:3 }, x: { ticks: 10, nice:true //tickFormat: d3.timeFormat("%b") //domain:[] }, y : { domain: [5,yScaleLimit_5], label: ` ${yLabel}`, }, color: { type: "ordinal", domain: ["world", "uk"], range: ['#0072bb','#F52D4F'], //"#70C8F2", "#096E76", "#E02685", "#695B78", "#E5B541", scheme: "category10", legend: "ramp" }, //'#0072bb' '#F52D4F' marks: [ // PlotW.frame(), // PlotW.line(data_merged, { // x: d=>d["date"], // y: d=>d["value"], // // z: "year", // stroke: "source", // strokeOpacity: 1, // // stroke: d=> focusYears.includes(d.year)? d["year"]:"#ccc", // // curve: "catmull-rom" // curve: "basis", // }), PlotW.line(data_merged, Plot.windowY({ x: "date", y: "value", k: 7, curve: "basis", stroke: "source", strokeWidth:1.5 })), // PlotW.line(data_merged, Plot.normalizeY(Plot.windowY({ // x: "date", y: "value", k: 7, curve: "basis", stroke: "source"}))), PlotW.text(data_merged, Plot.selectLast({ x: "date", y: "value", z: "source", text: "source", fill: "source", textAnchor: "start", // transform: t=>t.toLowerCase(), dx: 3 })), PlotW.text(data_merged, PlotW.selectFirst({ text: d=> `${d.topic}`, x: new Date("2019-05-31T23:00Z"), y: yScale100_5==="Yes"?100:85 })) ] }) : PlotW.plot({ grid: true, marginLeft: 30, marginRight: 30, inset: 10, width: 1050, height: 350, facet: { data: data_merged, x: "topic", columns:3 }, x: { ticks: 10, nice:true, //tickFormat: d3.timeFormat("%b") //domain:[] }, y : { // domain: [0.6,1.8], //label: "", tickFormat: (f => x => f((x - 1) * 100))(d3.format("d")), label: ` ${yLabelNorm}`, // x:200 }, color: { type: "ordinal", domain: ["world", "uk"], range: ['#0072bb','#F52D4F'], //"#70C8F2", "#096E76", "#E02685", "#695B78", "#E5B541", scheme: "category10", legend: "ramp" }, //'#0072bb' '#F52D4F' marks: [ // PlotW.areaY(data_merged, PlotW.normalizeY({ // x: "date", // y: "value", // // y2: "value", // curve: "basis", // fill: "#ccc" // })), PlotW.line(data_merged, PlotW.normalizeY(PlotW.windowY({ x: "date", y: "value", k: 7, curve: "basis", stroke: "source", strokeWidth:1.5 }))), PlotW.text(data_merged, PlotW.selectLast(PlotW.normalizeY({ x: "date", y: "value", z: "source", text: "source", fill: "source", textAnchor: "start", // transform: t=>t.toLowerCase(), dx: 3 }))), PlotW.text(data_merged, PlotW.selectFirst({ text: d=> `${d.topic}`, x: new Date("2019-05-31T23:00Z"), y: 2 })) ] }) const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize").style("fill", "black") const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey").style("stroke-width", "0.5px") const ticksPath = d3.select(plot).selectAll(".tick").selectAll("path").style("stroke-width", "0.5px") return html`

${plot}

` }51:T14c5,{ //need to fix annotations of labels - I know that this graph is messy, but it could be useful regardless //const topicFilter = 'jobs' const plot = normalized_6 === "No"? PlotW.plot({ grid: true, marginLeft: 30, marginRight: 100, inset: 10, width: 1020, height: 550, // facet: { // data: data_UK_agg, // x: "source", // columns:1 // }, x: { ticks: 10, nice:true, //tickFormat: d3.timeFormat("%b") //domain:[] }, y : { domain: [5,yScaleLimit], ticks:5, label: ` ${yLabel}`, }, color: { type: "ordinal", domain: topics, range:['#0072bb', '#F52D4F', '#746AB0', '#FFA500', '#37C871', '#70C8F2', '#096E76', '#E5B541', '#E02685', '#3AB3B6', '#1B2140', '#9D1A5F'], //"#009699", "#4d194d" }, marks: [ PlotW.line(data_UK_agg, PlotW.windowY({ x: "date", y: "value", k: 14, curve: "basis", // z:"topic", stroke: "topic", // stroke: d => d.topic===select_topicFinder? d.topic:d.source, strokeOpacity: 1, strokeWidth:3})), PlotW.text(data_UK_agg, Plot.selectLast({ x: "date", y: d=>d.topic==="jobs"?d.value+5.5: d.topic==="learning"?d.value+9: d.topic==="finance"?d.value+16.5: d.topic==="research"?d.value: d.topic==="support"?d.value+6: d.topic==="gaming"?d.value-12: d.topic==="real estate"?d.value+2.5: d.topic==="virtual"?d.value-11.5: d.topic==="leisure"?d.value+1.5: d.topic==="mental health"?d.value+13: d.topic==="sustainability"?d.value+10: d.topic==="used"?d.value-1.5:d.value, z: "topic", text: legendOrcurve!=="Legend"?"topic":"None", //fill: "topic", fill: "topic", textAnchor: "start", // dx: 30, // dy:-10, dx:-15, // opacity: d => d.topic===select_topicFinder? 1:0.2, })), ] }): PlotW.plot({ grid: true, marginLeft: 30, marginRight: 100, inset: 10, width: 1020, height: 550, // facet: { // data: data_UK_agg, // x: "source", // columns:1 // }, x: { ticks: 10, nice:true, //tickFormat: d3.timeFormat("%b") //domain:[] }, y : { // domain: [0.3,3], ticks:5, tickFormat: (f => x => f((x - 1) * 100))(d3.format("d")), label: ` ${yLabelNorm}`, }, color: { type: "ordinal", domain: topics, range:['#0072bb', '#F52D4F', '#746AB0', '#FFA500', '#37C871', '#70C8F2', '#096E76', '#E5B541', '#E02685', '#3AB3B6', '#1B2140', '#9D1A5F'], //"#009699", "#4d194d" }, marks: [ PlotW.line(data_UK_agg, PlotW.normalizeY(PlotW.windowY({ x: "date", y: "value", k: 14, curve: "basis", // z:"topic", stroke: "topic", // extent:[0,1], // stroke: d => d.topic===select_topicFinder? d.topic:d.source, strokeOpacity: 1, strokeWidth:3}))), PlotW.text(data_UK_agg, Plot.selectLast(PlotW.normalizeY({ x: "date", y: d=>d.topic==="sustainability"?d.value-2.5: d.topic==="jobs"?d.value+1000: d.topic==="mental health"?d.value+200: d.topic==="finance"?d.value+90: d.topic==="research"?d.value-5: d.topic==="support"?d.value+175: d.topic==="gaming"?d.value+70: d.topic==="real estate"?d.value-7: d.topic==="virtual"?d.value+30: d.topic==="leisure"?d.value+2: d.topic==="learning"?d.value+50: d.topic==="used"?d.value-7: d.value, z: "topic", text: legendOrcurve!=="Legend"?"topic":"None", // k: 14, // range:[0,1], //fill: "topic", fill: "topic", textAnchor: "start", // dx: 10, // dy:-60, // dy: d => d.topic==="sustainability"?"100px":0, dx:-15, // opacity: d => d.topic===select_topicFinder? 1:0.2, }))), // PlotW.text(data_merged, PlotW.selectFirst({text: ' ', x: new Date("2019-05-31T23:00Z"), y: 100 })) ] }) const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize").style("fill", "black") // const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey").style("stroke-width", "0.5px") const ticksPath = d3.select(plot).selectAll(".tick").selectAll("path").style("stroke", "lightgrey").style("stroke-width", "0.5px") return html`

