Vote Cone — UninterpolatedVote Cone TransitionsReflecting on “Vote Cones”Vote Cones in Vega-LiteElection 2020: How reliable are different results at different stages?Election 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 Labeled ContoursCalculate the percentage of remaining vote needed to overcome given absolute margin
Election 2020 Cone with Colored Contours
// outcome = svg`<svg width="${width}" height="45">
// <path
// d="${`M 0 0 L ${x(final)} 0 V 25 H 0 Z`}"
// fill="${color1}" stroke="white" />
// <path
// d="${`M ${x(final)} 0 L ${width} 0 V 25 H ${x(
// final
// )} Z`}" fill="${color2}" stroke="white" />
// <text text-anchor="middle" font-family="sans-serif" font-size="14" font-weight="bold" x="${x(
// final
// )}" y="25" dy="1.2em" fill="${final > .5 ? color1 : color2}">${
// final > .5 ? `${a.name_display} wins` : `${b.name_display} wins`
// }</text>
// </svg>`
// file = FileAttachment("florida@1.json").json()
// file = FileAttachment("arizona.json").json()
// file = FileAttachment("ohio.json").json()
// file = FileAttachment("pennsylvania.json").json()
// file = FileAttachment("iowa@1.json").json()
// file = FileAttachment("texas.json").json()
// file = FileAttachment("arizona (1).json").json()
// file = FileAttachment("pennsylvania (1).json").json()
// file = FileAttachment("massachusetts.json").json()
// file = FileAttachment("new-york.json").json()
// file = FileAttachment("north-dakota.json").json()
// file = FileAttachment("michigan.json").json()
// file = FileAttachment("michigan (1).json").json()
// file = FileAttachment("nevada.json").json()
// file = FileAttachment("nevada (1).json").json()
// file = FileAttachment("wisconsin.json").json()
// file = FileAttachment("georgia.json").json()
// file = FileAttachment("north-carolina.json").json()
// file = FileAttachment("florida@2.json").json()
// file = FileAttachment("wisconsin (1).json").json()
// file = FileAttachment("michigan (2).json").json()
// file = FileAttachment("pennsylvania (2).json").json()
// file = FileAttachment("georgia (1).json").json()
// file = FileAttachment("georgia@1.json").json()
// file = FileAttachment("arizona@1.json").json()
// file = FileAttachment("nevada@1.json").json()
file = FileAttachment("pennsylvania@1.json").json()
// file = FileAttachment("oregon.json").json()
a = file.data.races[0].candidates.find(d => d.party_id === "republican")
b = file.data.races[0].candidates.find(d => d.party_id === "democrat")
stateName = file.data.races[0].state_name
timeseries = file.data.races[0].timeseries.map(d => ({
x:
d.vote_shares[a.candidate_key] /
(d.vote_shares[a.candidate_key] + d.vote_shares[b.candidate_key]) || 0.5,
y: d.eevp / 100,
t: new Date(d.timestamp)
}))
final = timeseries.slice(-1)[0].x
color1 = "#dd2c35"
color2 = "#0080c9"
counties = []
// how's this file different??
(await FileAttachment("summary.json").json()).races[0].timeseries
accumulate = arr => {
const result = [];
const _N = d3.sum(arr, ({ n }) => n);
let _a = 0;
let _n = 0;
arr.forEach(function({ a, n }) {
_a += a;
_n += n;
result.push({ x: _a / _n, y: _n / _N });
});
return result;
}
getBest = data =>
accumulate(data.slice().sort((a, b) => d3.descending(a.a / a.n, b.a / b.n)))
getWorst = data =>
accumulate(data.slice().sort((a, b) => d3.ascending(a.a / a.n, b.a / b.n)))
bestRun = getBest(counties)
worstRun = getWorst(counties)
line = d3
.line()
.x(d => x(d.x))
.y(d => y(d.y))
function halo(text) {
text
.select(function() {
return this.parentNode.insertBefore(this.cloneNode(true), this);
})
.attr("fill", "none")
.attr("stroke", "white")
.attr("stroke-width", 4)
.attr("stroke-linejoin", "round");
}
renderHypotheticals = g =>
g
.selectAll("g.hypothetical")
.data([bestRun, worstRun])
.join("g")
.attr("class", "hypothetical")
.each(function(d, i) {
const uid = DOM.uid("hypo");
const sel = d3.select(this);
const path = sel
.append("path")
.attr("id", uid.id)
.attr("stroke-dasharray", "3,3")
