margin = ({top: 10, right: 40, bottom: 30, left: 100})
brandsX = d3.scaleLinear()
.domain([0, d3.max(brandData, d => d.value)])
.range([margin.left, width - margin.right])
brandsY = d3.scaleBand()
.domain(brandsOfYear_top20.map(d => d.name))
.range([margin.top, height - margin.bottom])
.padding(0.1)
brandsColor = d3.scaleOrdinal()
.domain(brandData.map(d => d.category))
.range(d3.schemeAccent)
viewof year = {
const years = d3.extent(brandData, d => d.date.getFullYear());
return Scrubber(
d3.range(years[0], years[1] + 1, 1), // min to max years in 1 year increments
{ autoplay: false, delay: transitionDuration, loop: false } // experiment with these settings!
);
}
import {Scrubber} from "@mbostock/scrubber"
chartElement = {
const svg = d3.create("svg"); // selectAll("svg.canvas").data([1]).join("svg").attr("class", "canvas");
svg.attr("width", width);
svg.attr("height", height)
.attr("font-family", "sans-serif");
return svg.node();
}
{
const svg = d3.select(chartElement);
const marksArea = svg.selectAll("g.marksArea").data([1])
.join("g")
.attr("class", "marksArea");
const groups = marksArea.selectAll("g.datapoint")
.data(brandsOfYear_top20, d => d.name)
.join(
enter => enter.append("g")
.attr("class", "datapoint")
.attr("transform", (d) => `translate(${brandsX(0)}, ${brandsY.range()[1]})`)
.call(appendBars),
update => update.call(updateBars),
exit => exit.remove()
)
.call(
groups => groups.transition().duration(transitionDuration)
.attr("transform", (d) => `translate(${brandsX(0)}, ${brandsY(d.name)})`)
);
svg.selectAll("g.axisLeft").data([1])
.join("g")
.attr("class", "axisLeft")
.attr("transform", `translate(${margin.left}, 0)`)
.transition().duration(transitionDuration).ease(d3.easeLinear)
.call(d3.axisLeft(brandsY));
svg.selectAll("g.axisBottom").data([1])
.join("g")
.attr("class", "axisBottom")
.attr("transform", `translate(0, ${height - margin.bottom})`)
.transition().duration(transitionDuration)
.call(d3.axisBottom(brandsX));
return svg.node();
}
appendBars = (group) => {
const bars = group.append("rect")
.attr("height", brandsY.bandwidth())
.attr("width", (d) => brandsX(d.value) - brandsX(0))
.attr("fill", (d) => brandsColor(d.category));
bars.append("title")
.text(d => `${d.name} (${d.category}): ${d.value}`);
group.append("text")
.text(d => d.name)
.attr("x", d => brandsX(d.value) - brandsX(0) - 5)
.attr("y", brandsY.bandwidth() / 2)
.attr("text-anchor", "end")
.attr("dominant-baseline", "middle")
.attr("font-size", "11px");
}
updateBars = (group) => {
// update bar width
const bars = group.select("rect")
.call(b => b.transition().duration(transitionDuration)
.attr("width", (d) => brandsX(d.value) - brandsX(0))
);
// update hover tooltip
bars.select("title")
.text(d => `${d.name} (${d.category}): ${d.value}`);
// update label position
group.select("text").transition().duration(transitionDuration)
.attr("x", d => brandsX(d.value) - brandsX(0) - 5);
}
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.