${plot}

` }52:T144b,{ const topicFilter = 'jobs' const plot = normalized_7==="No"? PlotW.plot({ grid: true, marginLeft: 30, marginRight: 100, inset: 10, width: 1020, height: 400, // facet: { // data: data_UK_agg, // x: "source", // columns:1 // }, x: { ticks: 10, nice:true, //tickFormat: d3.timeFormat("%b") //domain:[] }, y : { domain: [5,yScaleLimit], ticks:5, label: ` ${yLabel}`, }, color: { type: "ordinal", domain: topics, range:['#0072bb', '#F52D4F', '#746AB0', '#FFA500', '#37C871', '#70C8F2', '#096E76', '#E5B541', '#E02685', '#3AB3B6', '#1B2140', '#9D1A5F'], //"#009699", "#4d194d" }, marks: [ PlotW.line(data_UK_agg, PlotW.windowY({ x: "date", y: "value", k: 14, curve: "basis", // z:"topic", stroke: "topic", // stroke: d => d.topic===select_topicFinder? d.topic:d.source, strokeOpacity: d => d.topic===select_topicFinder? 1:0.15, strokeWidth:3})), PlotW.text(data_UK_agg, Plot.selectLast({ x: "date", y: d=>d.topic==="jobs"?d.value+4: d.topic==="learning"?d.value+7: d.topic==="finance"?d.value+13: d.topic==="research"?d.value-2: d.topic==="support"?d.value+4: d.topic==="gaming"?d.value-9: d.topic==="real estate"?d.value-1: d.topic==="virtual"?d.value-8: d.topic==="leisure"?d.value-1: d.topic==="mental health"?d.value+10: d.topic==="sustainability"?d.value+8: d.topic==="used"?d.value-3:d.value, z: "topic", text: "topic", //fill: "topic", fill: d => d.topic===select_topicFinder? d.topic:"none", textAnchor: "start", // dx: 30, // dy:-10, dx:-15, // opacity: d => d.topic===select_topicFinder? 1:0.2, })), PlotW.text(data_merged, PlotW.selectFirst({text: ' ', x: new Date("2019-05-31T23:00Z"), y: 100 })) ] }) : PlotW.plot({ grid: true, marginLeft: 30, marginRight: 100, inset: 10, width: 1020, height: 400, // facet: { // data: data_UK_agg, // x: "source", // columns:1 // }, x: { ticks: 10, nice:true, //tickFormat: d3.timeFormat("%b") //domain:[] }, y : { // domain: [0.3,3], ticks:5, tickFormat: (f => x => f((x - 1) * 100))(d3.format("d")), label: ` ${yLabelNorm}`, }, color: { type: "ordinal", domain: topics, range:['#0072bb', '#F52D4F', '#746AB0', '#FFA500', '#37C871', '#70C8F2', '#096E76', '#E5B541', '#E02685', '#3AB3B6', '#1B2140', '#9D1A5F'], //"#009699", "#4d194d" }, marks: [ PlotW.line(data_UK_agg, PlotW.normalizeY(PlotW.windowY({ x: "date", y: "value", k: 14, curve: "basis", // z:"topic", stroke: "topic", // extent:[0,1], // stroke: d => d.topic===select_topicFinder? d.topic:d.source, strokeOpacity: d => d.topic===select_topicFinder? 1:0.15, strokeWidth:3}))), PlotW.text(data_UK_agg, Plot.selectLast(PlotW.normalizeY({ x: "date", y: d=>d.topic==="sustainability"?d.value+4: d.topic==="mental health"?d.value+100: d.topic==="jobs"?d.value+150: d.topic==="learning"?d.value+50: d.topic==="finance"?d.value+50: d.topic==="support"?d.value+85: d.topic==="virtual"?d.value+25: d.topic==="gaming"?d.value+100:d.value, z: "topic", text: "topic", // k: 14, // range:[0,1], //fill: "topic", fill: d => d.topic===select_topicFinder? d.topic:"none", textAnchor: "start", // dx: 10, // dy:-60, // dy: d => d.topic==="sustainability"?"100px":0, dx:-15, // opacity: d => d.topic===select_topicFinder? 1:0.2, }))), // PlotW.text(data_merged, PlotW.selectFirst({text: ' ', x: new Date("2019-05-31T23:00Z"), y: 100 })) ] }) const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize").style("fill", "black") // const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey").style("stroke-width", "0.5px") const ticksPath = d3.select(plot).selectAll(".tick").selectAll("path").style("stroke", "lightgrey").style("stroke-width", "0.5px") return html`

