CS PhD student at UAM, interested in: DataViz, XAI, ML, RecSys, NLP and Big Data.
chart = {
const svg = d3.create("svg")
.attr("viewBox", [0, 0, width, height]);
svg.append("g")
.attr("fill-opacity", 0.8)
.selectAll("path")
.data(series)
.join("path")
.attr("fill", ({key}) => color(regionByState.get(key)))
.attr("d", area)
.append("title")
.text(({key}) => key);
svg.append("g")
.attr("fill", "none")
.attr("stroke-width", 0.75)
.selectAll("path")
.data(series)
.join("path")
.attr("stroke", ({key}) => d3.lab(color(regionByState.get(key))).darker())
.attr("d", area.lineY1());
svg.append("defs")
.selectAll("path")
.data(series)
.join("path")
.attr("id", d => (d.id = DOM.uid("state")).id)
.attr("d", midline);
svg.append("g")
.style("font", "10px sans-serif")
.attr("text-anchor", "middle")
.selectAll("text")
.data(series)
.join("text")
.attr("dy", "0.35em")
.append("textPath")
.attr("href", d => d.id.href)
.attr("startOffset", d => `${Math.max(0.05, Math.min(0.95, d.offset = d3.maxIndex(d, d => d[1] - d[0]) / (d.length - 1))) * 100}%`)
.text(d => d.key);
svg.append("g")
.call(xAxis);
svg.append("g")
.call(yAxis);
return svg.node();
}
data = {
const years = d3.range(1790, 2000, 10);
const states = d3.tsvParse(await FileAttachment("population.tsv").text(), (d, i) => i === 0 ? null : ({name: d[""], values: years.map(y => +d[y].replace(/,/g, "") || 0)}));
states.sort((a, b) => d3.ascending(regionRank.indexOf(regionByState.get(a.name)), regionRank.indexOf(regionByState.get(b.name))) || d3.descending(d3.sum(a.values), d3.sum(b.values)));
return Object.assign(years.map((y, i) => Object.fromEntries([["date", new Date(Date.UTC(y, 0, 1))]].concat(states.map(s => [s.name, s.values[i]])))), {columns: ["date", ...states.map(s => s.name)]});
}
series = d3.stack()
.keys(data.columns.slice(1))
.offset(d3.stackOffsetExpand)
(data)
regionRank = [
"New England",
"Middle Atlantic",
"South Atlantic",
"East South Central",
"West South Central",
"East North Central",
"West North Central",
"Mountain",
"Pacific"
]
regionByState = {
const regions = await d3.csv("https://raw.githubusercontent.com/cphalpert/census-regions/7bdc6aa1cb0892361e90ce9ad54983041c2ad015/us%20census%20bureau%20regions%20and%20divisions.csv");
return new Map(regions.map(d => [d.State, d.Division]));
}
area = d3.area()
.x(d => x(d.data.date))
.y0(d => y(d[0]))
.y1(d => y(d[1]))
midline = d3.line()
.curve(d3.curveBasis)
.x(d => x(d.data.date))
.y(d => y((d[0] + d[1]) / 2))
x = d3.scaleUtc()
.domain(d3.extent(data, d => d.date))
.range([margin.left, width - margin.right])
y = d3.scaleLinear()
.range([height - margin.bottom, margin.top])
color = d3.scaleOrdinal()
.domain(regionRank)
.range(d3.schemeCategory10)
.unknown("gray")
xAxis = g => g
.attr("transform", `translate(0,${height - margin.bottom})`)
.call(d3.axisBottom(x).ticks(width / 80).tickSizeOuter(0))
yAxis = g => g
.attr("transform", `translate(${margin.left},0)`)
.call(d3.axisLeft(y).ticks(10, "%"))
.call(g => g.select(".domain").remove())
height = 720
margin = ({top: 10, right: 10, bottom: 30, left: 40})
d3 = require("d3@5", "d3-array@2")
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.