2d:null 2e:["$","title",null,{"children":"Italy Economic Analysis | leonardo Nicoletti | Observable"}] 33:Te22,stacked = { const data1 = gdpProd.filter(d=>d.timePeriod==="1995-2020") const data2 = gdpProd.filter(d=>d.timePeriod==="1995-2005") const data3 = gdpProd.filter(d=>d.timePeriod==="2010-2020") const plot = Plot.plot({ width: 400, marginLeft: 60, // color: {legend: true}, color: { // type: "ordinal", // scheme: "category10", legend: true }, y: {label:"1995-2020", domain: gdpProd.filter(d=>d.variable==="GdpCapita"&&d.timePeriod==="1995-2020").sort((a,b)=>d3.descending(a.value, b.value)).map(d=>d.country)}, // color: {range: ["blue", "red"]}, x: { // tickFormat: d => d.toLocaleString("en", {month: "narrow"}), label: null }, marks: [ Plot.barX(data1.filter(d=>d.variable!=="GdpCapita"), {y: "country", x: "value", fill: "variable"}), Plot.barX(data1.filter(d=>d.country==="ITA"&&d.variable!=="GdpCapita"), {y: "country", x: "value", fill: "none", stroke:"red", strokeWidth:2}), Plot.dot(data1.filter(d=>d.variable==="GdpCapita"), {y: "country", x: "value", fill: "white", stroke:"black"}), Plot.ruleX([0]) ], // color: {legend: true} }) const plot2 = Plot.plot({ width: 400, // color: {legend: true}, color: { // type: "ordinal", // scheme: "category10", legend: true }, y: {label:"1995-2005", tickFormat: d => "", tickSize:0, domain: gdpProd.filter(d=>d.variable==="GdpCapita"&&d.timePeriod==="1995-2020").sort((a,b)=>d3.descending(a.value, b.value)).map(d=>d.country) }, // color: {range: ["blue", "red"]}, // x: { // tickFormat: d => d.toLocaleString("en", {month: "narrow"}), // label: null // }, marks: [ Plot.barX(data2.filter(d=>d.variable!=="GdpCapita"), {y: "country", x: "value", fill: "variable"}), Plot.dot(data2.filter(d=>d.variable==="GdpCapita"), {y: "country", x: "value", fill: "white", stroke:"black"}), Plot.barX(data2.filter(d=>d.country==="ITA"&&d.variable==="GdpCapita"), {y: "country", x: "value", fill: "none", stroke:"red", strokeWidth:2}), Plot.ruleX([0]) ], // color: {legend: true} }) const plot3 = Plot.plot({ width: 400, // color: {legend: true}, color: { // type: "ordinal", // scheme: "category10", legend: true }, y: {label:"2010-2020", tickFormat: d => "", tickSize:0, domain: gdpProd.filter(d=>d.variable==="GdpCapita"&&d.timePeriod==="1995-2020").sort((a,b)=>d3.descending(a.value, b.value)).map(d=>d.country) }, // color: {range: ["blue", "red"]}, // x: { // tickFormat: d => d.toLocaleString("en", {month: "narrow"}), // label: null // }, marks: [ Plot.barX(data3.filter(d=>d.variable!=="GdpCapita"), {y: "country", x: "value", fill: "variable"}), Plot.dot(data3.filter(d=>d.variable==="GdpCapita"), {y: "country", x: "value", fill: "white", stroke:"black"}), Plot.barX(data3.filter(d=>d.country==="ITA"&&d.variable!=="GdpCapita"), {y: "country", x: "value", fill: "none", stroke:"red", strokeWidth:2}), Plot.ruleX([0]) ], // color: {legend: true} }) d3.select(plot).selectAll("text").attr("font-size", 15) d3.select(plot2).selectAll("text").attr("font-size", 15) d3.select(plot3).selectAll("text").attr("font-size", 15) return html`

${[plot, plot2, plot3]}

