30:T54d,I wanted to take a look at how new mail-in voting laws may affect upcoming elections. What I found was that people of color still aren't turning out like the should. But first let's look at what propelled Biden to victory. What I found were seven indicators that predicted the outcome of winning a county with an accuracy of 87 percent.

Much of what I found is confirmed by the data we've been seeing in recent elections. There is a growing divide between groups in America, particularly between [urban and rural America](https://fivethirtyeight.com/features/how-urban-or-rural-is-your-state-and-what-does-that-mean-for-the-2020-election/), and the [college educated and uneducated](https://www.nbcnews.com/politics/meet-the-press/gop-faces-massive-realignment-it-sheds-college-educated-voters-n1264425).

I ran models with looking at just black, white, asian, hispanic and other racial/ethnic voters. What I found was that looking at all minority groups provided the best accuracy. I have graphed the impact of each of the variables below. What it shows is how a one unit increase in each variable changes the probability of winning a county. The units are standard deviations, which is convoluted, so you can just ignore it. Just know that it allows you to compare across all variables even though they were measured in different units.31:T637,When you toggle between candidates you can see that Biden had a lot more to gain from his top three groups than Trump did from his. Trump needed to limit votes from urban areas, the college educated and minorities, and to turn out conservative voters who tended to be older, married with children and live in areas with higher Covid rates. Covid is just an indicator of how conservative a county is, and suggests that people in those counties listened to Trump's downplaying of the virus. For Trump to win it makes sense to try on limit the turnout of Biden's most likely voters. There's just more to gain by limiting his than turning out your own. So let's look at turnout for these groups. There is decent correlation for turnout among three groups - minorities, college educated and older voters. _(Correlation (R) goes from negative one to positive one. A -1 or +1 one means there is a perfect correalation between the two variables, in a negative or positive direction respectively. A 0 means there is absolutley no correlation.)_ Two of those groups, minorities and the college educated, had a signifigant impact on the odds of Biden winning a county. The third, older voters, helped Trump's odds. So finally let's see if vote-by-mail helped increase turnout. While the data is limited it gives us some estimate on turnout for these groups. What you see is that there isn't any real correlation between mail-in voting and turnout in these groups. While mail-in voting may have boosted the overall turnout, it doesn't appear to be increase the turnout rate for any particular group. 32:T793,chartTurnout = { const svg = d3.create("svg") .attr("viewBox", [0, 0, width, height]); svg.selectAll("circle") .data(turnout) .join("circle") .attr("cx" , d => xInitial(d.minority)) .attr("cy" , d => yTurnout(d.turnout)) .attr("class", function(d) { return "circle circle--" + (d.turnout >= 67 ? "positive" : "negative");}) .attr("r" , 5) .attr("opacity" , 0.5); svg.append("circle") .attr("cx" , 275) .attr("cy" , 7) .attr("r" , 5) .attr("fill" , "crimson") .attr("class" , "legend"); svg.append("circle") .attr("cx" , margin.left+25) .attr("cy" , 7) .attr("r" , 5) .attr("fill" , "steelblue") .attr("class" , "legend"); svg.append("text") .attr("x" , margin.left+35) .attr("y", 12) .attr("class" , "text") .text("At or Above National Turnout"); svg.append("text") .attr("x" , 285) .attr("y", 12) .attr("class" , "text") .text("Below National Turnout"); svg.append("path") .attr("fill", "#ccc") .attr("fill-opacity", 