I'm a professor of mathematics at the University of North Carolina Asheville. I've also done a fair amount of consulting work over the years focusing on scientific and data visualization.
I'm happy to be part of the first Observable Ambassador's cohort.
Published unlisted
Edited
Apr 20, 2022
chart = {
let xmin = 0.45;
let xmax = 0.65;
let ymin = 0.47;
let ymax = 1;
let w = 0.9 * width;
let h = w * 0.6;
// h = (w * (ymax - ymin)) / (xmax - xmin);
let pad = 35;
let xScale = d3
.scaleLinear()
.domain([xmin, xmax])
.range([pad, w - pad]);
let yScale = d3
.scaleLinear()
.domain([ymin, ymax])
.range([h - pad, pad]);
let div = d3.create("div").style("width", `${w}px`).style("height", `${h}px`);
let chart = div.append("svg").attr("width", w).attr("height", h);
let lines = chart.append("g");
lines
.append("line")
.attr("x1", xScale(0.5))
.attr("x2", xScale(0.5))
.attr("y1", yScale(ymin))
.attr("y2", yScale(ymax))
.style("stroke", "black")
.style("stroke-width", 1)
.attr("stroke-dasharray", "2 3");
let pts = [];
let x0 = 0.5006;
let y0 = 0.58;
let circles = chart.append("g");
for (let [year, d] of data) {
let all_electoral_votes = d.map((o) => parseInt(o["Electoral Votes"]));
let electoral_1st = all_electoral_votes[0];
let electoral_2nd = all_electoral_votes[1];
let total_electoral_votes = electoral_1st + electoral_2nd;
let electoral_winner_electoral_proportion =
electoral_1st / total_electoral_votes;
let all_popular_votes = d.map((o) => parseInt(o["Popular Votes"]));
let electoral_1st_popular_vote = all_popular_votes[0];
let electoral_2nd_popular_vote = all_popular_votes[1];
let total_popular_votes =
electoral_1st_popular_vote + electoral_2nd_popular_vote;
let electoral_winner_popular_proportion =
electoral_1st_popular_vote / total_popular_votes;
let x = electoral_winner_popular_proportion;
let y = electoral_winner_electoral_proportion;
pts.push([x, y]);
let close_class;
if (Math.abs(x - x0) < 0.005 && Math.abs(y - y0) < 0.005) {
close_class = "close";
} else {
close_class = "far";
}
let circle = circles
.append("circle")
.attr("class", close_class)
.attr("cx", xScale(x))
.attr("cy", yScale(y))
.attr("r", 3)
.style("fill", "#ccc")
.style("stroke", "black")
.style("stroke-width", "0.5px")
.on("mouseenter", function () {
d3.select(this).attr("r", 5).style("stroke-width", "1.5px");
})
.on("mouseleave", function () {
d3.select(this).attr("r", 3).style("stroke-width", "0.5px");
});
tippy(circle.node(), {
allowHTML: true,
content: `
<div>${year}</div>
<div>Winner: ${d[0]["Candidate"]}</div>
<div style="margin-left: 10px">Popular percentage: ${d3.format(
"0.2%"
)(x)}</div>
<div style="margin-left: 10px">Electoral percentage: ${d3.format(
"0.2%"
)(y)}</div>
<div>Loser: ${d[1]["Candidate"]}</div>`
});
}
chart.selectAll(".close").each(function (_, i) {
let c = d3.select(this);
let this_y = c.attr("cy");
c.attr("cy", parseFloat(this_y) + (-1) ** i * 4);
});
let regress = d3.regressionLinear().domain([xmin, xmax])(pts);
let active = true;
lines
.append("line")
.attr("x1", xScale(regress[0][0]))
.attr("x2", xScale(regress[1][0]))
.attr("y1", yScale(regress[0][1]))
.attr("y2", yScale(regress[1][1]))
.style("stroke", "black")
.style("stroke-width", 1);
// chart
// .append('line')
// .attr('x1', xScale(regress[0][0]))
// .attr('x2', xScale(regress[1][0]))
// .attr('y1', yScale(regress[0][1]))
// .attr('y2', yScale(regress[1][1]))
// .style('stroke', 'black')
// .style('stroke-width', 3)
// .style('opacity', 0);
// chart
// .append('circle')
// .attr('cx', xScale(x0))
// .attr('cy', yScale(y0))
// .attr('r', 12)
// .attr('fill', 'yellow')
// .attr('stroke', 'black')
// .attr('opacity', 0.2)
// .on('mouseenter', function() {
// chart.selectAll('.close').each(function(_, i) {
// let c = d3.select(this);
// let this_y = c.attr('cy');
// c.attr('cy', parseFloat(this_y) + (-1) ** i * 4);
// });
// });
chart
.append("g")
.attr("transform", `translate(0, ${h - pad})`)
.call(d3.axisBottom(xScale));
chart
.append("g")
.attr("transform", `translate(${pad})`)
.call(d3.axisLeft(yScale));
let tree = d3.quadtree(pts);
console.log(tree.find(0.5, 0.5, 1));
chart
.on("mousemove", function (evt) {
chart.selectAll(".predict").remove();
let mouse_x = xScale.invert(evt.layerX);
let mouse_y = yScale.invert(evt.layerY);
if (tree.find(mouse_x, mouse_y, 0.01) == undefined) {
let predict_y = regress.predict(mouse_x);
if (Math.abs(mouse_y - predict_y) < 0.01) {
console.log(mouse_x);
chart
.append("circle")
.attr("class", "predict")
.attr("cx", evt.layerX)
.attr("cy", yScale(predict_y))
.attr("r", 3)
.style("fill", "#d00")
.style("stroke", "black")
.style("stroke-width", "1px");
chart
.append("text")
.attr("class", "predict")
.attr("pointer-events", "none")
.attr("x", evt.layerX)
.attr("y", yScale(predict_y))
.attr("text-anchor", evt.layerX < 0.7 * w ? "start" : "end")
.text(
`input: ${d3.format("0.3f")(
xScale.invert(evt.layerX)
)}, prediction: ${d3.format("0.3f")(yScale.invert(evt.layerY))}`
);
}
}
})
.on("mouseleave", function () {
chart.selectAll(".predict").remove();
});
div.attr("a", regress.a);
div.attr("b", regress.b);
return div.node();
}
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