${plot}

` }53:Ta32,yearPlot = { const plot = PlotW.plot({ grid: true, inset: 0, width: 1350, height: 300, facet: { data: data_UK_agg, x: "year", columns:5, }, x: { ticks: 10, // domain:["January", "February", "March"] tickFormat: d3.timeFormat("%b"), nice:false, }, y : { domain: normalized_8==="Yes"?[0.3, 1.7]:[5,100], tickFormat: normalized_8==="Yes"?(f => x => f((x - 1) * 100))(d3.format("d")):(f => x => f((x)))(d3.format("d")), label: ` ${normalized_8==="Yes"?yLabelNorm:yLabel}`, }, color: { type: "ordinal", domain: topics, range:['#0072bb', '#F52D4F', '#746AB0', '#FFA500', '#37C871', '#70C8F2', '#096E76', '#E5B541', '#E02685', '#3AB3B6', '#1B2140', '#9D1A5F'], // legend: "ramp" }, marks: [ // PlotW.frame(), PlotW.line(data_UK_agg, normalized_8==="Yes"? PlotW.normalizeY(PlotW.windowY({ x: d=>d["dateMonth"], y: d=>d["value"], k:7, // z: "year", stroke: "topic", curve: "basis", // curve: "catmull-rom" })) : PlotW.windowY({ x: d=>d["dateMonth"], y: d=>d["value"], k:7, // z: "year", stroke: "topic", curve: "basis", // curve: "catmull-rom" }) ), PlotW.text(data_UK_agg, PlotW.selectFirst({ text: d=> `${d.year}`, x: new Date("2001-05-31T23:00Z"), y: normalized_8==="Yes"?1.7:100 })) ] }) // const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") //const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize") //const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey") const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize").style("fill", "black") const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey").style("stroke-width", "0.5px") const ticksPath = d3.select(plot).selectAll(".tick").selectAll("path").style("stroke-width", "0.5px") return plot }54:T929,yearPlotBarUK = { const plot = PlotW.plot({ grid: true, inset: 0, width: 1350, height: 300, marginLeft:80, facet: { data: data_UK_agg, x: "year", columns:5, }, y: { // ticks: 10, // domain:["January", "February", "March"] // tickFormat: d3.timeFormat("%b"), nice:false, }, x : { domain: [0,100], ticks:6, tickFormat: (f => x => f((x)))(d3.format("d")), label: ` ${sumStatBarUk+" "+yLabel}`, }, color: { type: "ordinal", domain: topics, range:['#0072bb', '#F52D4F', '#746AB0', '#FFA500', '#37C871', '#70C8F2', '#096E76', '#E5B541', '#E02685', '#3AB3B6', '#1B2140', '#9D1A5F'], // legend: "ramp" }, marks: [ // PlotW.frame(), PlotW.barX(data_UK_agg, Plot.groupY({x: sumStatBarUk}, { y: "topic", x: "value", sort: {y: "x", reverse: true}, // reducer:"mean", // z: "year", fill: "topic", // curve: "basis", // curve: "catmull-rom" })) , PlotW.text(data_UK_agg, PlotW.selectFirst({ text: d=> `${d.year}`, x: new Date("2001-05-31T23:00Z"), y: normalized_9==="Yes"?2:100 })) ] }) // const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") //const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize") //const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey") const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize").style("fill", "black") const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey").style("stroke-width", "0.5px") const ticksPath = d3.select(plot).selectAll(".tick").selectAll("path").style("stroke-width", "0.5px") return plot }55:Ta38,yearPlot2 = { const plot = PlotW.plot({ grid: true, inset: 0, width: 1350, height: 300, facet: { data: data_world_agg, x: "year", columns:5, }, x: { ticks: 10, // domain:["January", "February", "March"] tickFormat: d3.timeFormat("%b"), nice:false, }, y : { domain: normalized_9==="Yes"?[0.3, 2]:[5,100], tickFormat: normalized_9==="Yes"?(f => x => f((x - 1) * 100))(d3.format("d")):(f => x => f((x)))(d3.format("d")), label: ` ${normalized_9==="Yes"?yLabelNorm:yLabel}`, }, color: { type: "ordinal", domain: topics, range:['#0072bb', '#F52D4F', '#746AB0', '#FFA500', '#37C871', '#70C8F2', '#096E76', '#E5B541', '#E02685', '#3AB3B6', '#1B2140', '#9D1A5F'], // legend: "ramp" }, marks: [ // PlotW.frame(), PlotW.line(data_world_agg, normalized_9==="Yes"? PlotW.normalizeY(PlotW.windowY({ x: d=>d["dateMonth"], y: d=>d["value"], k:7, // z: "year", stroke: "topic", curve: "basis", // curve: "catmull-rom" })) : PlotW.windowY({ x: d=>d["dateMonth"], y: d=>d["value"], k:7, // z: "year", stroke: "topic", curve: "basis", // curve: "catmull-rom" }) ), PlotW.text(data_world_agg, PlotW.selectFirst({ text: d=> `${d.year}`, x: new Date("2001-05-31T23:00Z"), y: normalized_9==="Yes"?2:100 })) ] }) // const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") //const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize") //const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey") const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize").style("fill", "black") const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey").style("stroke-width", "0.5px") const ticksPath = d3.select(plot).selectAll(".tick").selectAll("path").style("stroke-width", "0.5px") return plot }56:T92c,yearPlotBar = { const plot = PlotW.plot({ grid: true, inset: 0, width: 1350, height: 300, marginLeft:80, facet: { data: data_world_agg, x: "year", columns:5, }, y: { // ticks: 10, // domain:["January", "February", "March"] // tickFormat: d3.timeFormat("%b"), nice:false, }, x : { ticks:6, domain: [0,100], tickFormat: (f => x => f((x)))(d3.format("d")), label: ` ${sumStatBar+" "+yLabel}`, }, color: { type: "ordinal", domain: topics, range:['#0072bb', '#F52D4F', '#746AB0', '#FFA500', '#37C871', '#70C8F2', '#096E76', '#E5B541', '#E02685', '#3AB3B6', '#1B2140', '#9D1A5F'], // legend: "ramp" }, marks: [ // PlotW.frame(), PlotW.barX(data_world_agg, Plot.groupY({x: sumStatBar}, { y: "topic", x: "value", sort: {y: "x", reverse: true}, // reducer:"mean", // z: "year", fill: "topic", // curve: "basis", // curve: "catmull-rom" })) , PlotW.text(data_world_agg, PlotW.selectFirst({ text: d=> `${d.year}`, x: new Date("2001-05-31T23:00Z"), y: normalized_9==="Yes"?2:100 })) ] }) // const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") //const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize") //const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey") const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize").style("fill", "black") const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey").style("stroke-width", "0.5px") const ticksPath = d3.select(plot).selectAll(".tick").selectAll("path").style("stroke-width", "0.5px") return plot }57:T7d6,{ const focusYears = [2020, 2021] const plot = PlotW.plot({ grid: true, marginLeft: 100, inset: 10, width: 1050, height: 740, facet: { data: data_UK_UK_agg, x: "variable", columns:3 }, y : { domain: [5,yScaleLimit_6], label: ` ${yLabel}`, }, x: { ticks: 10, tickFormat: d3.timeFormat("%b") // domain:["January", "February", "March"] }, color: { type: "ordinal", domain: [2017, 2018, 2019, 2020, 2021], range: ["#E5B541", "#695B78","#096E76","#70C8F2", "#E02685"] //"#70C8F2", "#096E76", "#E02685", "#695B78", "#E5B541", // scheme: "category10", // legend: "ramp" }, marks: [ // PlotW.frame(), PlotW.line(data_UK_UK_agg, { x: d=>d["dateMonth"], y: d=>d["value"], // z: "year", stroke: "year", strokeOpacity: d => focusYears.includes(d.year)? 1:lineOpacity3, // stroke: d=> focusYears.includes(d.year)? d["year"]:"#ccc", // curve: "catmull-rom" curve: "basis" }), PlotW.text(data_UK_UK_agg, PlotW.selectFirst({ text: d=> `${d.topic}`, x: new Date("2001-05-31T23:00Z"), y: yScale100_6==="Yes"? 100:85 })) ] }) const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize").style("fill", "black") const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey").style("stroke-width", "0.5px") const ticksPath = d3.select(plot).selectAll(".tick").selectAll("path").style("stroke-width", "0.5px") return html`

