44:["$","title",null,{"children":"Model Prototype | Jeff MacInnes | Observable"}] 4c:T579,{ let city = citySelector; // get current city from city selector let cityPop = modelsData.filter(d => d.city == city)[0]['pop_cbsa']; let cityVD = modelsData.filter(d => d.city == city)[0]['vdoct19']; let cityVentures = cityVD * cityPop/100; let predictedChangeVD = makePrediction(changeVD_inputVals, changeVD_coefs); let predictedNewVentures = predictedChangeVD * cityPop/100; let predictedVD = cityVD + predictedChangeVD; let predictedChangeVD_perc = predictedChangeVD / cityVD * 100 let changeText = predictedChangeVD >= 0 ? 'increase' : 'decrease'; let changeVerb = predictedChangeVD >= 0 ? 'add' : 'take away'; let cityStdDev = d3.deviation(modelsData.map(d => d['change_vd_nov18oct19'])); let cityDiff= (predictedChangeVD/cityStdDev); return md` **${city}** (pop. **${numeral(cityPop).format('0,0')}**) has **${cityVD.toFixed(2)}** ventures per 100 (or **${numeral(cityVentures).format('0,0')}** ventures total) The predicted change is **${predictedChangeVD.toFixed(2)}** ventures per 100 (a **${predictedChangeVD_perc.toFixed(1)}%** ${changeText}) In other words, this would ${changeVerb} **${numeral(predictedNewVentures).format('0,0')}** ventures, for a new density of **${predictedVD.toFixed(2)}** per 100 This is a change of **${cityDiff.toFixed(2)}** standard deviations ` } 4d:T502,genSlider = function(modelName, varName){ // Generate a slider for the specified input variable let data = dsByModel[modelName + '_ds'] // get the dataset for the specified model let city = citySelector; // get the current city from the selector let cityValue = data.filter(d => d.city === city)[0][varName]; // get city's value for this var let max = d3.max(data.map(d => d[varName])); let min = d3.min(data.map(d => d[varName])); let step = (max-min)/100; const div = html` ${varName} `; const range = div.querySelector("[type=range]"); const number = div.querySelector("[type=number]"); div.value = range.value = number.value = cityValue.toFixed(3); range.oninput = () => number.value = div.value = range.valueAsNumber.toFixed(3); number.oninput = () => range.value = div.value = number.valueAsNumber.toFixed(3); div.addEventListener("resetSliders",() => { div.value = range.value = number.value = cityValue.toFixed(3); div.dispatchEvent(new CustomEvent('input')) }) return div; }4e:T98e,drawChangeVDChart = { // set the dimensions and margins of the graph var margin = {top: 60, right: 30, bottom: 30, left: 40}, width = 600 - margin.left - margin.right, height = 200 - margin.top - margin.bottom; // append the svg object to the body of the page d3.select("#changeVD-vis").html(""); var svg = d3.select("#changeVD-vis") .append("svg") .attr("width", width + margin.left + margin.right) .attr("height", height + margin.top + margin.bottom) .append("g") .attr("transform", "translate(" + margin.left + "," + margin.top + ")"); // set up the data range var data = dsByModel['changeVD_ds']; var predictedVal = makePrediction(changeVD_inputVals, changeVD_coefs); var maxChangeVD = d3.max(data.map(d => d['change_vd_nov18oct19'])); var minChangeVD = d3.min(data.map(d => d['change_vd_nov18oct19'])); // X axis: scale and draw: var x = d3.scaleLinear() .domain([ d3.min([minChangeVD, predictedVal]), d3.max([maxChangeVD, predictedVal]) ]) .range([0, width]); svg.append("g") .attr("transform", "translate(0," + height + ")") .call(d3.axisBottom(x)); svg.append('text') .attr('x', 0) .attr('y', -20) .style('font', '16px Helvetica') .style('font-weight', 'bold') .style('fill', '#69b3a2') .text('Change in Venture Density') // set up the histogram var hist = d3.histogram() .value(d => d['change_vd_nov18oct19']) .domain(x.domain()) .thresholds(x.ticks(70)); var bins = hist(data); // y axis var y = d3.scaleLinear() .range([height, 