31:Tc97,chart = { let root = pack(dataPure); const svg = d3.create("svg") .attr("viewBox", [0, 0, width, height]) .attr("font-size", 10) .attr("font-family", "sans-serif") .attr("text-anchor", "middle"); const leaf = svg.selectAll("g") //.data(root.leaves()) .data(root.descendants()) .join("g") .attr("transform", d => `translate(${d.x + 1},${d.y + 1})`); leaf.append("circle") .attr("r", d => d.r) //.attr("stroke", d => d.children ? "gray" : "none") //add color for group circles .attr("stroke", "none") .attr("stroke-width", "0.2px") .attr("fill-opacity", 1.0) .attr("fill", d => { let fill if (d.children) { fill = "none" } else { //fill = color(d.data) if (d.data.incomePerCapMon > highIncome) { fill = d3.hsl(160, 0.25, 0.7) } else if (d.data.incomePerCapMon > middleIncome) { fill = d3.hsl(47, 1.0, 0.6) } else { fill = d3.hsl(10, 0.98, 0.8) } } return fill }); leaf.append("circle") .attr("r", d => { return Math.sqrt(lowIncome/d.data.incomePerCapMon)*d.r; }) .attr("stroke", "white") .attr("stroke-width", d => { let width = 0.0 if (d.data.incomePerCapMon>lowIncome) { width = 0.5 } return width }) .attr("fill-opacity", 0.0) .attr("fill", "none"); leaf.append("circle") .attr("r", d => { return Math.sqrt(middleIncome/d.data.incomePerCapMon)*d.r; }) .attr("stroke", "white") .attr("stroke-width", d => { let width = 0.0 if (d.data.incomePerCapMon>middleIncome) { width = 0.5 } return width }) .attr("fill-opacity", 0.0) .attr("fill", "none"); leaf.append("circle") .attr("r", d => { return Math.sqrt(highIncome/d.data.incomePerCapMon)*d.r; }) .attr("stroke", "white") .attr("stroke-width", d => { let width = 0.0 if (d.data.incomePerCapMon>highIncome) { width = 0.5 } return width }) .attr("fill-opacity", 0.0) .attr("fill", "none"); leaf.append("circle") .attr("r", d => { return Math.sqrt(veryHighIncome/d.data.incomePerCapMon)*d.r; }) .attr("stroke", "white") .attr("stroke-width", d => { let width = 0.0 if (d.data.incomePerCapMon>veryHighIncome) { width = 0.5 } return width }) .attr("fill-opacity", 0.0) .attr("fill", "none"); leaf.append("circle") .attr("r", d => { let scale = d3.scaleSqrt() .domain([0, 146000000]) .range([0, 50]); return scale(d.data.population) }) .attr("fill", d3.hsl(360, 0, 0.3)); /*svg.selectAll(".categories") .data(root.children) .enter() .append("g") .attr("transform", d => `translate(${d.x + 1},${d.y + d.r + 1})`) .append("text") .text(d => d.data.name) .attr("font-size", 12) .attr("font-family", "sans-serif") .attr("text-anchor", "middle");*/ return svg.node(); }32:T44d,dataSocialGroups = { let dataTmp = [] let lowIncomeClass = [] let middleClass = [] let upperMiddleClass = [] let highIncomeClass = [] let veryHighIncomeClass = [] for (let i in data.children) { if (data.children[i].incomePerCapMon < lowIncome) { lowIncomeClass.push(data.children[i]) } else if (data.children[i].incomePerCapMon < middleIncome) { middleClass.push(data.children[i]) } else if (data.children[i].incomePerCapMon < highIncome) { upperMiddleClass.push(data.children[i]) } else if (data.children[i].incomePerCapMon < veryHighIncome) { highIncomeClass.push(data.children[i]) } else { veryHighIncomeClass.push(data.children[i]) } } return {name: "Income Distribution", children: [{name: "Low Income", children: lowIncomeClass}, {name: "Middle Income", children: middleClass}, {name: "Upper Middle Income", children: upperMiddleClass}, {name: "High Income", children: highIncomeClass}, {name: "Very High Income", children: veryHighIncomeClass}] }; }33:T73e,chartDistr = { const w = 900 const h = 400 const padding = 30 const xDomain = 200000 const yDomain = 3e-5 let svg = d3.select(DOM.svg(w, h)) let xScale = d3.scaleLinear([0, xDomain], [padding, w - padding]) let yScale = d3.scaleLinear([yDomain, 0], [padding, h - padding]) // Clip path let clipPath = svg.append('clipPath') .attr('id', 'chart-area') .append('rect') .attr('x', padding) .attr('y', padding) .attr('width', w - 2 * padding) .attr('height', h - 2 * padding) let myLine = d3.line() .x(d => xScale(d[0])) .y(d => yScale(d[1])) svg.append('path') .datum(incomePopulationDensityData) .attr("clip-path", "url(#chart-area)") .attr('fill', 