` } 34:T419,stacked2 = { const data1 = gdpProd.filter(d=>d.variable!=="GdpCapita") const data2 = gdpProd.filter(d=>d.variable==="GdpCapita") // const data1 = gdpProd//.filter(d=>d.variable!=="GdpCapita") // const data2 = gdpProd.filter(d=>d.variable==="GdpCapita") // const data3 = gdpProd.filter(d=>d.timePeriod==="2010-2020") const plot = Plot.plot({ width: 1000, // color: {legend: true}, color: { // type: "ordinal", // scheme: "category10", legend: true }, y: {label:""}, facet: { data: data1, x: "timePeriod" }, // color: {range: ["blue", "red"]}, // x: { // tickFormat: d => d.toLocaleString("en", {month: "narrow"}), // label: null // }, marks: [ Plot.barX(data1, {y: "country", x: "value", fill: "variable"}), Plot.dot(data2, {y: "country", fy:"timePeriod", x: "value", fill: "white", stroke:"black"}), Plot.ruleX([0]) ], // color: {legend: true} }) // return plot }35:T12b7,provMap = { var active = d3.select(null); let svg = d3.select(DOM.svg(widthMap*3, heightMap)) // let svg2 = d3.select(DOM.svg(widthMap*2, heightMap)) // //bg rect for reset // svg.append("rect") // .attr("width", widthMap) // .attr("height", heightMap) // .attr("fill", 'white') // .on("click", reset); let g = svg.append('g').attr("transform", "translate (0, 50)") let g2 = svg.append('g').attr("transform", "translate (500, 50)") g.append("text") .attr("class", "FigTitle") // .attr("x", 0) .attr("x", widthMap/1.4) .attr("y", -30) .style('font-weight', 400) // .attr("text-anchor", "left") .text(selectGDP==="Nominal GDP"?`Nominal GDP per capita by `:`Real GDP per capita by `) .append('tspan') .attr("id", "numStories") .style('font-weight', 700) .text(`province`) // .style("fill", "#E02685") // .append('tspan') // .style('font-weight', 900) // .text('stories ') g.append("text") .attr("class", "FigTitle") // .attr("x", 0) .attr("x", 100) .attr("y", 0) .style('font-weight', 700) // .attr("text-anchor", "left") .text(`2000`) g2.append("text") .attr("class", "FigTitle") // .attr("x", 0) .attr("x", 100) .attr("y", 0) .style('font-weight', 700) // .attr("text-anchor", "left") .text(`2018`) function reset() { active.classed("active", false); active = d3.select(null); g.transition() .duration(750) .style("stroke-width", "1.5px") .attr("transform", "") } function clicked(d) { if (active.node() === this) return reset(); active.classed("active", false); active = d3.select(this).classed("active", true); let bounds = italyPathGen.bounds(d), dx = bounds[1][0] - bounds[0][0], dy = bounds[1][1] - bounds[0][1], x = (bounds[0][0] + bounds[1][0]) / 2, y = (bounds[0][1] + bounds[1][1]) / 2, scale = .9 / Math.max(dx / width, dy / heightMap), translate = [width / 2 - scale * x, heightMap / 2 - scale * y]; // let filtered = wineries.filter(w => w.province.toUpperCase() == d.properties.NOME_REG) g.transition() .duration(750) .style("stroke-width", 1.5 / scale + "px") .attr("transform", "translate(" + translate + ")scale(" + scale + ")") } // draw the map g.selectAll('path') .data(italyProv.features) .enter() .append('path') .attr('d', italyPathGen) // .attr('fill', '#999999') .attr("fill", d=> gdpCapitaProvs.filter(c=>c.GEO.toLowerCase().match(d.properties.NOME_PRO.toLowerCase()))[0]!==undefined?gdpScale(gdpCapitaProvs.filter(c=>c.GEO.toLowerCase().match(d.properties.NOME_PRO.toLowerCase()))[0].gdpCapita):"lightgrey") .attr('stroke', 'white') .attr("class", "feature") // .attr('fill-opacity', 0.25) .attr('stroke-width', '0.8') .append("svg:title") .text(d=>d.properties.NOME_PRO) .on("mouseover", (event, d) => d3.select(this).attr("stroke-width", 2)) .on("mouseout", (event, d) => d3.select(this).attr("stroke-width", 0.8)) .on("click", clicked); // draw the map g2.selectAll('path') .data(italyProv.features) .enter() .append('path') .attr('d', italyPathGen) // .attr('fill', '#999999') .attr("fill", d=> gdpCapitaProvs.filter(c=>c.GEO.toLowerCase().match(d.properties.NOME_PRO.toLowerCase()))[1]!