0.75) .attr("stroke", "none") .attr("class" , "interval") .attr("d", area(intervalDataInitial)); svg.append("path") .attr("class", "regression") .attr("fill" , "none") .attr("stroke" , "black") .attr("d" , lineInitial(regressionInitial)) .attr("opacity" , 0); svg.append("rect") .attr("height" , 50) .attr("width" , width) .attr("y" , height-margin.bottom) .attr("fill" , "white"); svg.append("g") .call(yAxisTurnout); svg.append("g") .call(xAxisLinear); svg.append("text") .attr("class" , "text") .attr("x" , margin.left+35) .attr("y" , 30) .text("R:"); svg.append("text") .attr("class" , "r") .attr("x" , margin.left+50) .attr("y" , 30) .text(data[0].rTurnout); return svg.node(); }33:T422,callout = (g, value) => { if (!value) return g.style("display", "none"); g .style("display", null) .style("pointer-events", "none") .style("font", "12px sans-serif"); const path = g.selectAll("path") .data([null]) .attr("class" , "thepath") .join("path") .attr("fill" , "white") .attr("stroke", "black"); var text = g.selectAll("text") .data([null]) .join("text") .call(text => text .selectAll("tspan") .data((value + "").split(/\n/)) .join("tspan") .attr("x", 0) .attr("y", (d, i) => `${i * 1.5}em`) .style("font-weight", (_, i) => i == 2 ? null : i == 3 ? null : i ==4 ? null : i ==5 ? null:"bold") .style("fill" , (_, i) => i == 0 ? null: i == 1 ? null: i == 2 ? "red" : i == 3 ? "steelblue": i == 4 ? "purple":"black") .text(d => d)); const {x, y, width: w, height: h} = text.node().getBBox(); text.attr("transform", `translate(${-w / 2},${15 - y})`); path.attr("d", `M${-w/2 - 10},5H${w/2 + 10}v${h + 20}h-${w + 20}z`); }34:T4cb,function confidenceUpdate(data, predict) { const mean = d3.sum(data, dropdownR == "bachelor" ? d => d.bachelor:dropdownR == "cohab" ? d=> d.cohab:dropdownR == "married" ? d => d.married:dropdownR == "age" ? d => d.age:dropdownR == "density" ? d => d.density:dropdownR == "covid" ? d => d.covid:d => d.minority) / data.length; let a = 0, b = 0; for (let i = 0; i < data.length; ++i) { a += Math.pow((dropdownR == "bachelor" ? data[i].bachelor:dropdownR == "cohab" ? data[i].cohab:dropdownR == "married" ? data[i].married:dropdownR == "age" ? data[i].age:dropdownR == "density" ? data[i].density:dropdownR == "covid" ? data[i].covid:data[i].minority) - mean, 2); b += Math.pow(data[i].turnout - predict(dropdownR == "bachelor" ? data[i].bachelor:dropdownR == "cohab" ? data[i].cohab:dropdownR == "married" ? data[i].married:dropdownR == "age" ? data[i].age:dropdownR == "density" ? data[i].density:dropdownR == "covid" ? data[i].covid:data[i].minority), 2); } const sy = Math.sqrt(b / (data.length - 2)); const t = 1.9638 return function(x) { const Y = predict(x); const se = sy * Math.sqrt(1 / data.length + Math.pow(x - mean, 2) / a); return { x, lower: Y - t * se, upper: Y + t * se }; } }35:T27cf,function updateScatter (input, select) { const circle = d3.selectAll(".circle").transition().duration(1000); const label = d3.selectAll(".label").attr("opacity" , 0); const intervalTrans = d3.selectAll(".interval").transition().delay(500).attr("opacity" , ch == "toggle" ? 1:0); const regressTrans = d3.selectAll(".regression").transition().delay(500).attr("opacity" , ch == "toggle" ? 1:0); const r = d3.selectAll(".r").transition().duration(1000); if (input == "bachelor" & select == "turnout") { circle.attr("cx" , d => xTurnout(d.bachelor)), d3.selectAll(".linearAxis").transition().duration(1000).call(d3.axisBottom(xTurnout).tickSizeOuter(0).tickFormat(d => d + "%")), label.text("Bachelor Degree or Above").attr("opacity" , 1), d3.selectAll(".regression").attr("opacity" , 0).attr("d" , line(regressionTurnout)), d3.selectAll(".interval").attr("opacity" , 0).attr("d" , areaUpdate(intervalDataTurnout)), r.textTween(function(d) { const i = d3.interpolate(d3.select(this).text(), data[1].rTurnout); return function(t) { return this._current = Math.round((i(t) + Number.EPSILON)*1000)/1000}; }) } else if (input == "cohab" & select == "turnout"){ circle.attr("cx" , d => xTurnout(d.cohab)), d3.selectAll(".linearAxis").transition().duration(1000).call(d3.axisBottom(xTurnout).tickSizeOuter(0).tickFormat(d => d + "%")), label.text("Unmarried Living Together").attr("opacity" , 1), d3.selectAll(".regression").attr("opacity" , 0).attr("d" , line(regressionTurnout)), d3.selectAll(".interval").attr("opacity" , 0).attr("d" , areaUpdate(intervalDataTurnout)), r.textTween(function(d) { const i = d3.interpolate(d3.select(this).text(), data[2].rTurnout); return function(t) { return this._current = Math.round((i(t) + Number.EPSILON)*1000)/1000}; }) } else if (input == "married" & select == "turnout"){ circle.attr("cx" , d => xTurnout(d.married)), d3.selectAll(".linearAxis").transition().duration(1000).call(d3.axisBottom(xTurnout).tickSizeOuter(0).tickFormat(d => d + "%")), label.text("Married With Kids").attr("opacity" , 1), d3.selectAll(".regression").attr("opacity" , 0).attr("d" , line(regressionTurnout)), d3.selectAll(".interval").attr("opacity" , 0).attr("d" , areaUpdate(intervalDataTurnout)), r.textTween(function(d) { const i = d3.interpolate(d3.select(this).text(), data[3].rTurnout); return function(t) { return this._current = Math.round((i(t) + Number.EPSILON)*1000)/1000}; }) } else if (input == "age" & select == "turnout") { circle.attr("cx" , d => xTurnout(d.age)), d3.selectAll(".linearAxis").transition().duration(1000).call(d3.axisBottom(xTurnout).tickSizeOuter(0)), label.text("Median Age").attr("opacity" , 1), d3.selectAll(".regression").attr("opacity" , 0).attr("d" , line(regressionTurnout)), d3.selectAll(".interval").attr("opacity" , 0).attr("d" , areaUpdate(intervalDataTurnout)), r.textTween(function(d) { const i = d3.interpolate(d3.select(this).text(), data[4].rTurnout); return function(t) { return this._current = Math.round((i(t) + Number.EPSILON)*1000)/1000}; }) } else if (input == "density" & select == "turnout") { circle.attr("cx" , d => xTurnout(d.density)), d3.selectAll(".linearAxis").transition().duration(1000).call(d3.axisBottom(xTurnout).tickSizeOuter(0)), label.text("People Per Square Mile").attr("opacity" , 1), d3.selectAll(".regression").attr("opacity" , 0).attr("d" , line(regressionTurnout)), d3.selectAll(".interval").attr("opacity" , 0).attr("d" , areaUpdate(intervalDataTurnout)), r.textTween(function(d) { const i = d3.interpolate(d3.select(this).text(), data[5].rTurnout); return function(t) { return this._current = Math.round((i(t) + Number.EPSILON)*1000)/1000}; }) } else if (input == "covid" & select == "turnout") { circle.attr("cx" , d => xTurnout(d.covid)), d3.selectAll(".linearAxis").transition().duration(1000).call(d3.axisBottom(xTurnout).tickSizeOuter(0)), label.text("Cases Per 100k").attr("opacity" , 1), d3.selectAll(".regression").attr("opacity" , 0).attr("d" , line(regressionTurnout)), d3.selectAll(".interval").attr("opacity" , 0).attr("d" , areaUpdate(intervalDataTurnout)), r.textTween(function(d) { const i = d3.interpolate(d3.select(this).text(), data[6].rTurnout); return function(t) { return this._current = Math.round((i(t) + Number.EPSILON)*1000)/1000}; }) } else if (input == "minority" & select == "turnout"){ circle.attr("cx" , d => xTurnout(d.minority)), d3.selectAll(".linearAxis").transition().duration(1000).call(d3.axisBottom(xTurnout).tickSizeOuter(0).tickFormat(d => d + "%")), label.text("Minority").attr("opacity" , 1), d3.selectAll(".regression").attr("opacity" , 0).attr("d" , line(regressionTurnout)), d3.selectAll(".interval").attr("opacity" , 0).attr("d" , areaUpdate(intervalDataTurnout)), r.textTween(function(d) { const i = d3.interpolate(d3.select(this).text(), data[0].rTurnout); return function(t) { return this._current = Math.round((i(t) + Number.EPSILON)*1000)/1000}; }) } else if (input == "bachelor" & select == "mail") { circle.attr("cx" , d => xTurnout(d.bachelor)), d3.selectAll(".linearAxis").transition().duration(1000).call(d3.axisBottom(xTurnout).tickSizeOuter(0).tickFormat(d => d + "%")), label.text("Bachelor Degree or Above").attr("opacity" , 