${plot}

` }58:T784,{ const focusYears = [2020, 2021] const plot = PlotW.plot({ grid: true, marginLeft: 100, inset: 10, width: 1050, height: 740, facet: { data: data_UK_UK_agg, x: "topic", columns:3 }, x: { ticks: 10, tickFormat: d3.timeFormat("%b") // domain:["January", "February", "March"] }, y : { domain: [5,yScaleLimit_7], label: ` ${yLabel}`, }, color: { type: "ordinal", domain: [2017, 2018, 2019, 2020, 2021], range: ["#ccc", "#ccc","#ccc","#70C8F2", "#E02685"] // scheme: "category10", // legend: "ramp" }, marks: [ // PlotW.frame(), PlotW.line(data_UK_UK_agg, { x: d=>d["dateMonth"], y: d=>d["value"], // z: "year", stroke: "year", strokeWidth: d => focusYears.includes(d.year)? 2:1.2, // stroke: d=> focusYears.includes(d.year)? d["year"]:"#ccc", // curve: "catmull-rom" curve: "basis" }), PlotW.text(data_UK_UK_agg, PlotW.selectFirst({ text: d=> `${d.topic}`, x: new Date("2001-05-31T23:00Z"), y: yScale100_7==="Yes"? 100:85})) ] }) const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize").style("fill", "black") const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey").style("stroke-width", "0.5px") const ticksPath = d3.select(plot).selectAll(".tick").selectAll("path").style("stroke-width", "0.5px") return html`

