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Aug 22, 2023
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Walmart’s growth
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chart = {
const svg = d3.create("svg")
.attr("viewBox", [0, 0, 960, 600]);
svg.append("path")
.datum(topojson.merge(us, us.objects.lower48.geometries))
.attr("fill", "#ddd")
.attr("d", d3.geoPath());
svg.append("path")
.datum(topojson.mesh(us, us.objects.lower48, (a, b) => a !== b))
.attr("fill", "none")
.attr("stroke", "white")
.attr("stroke-linejoin", "round")
.attr("d", d3.geoPath());
const g = svg.append("g")
.attr("fill", "none")
.attr("stroke", "black");
const dot = g.selectAll("circle")
.data(data)
.join("circle")
.attr("transform", d => `translate(${d})`);
svg.append("circle")
.attr("fill", "blue")
.attr("transform", `translate(${data[0]})`)
.attr("r", 3);
let previousDate = -Infinity;
return Object.assign(svg.node(), {
update(date) {
dot // enter
.filter(d => d.date > previousDate && d.date <= date)
.transition().attr("r", 3);
dot // exit
.filter(d => d.date <= previousDate && d.date > date)
.transition().attr("r", 0);
previousDate = date;
}
});
}
update = chart.update(date)
data = (await FileAttachment("walmart.tsv").tsv())
.map(d => {
const p = projection(d);
p.date = parseDate(d.date);
return p;
})
.sort((a, b) => a.date - b.date)
parseDate = d3.utcParse("%m/%d/%Y")
projection = d3.geoAlbersUsa().scale(1280).translate([480, 300])
us = {
const us = await d3.json("https://cdn.jsdelivr.net/npm/us-atlas@1/us/10m.json");
us.objects.lower48 = {
type: "GeometryCollection",
geometries: us.objects.states.geometries.filter(d => d.id !== "02" && d.id !== "15")
};
return us;
}
import {Scrubber} from "@mbostock/scrubber"
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.