1), d3.selectAll(".regression").attr("opacity" , 0).attr("d" , line(regressionMail)), d3.selectAll(".interval").attr("opacity" , 0).attr("d" , areaUpdate(intervalDataMail)), r.textTween(function(d) { const i = d3.interpolate(d3.select(this).text(), data[1].rMail); return function(t) { return this._current = Math.round((i(t) + Number.EPSILON)*1000)/1000}; }) } else if (input == "cohab" & select == "mail") { circle.attr("cx" , d => xTurnout(d.cohab)), d3.selectAll(".linearAxis").transition().duration(1000).call(d3.axisBottom(xTurnout).tickSizeOuter(0).tickFormat(d => d + "%")), label.text("Unmarried Living Together").attr("opacity" , 1), d3.selectAll(".regression").attr("opacity" , 0).attr("d" , line(regressionMail)), d3.selectAll(".interval").attr("opacity" , 0).attr("d" , areaUpdate(intervalDataMail)), r.textTween(function(d) { const i = d3.interpolate(d3.select(this).text(), data[2].rMail); return function(t) { return this._current = Math.round((i(t) + Number.EPSILON)*1000)/1000}; }) } else if (input == "married" & select == "mail") { circle.attr("cx" , d => xTurnout(d.married)), d3.selectAll(".linearAxis").transition().duration(1000).call(d3.axisBottom(xTurnout).tickSizeOuter(0).tickFormat(d => d + "%")), label.text("Married With Kids").attr("opacity" , 1), d3.selectAll(".regression").attr("opacity" , 0).attr("d" , line(regressionMail)), d3.selectAll(".interval").attr("opacity" , 0).attr("d" , areaUpdate(intervalDataMail)), r.textTween(function(d) { const i = d3.interpolate(d3.select(this).text(), data[3].rMail); return function(t) { return this._current = Math.round((i(t) + Number.EPSILON)*1000)/1000}; }) } else if (input == "age" & select == "mail") { circle.attr("cx" , d => xTurnout(d.age)), d3.selectAll(".linearAxis").transition().duration(1000).call(d3.axisBottom(xTurnout).tickSizeOuter(0)), label.text("Median Age").attr("opacity" , 1), d3.selectAll(".regression").attr("opacity" , 0).attr("d" , line(regressionMail)), d3.selectAll(".interval").attr("opacity" , 0).attr("d" , areaUpdate(intervalDataMail)), r.textTween(function(d) { const i = d3.interpolate(d3.select(this).text(), data[4].rMail); return function(t) { return this._current = Math.round((i(t) + Number.EPSILON)*1000)/1000}; }) } else if (input == "density" & select == "mail") { circle.attr("cx" , d => xTurnout(d.density)), d3.selectAll(".linearAxis").transition().duration(1000).call(d3.axisBottom(xTurnout).tickSizeOuter(0)), label.text("People Per Square Mile").attr("opacity" , 1), d3.selectAll(".regression").attr("opacity" , 0).attr("d" , line(regressionMail)), d3.selectAll(".interval").attr("opacity" , 0).attr("d" , areaUpdate(intervalDataMail)), r.textTween(function(d) { const i = d3.interpolate(d3.select(this).text(), data[5].rMail); return function(t) { return this._current = Math.round((i(t) + Number.EPSILON)*1000)/1000}; }) } else if (input == "covid" & select == "mail") { circle.attr("cx" , d => xTurnout(d.covid)), d3.selectAll(".linearAxis").transition().duration(1000).call(d3.axisBottom(xTurnout).tickSizeOuter(0)), label.text("Cases Per 100k").attr("opacity" , 1), d3.selectAll(".regression").attr("opacity" , 0).attr("d" , line(regressionMail)), d3.selectAll(".interval").attr("opacity" , 0).attr("d" , areaUpdate(intervalDataMail)), r.textTween(function(d) { const i = d3.interpolate(d3.select(this).text(), data[6].rMail); return function(t) { return this._current = Math.round((i(t) + Number.EPSILON)*1000)/1000}; }) } else if (input == "minority" & select == "mail"){ circle.attr("cx" , d => xTurnout(d.minority)), d3.selectAll(".linearAxis").transition().duration(1000).call(d3.axisBottom(xTurnout).tickSizeOuter(0).tickFormat(d => d + "%")), label.text("Minority").attr("opacity" , 1), d3.selectAll(".regression").attr("opacity" , 0).attr("d" , line(regressionMail)), d3.selectAll(".interval").attr("opacity" , 0).attr("d" , areaUpdate(intervalDataMail)), r.textTween(function(d) { const i = d3.interpolate(d3.select(this).text(), data[0].rMail); return function(t) { return this._current = Math.round((i(t) + Number.EPSILON)*1000)/1000}; }) } }36:[{"id":"b7c707684981edc3b96e214ac8d15e59f4e9d02822dbe54b214f48c65545a3a2079bd649a6fc8bf757eb5b8e8ed6a6a16974abb5d2ca1c7c930aee5c633b5825","href":"https://static.observableusercontent.com/files/b7c707684981edc3b96e214ac8d15e59f4e9d02822dbe54b214f48c65545a3a2079bd649a6fc8bf757eb5b8e8ed6a6a16974abb5d2ca1c7c930aee5c633b5825","name":"electionCoef.csv","type":"text/csv","lastModified":1651423243035.861,"size":348},{"id":"f9688dd8cd242c3b9591b96f38ffb3734ea4ce3acd23d9ea2def0227274fb3734b60e650e23632848bb81a3f2d91d395a0e259f6aea8a656c21c0a835c457112","href":"https://static.observableusercontent.com/files/f9688dd8cd242c3b9591b96f38ffb3734ea4ce3acd23d9ea2def0227274fb3734b60e650e23632848bb81a3f2d91d395a0e259f6aea8a656c21c0a835c457112","name":"turnout@8.csv","type":"text/csv","lastModified":1651423243035.861,"size":130584}] 2e:{"title":"Mail-In Voting","cells":[{"id":0,"value":"# Mail-In Voting","pinned":false,"hidden":false,"mode":"md"},{"id":258,"value":"$30","pinned":false,"hidden":false,"mode":"md"},{"id":524,"value":"viewof dropdown = select({\n options: [\n {label: \"Biden\" , value: \"biden\"},\n {label: \"Trump\" , value: \"trump\"},\n ],\n value: \"biden\",\n title: \"Candidate\",\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":33,"value":"chartVar = {\n const svg = d3.create(\"svg\")\n .attr(\"viewBox\", [0, 0, width, height]);\n \n svg.selectAll(\".bar\")\n .data(data)\n .enter()\n .append(\"rect\")\n .attr(\"x\" , d => x(d.indicator))\n .attr(\"y\" , d => y(Math.max(0, d.biden)))\n .attr(\"height\" , d => Math.abs(y(d.biden) - y(0)))\n .attr(\"width\" , x.bandwidth())\n .attr(\"class\", \"bar\")\n .attr(\"fill\" , d => d.biden >0 ? \"steelblue\":\"crimson\");\n \n svg.append(\"g\")\n .call(yAxis);\n\n svg.append(\"g\")\n .call(xAxis);\n \n return svg.node();\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1964,"value":"$31","pinned":false,"hidden":false,"mode":"md"},{"id":673,"value":"viewof dropdownR = select({\n options: [\n {label: \"Minority\" , value: \"minority\"},\n {label: \"Bachelor and Above\" , value: \"bachelor\"},\n {label: \"Unmarried Living Together\" , value: \"cohab\"},\n {label: \"Married with Kids\" , value: \"married\"},\n {label: \"Age\" , value: \"age\"},\n {label: \"Covid\" , value: \"covid\"},\n {label: \"Density\" , value: \"density\"}\n ],\n value: \"minority\",\n title: \"Variable\",\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2618,"value":"viewof r1 = radio({\n options: [\n { label: 'Turnout', value: 'turnout' },\n { label: 'Mail-In', value: 'mail' },\n ],\n value: 'turnout'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2070,"value":"viewof ch = checkbox({\n options: [{ value: \"toggle\", label: \"Regression Line\" }],\n value: false\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":645,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":3640,"value":"This suggests that laws restricting mail-in votes may just reduce turnout across all groups. But there is more to this story. When you look deeper into the mail-in data you see [evidence of discrimination](https://www.brennancenter.org/our-work/research-reports/digging-georgia-primary). In fact, the takeaway from this analysis is that even in an election with record turnout, minority communities were still **less likely** to vote. We should really be asking ourselves why? ","pinned":false,"hidden":false,"mode":"md"},{"id":3670,"value":"html`\n\n\n\n\n\n\n\n\n\n\n

Predictor	Coefficient	p-value	Standard Dev
Minority	1.8961	< 2e-16	18.25
Bachelor or Above	2.1812	< 2e-16	9.63
Living Together - Unmarried	0.9390	2.97e-08	1.01
Married With Kids	-1.3555	9.23e-12	2.11
Age	-0.7511	1.65e-05	4.74
Density	2.5023	4.37e-05	1396.43
Covid	-1.2616	8.52e-07	4.18
Null deviance: 819.98 on 616 degrees of freedom
Residual deviance: 358.54 on 609 degrees of freedom