${plot}

` }59:T1453,{ const plot = normalized_10 === "No"? PlotW.plot({ grid: true, marginLeft: 30, marginRight: 100, inset: 10, width: 1020, height: 550, // facet: { // data: data_UK_agg, // x: "source", // columns:1 // }, x: { ticks: 10, nice:true, //tickFormat: d3.timeFormat("%b") //domain:[] }, y : { domain: [5,yScaleLimit], ticks:5, label: ` ${yLabel}`, }, color: { type: "ordinal", domain: topics, range:['#0072bb', '#F52D4F', '#746AB0', '#FFA500', '#37C871', '#70C8F2', '#096E76', '#E5B541', '#E02685', '#3AB3B6', '#1B2140', '#9D1A5F'], //"#009699", "#4d194d" }, marks: [ PlotW.line(data_UK_UK_agg, PlotW.windowY({ x: "date", y: "value", k: 14, curve: "basis", // z:"topic", stroke: "topic", // stroke: d => d.topic===select_topicFinder? d.topic:d.source, strokeOpacity: 1, strokeWidth:3})), PlotW.text(data_UK_UK_agg, Plot.selectLast({ x: "date", y: d=>d.topic==="jobs"?d.value+4: d.topic==="learning"?d.value+9.5: d.topic==="finance"?d.value+16.5: d.topic==="research"?d.value: d.topic==="support"?d.value+5.5: d.topic==="gaming"?d.value-2500: d.topic==="real estate"?d.value+8.5: d.topic==="virtual"?d.value-11.5: d.topic==="leisure"?d.value+.5: d.topic==="mental health"?d.value+13: d.topic==="sustainability"?d.value+10: d.topic==="used"?d.value-2.5:d.value, z: "topic", text: legendOrcurve_1!=="Legend"?"topic":"None", //fill: "topic", fill: "topic", textAnchor: "start", // dx: 30, // dy:-10, dx:-15, // opacity: d => d.topic===select_topicFinder? 1:0.2, })), ] }): PlotW.plot({ grid: true, marginLeft: 30, marginRight: 100, inset: 10, width: 1020, height: 550, // facet: { // data: data_UK_agg, // x: "source", // columns:1 // }, x: { ticks: 10, nice:true, //tickFormat: d3.timeFormat("%b") //domain:[] }, y : { // domain: [0.3,3], ticks:5, tickFormat: (f => x => f((x - 1) * 100))(d3.format("d")), label: ` ${yLabelNorm}`, }, color: { type: "ordinal", domain: topics, range:['#0072bb', '#F52D4F', '#746AB0', '#FFA500', '#37C871', '#70C8F2', '#096E76', '#E5B541', '#E02685', '#3AB3B6', '#1B2140', '#9D1A5F'], //"#009699", "#4d194d" }, marks: [ PlotW.line(data_UK_UK_agg, PlotW.normalizeY(PlotW.windowY({ x: "date", y: "value", k: 14, curve: "basis", // z:"topic", stroke: "topic", // extent:[0,1], // stroke: d => d.topic===select_topicFinder? d.topic:d.source, strokeOpacity: 1, strokeWidth:3}))), PlotW.text(data_UK_UK_agg, Plot.selectLast(PlotW.normalizeY({ x: "date", y: d=>d.topic==="sustainability"?d.value+2500: d.topic==="jobs"?d.value+50: d.topic==="mental health"?d.value+9500: d.topic==="finance"?d.value+41: d.topic==="research"?d.value-5: d.topic==="support"?d.value+74: d.topic==="gaming"?d.value+9: d.topic==="real estate"?d.value-9.5: d.topic==="virtual"?d.value+30: d.topic==="leisure"?d.value-23: d.topic==="learning"?d.value+20: d.topic==="used"?d.value-20: d.value, z: "topic", text: legendOrcurve_1!=="Legend"?"topic":"None", // k: 14, // range:[0,1], //fill: "topic", fill: "topic", textAnchor: "start", // dx: 10, // dy:-60, // dy: d => d.topic==="sustainability"?"100px":0, dx:-15, // opacity: d => d.topic===select_topicFinder? 1:0.2, }))), // PlotW.text(data_merged, PlotW.selectFirst({text: ' ', x: new Date("2019-05-31T23:00Z"), y: 100 })) ] }) const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize").style("fill", "black") // const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey").style("stroke-width", "0.5px") const ticksPath = d3.select(plot).selectAll(".tick").selectAll("path").style("stroke", "lightgrey").style("stroke-width", "0.5px") return html`

${plot}

` }5a:T14f0,{ const topicFilter = 'jobs' const plot = normalized_11==="No"? PlotW.plot({ grid: true, marginLeft: 30, marginRight: 100, inset: 10, width: 1020, height: 400, // facet: { // data: data_UK_agg, // x: "source", // columns:1 // }, x: { ticks: 10, nice:true, //tickFormat: d3.timeFormat("%b") //domain:[] }, y : { domain: [5,yScaleLimit], ticks:5, label: ` ${yLabel}`, }, color: { type: "ordinal", domain: topics, range:['#0072bb', '#F52D4F', '#746AB0', '#FFA500', '#37C871', '#70C8F2', '#096E76', '#E5B541', '#E02685', '#3AB3B6', '#1B2140', '#9D1A5F'], //"#009699", "#4d194d" }, marks: [ PlotW.line(data_UK_UK_agg, PlotW.windowY({ x: "date", y: "value", k: 14, curve: "basis", // z:"topic", stroke: "topic", // stroke: d => d.topic===select_topicFinder? d.topic:d.source, strokeOpacity: d => d.topic===select_topicFinder_1? 1:0.15, strokeWidth:3})), PlotW.text(data_UK_UK_agg, Plot.selectLast({ x: "date", y: d=>d.topic==="jobs"?d.value+4: d.topic==="learning"?d.value+7: d.topic==="finance"?d.value+13: d.topic==="research"?d.value-2: d.topic==="support"?d.value+4: d.topic==="gaming"?d.value-9: d.topic==="real estate"?d.value-1: d.topic==="virtual"?d.value-8: d.topic==="leisure"?d.value-1: d.topic==="mental health"?d.value+10: d.topic==="sustainability"?d.value+8: d.topic==="used"?d.value-3:d.value, z: "topic", text: "topic", //fill: "topic", fill: d => d.topic===select_topicFinder_1? d.topic:"none", textAnchor: "start", // dx: 30, // dy:-10, dx:-15, // opacity: d => d.topic===select_topicFinder? 1:0.2, })), PlotW.text(data_merged, PlotW.selectFirst({text: ' ', x: new Date("2019-05-31T23:00Z"), y: 100 })) ] }) : PlotW.plot({ grid: true, marginLeft: 30, marginRight: 100, inset: 10, width: 1020, height: 400, // facet: { // data: data_UK_agg, // x: "source", // columns:1 // }, x: { ticks: 10, nice:true, //tickFormat: d3.timeFormat("%b") //domain:[] }, y : { // domain: [0.3,3], ticks:5, tickFormat: (f => x => f((x - 1) * 100))(d3.format("d")), label: ` ${yLabelNorm}`, }, color: { type: "ordinal", domain: topics, range:['#0072bb', '#F52D4F', '#746AB0', '#FFA500', '#37C871', '#70C8F2', '#096E76', '#E5B541', '#E02685', '#3AB3B6', '#1B2140', '#9D1A5F'], //"#009699", "#4d194d" }, marks: [ PlotW.line(data_UK_UK_agg, PlotW.normalizeY(PlotW.windowY({ x: "date", y: "value", k: 14, curve: "basis", // z:"topic", stroke: "topic", // extent:[0,1], // stroke: d => d.topic===select_topicFinder? d.topic:d.source, strokeOpacity: d => d.topic===select_topicFinder_1? 1:0.15, strokeWidth:3}))), PlotW.text(data_UK_UK_agg, Plot.selectLast(PlotW.normalizeY({ x: "date", y: d=>d.topic==="sustainability"?d.value+4: d.topic==="mental health"?d.value+275: d.topic==="jobs"?d.value+150: d.topic==="learning"?d.value+50: d.topic==="finance"?d.value+50: d.topic==="support"?d.value+85: d.topic==="virtual"?d.value+35: d.topic==="leisure"?d.value-15: d.topic==="real estate"?d.value-8: d.topic==="used"?d.value-22: d.topic==="gaming"?d.value+100:d.value, z: "topic", text: "topic", // k: 14, // range:[0,1], //fill: "topic", fill: d => d.topic===select_topicFinder_1? d.topic:"none", textAnchor: "start", // dx: 10, // dy:-60, // dy: d => d.topic==="sustainability"?"100px":0, dx:-15, // opacity: d => d.topic===select_topicFinder? 1:0.2, }))), // PlotW.text(data_merged, PlotW.selectFirst({text: ' ', x: new Date("2019-05-31T23:00Z"), y: 100 })) ] }) const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize").style("fill", "black") // const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey").style("stroke-width", "0.5px") const ticksPath = d3.select(plot).selectAll(".tick").selectAll("path").style("stroke", "lightgrey").style("stroke-width", "0.5px") return html`