0]); y.domain([0, d3.max(bins, function(d) { return d.length; })]); svg.append("g") .call(d3.axisLeft(y)); // append the bar rectangles to the svg element svg.selectAll("rect") .data(bins) .enter() .append("rect") .attr("x", 1) .attr("transform", function(d) { return "translate(" + x(d.x0) + "," + y(d.length) + ")"; }) .attr("width", function(d) { return x(d.x1) - x(d.x0) -1 ; }) .attr("height", function(d) { return height - y(d.length); }) .style("fill", "#69b3a2") .style("opacity", .5); // show line for the current predicted value svg.append('line') .attr("x1", x(predictedVal)) .attr("x2", x(predictedVal)) .attr("y1", 0) .attr("y1", height) .style("stroke", "495FBC") .style("stroke-width", 2); }4f:T9a5,drawVdProsperity2016Chart = { // set the dimensions and margins of the graph var margin = {top: 60, right: 30, bottom: 30, left: 40}, width = 600 - margin.left - margin.right, height = 200 - margin.top - margin.bottom; // append the svg object to the body of the page d3.select("#vdProsperity2016-vis").html(""); var svg = d3.select("#vdProsperity2016-vis") .append("svg") .attr("width", width + margin.left + margin.right) .attr("height", height + margin.top + margin.bottom) .append("g") .attr("transform", "translate(" + margin.left + "," + margin.top + ")"); // set up the data range var data = dsByModel['vdProsperity2016_ds']; var predictedVal = makePrediction(vdProsperity2016_inputVals, vdProsperity2016_coefs); var maxChangeVD = d3.max(data.map(d => d['prosperity2016'])); var minChangeVD = d3.min(data.map(d => d['prosperity2016'])); // X axis: scale and draw: var x = d3.scaleLinear() .domain([ d3.min([minChangeVD, predictedVal]), d3.max([maxChangeVD, predictedVal]) ]) .range([0, width]); svg.append("g") .attr("transform", "translate(0," + height + ")") .call(d3.axisBottom(x)); svg.append('text') .attr('x', 0) .attr('y', -20) .style('font', '16px Helvetica') .style('font-weight', 'bold') .style('fill', '#6293E4') .text('Prosperity Score') // set up the histogram var hist = d3.histogram() .value(d => d['prosperity2016']) .domain(x.domain()) .thresholds(x.ticks(70)); var bins = hist(data); // y axis var y = d3.scaleLinear() .range([height, 0]); y.domain([0, d3.max(bins, function(d) { return d.length; })]); svg.append("g") .call(d3.axisLeft(y)); // append the bar rectangles to the svg element svg.selectAll("rect") .data(bins) .enter() .append("rect") .attr("x", 1) .attr("transform", function(d) { return "translate(" + x(d.x0) + "," + y(d.length) + ")"; }) .attr("width", function(d) { return x(d.x1) - x(d.x0) -1 ; }) .attr("height", function(d) { return height - y(d.length); }) .style("fill", "#6293E4") .style("opacity", .25); // show line for the current predicted value svg.append('line') .attr("x1", x(predictedVal)) .attr("x2", x(predictedVal)) .attr("y1", 0) .attr("y1", height) .style("stroke", "#60A6A8") .style("stroke-width", 2); }50:T9c6,drawVdChangeProsperityChart = { // set the dimensions and margins of the graph var margin = {top: 60, right: 30, bottom: 30, left: 40}, width = 600 - margin.left - margin.right, height = 200 - margin.top - margin.bottom; // append the svg object to the body of the page d3.select("#vdChangeProsperity-vis").html(""); var svg = d3.select("#vdChangeProsperity-vis") .append("svg") .attr("width", width + margin.left + margin.right) .attr("height", height + margin.top + margin.bottom) .append("g") .attr("transform", "translate(" + margin.left + "," + margin.top + ")"); // set up the data range var data = dsByModel['vdChangeProsperity_ds']; var predictedVal = makePrediction(vdChangeProsperity_inputVals, vdChangeProsperity_coefs); var maxChangeVD = d3.max(data.map(d => d['change_prosperity'])); var minChangeVD = d3.min(data.map(d => d['change_prosperity'])); // X axis: scale and draw: var x = d3.scaleLinear() .domain([ d3.min([minChangeVD, predictedVal]), d3.max([maxChangeVD, predictedVal]) ]) .range([0, width]); svg.append("g") .attr("transform", "translate(0," + height + ")") .call(d3.axisBottom(x)); svg.append('text') .attr('x', 0) .attr('y', -20) .style('font', '16px Helvetica') .style('font-weight', 'bold') .style('fill', '#6293E4') .text('Recovery from Recession') // set up the histogram var hist = d3.histogram() .value(d => d['change_prosperity']) .domain(x.domain()) .thresholds(x.ticks(70)); var bins = hist(data); // y axis var y = d3.scaleLinear() .range([height, 0]); y.domain([0, d3.max(bins, function(d) { return d.length; })]); svg.append("g") .call(d3.axisLeft(y)); // append the bar rectangles to the svg element svg.selectAll("rect") .data(bins) .enter() .append("rect") .attr("x", 1) .attr("transform", function(d) { return "translate(" + x(d.x0) + "," + y(d.length) + ")"; }) .attr("width", function(d) { return x(d.x1) - x(d.x0) -1 ; }) .attr("height", function(d) { return height - y(d.length); }) .style("fill", "#6293E4") .style("opacity", .25); // show line for the current predicted value svg.append('line') .attr("x1", x(predictedVal)) .attr("x2", x(predictedVal)) .attr("y1", 0) .attr("y1", height) .style("stroke", "#60A6A8") .style("stroke-width", 2); } 51:T9b9,drawVdChangeMedIncomeChart = { // set the dimensions and margins of the graph var margin = {top: 60, right: 30, bottom: 30, left: 40}, width = 600 - margin.left - margin.right, height = 200 - margin.top - margin.bottom; // append the svg object to the body of the page d3.select("#vdChangeMedIncome-vis").html(""); var svg = d3.select("#vdChangeMedIncome-vis") .append("svg") .attr("width", width + margin.left + margin.right) .attr("height", height + margin.top + margin.bottom) .append("g") .attr("transform", "translate(" + margin.left + "," + margin.top + ")"); // set up the data range var data = dsByModel['vdChangeMedIncome_ds']; var predictedVal = makePrediction(vdChangeMedIncome_inputVals, vdChangeMedIncome_coefs); var maxChangeVD = d3.max(data.map(d => d['new_change_medinc'])); var minChangeVD = d3.min(data.map(d => d['new_change_medinc'])); // X axis: scale and draw: var x = d3.scaleLinear() .domain([ d3.min([minChangeVD, predictedVal]), d3.max([maxChangeVD, predictedVal]) ]) .range([0, width]); svg.append("g") .attr("transform", "translate(0," + height + ")") .call(d3.axisBottom(x)); svg.append('text') .attr('x', 0) .attr('y', -20) .style('font', '16px Helvetica') .style('font-weight', 'bold') .style('fill', '#6293E4') .text('Change in Med. Income') // set up the histogram var hist = d3.histogram() .value(d => d['new_change_medinc']) .domain(x.domain()) .thresholds(x.ticks(70)); var bins = hist(data); // y axis var y = d3.scaleLinear() .range([height, 0]); y.domain([0, d3.max(bins, function(d) { return d.length; })]); svg.append("g") .call(d3.axisLeft(y)); // append the bar rectangles to the svg element svg.selectAll("rect") .data(bins) .enter() .append("rect") .attr("x", 1) .attr("transform", function(d) { return "translate(" + x(d.x0) + "," + y(d.length) + ")"; }) .attr("width", function(d) { return x(d.x1) - x(d.x0) -1 ; }) .attr("height", function(d) { return height - y(d.length); }) .style("fill", "#6293E4") .style("opacity", .25); // show line for the current predicted value svg.append('line') .attr("x1", x(predictedVal)) .attr("x2", x(predictedVal)) .attr("y1", 0) .attr("y1", height) .style("stroke", "#60A6A8") .style("stroke-width", 2); 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Density`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":485,"value":"viewof citySelector = select({\n title: \"Select a city\", \n options: getCompleteCities()\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":223,"value":"viewof bblessthan20_slider = genSlider('changeVD', 'bblessthan20')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":269,"value":"viewof