chart = {
// Specify the chart’s dimensions.
const width = 928;
const height = 500;
const marginTop = 20;
const marginRight = 30;
const marginBottom = 34;
const marginLeft = 0;
const yearStep = 1;
const yearMin = d3.min(data, d => d.year);
// Construct the scales.
const x = d3.scaleBand()
.domain(Array.from(d3.group(data, d => d.age).keys()).sort(d3.ascending))
.range([width - marginRight, marginLeft]);
const y = d3.scaleLinear()
.domain([0, d3.max(data, d => d.value)])
.range([height - marginBottom, marginTop]);
const color = d3.scaleOrdinal(["M", "F"], ["#4e79a7", "#e15759"]);
// Create the SVG container.
const svg = d3.create("svg")
.attr("viewBox", `0 0 ${width} ${height}`)
.attr("width", width)
.attr("height", height)
.attr("style", `max-width: 100%; height: auto;`);
// Add the axes.
svg.append("g")
.attr("transform", `translate(0,${height - marginBottom})`)
.call(d3.axisBottom(x)
.tickValues(d3.ticks(...d3.extent(data, d => d.age), width / 40))
.tickSizeOuter(0))
.call(g => g.append("text")
.attr("x", marginRight)
.attr("y", marginBottom - 4)
.attr("fill", "currentColor")
.attr("text-anchor", "end")
.text("← Age"));
svg.append("g")
.attr("transform", `translate(${width - marginRight},0)`)
.call(d3.axisRight(y).ticks(null, "s"))
.call(g => g.select(".domain").remove())
.call(g => g.append("text")
.attr("x", marginRight)
.attr("y", 10)
.attr("fill", "currentColor")
.attr("text-anchor", "end")
.text("Population ↑"));
const group = svg.append("g");
let rect = group.selectAll("rect");
return Object.assign(svg.node(), {
update(year) {
const dx = x.step() * (year - yearMin) / yearStep;
const t = svg.transition()
.ease(d3.easeLinear)
.duration(delay);
rect = rect
.data(data.filter(d => d.year === year), d => `${d.sex}:${d.year - d.age}`)
.join(
enter => enter.append("rect")
.style("mix-blend-mode", "darken")
.attr("fill", d => color(d.sex))
.attr("x", d => x(d.age) + dx)
.attr("y", d => y(0))
.attr("width", x.bandwidth() + 1)
.attr("height", 0),
update => update,
exit => exit.call(rect => rect.transition(t).remove()
.attr("y", y(0))
.attr("height", 0))
);
rect.transition(t)
.attr("y", d => y(d.value))
.attr("height", d => y(0) - y(d.value));
group.transition(t)
.attr("transform", `translate(${-dx},0)`);
},
scales: {color}
});
}
chart.update(year)
delay = 250 // controls both the speed of the scrubber and the transition’s duration
Plot.plot({
width: 960,
height: 500,
x: { reverse: true, line: true, transform: d => d - 0.45, tickSpacing: 30, label: "← Age", labelAnchor: "left" },
y: { label: "Population ↑", tickFormat: "s", axis: "right" },
color: {
legend: true,
domain: ["M", "F"],
range: ["#4e79a7", "#e15759"],
tickFormat: (d) => (d === "M" ? "Male" : "Female")
},
marks: [
Plot.rectY(data, {
filter: (d) => d.year === year,
x: "age",
interval: 1,
y2: "value",
fill: "sex",
mixBlendMode: "darken"
})
]
})
data = FileAttachment("icelandic-population.csv").csv({typed: true})
import {Scrubber} from "@mbostock/scrubber"
import {swatches} 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.