${plot}

` }5b:Ta42,yearPlot_1 = { const plot = PlotW.plot({ grid: true, inset: 0, width: 1350, height: 300, facet: { data: data_UK_UK_agg, x: "year", columns:5, }, x: { ticks: 10, // domain:["January", "February", "March"] tickFormat: d3.timeFormat("%b"), nice:false, }, y : { domain: normalized_12==="Yes"?[0.3, 1.7]:[5,100], tickFormat: normalized_12==="Yes"?(f => x => f((x - 1) * 100))(d3.format("d")):(f => x => f((x)))(d3.format("d")), label: ` ${normalized_12==="Yes"?yLabelNorm:yLabel}`, }, color: { type: "ordinal", domain: topics, range:['#0072bb', '#F52D4F', '#746AB0', '#FFA500', '#37C871', '#70C8F2', '#096E76', '#E5B541', '#E02685', '#3AB3B6', '#1B2140', '#9D1A5F'], // legend: "ramp" }, marks: [ // PlotW.frame(), PlotW.line(data_UK_UK_agg, normalized_12==="Yes"? PlotW.normalizeY(PlotW.windowY({ x: d=>d["dateMonth"], y: d=>d["value"], k:7, // z: "year", stroke: "topic", curve: "basis", // curve: "catmull-rom" })) : PlotW.windowY({ x: d=>d["dateMonth"], y: d=>d["value"], k:7, // z: "year", stroke: "topic", curve: "basis", // curve: "catmull-rom" }) ), PlotW.text(data_UK_UK_agg, PlotW.selectFirst({ text: d=> `${d.year}`, x: new Date("2001-05-31T23:00Z"), y: normalized_12==="Yes"?1.7:100 })) ] }) // const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") //const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize") //const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey") const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize").style("fill", "black") const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey").style("stroke-width", "0.5px") const ticksPath = d3.select(plot).selectAll(".tick").selectAll("path").style("stroke-width", "0.5px") return plot }5c:T939,yearPlotBarUK_1 = { const plot = PlotW.plot({ grid: true, inset: 0, width: 1350, height: 300, marginLeft:80, facet: { data: data_UK_UK_agg, x: "year", columns:5, }, y: { // ticks: 10, // domain:["January", "February", "March"] // tickFormat: d3.timeFormat("%b"), nice:false, }, x : { domain: [0,100], ticks:6, tickFormat: (f => x => f((x)))(d3.format("d")), label: ` ${sumStatBarUk_1+" "+yLabel}`, }, color: { type: "ordinal", domain: topics, range:['#0072bb', '#F52D4F', '#746AB0', '#FFA500', '#37C871', '#70C8F2', '#096E76', '#E5B541', '#E02685', '#3AB3B6', '#1B2140', '#9D1A5F'], // legend: "ramp" }, marks: [ // PlotW.frame(), PlotW.barX(data_UK_UK_agg, Plot.groupY({x: sumStatBarUk_1}, { y: "topic", x: "value", sort: {y: "x", reverse: true}, // reducer:"mean", // z: "year", fill: "topic", // curve: "basis", // curve: "catmull-rom" })) , PlotW.text(data_UK_UK_agg, PlotW.selectFirst({ text: d=> `${d.year}`, x: new Date("2001-05-31T23:00Z"), y: normalized_12==="Yes"?2:100 })) ] }) // const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") //const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize") //const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey") const figLabels = d3.select(plot).selectAll("text").style("font", "10px sans-serif").style("text-transform", "uppercase").style("font-weight", "bold") const ticks = d3.select(plot).selectAll(".tick").selectAll("text").style("font", "10px sans-serif").style("text-transform", "capitalize").style("fill", "black") const ticksLine = d3.select(plot).selectAll(".tick").selectAll("line").style("stroke", "lightgrey").style("stroke-width", "0.5px") const ticksPath = d3.select(plot).selectAll(".tick").selectAll("path").style("stroke-width", "0.5px") return plot }5d:T1156,html`Data Source

Data used in this analysis was sourced from Google Trends with the unofficial Google Trends API, Pytrends, for the period of January 1, 2017 - January 1, 2022. For complete comparisons of timeframes, only data for the years of 2017, 2018, 2019, 2020, and 2021 was visualised. With twelve pre-determined Google Search Topics related to jobs, learning, finance, research, support, gaming, real estate, virtual, leisure, mental health, sustainability, and used, data was queried with multiple search terms per topic. Groups of search terms for each topic were formed on the basis that search terms shared the same concept. Spelling variations were also taken into account when forming search term query groups.

