chart = {
const svg = d3.create("svg").attr("viewBox", `0, 0, ${width}, ${height}`);
const tooltip = d3.select("body").append("div")
.attr("class", "tooltip")
.style("position", "absolute")
.style("visibility", "hidden");
svg.selectAll("path")
.data(countries.features)
.enter()
.append("g")
.attr("class", "country-group")
.each(function (d) {
const countryGroup = d3.select(this);
// Append path element
countryGroup.append("path")
.attr("d", path)
.attr("stroke", "#111")
.attr("stroke-width", 0.5)
.on("mouseover", function (event, d) {
let text = d.properties.name
const mean_homo = d.properties.dataJoined.get(year)[0].mean_homo
const mean_homo_rounded = Math.round((mean_homo + Number.EPSILON) * 100) / 100
text = text + "\nJustifiability: " + mean_homo_rounded
showToolTip(text, [event.pageX, event.pageY])
})
.on("mousemove", function (event) {
d3.select(".tooltip")
.style("top", event.pageY - 10 + "px")
.style("left", event.pageX + 10 + "px")
})
.on("mouseout", function () {
d3.select(".tooltip").style("visibility", "hidden")
})
.attr("fill", d => d.properties.dataJoined?.get(year)
? cScale(d.properties.dataJoined.get(year)[0].mean_homo)
: "lightgrey");
// Append star symbol if when_legal equals the displayed year and not already added
const whenLegalYear = d.properties.dataJoined?.get(year) ? d.properties.dataJoined.get(year)[0].when_legal : null;
const symbolAdded = whenLegalYear !== null && whenLegalYear <= year;
console.log("Symbol Added:", symbolAdded); // Log the presence of the symbolAdded condition
if (symbolAdded) {
const [x, y] = path.centroid(d);
const countryArea = path.area(d);
const symbolSize = Math.log(countryArea)*2; // Adjust the scaling factor as needed
console.log("Symbol Size:", symbolSize); // Log the symbol size
countryGroup.append("text")
.attr("class", "symbol")
.attr("x", x)
.attr("y", y)
.text("\u2726") // Unicode for six-pointed star symbol
.attr("text-anchor", "middle")
.attr("alignment-baseline", "middle")
.style("font-size", `${symbolSize}px`)
.style("fill", "orange");
}
});
return svg.node();
};
Type JavaScript, then Shift-Enter. Ctrl-space for more options. Arrow ↑/↓ to switch modes.
showToolTip = (text, coords) => {
d3.select(".tooltip")
.text(text)
.style("top", coords[1] + "px")
.style("left", coords[0] + "px")
.style("visibility", "visible");
}
cScale = d3.scaleLinear()
.domain([
1,
10])
.range([-1, 0, 1])
.interpolate((a, b) => a < 0
? t => d3.interpolateBlues(t)
: t => d3.interpolateReds(1-t))
outline = ({ type: "Sphere" })
projection = d3.geoNaturalEarth1()
path = d3.geoPath(projection)
height = {
const [[x0, y0], [x1, y1]] = d3.geoPath(projection.fitWidth(width, outline)).bounds(outline);
const dy = Math.ceil(y1 - y0), l = Math.min(Math.ceil(x1 - x0), dy);
projection.scale(projection.scale() * (l - 1) / l).precision(0.2);
return dy;
}
countries = {
// group entities by name and year
const entities = d3.group(dataJoined, d => d.country, d => d.year);
// attach data to each country in properties
const countries = topojson.feature(world, world.objects.countries);
countries.features.forEach(country => {
country.properties.dataJoined = entities.get(country.properties.name);
})
return countries
}
data = d3.csvParse(await FileAttachment("Interpolated_Data_Cleaned.csv").text(), d => {
if (d.country === "United States") {
d.country = "United States of America"
}
return {
country: d.Country,
year: +d.Year,
wave: +d.Wave,
country_code: +d.Country_Code,
mean_homo: +d.mean_homo }
} )
// Assuming your second CSV file is named "legality_1.csv"
legalityData = await FileAttachment("legality_1@1.csv").csv({typed: true});
// Merge data from the second CSV into the original data array
dataJoined = data.map(d => {
const matchingRow = legalityData.find(row => row.Country === d.country);
// Add the new property 'when_legal' based on the additional data
return {
...d,
when_legal: matchingRow ? +matchingRow.Year : null
}
});
// Now, each object in the output array has a new 'when_legal' property based on the legality data
{for (let i = 0; i < data.length; i++) {
data[i].Year += cars[i] + "<br>";
}}
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.