Published
Edited
Nov 11, 2020
2 stars
Vote Cone — UninterpolatedVote Cone TransitionsReflecting on “Vote Cones”Vote Cones in Vega-LiteElection 2020 Vote–Time Correlation CartogramElection 2020 Vote Count Speed CartogramElection Cone — Iowa Senate 2016Election Cones — President 2016Election Cones — Senate 2016What’s the connection between these two election bar charts?Election 2020 DataElection 2020 Vote Margin CartogramElection 2020 Cone with Colored ContoursElection 2020 Cone with Labeled ContoursCalculate the percentage of remaining vote needed to overcome given absolute margin
Election 2020: How reliable are different results at different stages?
Also listed in…
Elections
states = Array.from(presidentByState.keys())
races = new Map([
["president", presidentRaces],
["state", stateRaces]
])
data = races.get(raceOption)
keys = Array.from(new Set(d3.range(0,1,0.01).map(grouper))).sort()
grouper = d => Math.floor(d * numberOfSections) / numberOfSections
groups = d3.group(d3.merge(data), ({ x }) => grouper(x), ({ y }) => grouper(y))
get = (x, y) =>
groups.has(x) && groups.get(x).has(y) ? groups.get(x).get(y) : []
fractionCorrect = arr =>
arr.length ? arr.filter(({ correct }) => correct).length / arr.length : NaN
sectionData = d3
.cross(keys, keys)
.map(([x, y]) => ({ x, y, value: fractionCorrect(get(x, y)) }))
presidentRaces = Array.from(presidentByState.values()).map(parseStateRace)
stateRaces = Array.from(state.values())
.flatMap(st => st.data.races
.filter(d => d.candidates && d.timeseries)
.map(parseStateRace)
)
.filter(d => d)
parseStateRace = race => {
const [a, b] = ["republican", "democrat"].map(party =>
race.candidates.find(d => d.party_id === party)
);
if (!a || !b) return;
return transformTimeseries(race.timeseries, a, b);
}
transformTimeseries = (ts, a, b) =>
ts
.map(d => {
const x =
d.vote_shares[a.candidate_key] /
(d.vote_shares[a.candidate_key] + d.vote_shares[b.candidate_key]);
const y = d.eevp / 100;
return { x, y };
})
.map(({ x, y }, i, arr) => ({
x,
y,
correct: x > 0.5 === arr[arr.length - 1].x > 0.5
}))
margin = ({ top: 70, right: 80, bottom: 35, left: 10 })
height = width - margin.left - margin.right + margin.top + margin.bottom
x = d3.scaleLinear([0, 1], [margin.left, width - margin.right])
y = d3.scaleLinear([0, 1], [margin.top, height - margin.bottom])
xs = x(keys[1]) - x(keys[0])
ys = y(keys[1]) - y(keys[0])
color = d3.scaleSequential([0, 1], d3.interpolateRdYlBu)
line = d3.line().x(d => x(d.x)).y(d => y(d.y)).defined(d => !isNaN(d.x) && !isNaN(d.y))
xAxis = g =>
g
.attr("transform", `translate(0, ${height - margin.bottom})`)
.call(d3.axisBottom(x).tickFormat(d3.format(".0%")))
.call(g =>
g
.select(".tick:last-of-type text")
.clone()
.attr("x", 3)
.attr("dy", "2em")
.attr("text-anchor", "end")
.attr("font-weight", "bold")
.text("Vote share for " + candidateName)
)
yAxis = g =>
g
.attr("transform", `translate(${width - margin.right}, 0)`)
.call(d3.axisRight(y).tickFormat(d3.format(".0%")))
.call(g => g.select(".tick:first-of-type text").clone()
.attr("dy", "-1em")
.attr("text-anchor", "start")
.attr("font-weight", "bold")
.text("Votes counted"))
candidateName = raceOption === "president"
? presidentByState
.values()
.next()
.value.candidates.find(d => d.party_id === "republican").last_name
: "Republican"
leg = legend({
color,
title: `Percentage of incomplete results that match latest results (2020
${raceOption === "president" ? "presidential" : "state and presidential"}
elections)`,
tickFormat: d3.format(".0%")
})
d3 = require("d3")
import { legend } from "@d3/color-legend"
import { state, president, presidentByState } from "1342cadc9354cc67"
import {checkbox, slider, radio} from "@jashkenas/inputs"
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