For example, related to the Search Topic of learning, "e-learning", "elearning", and "e learning" were some of the few search terms included in the learning topic's search term query group. For an exhaustive list of search terms used for each Search Topic, click here

Data Metrics

Notably, data has been analysed in terms of both 1) absolute popularity and 2) relative popularity.

absolute popularity

absolute popularity of each Google Search Topic is represented by all charts and figures where the data is not normalised. In representing the data this way, the reader can observe the absolute popularity of each Google Search Topic over time. Thus, in a non-normalised chart or figure, it is apparent which search topics are most popular and least popular.

In representing the absolute popularity of each Google Search Topic, the Google Trends Metric is used. According to Google Trends, With this metric, values are calculated on a scale from 0-100. 100 on this scale represents a Google search with the most popularity as a fraction of total searches. Similarly, 50 on this scale represents a Google search that is half as popular as the most popular Google search. On this scale, a value of 0 represents negligible popularity in a Google Search.

relative popularity

relative popularity of each Google Search Topic is represented by all charts and figures where the data is normalised. In representing the data this way, comparisons over time can be made both across locations (i.e., UK vs. global) and across Google Search Topics (i.e., jobs vs. learning, etc.). As such, the reader can observe the relative change in Google Search Topic popularity over time. For example, regardless of the fact that real estate may be more popular in absolute than leisure for the year of 2021, normalised data indicates that over this timeframe, leisure is actually relatively more popular than real estate, given the percent relative change in the leisure Google Search Topic's popularity compared to the real estate Google Search Topic's popularity.

Section 1.0 Data Download

Click here to download the data relevant to UK level Google Search Trends that can be compared to global level Google Search Trends.

Click here to download the data relevant to global level Google Search Trends.

Section 2.0 Data Download

Click here to download the UK level Google Search Trends data that includes UK-specific brands and that should not be compared to global level Google Search Trends.

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Popular Google Search Queries in the United Kingdom (UK) and Beyond

A Comparison of Google Trends from 2017-2022

Prepared for Enders Analysis by Caroline Cullinan & Leonardo Nicoletti\n

Section 1.0 Google Trends across Search Topics in both the UK and the rest of the world

Popular Google Search Queries in the United Kingdom (UK)

${makeFocusLegend()}

Popular Google Search Queries Globally

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A UK vs. Global Comparison of Google Search Queries Relevant to Jobs, Learning, and Virtual from 2017-2022

Search topics compared by ${normalized_5===\"Yes\"?yLabelNorm:yLabel}

A UK Comparison of Popular Google Search Queries from 2017-2022

Search topics compared by ${normalized_6===\"Yes\"?yLabelNorm:yLabel}

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A UK Comparison of Popular Google Search Queries from 2017-2022

Search topics compared by ${normalized_7===\"Yes\"?yLabelNorm:yLabel}

A UK Comparison of Popular Google Search Queries across 2017-2022

Search topics compared by ${normalized_8===\"Yes\"?yLabelNorm:yLabel}

${makeLegend_UK(\"topic\",10, 1200)}

A Global Comparison of Popular Google Search Queries across 2017-2022

Search topics compared by ${normalized_9===\"Yes\"?yLabelNorm:yLabel}

${makeLegend_world(\"topic\",10, 1200)}

Section 2.0 Google Trends across Search Topics in the UK (only)

Popular Google Search Queries in the United Kingdom (UK)

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${makeLegend_UK(\"topic\",10, 1200)}