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":3704,"value":"MIT Election Data and Science Lab, 2018, \"County Presidential Election Returns 2000-2020\", https://doi.org/10.7910/DVN/VOQCHQ, Harvard Dataverse, V10, UNF:6:pVAMya52q7VM1Pl7EZMW0Q== [fileUNF]\n\nMichael McDonald, Professor, University of Florida, 2020, \"2020 General Election early Vote Statistics\", https://electproject.github.io/Early-Vote-2020G/index.html\n\nDemographic Data from [US Census American Community Survey](https://data.census.gov/cedsci/table?q=United%20States&g=0100000US,%240500000&d=ACS%201-Year%20Estimates%20Detailed%20Tables)","pinned":false,"hidden":false,"mode":"md"},{"id":113,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":418,"value":"","pinned":false,"hidden":false,"mode":"html"},{"id":142,"value":"format = d3.format(\".0%\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":16,"value":"margin = ({top: 40, right: 10, bottom: 50, left: 60})","pinned":false,"hidden":false,"mode":"ojs"},{"id":27,"value":"yAxis = g => g\n .attr(\"transform\", `translate(${margin.left},0)`)\n .attr(\"class\" , \"y_axis\")\n .call(d3.axisLeft(y).tickFormat(format))\n .call(g => g.append(\"text\")\n .attr(\"class\" , \"text\")\n .attr(\"transform\", \"rotate(-90)\")\n .attr(\"y\", 15)\n .attr(\"x\" , -height/2+margin.top)\n .attr(\"text-anchor\", \"middle\")\n .attr(\"fill\", \"black\")\n .text(\"Odds\"));","pinned":false,"hidden":false,"mode":"ojs"},{"id":12,"value":"y = d3.scaleLinear()\n\t .domain([-1, 12])\n\t .range([height-margin.bottom,margin.top])\n .nice()","pinned":true,"hidden":false,"mode":"ojs"},{"id":22,"value":"x = d3.scaleBand()\n .domain(data.map(d => d.indicator))\n .range([margin.left, width - margin.right])\n .padding(0.2)","pinned":true,"hidden":false,"mode":"ojs"},{"id":3739,"value":"data.map(d => d.indicator)","pinned":false,"hidden":false,"mode":"ojs"},{"id":31,"value":"xAxis = (g) =>\n g\n .attr(\"transform\", `translate(0,${height - 82})`)\n .attr(\"class\", \"text\")\n .call(d3.axisTop(x))\n .selectAll(\"text\")\n .attr(\"transform\", \"rotate(-90)\")\n .attr(\"y\", 5)\n .attr(\"x\", 10)\n .style(\"text-anchor\", \"start\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":624,"value":"xInitial = d3.scaleLinear()\n .domain(d3.extent(turnout, d => d.minority))\n .range([margin.left, width - margin.right]);","pinned":true,"hidden":false,"mode":"ojs"},{"id":1167,"value":"xTurnout = d3.scaleLinear()\n .domain(d3.extent(turnout, dropdownR == \"bachelor\" ? d => d.bachelor:dropdownR == \"cohab\" ? d => d.cohab:dropdownR == \"married\" ? d => d.married:dropdownR == \"age\" ? d => d.age:dropdownR == \"density\" ? d => d.density:dropdownR == \"covid\" ? d => d.covid:d => d.minority))\n .range([margin.left, width - margin.right])","pinned":false,"hidden":false,"mode":"ojs"},{"id":631,"value":"xAxisLinear = g => g\n .attr(\"transform\", `translate(0,${height - margin.bottom})`)\n .attr(\"class\" , \"linearAxis\")\n .call(d3.axisBottom(xInitial).tickSizeOuter(0).tickFormat(d => d + \"%\"))\n .call(g => g.append(\"text\")\n .attr(\"class\" , \"label\")\n .attr(\"x\", width/2)\n .attr(\"y\", -4)\n .attr(\"text-anchor\", \"middle\")\n .attr(\"fill\", \"black\")\n .text(\"Minority\"));","pinned":true,"hidden":false,"mode":"ojs"},{"id":633,"value":"yTurnout = d3.scaleLinear()\n .domain([0, 100])\n .range([height - margin.bottom, margin.top]);","pinned":false,"hidden":false,"mode":"ojs"},{"id":640,"value":"yAxisTurnout = g => g\n .attr(\"transform\", `translate(${margin.left},0)`)\n .attr(\"class\" , \"y_axis\")\n .call(d3.axisLeft(yTurnout).tickFormat(d => d + \"%\"))\n .call(g => g.append(\"text\")\n .attr(\"class\" , \"yAxis\")\n .attr(\"transform\", \"rotate(-90)\")\n .attr(\"y\", 15)\n .attr(\"x\" , -height/2+margin.top)\n .attr(\"text-anchor\", \"middle\")\n .attr(\"fill\", \"black\")\n .text(\"Turnout\"));","pinned":false,"hidden":false,"mode":"ojs"},{"id":531,"value":"function