==undefined&&selectGDP==="Nominal GDP"?gdpScale(gdpCapitaProvs.filter(c=>c.GEO.toLowerCase().match(d.properties.NOME_PRO.toLowerCase()))[1].gdpCapita):gdpCapitaProvs.filter(c=>c.GEO.toLowerCase().match(d.properties.NOME_PRO.toLowerCase()))[1]!==undefined&&selectGDP==="Real GDP"?gdpScale(gdpCapitaProvs.filter(c=>c.GEO.toLowerCase().match(d.properties.NOME_PRO.toLowerCase()))[1].gdpCapita/1.339):"lightgrey") .attr('stroke', 'white') .attr("class", "feature") // .attr('fill-opacity', 0.25) .attr('stroke-width', '0.8') .append("svg:title") .text(d=>d.properties.NOME_PRO) .on("mouseover", (event, d) => d3.select(this).attr("stroke-width", 2)) .on("mouseout", (event, d) => d3.select(this).attr("stroke-width", 0.8)) .on("click", clicked); // g.selectAll("circle") // .data(wineries) // .enter() // .append("circle") // .attr("cx", w => italyProjection([w.E, w.N])[0]) // .attr("cy", w => italyProjection([w.E, w.N])[1]) // .attr("r", "1px") // .attr("fill", "#999999") // .attr('fill-opacity', 0.5) // .attr("data-winery", w => w.winery) return svg.node() }36:T12a7,streetsMap = { var active = d3.select(null); let svg = d3.select(DOM.svg(widthMap*3, heightMap)) // let svg2 = d3.select(DOM.svg(widthMap*2, heightMap)) // //bg rect for reset // svg.append("rect") // .attr("width", widthMap) // .attr("height", heightMap) // .attr("fill", 'white') // .on("click", reset); let g = svg.append('g').attr("transform", "translate (0, 50)") let g2 = svg.append('g').attr("transform", "translate (500, 50)") g.append("text") .attr("class", "FigTitle") // .attr("x", 0) .attr("x", widthMap/1.4) .attr("y", -30) .style('font-weight', 400) // .attr("text-anchor", "left") .text(selectGDP==="Nominal GDP"?`Nominal GDP per capita by `:`Real GDP per capita by `) .append('tspan') .attr("id", "numStories") .style('font-weight', 700) .text(`region`) // .style("fill", "#E02685") // .append('tspan') // .style('font-weight', 900) // .text('stories ') g.append("text") .attr("class", "FigTitle") // .attr("x", 0) .attr("x", 100) .attr("y", 0) .style('font-weight', 700) // .attr("text-anchor", "left") .text(`2000`) g2.append("text") .attr("class", "FigTitle") // .attr("x", 0) .attr("x", 100) .attr("y", 0) .style('font-weight', 700) // .attr("text-anchor", "left") .text(`2018`) function reset() { active.classed("active", false); active = d3.select(null); g.transition() .duration(750) .style("stroke-width", "1.5px") .attr("transform", "") } function clicked(d) { if (active.node() === this) return reset(); active.classed("active", false); active = d3.select(this).classed("active", true); let bounds = italyPathGen.bounds(d), dx = bounds[1][0] - bounds[0][0], dy = bounds[1][1] - bounds[0][1], x = (bounds[0][0] + bounds[1][0]) / 2, y = (bounds[0][1] + bounds[1][1]) / 2, scale = .9 / Math.max(dx / width, dy / heightMap), translate = [width / 2 - scale * x, heightMap / 2 - scale * y]; // let filtered = wineries.filter(w => w.province.toUpperCase() == d.properties.NOME_REG) g.transition() .duration(750) .style("stroke-width", 1.5 / scale + "px") .attr("transform", "translate(" + translate + ")scale(" + scale + ")") } // draw the map g.selectAll('path') .data(italy.features) .enter() .append('path') .attr('d', italyPathGen) // .attr('fill', '#999999') .attr("fill", d=> gdpCapitaProvs.filter(c=>c.GEO.toLowerCase().match(d.properties.NOME_REG.toLowerCase()))[0]!