foreignborn_slider = genSlider('changeVD', 'foreignborn')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":271,"value":"viewof pctinfo_slider = genSlider('changeVD', 'pctinfo')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":275,"value":"viewof bachelors_slider = genSlider('changeVD', 'bachelors')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":277,"value":"viewof age25to44_slider = genSlider('changeVD', 'age25to44')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":190,"value":"{const form = html``;\n form.reset.addEventListener(\"click\", () => {\n viewof bblessthan20_slider.dispatchEvent(new CustomEvent(\"resetSliders\"));\n viewof foreignborn_slider.dispatchEvent(new CustomEvent(\"resetSliders\"));\n viewof pctinfo_slider.dispatchEvent(new CustomEvent(\"resetSliders\"));\n viewof bachelors_slider.dispatchEvent(new CustomEvent(\"resetSliders\"));\n viewof age25to44_slider.dispatchEvent(new CustomEvent(\"resetSliders\"));\n });\n \n return form;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":134,"value":"html` \n \n\n

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Data`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"modelsData = d3.csvParse(await FileAttachment(\"models_data.csv\").text(), d3.autoType)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":372,"value":"models = await FileAttachment(\"models.json\").json()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":452,"value":"md`#### Create datasets specific to each model, store in object`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":461,"value":"dsByModel = {\n let model_datasets = {};\n \n // loop over all models and create a dataset for each\n Object.keys(models).forEach(modelName => {\n model_datasets[modelName + '_ds'] = getModelData(modelName)\n });\n \n return model_datasets;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":108,"value":"md`### set coefficients for each model`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"changeVD_coefs = ({\n 'bblessthan20': .0021762,\n 'foreignborn': .0066928,\n 'pctblack': .003244,\n 'pctinfo': .012803,\n 'hsgrad': -.0006706,\n 'bachelors': .0033515,\n 'age18to24': -.0015455,\n 'age25to44': .0145823,\n 'age45to54': -.0061798,\n 'age55to64': .0015114,\n 'change_pop_100000': .0355974,\n 'new_change_medinc': .0276445,\n 'new_medinc2017': -.0116502,\n 'pctpoverty': -.0221746,\n 'metro': .0492244,\n '_cons': .5078916\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":638,"value":"vdChangeProsperity_coefs = ({\n 'vdoct19': 1.445077,\n 'bb': .1815903,\n 'smallbusiness_per_100': -.6126545,\n 'pctblack': .0006715,\n 'pctnative': -.0612036,\n 'pctasian': -.0916979,\n 'pcthisp': -.0822609,\n 'pctag': -.3599052,\n 'pctconstruct': -.2641533,\n 'pctwhole': -.4410969,\n 'pctretail': 1.162467,\n 'pcttrans': -.4665026,\n 'pctinfo': -.7975115,\n 'pctfinance': -.3766198,\n 'pctprof': .305368,\n 'pcteduc': -.7226615, \n 'pctother': -.0006475,\n 'pctpublic': -.3696497,\n 'age18to24': 0.379897,\n 'age25to44': -.3665635,\n 'age45to64': -.2267651,\n 'hsgrad': -.653645,\n 'somecollege': -.0449509,\n 'bachelors': -.6168075,\n 'graduate': -.2495419,\n 'newmetro': .6907674,\n '_cons': 46.77329\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":720,"value":"vdProsperity2016_coefs = ({\n 'vdoct19': .5988529,\n 'bb': .6477374,\n 'smallbusiness_per_100': .1553095,\n 'pctblack': -.1722428,\n 'pctnative': -.0553936,\n 'pctasian': -.0331354,\n 'pcthisp': .1062561,\n 'pctag': -.1387641,\n 'pctconstruct': .2848725,\n 'pctwhole': 1.970283,\n 'pctretail': -.8703661,\n 'pcttrans': -.3825853,\n 'pctinfo': -1.256704,\n 'pctfinance': -.3653741,\n 'pctprof': -.7247396,\n 'pcteduc': -.736046,\n 'pctother': -.064433,\n 'pctpublic': -.2270864,\n 'age18to24': -.95536,\n 'age25to44': .212319,\n 