update (input) {\n if (input == \"trump\") {d3.selectAll(\".bar\").transition().duration(1000).attr(\"y\" , d => y(Math.max(0, d.trump))).attr(\"height\" , d => Math.abs(y(d.trump) - y(0))).attr(\"fill\" , d => d.trump > 0 ? \"steelblue\":\"crimson\")}\n else {d3.selectAll(\".bar\").transition().duration(1000).attr(\"y\" , d => y(Math.max(0, d.biden))).attr(\"height\" , d => Math.abs(y(d.biden) - y(0))).attr(\"fill\" , d => d.biden > 0 ? \"steelblue\":\"crimson\")}\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":537,"value":"update(dropdown)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1066,"value":"equationTurnout = x => dropdownR == \"bachelor\" ? 51.1804593 + 0.4517004*x:dropdownR == \"cohab\" ? 61.4621739 + 0.7045604*x:dropdownR == \"married\" ? 56.1209949 + 0.8974915*x:dropdownR == \"age\" ? 27.3698251 + 0.9221773*x:dropdownR == \"density\" ? 64.1001134040 - 0.0004314136*x:dropdownR == \"covid\" ? 67.5770098 - 0.5273789*x:70.5321924 - 0.2704017*x","pinned":false,"hidden":false,"mode":"ojs"},{"id":3218,"value":"equationMail = x => dropdownR == \"bachelor\" ? 33.6546392 + 0.2316483*x:dropdownR == \"cohab\" ? 34.691743 + 1.449034*x:dropdownR == \"married\" ? 39.50698865 + 0.04166326*x:dropdownR == \"age\" ? 44.7282599 - 0.1173653*x:dropdownR == \"density\" ? 38.881584720 + 0.001605258*x:dropdownR == \"covid\" ? 45.9845781 - 0.9011156*x:39.794692874 + 0.002581484*x","pinned":false,"hidden":false,"mode":"ojs"},{"id":1095,"value":"equationInitial = x => 70.5321924 - 0.2704017*x","pinned":false,"hidden":false,"mode":"ojs"},{"id":1104,"value":"line = d3.line()\n .x(dropdownR == \"bachelor\" ? d => xTurnout(d.x):dropdownR == \"cohab\" ? d => xTurnout(d.x):dropdownR == \"married\" ? d => xTurnout(d.x):dropdownR == \"age\" ? d => xTurnout(d.x):d => xTurnout(d.x))\n .y(d => yTurnout(d.y))","pinned":false,"hidden":false,"mode":"ojs"},{"id":851,"value":"lineInitial = d3.line()\n .x(d => xInitial(d.x))\n .y(d => yTurnout(d.y))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1441,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":1900,"value":"function confidenceInitial(data, predict) {\n const mean = d3.sum(data, d => d.minority) / data.length;\n let a = 0, b = 0;\n for (let i = 0; i < data.length; ++i) {\n a += Math.pow(data[i].minority - mean, 2);\n b += Math.pow(data[i].turnout - predict(data[i].minority), 2);\n }\n const sy = Math.sqrt(b / (data.length - 2));\n const t = 1.9638 \n return function(x) {\n const Y = predict(x);\n const se = sy * Math.sqrt(1 / data.length + Math.pow(x - mean, 2) / a);\n return { x, lower: Y - t * se, upper: Y + t * se };\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1646,"value":"intervalInitial = confidenceInitial(turnout, equationInitial)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1313,"value":"intervalDataInitial = {\n const domain = xInitial.domain();\n const step = (domain[1] - domain[0]) / 100;\n return d3.range(domain[0], domain[1] + step, step).map(d => intervalInitial(d));\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1734,"value":"intervalUpdateTurnout = confidenceUpdate(turnout, equationTurnout)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3337,"value":"intervalUpdateMail = confidenceUpdate(turnout, equationMail)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1732,"value":"intervalDataTurnout = {\n const domain = xTurnout.domain();\n return d3.range(d3.min(turnout, dropdownR == \"bachelor\" ? d => d.bachelor:dropdownR == \"cohab\" ? d=> d.cohab:dropdownR == \"married\" ? d => d.married:dropdownR == \"age\" ? d => d.age:dropdownR == \"density\" ? d => d.density:dropdownR == \"covid\" ? d => d.covid:d => d.minority), dropdownR == \"density\" ? 20000:100).map(d => intervalUpdateTurnout(d));\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3342,"value":"intervalDataMail = {\n const domain = xTurnout.domain();\n return d3.range(d3.min(turnout, dropdownR == \"bachelor\" ? d => d.bachelor:dropdownR == \"cohab\" ? d=> d.cohab:dropdownR == \"married\" ? d => d.married:dropdownR == \"age\" ? d => d.age:dropdownR == \"density\" ? d => d.density:dropdownR == \"covid\" ? d => d.covid:d => d.minority), dropdownR == \"density\" ? 