'none') .attr('stroke', 'teal') .attr('stroke-width', 2) .attr('d', myLine) let xAxis = d3.axisBottom(xScale) let yAxis = d3.axisLeft(yScale) svg.append('g') .attr('transform', `translate(0,${h-padding})`) .call(xAxis) svg.append('g') .attr('transform', `translate(${padding},0)`) .call(yAxis) svg.append('g') .append('line') .attr('y1', h-padding) .attr('y2', padding) .attr('stroke', 'black') .attr('transform', `translate(${xScale(lowIncome)}, 0)`) svg.append('g') .append('line') .attr('y1', h-padding) .attr('y2', padding) .attr('stroke', 'black') .attr('transform', `translate(${xScale(middleIncome)}, 0)`) svg.append('g') .append('line') .attr('y1', h-padding) .attr('y2', padding) .attr('stroke', 'black') .attr('transform', `translate(${xScale(highIncome)}, 0)`) svg.append('g') .append('line') .attr('y1', h-padding) .attr('y2', padding) .attr('stroke', 'red') .attr('transform', `translate(${xScale(incomePerCapMed)}, 0)`) return svg.node() }34:T41b,chartCDF = { const w = 900 const h = 400 const padding = 30 const xDomain = 200000 let svg = d3.select(DOM.svg(w, h)) let xScale = d3.scaleLinear([0, xDomain], [padding, w - padding]) let yScale = d3.scaleLinear([1, 0], [padding, h - padding]) // Clip path let clipPath = svg.append('clipPath') .attr('id', 'chart-area') .append('rect') .attr('x', padding) .attr('y', padding) .attr('width', w - 2 * padding) .attr('height', h - 2 * padding) let myLine = d3.line() .x(d => xScale(d[0])) .y(d => yScale(d[1])) svg.append('path') .datum(incomePopulationCDF) .attr("clip-path", "url(#chart-area)") .attr('fill', 'none') .attr('stroke', 'teal') .attr('stroke-width', 2) .attr('d', myLine) let xAxis = d3.axisBottom(xScale) let yAxis = d3.axisLeft(yScale) svg.append('g') .attr('transform', `translate(0,${h-padding})`) .call(xAxis) svg.append('g') .attr('transform', `translate(${padding},0)`) .call(yAxis) return svg.node() }35:T5cd,chartLorenzCurve = { const w = 900 const h = 400 const padding = 30 const xDomain = 1 const yDomain = 1 let svg = d3.select(DOM.svg(w, h)) let xScale = d3.scaleLinear([0, xDomain], [padding, w - padding]) let yScale = d3.scaleLinear([yDomain, 0], [padding, h - padding]) // Clip path let clipPath = svg.append('clipPath') .attr('id', 'chart-area') .append('rect') .attr('x', padding) .attr('y', padding) .attr('width', w - 2 * padding) .attr('height', h - 2 * padding) let myLine = d3.line() .x(d => xScale(d[0])) .y(d => yScale(d[1])) let lorenzFunctionTest =[] let num=5 let xTemp, yTemp for (let i=0; i<=num; i++) { xTemp = i/num if (xTemp>0) { yTemp = lorenzCurve(xTemp) } else { yTemp = 0} lorenzFunctionTest.push([xTemp, yTemp]) } svg.append('path') .datum(lorenzData) .attr("clip-path", "url(#chart-area)") .attr('fill', 'none') .attr('stroke', 'teal') .attr('stroke-width', 2) .attr('d', myLine) svg.selectAll("circle") .data(lorenzDataRosstat) .enter().append("circle") .attr("r", 3) .attr("cx", d => xScale(d.x)) .attr("cy", d => yScale(d.y)); let xAxis = d3.axisBottom(xScale) let yAxis = d3.axisLeft(yScale) svg.append('g') .attr('transform', `translate(0,${h-padding})`) .call(xAxis) svg.append('g') .attr('transform', `translate(${padding},0)`) .call(yAxis) return svg.node() }36:[] 2f:{"title":"Income distribution in Russia in 2020","cells":[{"id":202,"value":"md`# Income distribution in Russia in 2020\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1178,"value":"numberOfGroups = 1465\n","pinned":true,"hidden":false,"mode":"ojs"},{"id":113,"value":"$31","pinned":true,"hidden":false,"mode":"ojs"},{"id":1337,"value":"data = {\n let data\n let dataset = incomeDistr(numberOfGroups, lorenzCurve)\n \n data = {name: \"Income Distribution\", children: dataset}\n return data\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1497,"value":"$32","pinned":true,"hidden":false,"mode":"ojs"},{"id":2013,"value":"dataPure = {\n let child = []\n\n for (let i in data.children) {\n child.push(data.children[i])\n }\n return {name: \"Income Distribution\",\n children: [{name: \"Entire Population\", children: child}]\n };\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":2023,"value":"dataPure.children[0].children","pinned":true,"hidden":false,"mode":"ojs"},{"id":111,"value":"pack = data => d3.pack()\n .size([width - 2, height - 2])\n .padding(1)\n (d3.hierarchy(data)\n .sum(d => d.incomePerCapMon)\n //.sum(d => d.population)\n .sort((a, b) => d3.descending(a.value, b.value)))","pinned":true,"hidden":false,"mode":"ojs"},{"id":1654,"value":"pack(dataSocialGroups)","pinned":true,"hidden":false,"mode":"ojs"},{"id":102,"value":"width = 500","pinned":false,"hidden":false,"mode":"ojs"},{"id":105,"value":"height = 500","pinned":false,"hidden":false,"mode":"ojs"},{"id":1183,"value":"dataTest = {\n let data = [];\n for (let i = 0; i<5; i++) {\n data.push({\n population: dataDimensionless[i].population*populationTotal, \n income: dataDimensionless[i].income*incomeTotal,\n incomePerCapMon: dataDimensionless[i].income*incomeTotal/(dataDimensionless[i].population*populationTotal)/12});\n }\n return data;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":141,"value":"format = d3.format(\",d\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":143,"value":"color = d => {return d3.hsl(160, 0.26, 0.9-d.incomePerCapMon/200000*0.4)}","pinned":true,"hidden":false,"mode":"ojs"},{"id":135,"value":"d3 = require(\"d3@7\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":15,"value":"md`## Calculations`","pinned":false,"hidden":false,"mode":"ojs"},{"id":0,"value":"incomeTotal = 62700.8e9; //RUB","pinned":false,"hidden":false,"mode":"ojs"},{"id":229,"value":"populationEnd2019 = 146748590","pinned":false,"hidden":false,"mode":"ojs"},{"id":230,"value":"populationEnd2020 = 146171015","pinned":false,"hidden":false,"mode":"ojs"},{"id":12,"value":"populationTotal = 0.5*(populationEnd2019+populationEnd2020)","pinned":false,"hidden":false,"mode":"ojs"},{"id":19,"value":"The total population income for the year 2020 is **${incomeTotal} ₽**","pinned":false,"hidden":false,"mode":"md"},{"id":225,"value":"In Russia in 2020, there was an average of **${populationTotal}** people.","pinned":false,"hidden":false,"mode":"md"},{"id":242,"value":"Distribution of total income in Russia ind 2020:","pinned":false,"hidden":false,"mode":"md"},{"id":34,"value":"dataDimensionless=[{population: 0.2, income: 0.055},{population: 0.2, income: 0.103}, {population: 0.2, income: 0.153},{population: 0.2, income: 0.227}, {population: 0.2, income: 0.462}]","pinned":false,"hidden":false,"mode":"ojs"},{"id":249,"value":"Calculation of absolute values of income and monthly per capita income:","pinned":false,"hidden":false,"mode":"md"},{"id":257,"value":"md`## Data interpolation`","pinned":false,"hidden":false,"mode":"ojs"},{"id":562,"value":"incomePerCapMed = 27036.4","pinned":true,"hidden":false,"mode":"ojs"},{"id":616,"value":"incomeModal = 15527.4","pinned":true,"hidden":false,"mode":"ojs"},{"id":1244,"value":"incomeMean = 35675.8","pinned":true,"hidden":false,"mode":"ojs"},{"id":1467,"value":"md`### Social Groups`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1469,"value":"lowIncome = 16000","pinned":true,"hidden":false,"mode":"ojs"},{"id":1476,"value":"middleIncome = 40000","pinned":true,"hidden":false,"mode":"ojs"},{"id":1478,"value":"highIncome = 70000","pinned":true,"hidden":false,"mode":"ojs"},{"id":1588,"value":"veryHighIncome = 100000","pinned":true,"hidden":false,"mode":"ojs"},{"id":354,"value":"md`### Lognormal Distribution`","pinned":false,"hidden":false,"mode":"ojs"},{"id":359,"value":"freqLogNorm = function(x, mu, sigma) {\n return 1/(x*Math.sqrt(2*Math.PI*sigma*sigma))*Math.exp(-Math.pow((Math.log(x)-mu),2)/(2*sigma*sigma));\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":434,"value":"mu = Math.log(incomePerCapMed)\n","pinned":true,"hidden":false,"mode":"ojs"},{"id":436,"value":"\nsigma = Math.sqrt(2*(-mu + Math.log(incomeMean)))","pinned":true,"hidden":false,"mode":"ojs"},{"id":1250,"value":"sigma2 = Math.sqrt(mu - Math.log(incomeModal))","pinned":true,"hidden":false,"mode":"ojs"},{"id":858,"value":"incomeMax = 500e3","pinned":true,"hidden":false,"mode":"ojs"},{"id":644,"value":"nPoints = 1000","pinned":true,"hidden":false,"mode":"ojs"},{"id":854,"value":"incomePopulationDensityData = {\n let data = []\n let xStart = 0;\n let xEnd = incomeMax;\n let xTemp, yTemp\n data.push([0, 0])\n for (let i = 1; i<=nPoints; i++) {\n xTemp = xStart + (xEnd-xStart)*i/nPoints;\n yTemp = freqLogNorm(xTemp,mu,sigma);\n data.push([xTemp, yTemp]);\n }\n return data\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":392,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":448,"value":"md`### Cumulative Distribution Function`","pinned":false,"hidden":false,"mode":"ojs"},{"id":868,"value":"incomePopulationCDF = {\n let data = []\n let xTemp = 0\n let yTemp = 0\n let input = incomePopulationDensityData\n for (let i = 0; i<=nPoints; i++) {\n xTemp = input[i][0]\n if (i>0) yTemp += 0.5*(input[i][1] + input[i-1][1])*(input[i][0]-input[i-1][0]);\n data.push([xTemp, yTemp]);\n }\n return data\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":621,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":341,"value":"md`### Lorenz Curve`","pinned":false,"hidden":false,"mode":"ojs"},{"id":892,"value":"lorenzData = {\n let data = []\n let xTemp = 0\n let yTemp = 0\n data.push([0, 0])\n for (let i = 1; i<=nPoints; i++) {\n let populationSize = (incomePopulationCDF[i][1]-incomePopulationCDF[i-1][1])*populationTotal\n let incomeMeanPerYear = 0.5*(incomePopulationCDF[i][0]+incomePopulationCDF[i-1][0])*12*populationSize\n xTemp += populationSize/populationTotal;\n yTemp += incomeMeanPerYear/incomeTotal;\n data.push([xTemp, yTemp]);\n }\n data.push([1, 1])\n return data\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":899,"value":"lorenzDataRosstat={\n let dataRosstat = [{population: 0.2, income: 0.055},{population: 0.2, income: 0.103}, {population: 0.2, income: 0.153},{population: 0.2, income: 0.227}, {population: 0.2, income: 0.462}]\n\n \n let dataRosstatPlot=[\n {x: dataRosstat[0].population,\n y: dataRosstat[0].income}\n ];\n for (let i = 1; i<5; i++) {\n dataRosstatPlot.push({x: dataRosstatPlot[i-1].x+dataRosstat[i].population,\n y: dataRosstatPlot[i-1].y+dataRosstat[i].income\n });\n }\n return dataRosstatPlot\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1157,"value":"lorenzCurve(1)-lorenzCurve(0.8)","pinned":true,"hidden":false,"mode":"ojs"},{"id":639,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":964,"value":"lorenzCurve ={\n let data = lorenzData;\n let dataX = []\n let dataY = []\n for (let i=0; i 0 && x < 1) {\n let index = dataX.findIndex(element => element>=x)\n y = dataY[index-1] + (dataY[index]-dataY[index-1])*(x-dataX[index-1])/(dataX[index]-dataX[index-1])\n } else if (x<=0) {\n y = 0\n } else {\n y = 1\n }\n \n return y\n }\n return interpolate\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":959,"value":"incomeDistr = {\n function incomeDistribution(nGroups, lorenzCurve) {\n let income = []\n let groupSize = 1/nGroups\n\n let x1 = 0.0, x2\n let incomeTemp = 0\n let populationTemp\n let incomePerCapMonTemp\n for (let i = 0; i < nGroups; i++) {\n x2 = x1 + groupSize;\n populationTemp = (x2 - x1)*populationTotal\n incomeTemp = (lorenzCurve(x2) - lorenzCurve(x1))*incomeTotal\n incomePerCapMonTemp = incomeTemp/12/populationTemp\n \n income.push({population: populationTemp, income: incomeTemp, incomePerCapMon: incomePerCapMonTemp, betterThen: x1})\n\n x1 = x2;\n }\n return income;\n \n }\n return incomeDistribution\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1117,"value":"test = incomeDistr(5, lorenzCurve)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1138,"value":"dataTest","pinned":true,"hidden":false,"mode":"ojs"},{"id":279,"value":"margin = ({left: 29, right: 8, top: 13, bottom: 24});","pinned":false,"hidden":false,"mode":"ojs"},{"id":281,"value":"base = 400","pinned":false,"hidden":false,"mode":"ojs"},{"id":283,"value":"innerWidth = base - margin.left - margin.right;","pinned":true,"hidden":false,"mode":"ojs"},{"id":285,"value":"innerHeight = base - margin.top - margin.bottom;","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W36","stdlib":"1","titling":"auto"}