.attr("d", line(i ? d : d.slice().reverse()));
sel
.append("text")
.attr("x", d => path.node().getTotalLength() / 2)
.attr("dy", 15)
.attr("fill", "white")
.attr("stroke", "none")
.attr("text-anchor", "middle")
.append("textPath")
.attr("href", uid.href)
.text(`If ${(i ? b : a).last_name} counties reported first`);
})
timeTicks = [
d3.min(timeseries, d => d.t),
...d3.timeHour
.range(...d3.extent(timeseries, d => d.t))
.filter(d => timeScale(d) < .99)
]
timeScale = d3.scaleTime(timeseries.map(d => d.t), timeseries.map(d => d.y))
timeFormat = d3.timeFormat("%I:%M %p")
timeAxis = g =>
g
.attr("fill", "white")
.selectAll("g")
.data(timeTicks)
.join("g")
.attr("transform", d => `translate(${width}, ${y(timeScale(d))})`)
.call(g =>
g
.append("line")
.attr("stroke", "white")
.attr("x2", -5)
)
.call(g =>
g
.append("text")
.attr("text-anchor", "end")
.attr("x", -7)
.attr("dy", "0.3em")
.text(d => timeFormat(d))
)
pctAxis = g =>
g
.attr("fill", "white")
.selectAll("g")
.data([.25, .5, .75])
.join("g")
.attr("transform", d => `translate(0, ${y(d)})`)
.call(g =>
g
.append("line")
.attr("stroke", "white")
.attr("x2", 5)
)
.call(g =>
g
.append("text")
.attr("x", 7)
.attr("dy", "0.3em")
.text(d => pct(d))
)
pct = d3.format(".0%")
// currentShare ∈ [0,1] fraction of votes so far cast for Party A
// votesCounted ∈ [0,1] fraction of total votes that have been counted
// shareNeeded ∈ [0,1] fraction of remaining votes needed for Party A to win
value = (currentShare, votesCounted) =>
votesCounted === 1
? currentShare > .5
? -Infinity
: currentShare < .5
? Infinity
: NaN
: (currentShare * votesCounted - 0.5) / (votesCounted - 1)
_color = d3.scaleSequential(
[0, 1],
d3.interpolateRgb.gamma(2.2)(color1, color2)
)
color = d =>
d >= 1
? `url(${window.location.href}#checkerboard2)`
: d < 0
? `url(${window.location.href}#checkerboard1)`
: _color(d)
defs = `
${makeCheckerboard("checkerboard1", color1, checkSize)}
${makeCheckerboard("checkerboard2", color2, checkSize)}
${makeCheckerboard("smallCheckerboard1", color1, 5)}
${makeCheckerboard("smallCheckerboard2", color2, 5)}
</pattern>
`
makeCheckerboard = (name, color, checkSize) => `
<pattern id="${name}" patternUnits="userSpaceOnUse" width="${checkSize *
2}" height="${checkSize * 2}">
<rect width="${checkSize * 2}" height="${checkSize *
2}" x="0" y="0" fill="#ddd" />
<rect width="${checkSize * 2}" height="${checkSize *
2}" x="0" y="0" fill="${color}" opacity="0.3"/>
<rect width="${checkSize}" height="${checkSize}" x="0" y="0" fill="${color}" opacity="${
checkSize < 10 ? 1 : 0.2
}" />
<rect width="${checkSize}" height="${checkSize}" x="${checkSize}" y="${checkSize}" fill="${color}" opacity="${
checkSize < 10 ? 1 : 0.2
}" />`
checkSize = 16
thresholds = [-Infinity, ...d3.range(0, 1, .1), 1]
grid = {
const q = 4; // The level of detail, e.g., sample every 4 pixels in x and y.
const x0 = x.range()[0] - q / 2,
x1 = x.range()[1] + q;
const y0 = y.range()[0] - q / 2,
y1 = y.range()[1] + q;
const n = Math.ceil((x1 - x0) / q);
const m = Math.ceil((y1 - y0) / q);
const grid = new Array(n * m);
for (let j = 0; j < m; ++j) {
for (let i = 0; i < n; ++i) {
grid[j * n + i] = value(x.invert(i * q + x0), y.invert(j * q + y0));
}
}
grid.x = -q;
grid.y = -q;
grid.k = q;
grid.n = n;
grid.m = m;
return grid;
}
// Converts from grid coordinates (indexes) to screen coordinates (pixels).
transform = ({type, value, coordinates}) => {
return {type, value, coordinates: coordinates.map(rings => {
return rings.map(points => {
return points.map(([x, y]) => ([
grid.x + grid.k * x,
grid.y + grid.k * y
]));
});
})};
}
contours = d3
.contours()
.size([grid.n, grid.m])
.thresholds(thresholds)(grid)
.map(transform)
x = d3.scaleLinear([0, 1], [0, width])
y = d3.scaleLinear([0, 1], [0, height])
height = Math.floor((width + 28) / 4) * 4
d3 = require("d3@6")
import { ramp } from "@d3/color-legend"
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