20000:100).map(d => intervalUpdateMail(d));\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":946,"value":"regressionInitial = d3.range(d3.min(turnout, d => d.minority),100).map(function (d) {return {x:d , y:equationInitial(d)}})","pinned":false,"hidden":false,"mode":"ojs"},{"id":835,"value":"regressionTurnout = d3.range(d3.min(turnout, dropdownR == \"bachelor\" ? d => d.bachelor:dropdownR == \"cohab\" ? d=> d.cohab:dropdownR == \"married\" ? d => d.married:dropdownR == \"age\" ? d => d.age:dropdownR == \"density\" ? d => d.density:dropdownR == \"covid\" ? d => d.covid:d => d.minority), dropdownR == \"density\" ? 20000:100).map(function (d) {return {x:d, y:equationTurnout(d)} })","pinned":false,"hidden":false,"mode":"ojs"},{"id":3303,"value":"regressionMail = d3.range(d3.min(turnout, dropdownR == \"bachelor\" ? d => d.bachelor:dropdownR == \"cohab\" ? d=> d.cohab:dropdownR == \"married\" ? d => d.married:dropdownR == \"age\" ? d => d.age:dropdownR == \"density\" ? d => d.density:dropdownR == \"covid\" ? d => d.covid:d => d.minority), dropdownR == \"density\" ? 20000:100).map(function (d) {return {x:d, y:equationMail(d)} })","pinned":false,"hidden":false,"mode":"ojs"},{"id":1264,"value":"area = d3.area()\n .x(d => xInitial(d.x))\n .y0(d => yTurnout(d.lower))\n .y1(d => yTurnout(d.upper))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1730,"value":"areaUpdate = d3.area()\n .x(d => xTurnout(d.x))\n .y0(d => yTurnout(d.lower))\n .y1(d => yTurnout(d.upper))","pinned":false,"hidden":false,"mode":"ojs"},{"id":3180,"value":"function toggle (select) {\n if (select == \"mail\") {\n d3.selectAll(\".circle\").transition().duration(1000).ease(d3.easeSinIn).delay((d,i) => i*Math.random())\n .attr(\"cy\" , d => d.percentMail == 0 ? yTurnout(-height+margin.bottom) : yTurnout(d.percentMail)),\n d3.selectAll(\".yAxis\").attr(\"opacity\" , 0).text(\"Vote By Mail\").transition().duration(1500).attr(\"opacity\" , 1)\n }\n else {d3.selectAll(\".circle\").transition().duration(1000).ease(d3.easeExpIn).delay((d,i) => i*Math.random()).attr(\"cy\" , d => yTurnout(d.turnout)),\n d3.selectAll(\".yAxis\").attr(\"opacity\" , 0).text(\"Turnout\").transition().duration(1500).attr(\"opacity\" , 1)}\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3193,"value":"toggle(r1)","pinned":false,"hidden":false,"mode":"ojs"},{"id":677,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":680,"value":"updateScatter(dropdownR, r1)","pinned":false,"hidden":false,"mode":"ojs"},{"id":3512,"value":"height = 500","pinned":true,"hidden":false,"mode":"ojs"},{"id":3566,"value":"width = 900","pinned":true,"hidden":false,"mode":"ojs"},{"id":306,"value":"data = d3.csvParse(await FileAttachment(\"electionCoef.csv\").text(), ({indicator, Biden, Trump, rTurnout, rMail}) => ({indicator: indicator, biden: +Biden, trump: +Trump, rTurnout: +rTurnout, rMail: +rMail}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":618,"value":"turnout = d3.csvParse(await FileAttachment(\"turnout@8.csv\").text(), ({county, state, percentMinority, percentBachelorAbove, percentCohab, percentMarriedKids, age, density, covid, turnout, percentMail}) => ({county: county, state: state, turnout: +turnout, minority: +percentMinority, bachelor: +percentBachelorAbove, cohab: +percentCohab, married: +percentMarriedKids, age: +age, density: +density, covid: +covid, percentMail: +percentMail}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":522,"value":"import {select} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2072,"value":"import {checkbox} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2615,"value":"import {radio} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W36","stdlib":"1","titling":"auto"}