//import d3 library
d3 = require("d3@5")
//import migration data and parse values as numbers
migration = d3.csv("https://raw.githubusercontent.com/sw2279/data/b3ad3a358eabc4c40670d9ca5d83d762cd79e10b/migration_forbarchart.csv", ({YEARS, Acquisition, In_asylum, In_foreign, Out_foreign, Stock_foreign_labor,Stock_foreign_pop}) => ({YEARS: +YEARS, Acquisition: +Acquisition, In_asylum: +In_asylum, In_foreign: +In_foreign, Out_foreign: +Out_foreign, Stock_foreign_labor: +Stock_foreign_labor, Stock_foreign_pop: +Stock_foreign_pop}))
//example graph
//set margins for the graph display; took defaults from stacked bar graph link above
margin = ({top: 10, right: 10, bottom: 20, left: 40})
//set height for graph display
height = 600
// your code here
chart = {
//initiate SVG
const svg = d3.select(DOM.svg(width, height));
//set attributes for svg including the shapes (rectangles), axis, height, width
svg.append("g")
.selectAll("g")
.data(series)
.join("g")
.attr("fill", d => color(d.key))
.selectAll("rect")
.data(d => d)
.join("rect")
.attr("x", (d, i) => x(d.data.YEARS))
.attr("y", d => y(d[1]))
.attr("height", d => y(d[0]) - y(d[1]))
.attr("width", x.bandwidth());
//add on the axes labels and legend
svg.append("g")
.call(xAxis);
svg.append("g")
.call(yAxis);
svg.append("g")
.call(legend);
//return the graph
return svg.node();
}
//reshapes the df such that each object in the array now represents all the values for a variable (acquisition, in-flows, etc) rather than for a year
series = d3.stack().keys(migration.columns.slice(1))(migration)
//scale x-axis values (which reflect years)
x = d3.scaleBand()
.domain(migration.map(d => d.YEARS))
.range([margin.left, width - margin.right-150])
.padding(.2)
//scale y-axis values (migration numbers))
y = d3.scaleLinear()
.domain([0, d3.max(series, d => d3.max(d, d => d[1]))])
.rangeRound([height - margin.bottom, margin.top])
.nice()
// define colors
color = d3.scaleOrdinal()
.domain(series.map(d => d.key)) //maps
.range(d3.quantize(t => d3.interpolateSpectral(t * 0.9 + 0.1), series.length).reverse())
.unknown("#ccc")
// set x-axis ticks, labels
xAxis = g => g
.attr("transform", `translate(0,${height - margin.bottom})`)
.call(d3.axisBottom(x).tickSizeOuter(0))
.call(g => g.selectAll(".domain").remove())
// set y-axis ticks, labels
yAxis = g => g
.attr("transform", `translate(${margin.left},0)`)
.call(d3.axisLeft(y).ticks(null, "s"))
.call(g => g.selectAll(".domain").remove())
legend = svg => {
//sets format for legend
const g = svg
.attr("font-family", "sans-serif")
.attr("font-size", 12)
.attr("text-anchor", "end")
.attr("transform", `translate(${width - margin.right},${margin.top})`)
.selectAll("g")
.data(series.slice().reverse())
.join("g")
.attr("transform", (d, i) => `translate(0,${i * 25})`);
//shape and location of legend
g.append("rect")
.attr("x", -19)
.attr("width", 19)
.attr("height", 19)
.attr("fill", d => color(d.key));
//shape and location of legend text
g.append("text")
.attr("x", -24)
.attr("y", 9.5)
.attr("dy", "0.35em")
.text(d => d.key);
}
// see Stellar submissions for files
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.