`:html`

A UK Comparison of Popular Google Search Queries from 2017-2022

Search topics compared by ${normalized_10===\"Yes\"?yLabelNorm:yLabel}

A UK Comparison of Popular Google Search Queries from 2017-2022

Search topics compared by ${normalized_11===\"Yes\"?yLabelNorm:yLabel}

A UK Comparison of Popular Google Search Queries across 2017-2022

Search topics compared by ${normalized_12===\"Yes\"?yLabelNorm:yLabel}

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\"learning_worl\", \"jobs_worl\", \"search_worl\", \"support_worl\", \"gaming_worl\", \"alestate_worl\", \"virtual_worl\", \"leisure_worl\", \"mentalhealth_worl\", \"sustainability_worl\", \"used_worl\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":319,"value":"topics = [\"jobs\", \"learning\", \"finance\", \"research\", \"support\", \"gaming\", \"real estate\", \"virtual\", \"leisure\", \"mental health\", \"sustainability\", \"used\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3059,"value":"topicDict_world = topicsAbbrv_world.map((d, i)=>{\n return {\n topicAbbrv_world:d,\n topicLabel: topics[i]\n }\n}).reduce((map, obj) => (map[obj.topicAbbrv_world] = obj.topicLabel, map), {})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2204,"value":"dataTopicMean = \nd3.rollups(data_UK_agg, v=>d3.mean(v, d=>d.value), d=>d.topic, d=>d.year).map(d=>{return{\n topic: d[0],\n data: d[1]\n // [0][1],\n // learning: d[1][1][1],\n // finance: d[1][2][1],\n // research: d[1][3][1],\n // support: d[1][4][1],\n // gaming: d[1][5][1],\n // \"real estate\": d[1][6][1],\n // virtual: d[1][7][1],\n // leisure: d[1][8][1],\n // \"mental health\": d[1][9][1],\n // sustainability: d[1][10][1],\n // used: d[1][11][1]\n}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2194,"value":"dataYearMean = \nd3.rollups(data_UK_agg, v=>d3.mean(v, d=>d.value), d=>d.year, d=>d.topic).map(d=>{return{\n year: d[0],\n jobs: d[1][0][1],\n learning: d[1][1][1],\n finance: d[1][2][1],\n research: d[1][3][1],\n support: d[1][4][1],\n gaming: d[1][5][1],\n \"real estate\": d[1][6][1],\n virtual: d[1][7][1],\n leisure: d[1][8][1],\n \"mental health\": d[1][9][1],\n sustainability: d[1][10][1],\n used: d[1][11][1]\n}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":63,"value":"data_UK_groupYear = d3.groups(data_UK_agg, d=>d.year)\n.map(d=>{return {\n year:d[0],\n data:d[1]\n}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":72,"value":"data_UK_groupTopic = d3.groups(data_UK_agg, d=>d.topic)\n.map(d=>{\n const options = { month: 'long', day: 'numeric' }\n return {\n topic:d[0],\n data:d[1]\n // data:d[1].map(d=>({...d, dateMonth: `2020-${d.date.getMonth()}-${d.date.getDay()}`}))\n}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1589,"value":"data_world_groupYear = d3.groups(data_world_agg, d=>d.year)\n.map(d=>{return {\n year:d[0],\n data:d[1]\n}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1592,"value":"data_world_groupTopic = d3.groups(data_world_agg, d=>d.topic)\n.map(d=>{\n const options = { month: 'long', day: 'numeric' }\n return {\n topic:d[0],\n data:d[1]\n // data:d[1].map(d=>({...d, dateMonth: `2020-${d.date.getMonth()}-${d.date.getDay()}`}))\n}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":145,"value":"options = [{ month: 'long', day: 'numeric' }]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":867,"value":"data_merged = d3.merge([data_UK_agg, data_world_agg]).filter(d=>['jobs', 'learning', 'virtual'].includes(d.topic))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":38,"value":"data_UK_agg = data_UK.filter(d=>String(d.variable).match(\"mean\")).map(d=>({...d, dateMonth: new Date(d.date.toLocaleDateString('eng', options[0])),\n year: d.date.getFullYear(),\n source: 'uk',\n topic: topicDict_world[d.topic]})).filter(d=>d.year!==2022)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":57,"value":"data_world_agg = data_world.filter(d=>String(d.variable).match(\"mean\")).map(d=>({...d, dateMonth: new Date(d.date.toLocaleDateString('eng', options[0])),\n year: d.date.getFullYear(),\n source: 'world',\n topic: topicDict_world[d.topic],\n \n })).filter(d=>d.year!==2022)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"data_world = //FileAttachment(\"trends_all_world.csv\").csv({typed:true})\nFileAttachment(\"trends_all_world_09042022.csv\").csv({typed:true})\n// d3.csv(\"https://gist.githubusercontent.com/lnicoletti/afea9e19245a28ff7f0635dcda47658a/raw/af440552658e67f87004d6f01404e73d598c1c6e/trends_all_world_09042022.csv\", d3.autoType)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":10,"value":"data_UK = //FileAttachment(\"trends_all_UK.csv\").csv({typed:true})\nFileAttachment(\"trends_all_GB_world_09042022.csv\").csv({typed:true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2801,"value":"// data_world.map(d=>d.variable).filter(onlyUnique)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2866,"value":"// data_world.filter(d=>d.topic===\"financ\")//.map(d=>d.variable).filter(onlyUnique)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":154,"value":"PlotW = require(await FileAttachment(\"plot-7.umd.js\").url())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2959,"value":"md`##### Section 2.0 Data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3069,"value":"topicsAbbrv_UK_UK = //[\"jobs\", \"learning\", \"financ\", \"search\", \"support\", \"gaming\", \"alestat\", \"virtual\", \"leisu\", \"mentalhealth\", \"sustainabilit\", \"us\"]\n//data_world.map(d=>d.topic).filter(onlyUnique)\n[\"finance_UK\", \"learning_UK\", \"jobs_UK\", \"search_UK\", \"support_UK\", \"gaming_UK\", \"alestate_UK\", \"virtual_UK\", \"leisure_UK\", \"mentalhealth_UK\", \"sustainability_UK\", \"used_UK\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3072,"value":"topicDict_UK_UK = topicsAbbrv_UK_UK.map((d, i)=>{\n return {\n topicAbbrv_UK_UK:d,\n topicLabel: topics[i]\n }\n}).reduce((map, obj) => (map[obj.topicAbbrv_UK_UK] = obj.topicLabel, map), {})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3083,"value":"dataTopicMean_UK_UK = \nd3.rollups(data_UK_UK_agg, v=>d3.mean(v, d=>d.value), d=>d.topic, d=>d.year).map(d=>{return{\n topic: d[0],\n data: d[1]\n // [0][1],\n // learning: d[1][1][1],\n // finance: d[1][2][1],\n // research: d[1][3][1],\n // support: d[1][4][1],\n // gaming: d[1][5][1],\n // \"real estate\": d[1][6][1],\n // virtual: d[1][7][1],\n // leisure: d[1][8][1],\n // \"mental health\": d[1][9][1],\n // sustainability: d[1][10][1],\n // used: d[1][11][1]\n}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3086,"value":"dataYearMean_UK_UK = \nd3.rollups(data_UK_UK_agg, v=>d3.mean(v, d=>d.value), d=>d.year, d=>d.topic).map(d=>{return{\n year: d[0],\n jobs: d[1][0][1],\n learning: d[1][1][1],\n finance: d[1][2][1],\n research: d[1][3][1],\n support: d[1][4][1],\n gaming: d[1][5][1],\n \"real estate\": d[1][6][1],\n virtual: d[1][7][1],\n leisure: d[1][8][1],\n \"mental health\": d[1][9][1],\n sustainability: d[1][10][1],\n used: d[1][11][1]\n}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3022,"value":"data_UK_UK_groupYear = d3.groups(data_UK_UK_agg, d=>d.year)\n.map(d=>{return {\n year:d[0],\n data:d[1]\n}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3010,"value":"data_UK_UK_groupTopic = d3.groups(data_UK_UK_agg, d=>d.topic)\n.map(d=>{\n const options = { month: 'long', day: 'numeric' }\n return {\n topic:d[0],\n data:d[1]\n // data:d[1].map(d=>({...d, dateMonth: `2020-${d.date.getMonth()}-${d.date.getDay()}`}))\n}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3012,"value":"data_UK_UK_agg = data_UK_UK.filter(d=>String(d.variable).match(\"mean\")).map(d=>({...d, dateMonth: new Date(d.date.toLocaleDateString('eng', options[0])),\n year: d.date.getFullYear(),\n source: 'uk',\n topic: topicDict_UK_UK[d.topic]})).filter(d=>d.year!==2022)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2962,"value":"data_UK_UK = FileAttachment(\"trends_GB_UK_09042022.csv\").csv({typed:true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1217,"value":"html`