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Jan 12, 2022
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Election
viewof switchDomain = Inputs.radio(
new Map([
["Show all possibilities", "all"],
["Focus on the more-probable middle", "some"]
]),
{ value: "some" }
)
drawOutcome = () => Math.round(cdfScale(Math.random()))
cdfScale = d3.scaleLinear(cdf.map(d => d[1]), cdf.map(d => d[0]))
bar = function(clinton, trump, total, format, label) {
const marginLeft = 60;
const h = 60;
const w = width - marginLeft;
return svg`<svg width=${width} height="${h}" style="font-family: sans-serif; cursor: default;">
<g transform="translate(${marginLeft * .4}, ${h * .6})">${getLabel(
label,
marginLeft / 3
)}</g>
<g transform="translate(${marginLeft}, 0)">
<text y="2em" fill="${blue}">
<tspan style="font-size: 2em">${format(clinton)}</tspan>
Hillary Clinton
</text>
<text x="${w}" text-anchor="end" y="2em" fill="${red}">
Donald Trump
<tspan style="font-size: 2em">${format(trump)}</tspan>
</text>
<g transform="translate(0, 40)">
<rect
width=${w * (clinton / (clinton + trump))}
height="20"
fill=${blue} />
<rect
width=${w * (trump / total)}
height="20"
fill=${red}
x="${w * (1 - trump / total)}" />
</g>
</g>
</svg>`;
}
getLabel = (letter, radius) => {
const fontSize = radius * 1.5;
const strokeWidth = radius / 8;
const _letter = letter.charAt(0);
const suffix = letter.substring(1);
return svg`<g>
<circle
r="${radius}"
fill="white"
stroke="black"
stroke-width="${strokeWidth}" />
<text
style="font-family: sans-serif; font-size: ${fontSize}px;"
text-anchor="middle"
dy="0.32em"
>
${_letter}
</text>
<text
style="font-family: sans-serif; font-size: ${fontSize}px;"
text-anchor="middle"
dx="0.4em"
dy="0.32em"
>
${suffix}
</text>
</g>`;
}
clintonProb = .714
trumpProb = 1 - clintonProb
clintonVotes = 232
trumpVotes = numberOfElectors - clintonVotes
pct = d3.format(".1%")
blue = "#0080c9"
red = "#dd2c35"
cdf = d3.zip(possibleOutcomes, d3.cumsum(pmf, d => d.prob))
cdfMap = new Map(cdf)
winProb = 1 - cdfMap.get(voteThreshold)
tieProb = pmf.find(d => d.votes === numberOfElectors / 2).prob
loseProb = cdfMap.get(voteThreshold - 1)
normalizeProbs = counts => {
const sum = d3.sum(counts.map(({ count }) => count));
const map = new Map(counts.map(({ votes, count }) => [votes, count]));
return possibleOutcomes.map(votes => ({
votes,
prob: (map.get(votes) || 0) / sum
}));
}
possibleOutcomes = d3.range(numberOfElectors + 1)
probableOutcomes = switchDomain === "all"
? possibleOutcomes
: d3.range(Math.round(randMean - 2 * randSd), randMean + 2 * randSd)
x = d3
.scaleBand(probableOutcomes, [margin.left, width - margin.right])
.paddingOuter(0)
.paddingInner(0)
xContinuous = d3.scaleLinear(
d3.extent(x.domain()),
d3.extent(x.domain()).map(x)
)
y = d3.scaleLinear(
[0, d3.max(Array.from(initialCounts.values()))],
[height - margin.bottom, margin.top]
)
y2 = y.copy().domain([0, 1])
xAxis = g =>
g.attr("transform", `translate(0,${height - margin.bottom})`).call(
d3
.axisBottom(xContinuous)
.ticks(width / 80)
.tickSizeOuter(0)
)
y2Axis = g =>
g
.attr("transform", `translate(${margin.left},0)`)
.call(
d3
.axisLeft(y2)
.ticks(5)
.tickFormat(d3.format(".0%"))
)
.call(g => g.select(".domain").remove())
.call(g =>
g
.select(".tick:last-of-type text")
.clone()
.attr("x", 3)
.attr("text-anchor", "start")
.attr("font-weight", "bold")
.text("Probability of Clinton getting up to this many votes")
)
_rand = d3.randomNormal(randMean, randSd)
randMean = numberOfElectors / 2
randSd = 2 * Math.sqrt(numberOfElectors)
rand = d =>
Math.min(
Math.max(_rand(), d3.min(possibleOutcomes)),
d3.max(possibleOutcomes)
)
initialCounts = d3.rollup(
d3.range(2000).map(rand),
arr => arr.length,
d => Math.round(d)
)
numberOfElectors = 538
voteThreshold = numberOfElectors / 2 + 1
height = width / 2
margin = ({ top: 10, right: 60, bottom: 20, left: 40 })
d3 = require("d3@6")
import { tweet } from "@mbostock/tweet"
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