==undefined?gdpScale(gdpCapitaProvs.filter(c=>c.GEO.toLowerCase().match(d.properties.NOME_REG.toLowerCase()))[0].gdpCapita):"lightgrey") .attr('stroke', 'white') .attr("class", "feature") // .attr('fill-opacity', 0.25) .attr('stroke-width', '0.8') .append("svg:title") .text(d=>d.properties.NOME_REG) .on("mouseover", (event, d) => d3.select(this).attr("stroke-width", 2)) .on("mouseout", (event, d) => d3.select(this).attr("stroke-width", 0.8)) .on("click", clicked); // draw the map g2.selectAll('path') .data(italy.features) .enter() .append('path') .attr('d', italyPathGen) // .attr('fill', '#999999') .attr("fill", d=> gdpCapitaProvs.filter(c=>c.GEO.toLowerCase().match(d.properties.NOME_REG.toLowerCase()))[1]!==undefined&&selectGDP==="Nominal GDP"?gdpScale(gdpCapitaProvs.filter(c=>c.GEO.toLowerCase().match(d.properties.NOME_REG.toLowerCase()))[1].gdpCapita):gdpCapitaProvs.filter(c=>c.GEO.toLowerCase().match(d.properties.NOME_REG.toLowerCase()))[1]!==undefined&&selectGDP==="Real GDP"?gdpScale(gdpCapitaProvs.filter(c=>c.GEO.toLowerCase().match(d.properties.NOME_REG.toLowerCase()))[1].gdpCapita/1.339):"lightgrey") .attr('stroke', 'white') .attr("class", "feature") // .attr('fill-opacity', 0.25) .attr('stroke-width', '0.8') .append("svg:title") .text(d=>d.properties.NOME_REG) .on("mouseover", (event, d) => d3.select(this).attr("stroke-width", 2)) .on("mouseout", (event, d) => d3.select(this).attr("stroke-width", 0.8)) .on("click", clicked); // g.selectAll("circle") // .data(wineries) // .enter() // .append("circle") // .attr("cx", w => italyProjection([w.E, w.N])[0]) // .attr("cy", w => italyProjection([w.E, w.N])[1]) // .attr("r", "1px") // .attr("fill", "#999999") // .attr('fill-opacity', 0.5) // .attr("data-winery", w => w.winery) return svg.node() }37:T768,{ const plotAll = Plot.plot({ width:width/2.2, y: {grid: true, label:"Average growth rate (2000-2018)"}, // x: {grid: true, label:"GDP/capita (2000)"}, x: {grid: true, label:"GDP/capita (2000)"}, marks: [ Plot.dot(data, { x: "gdpCapita2000", y: "avgGrowthRate", stroke: "steelblue" }), Plot.line( data, Plot.regression({ x: "gdpCapita2000", y: "avgGrowthRate", strokeDasharray: [3, 4], strokeWidth: 1.5, stroke: "red", strokeOpacity:0.5 }) ), ] }) const plotFil = Plot.plot({ width:width/2.2, y: {grid: true, label:"Average growth rate (2000-2018)"}, x: {grid: true, label:"GDP/capita (2000)"}, marks: [ Plot.dot(data.filter(d=>d.region!=="Caltanissetta"), { x: "gdpCapita2000", y: "avgGrowthRate", stroke: "steelblue" }), Plot.text(data.filter(d=>d.region!=="Caltanissetta"), {filter: d => d.gdpCapita2000> 1.3&&d.avgGrowthRate<1.268, x: "gdpCapita2000", y: "avgGrowthRate", text: "region", dy: 15, fontWeight:500}), Plot.text(data.filter(d=>d.region!=="Caltanissetta"), {filter: d => d.gdpCapita2000< 0.6&&d.avgGrowthRate>1.185&&d.avgGrowthRate<1.22, x: "gdpCapita2000", y: "avgGrowthRate", text: "region", dy: 15, fontWeight:500}), Plot.text(data.filter(d=>d.region!=="Caltanissetta"), {filter: d => d.gdpCapita2000< 1&&d.gdpCapita2000> 0.8&&d.avgGrowthRate>1.21&&d.avgGrowthRate<1.23, x: "gdpCapita2000", y: "avgGrowthRate", text: "region", dy: 15, fontWeight:500}), Plot.line( data.filter(d=>d.region!=="Caltanissetta"), Plot.regression({ x: "gdpCapita2000", y: "avgGrowthRate", strokeDasharray: [3, 4], strokeWidth: 1.5, stroke: "red", strokeOpacity:0.5 }) ), ] }) return html`