'age45to64': -.8111978,\n 'hsgrad': 1.206787,\n 'somecollege': .865509,\n 'bachelors': 2.142609,\n 'graduate': 1.816939,\n 'newmetro': 3.984972,\n '_cons': -61.66045\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":726,"value":"vdChangeMedIncome_coefs = ({\n 'vdoct19': .0716585,\n 'bb': .0238252,\n 'smallbusiness_per_100': -.0480746,\n 'pctblack': -.0243695,\n 'pctnative': -.0116564,\n 'pctasian': .0805543,\n 'pcthisp': -.0201668,\n 'pctag': -.0043033,\n 'pctconstruct': .023519,\n 'pctwhole': -.0923413,\n 'pctretail': .0144582,\n 'pcttrans': .0447202,\n 'pctinfo': -.084962,\n 'pctfinance': -.0308414,\n 'pctprof': .0186868,\n 'pcteduc': .0072303,\n 'pctother': -.0080855,\n 'pctpublic': -.0127175,\n 'age18to24': -.0349862,\n 'age25to44': .048063,\n 'age45to64': .0307359,\n 'hsgrad': -.082114,\n 'somecollege': -.0483312,\n 'bachelors': -.0221164,\n 'graduate': -.0279647,\n 'newmetro': -.1520177,\n '_cons': 3.395282\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":110,"value":"md`### define fixed inputs`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":50,"value":"changeVD_inputVals = {\n let inputs = {};\n \n // get inputs from sliders\n inputs['bblessthan20'] = bblessthan20_slider;\n inputs['foreignborn'] = foreignborn_slider;\n inputs['pctinfo'] = pctinfo_slider;\n inputs['bachelors'] = bachelors_slider;\n inputs['age25to44'] = age25to44_slider;\n \n // get the other inputs based on actual values from the current city\n let data = dsByModel['changeVD_ds'];\n let currentCityData = data.filter(d => d.city === citySelector)[0];\n console.log(currentCityData)\n let remainingVars = [\n 'pctblack',\n 'hsgrad',\n 'age18to24',\n 'age45to54',\n 'age55to64',\n 'change_pop_100000',\n 'new_change_medinc',\n 'new_medinc2017',\n 'pctpoverty',\n 'metro' \n ];\n for (let v of remainingVars){\n inputs[v] = currentCityData[v];\n }\n \n // add constant to model\n inputs['_cons'] = 1;\n \n return inputs;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":730,"value":"vdProsperity2016_inputVals = {\n let inputs = {};\n \n // get inputs from sliders\n inputs['vdoct19'] = vd_slider;\n \n // get the other inputs based on actual values from the current city\n let data = dsByModel['vdProsperity2016_ds'];\n let currentCityData = data.filter(d => d.city === citySelector)[0];\n\n let remainingVars = [\n 'bb', \n 'smallbusiness_per_100', \n 'pctblack',\n 'pctnative',\n 'pctasian',\n 'pcthisp', \n 'pctag',\n 'pctconstruct',\n 'pctwhole', \n 'pctretail',\n 'pcttrans',\n 'pctinfo',\n 'pctfinance',\n 'pctprof', \n 'pcteduc', \n 'pctother', \n 'pctpublic', \n 'age18to24', \n 'age25to44', \n 'age45to64', \n 'hsgrad', \n 'somecollege', \n 'bachelors', \n 'graduate', \n 'newmetro', \n ];\n for (let v of remainingVars){\n inputs[v] = currentCityData[v];\n }\n \n // add constant to model\n inputs['_cons'] = 1;\n \n return inputs;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":692,"value":"vdChangeProsperity_inputVals = {\n let inputs = {};\n \n // get inputs from sliders\n inputs['vdoct19'] = vd_slider;\n \n // get the other inputs based on actual values from the current city\n let data = dsByModel['vdChangeProsperity_ds'];\n let currentCityData = data.filter(d => d.city === citySelector)[0];\n\n let remainingVars = [\n 'bb', \n 'smallbusiness_per_100', \n 'pctblack',\n 'pctnative',\n 'pctasian',\n 'pcthisp', \n 'pctag',\n 'pctconstruct',\n 'pctwhole', \n 'pctretail',\n 'pcttrans',\n 'pctinfo',\n 'pctfinance',\n 'pctprof', \n 'pcteduc', \n 'pctother', \n 'pctpublic', \n 'age18to24', \n 'age25to44', \n 'age45to64', \n 'hsgrad', \n 'somecollege', \n 'bachelors', \n 'graduate', \n 'newmetro', \n ];\n for (let v of remainingVars){\n inputs[v] = currentCityData[v];\n }\n \n // add constant to model\n inputs['_cons'] = 1;\n \n return inputs;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":734,"value":"vdChangeMedIncome_inputVals = {\n let inputs = {};\n \n // get inputs from sliders\n inputs['vdoct19'] = vd_slider;\n \n // get the other inputs based on actual values from the current city\n let data = dsByModel['vdChangeMedIncome_ds'];\n let currentCityData = data.filter(d => d.city === citySelector)[0];\n\n let remainingVars = [\n 'bb', \n 'smallbusiness_per_100', \n 'pctblack',\n 'pctnative',\n 'pctasian',\n 'pcthisp', \n 'pctag',\n 'pctconstruct',\n 'pctwhole', \n 'pctretail',\n 'pcttrans',\n 'pctinfo',\n 'pctfinance',\n 'pctprof', \n 'pcteduc', \n 'pctother', \n 'pctpublic', \n 'age18to24', \n 'age25to44', \n 'age45to64', \n 'hsgrad', \n 'somecollege', \n 'bachelors', \n 'graduate', \n 'newmetro', \n ];\n for (let v of remainingVars){\n inputs[v] = currentCityData[v];\n }\n \n // add constant to model\n inputs['_cons'] = 1;\n \n return inputs;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":138,"value":"md`## Helper Functions`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":13,"value":"makePrediction = function(inputs, coefs){\n let predictors = Object.keys(inputs);\n \n // confirm that there is a coef for all predictors\n for (let p of predictors){\n if (!(p in coefs)){\n return `missing coef for predictor ${p}`;\n }\n }\n \n // confirm that there is predictor for all coefs\n for (let c of Object.keys(coefs)){\n if(!predictors.includes(c)){\n return `missing predictor for coef ${c}`;\n }\n }\n \n // build the equation\n let modelPrediction = 0;\n for (let p of predictors){\n modelPrediction += coefs[p] * inputs[p] // add this (coef*input) to the overall prediction\n }\n\n return modelPrediction\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":489,"value":"getCompleteCities = function(){\n // return an array of cities that have entries for all variables across all models\n let allVars = Object.keys(modelsData[0]);\n \n // filter out any city that has missing values for any variable\n let completeCities = modelsData.filter(row => {\n for (let v of allVars){\n if (row[v] === null){\n return false\n }\n }\n return true\n });\n \n completeCities = completeCities.map(d => d.city);\n \n return completeCities\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":219,"value":"$4d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":384,"value":"getModelData = function(modelName){\n // return a dataset that contains the variables for this model only. Remove rows with NaN\n let thisModel = models[modelName];\n let modelVars = [...thisModel['inputs'], thisModel['output'], 'city', 'pop_cbsa'];\n \n // grab these vars from the full models dataset \n let model_ds = modelsData.map(row => _.pick(row, modelVars));\n \n // filter out any rows with a missing values\n let numVars = modelVars.slice(0)\n numVars.splice(modelVars.indexOf('city'), 1); // remove the string var from the array\n model_ds = model_ds.filter(row => {\n for (let v of numVars){\n if (row[v] === null){\n return false\n }\n }\n return true\n \n });\n return model_ds\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":141,"value":"$4e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":737,"value":"$4f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":663,"value":"$50","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":754,"value":"$51","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":98,"value":"md`## imports`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":400,"value":"_ = require('lodash');","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":353,"value":"numeral = require('numeral')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":4,"value":"d3 = require('d3@v5');","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":64,"value":"import {slider, select} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W52","stdlib":"1"}} 24:null