barWidth = xBand.bandwidth()
barXPosition = d => xTime(d.year) - barWidth/2
lineXPosition = d => xTime(d.year)
xAxis = g => g
.attr("transform", `translate(0,${height - margin.bottom})`)
.call(d3.axisBottom(xTime)
.tickSizeOuter(0))
xTime = d3.scaleTime()
.domain(paddedExtent)
.rangeRound([margin.left, width - margin.right])
xBand = d3.scaleBand()
.domain(d3.timeYear.range(...xTime.domain()))
.rangeRound([margin.left, width - margin.right])
.padding(0.1)
// add one time unit of padding to make sure bars don't overlap with y-axis
paddedExtent = [
d3.min(dateData.map(d => d3.timeYear.offset(d.year, -1))),
d3.max(dateData.map(d => d3.timeYear.offset(d.year, 1)))
];
origData = Object.assign(d3.csvParse(await FileAttachment("new-passenger-cars.csv").text(), d3.autoType), {y1: "↑ New cars sold", y2: "Avg. fuel efficiency (mpg) ↑"})
dateData = origData.map(d => ({...d, year: new Date(d.year, 0, 1)}))
line = d3.line()
.x(lineXPosition)
.y(d => y2(d.efficiency))
y1 = d3.scaleLinear()
.domain([0, d3.max(dateData, d => d.sales)])
.rangeRound([height - margin.bottom, margin.top])
y2 = d3.scaleLinear()
.domain(d3.extent(dateData, d => d.efficiency))
.rangeRound([height - margin.bottom, margin.top])
y1Axis = g => g
.attr("transform", `translate(${margin.left},0)`)
.style("color", "steelblue")
.call(d3.axisLeft(y1).ticks(null, "s"))
.call(g => g.select(".domain").remove())
.call(g => g.append("text")
.attr("x", -margin.left)
.attr("y", 10)
.attr("fill", "currentColor")
.attr("text-anchor", "start")
.text(origData.y1))
y2Axis = g => g
.attr("transform", `translate(${width - margin.right},0)`)
.call(d3.axisRight(y2))
.call(g => g.select(".domain").remove())
.call(g => g.append("text")
.attr("x", margin.right)
.attr("y", 10)
.attr("fill", "currentColor")
.attr("text-anchor", "end")
.text(origData.y2))
height = 500
margin = ({top: 20, right: 30, bottom: 30, left: 40})
d3 = require("d3@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.