${[plotAll, plotFil]}

` }38:T852,{ const data = gdpCapita.filter(d=>(d.TIME===2000||d.TIME===2018)&&(d.gdpCapita!==Infinity)) const plot = Plot.plot({ width:width/2.2, // x: {domain:[0, 70000]}, facet: { data: data, y: "TIME" }, color: { type: "categorical", scheme: "tableau10", }, marks: [ Plot.areaY(data.filter(d=>(d.GEO!=="Italy")), Plot.binX({y2: "count"}, { x: "gdpCapitaW", thresholds:20, curve: "basis", // fill: "TIME", fillOpacity: 0.8, // mixBlendMode: "multiply" // stroke: "TIME" })), // Plot.ruleX([natMedian2000], { // strokeDasharray: [3, 4], // strokeWidth: 1.5, // stroke: "steelblue", // strokeOpacity:0.5 // }), // Plot.text(medians, {x: "median", y: "y", text: d => d.year + " median", dy: "dy", fill:"fill"}),//dy: -25, // Plot.ruleX([natMedian2018], { // strokeDasharray: [3, 4], // strokeWidth: 1.5, // stroke: "orange", // strokeOpacity:0.5 // }) ] }) const plotLog = Plot.plot({ width:width/2.2, x: {type:"log"}, // facet: { // data: data, // y: "TIME" // }, color: { type: "categorical", scheme: "tableau10", }, marks: [ Plot.areaY(data.filter(d=>(d.GEO!=="Italy")), Plot.binX({y2: "count"}, { x: "gdpCapitaW", thresholds:20, curve: "basis", fill: "TIME", fillOpacity: 0.8, mixBlendMode: "multiply" // stroke: "TIME" })), // Plot.ruleX([natMedian2000], { // strokeDasharray: [3, 4], // strokeWidth: 1.5, // stroke: "steelblue", // strokeOpacity:0.5 // }), // Plot.text(medians, {x: "median", y: 20, text: d => d.year + " median", dy: "dy", fill:"fill"}), // Plot.ruleX([natMedian2018], { // strokeDasharray: [3, 4], // strokeWidth: 1.5, // stroke: "orange", // strokeOpacity:0.5 // }) ] }) // return html`

${[plot, plotLog]}

` }39:[{"id":"c939c649ae912477ee91dfa5562123c1d12000e5ef4642dbfa392584f26584d97c6ed5b6d67bf0aeb908c9d3156b932c29d1717f17b704214767a8f5ac81343b","href":"https://static.observableusercontent.com/files/c939c649ae912477ee91dfa5562123c1d12000e5ef4642dbfa392584f26584d97c6ed5b6d67bf0aeb908c9d3156b932c29d1717f17b704214767a8f5ac81343b","name":"nama_10r_3gdp_1_Data@1.csv","type":"text/csv","lastModified":1637841729444.414,"size":108471},{"id":"9524681e53a8c0fb3a1abd4b3c5c5aa4c7839b6beea1802ce3364af1e32a9cf703cc185cf3396ca6e8afc90dfd54813f6a2eb8a3f767d51e9ec1d50c9302ce31","href":"https://static.observableusercontent.com/files/9524681e53a8c0fb3a1abd4b3c5c5aa4c7839b6beea1802ce3364af1e32a9cf703cc185cf3396ca6e8afc90dfd54813f6a2eb8a3f767d51e9ec1d50c9302ce31","name":"nama_10r_3popgdp_1_Data@1.csv","type":"text/csv","lastModified":1637841759103.501,"size":93527},{"id":"c8ceebba8a527484ae579d38555e4a63fe73d72f65291050ae645d58d5510382ebb1b7265dd8f39b47189e921b1ca6420cdd9df14571e8f6a20fdaccdc5f457e","href":"https://static.observableusercontent.com/files/c8ceebba8a527484ae579d38555e4a63fe73d72f65291050ae645d58d5510382ebb1b7265dd8f39b47189e921b1ca6420cdd9df14571e8f6a20fdaccdc5f457e","name":"dataGdpProductivityTidy@1.csv","type":"text/csv","lastModified":1640890611774.401,"size":26658}] 31:{"id":"eac027859136acdf","slug":"italy-economic-analysis","title":"Italy Economic Analysis","thumbnail":"474208f2215a16769b9e5940479d3dda0833bc1966155d9363c5d121cc76252b","access_level":"public","create_time":"$D2021-11-25T11:39:58.372Z","update_time":"$D2022-01-19T16:18:12.663Z","view_time":null,"first_public_time":"$D2022-04-21T13:20:24.642Z","has_unpublished_edits":null,"fork":false,"stars":1,"starred":false,"owner":{"type":"team","id":"405a122b348425fd","login":"lnicoletti","name":"leonardo Nicoletti","avatar_url":"https://avatars-next.observableusercontent.com/36092ac2cd0189ba9d5c4621680d6b2ef55948e600102b0814f4135b9cec7068","tier":"starter_2024"},"version":1335,"publish_version":1335,"role":null,"authored":false,"authors":[{"type":"individual","id":"8280c368bc7eaac0","login":"lnicoletti","name":"leonardo Nicoletti","avatar_url":"https://avatars-next.observableusercontent.com/36092ac2cd0189ba9d5c4621680d6b2ef55948e600102b0814f4135b9cec7068","tier":null}],"members":[{"type":"individual","id":"8280c368bc7eaac0","login":"lnicoletti","name":"leonardo Nicoletti","avatar_url":"https://avatars-next.observableusercontent.com/36092ac2cd0189ba9d5c4621680d6b2ef55948e600102b0814f4135b9cec7068","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"title":"Italy Economic Analysis","cells":[{"id":0,"value":"# Italy Economic Analysis","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1073,"value":"html`

\n ${Swatches(d3.scaleOrdinal([\"Labour Utilization\", \"GDP Per Hour Worked\"], [\"orange\", \"steelblue\"]))}\n \n Italy\n \n GDP Per Capita\n

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":977,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1163,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":921,"value":"viewof selectGDP = Inputs.select([\"Nominal GDP\", \"Real GDP\"], {label: \"Select metric\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":799,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":833,"value":"Legend(gdpScale, {title: \"GDP per capita\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":611,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":643,"value":"key = Legend(gdpScale, {title: \"GDP per capita\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":185,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":587,"value":"viewof select = Inputs.select([\"GDP/Capita Weighted\", \"GDP/Capita\"], {label: \"Select metric\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1308,"value":"viewof scaleMax = Inputs.number({label: \"Scale Maximum\", value:2.5})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":542,"value":"{\n const data = d3.merge([gdpCapita, fillerData]).filter(d=>(d.TIME===2000||d.TIME===2018)&&(d.gdpCapita!==Infinity))\n const plot = Plot.plot({\n grid: true,\n x: select=== \"GDP/Capita\"? {label:\"GDP per capita\", }: {label:\"GDP per capita weighted\", domain:[0.5, scaleMax]},//,\n facet: {\n data: data,\n y: \"TIME\"\n },\n color: {\n type: \"categorical\",\n scheme: \"tableau10\",\n },\n marks: [\n Plot.lineY(data, Plot.binX({y: \"count\"}, {\n x: select=== \"GDP/Capita\"?\"gdpCapita\":\"gdpCapitaW\", \n stroke: \"TIME\",\n strokeWidth: 4,\n curve: \"basis\",\n thresholds:20\n })),\n Plot.ruleY([0])\n ]\n })\n return plot\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":219,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":624,"value":"gdpScale = d3\n .scaleSequential(d3.extent(gdpCapitaRegions, d=>d.gdpCapita),\n d3.interpolateRdBu).nice()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":609,"value":"widthMap = 400","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":607,"value":"heightMap = 500","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":605,"value":"margin = 10","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":603,"value":"italyProjection = d3.geoMercator()\n.fitExtent([[margin, margin], [widthMap - margin, heightMap - margin]], italy)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":601,"value":"italyPathGen = d3.geoPath().projection(italyProjection)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":598,"value":"italy = d3.json('https://raw.githubusercontent.com/Dataninja/geo-shapes/master/italy/regions.geojson')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":791,"value":"italyProv = d3.json(\"https://raw.githubusercontent.com/Dataninja/geo-shapes/master/italy/provinces.geojson\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":380,"value":"medians = [{region:\"Italy\", median: natMedian2000, year:2000, fill:\"steelblue\", y:25},\n {region:\"Italy\", median: natMedian2018, year:2018, fill:\"orange\", y:24}]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":359,"value":"natMedian2018 = d3.mean(gdpCapita.filter(d=>(d.GEO!==\"Italy\")&&(d.TIME===2018)&&(d.gdpCapita!==Infinity)), c=>c.gdpCapitaW)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":347,"value":"natMedian2000 = d3.mean(gdpCapita.filter(d=>(d.GEO!==\"Italy\")&&(d.TIME===2000)&&(d.gdpCapita!==Infinity)), c=>c.gdpCapitaW)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":485,"value":"Inputs.table(data)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":722,"value":"fillerData = {return [{\n// TIME: 2000,\n// GEO: \"Filler\",\n// UNIT: \"Million euro\",\n// Value: 1241513,\n// \"Flag and Footnotes\": null,\n// gdpCapita: 60000,\n// gdpCapitaW: 2.3,\n// },\n// {\n// TIME: 2018,\n// GEO: \"Filler\",\n// UNIT: \"Million euro\",\n// Value: 1241513,\n// \"Flag and Footnotes\": null,\n// gdpCapita: 60000,\n// gdpCapitaW: 2.3,\n// },\n// {\nTIME: 2018,\n GEO: \"Filler2\",\n UNIT: \"Million euro\",\n Value: 1241513,\n \"Flag and Footnotes\": null,\n gdpCapita: 13000,\n gdpCapitaW: 0.4,\n}\n ]\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":720,"value":"d3.merge([gdpCapita, fillerData])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":151,"value":"data = mapToArray(dataMap).filter(d=>(d.region!==\"Italy\")&&(d.region!==\"Nord-Ovest\")&&d.avgGrowthRate!==0&&d.gdpCapita2000!==Infinity)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":95,"value":"dataMap = d3.rollup(gdpCapita.filter(d=>d.TIME===2000||d.TIME===2018), v => [Math.pow((((v[1].Value/v[0].Value)-1)*100), (1/18)), v[0].gdpCapitaW], d => d.GEO)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":630,"value":"gdpCapitaRegions = gdp.filter(d=>regions.includes(d.GEO)).filter(d=>!notRegions.includes(d.GEO)).map(d=> ({ ...d, \n gdpCapita: d.Value/pop.filter(c=>c.TIME===d.TIME&&c.GEO===d.GEO)[0].Value*1000, \n gdpCapitaW: (d.Value/pop.filter(c=>c.TIME===d.TIME&&c.GEO===d.GEO)[0].Value)/(gdp.filter(c=>c.TIME===d.TIME&&c.GEO===\"Italy\")[0].Value/pop.filter(c=>c.TIME===d.TIME&&c.GEO===\"Italy\")[0].Value)})).filter(d=>(d.TIME===2000||d.TIME===2018)&&(d.gdpCapita!==Infinity))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":796,"value":"gdpCapitaProvs = gdp.map(d=> ({ ...d, \n gdpCapita: d.Value/pop.filter(c=>c.TIME===d.TIME&&c.GEO===d.GEO)[0].Value*1000, \n gdpCapitaW: (d.Value/pop.filter(c=>c.TIME===d.TIME&&c.GEO===d.GEO)[0].Value)/(gdp.filter(c=>c.TIME===d.TIME&&c.GEO===\"Italy\")[0].Value/pop.filter(c=>c.TIME===d.TIME&&c.GEO===\"Italy\")[0].Value)})).filter(d=>(d.TIME===2000||d.TIME===2018)&&(d.gdpCapita!==Infinity))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":408,"value":"// gdpCapitaW = gdp.map(d=> ({ ...d, gdpCapita: d.Value/pop.filter(c=>c.TIME===d.TIME&&c.GEO===d.GEO)[0].Value*1000}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":461,"value":"// d3.rollup(gdpCapita, v => d3.median(v, d=>d.Value), d => d.GEO)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":477,"value":"Inputs.table(gdpCapita)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":55,"value":"gdpCapita = gdp.filter(d=>!regions.includes(d.GEO)).map(d=> ({ ...d, \n gdpCapita: d.Value/pop.filter(c=>c.TIME===d.TIME&&c.GEO===d.GEO)[0].Value*1000, \n gdpCapitaW: (d.Value/pop.filter(c=>c.TIME===d.TIME&&c.GEO===d.GEO)[0].Value)/(gdp.filter(c=>c.TIME===d.TIME&&c.GEO===\"Italy\")[0].Value/pop.filter(c=>c.TIME===d.TIME&&c.GEO===\"Italy\")[0].Value)}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":474,"value":"Inputs.table(pop)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":36,"value":"pop = FileAttachment(\"nama_10r_3popgdp_1_Data@1.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":467,"value":"Inputs.table(gdp)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":935,"value":"// gdpProd = FileAttachment(\"dataGdpProductivity.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1032,"value":"gdpProd=FileAttachment(\"dataGdpProductivityTidy@1.csv\").csv({typed: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":974,"value":"// gdpProd = FileAttachment(\"dataGdpProductivityTidy.csv\").csv({typed: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":35,"value":"gdp = FileAttachment(\"nama_10r_3gdp_1_Data@1.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":635,"value":"notRegions = [\"Centro (IT)\", \"Isole\", \"Nord-Est\", \"Sud\", \"Nord-Ovest\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":488,"value":"regions = [\"Abruzzo\", \"Basilicata\", \"Calabria\", \"Campania\", \"Centro (IT)\", \"Emilia-Romagna\", \"Isole\", \"Lazio\", \"Liguria\", \"Lombardia\", \"Marche\", \"Molise\", \"Nord-Est\", \"Piemonte\", \"Puglia\", \"Sardegna\", \"Sicilia\", \"Sud\", \"Toscana\", \"Umbria\", \"Veneto\", \"Nord-Ovest\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":21,"value":"md`### Dependencies`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":120,"value":"function mapToArray(map) {\n var res = [];\n map.forEach(function(val, key) {\n res.push({region: key, avgGrowthRate: val[0], gdpCapita2000: val[1]});\n});\n return res\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":16,"value":"Plot = addRegression(Plot_)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":25,"value":"reg = require(\"d3-regression@1\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":12,"value":"import {addRegression} from \"@fil/plot-regression\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7,"value":"import { Plot as Plot_ } from \"@fil/plot-splom\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":626,"value":"import {Legend, Swatches} from \"@d3/color-legend\"\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":774,"value":"html`\n`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W39","stdlib":"1","titling":"auto"}} 32:"$31:body"