chart = {
const svg = d3.select(DOM.svg(width, height));
svg.append("g")
.selectAll("g")
.data(data)
.join("g")
.attr("transform", d => `translate(${x0(d[groupKey])},0)`)
.selectAll("rect")
.data(d => keys.map(key => ({key, value: d[key]})))
.join("rect")
.attr("x", d => x1(d.key))
.attr("y", d => y(d.value))
.attr("width", x1.bandwidth())
.attr("height", d => y(0) - y(d.value))
.attr("fill", d => color(d.key));
svg.append("g")
.call(xAxis);
svg.append("g")
.call(yAxis);
svg.append("g")
.call(legend);
return svg.node();
}
legend = svg => {
const g = svg
.attr("transform", `translate(${width},0)`)
.attr("text-anchor", "end")
.attr("font-family", "sans-serif")
.attr("font-size", 10)
.selectAll("g")
.data(color.domain().slice().reverse())
.join("g")
.attr("transform", (d, i) => `translate(0,${i * 20})`);
g.append("rect")
.attr("x", -19)
.attr("width", 19)
.attr("height", 19)
.attr("fill", color);
g.append("text")
.attr("x", -24)
.attr("y", 9.5)
.attr("dy", "0.35em")
.text(d => d);
}
x0 = d3.scaleBand()
.domain(data.map(d => d[groupKey]))
.rangeRound([margin.left, width - margin.right])
.paddingInner(0.1)
x1 = d3.scaleBand()
.domain(keys)
.rangeRound([0, x0.bandwidth()])
.padding(0.05)
y = d3.scaleLinear()
.domain([0, d3.max(data, d => d3.max(keys, key => d[key]))]).nice()
.rangeRound([height - margin.bottom, margin.top])
color = d3.scaleOrdinal()
.range(["#98abc5", "#8a89a6", "#7b6888", "#6b486b"])
xAxis = g => g
.attr("transform", `translate(0,${height - margin.bottom})`)
.call(d3.axisBottom(x0).tickSizeOuter(0))
.call(g => g.select(".domain").remove())
yAxis = g => g
.attr("transform", `translate(${margin.left},0)`)
.call(d3.axisLeft(y).ticks(null, "s"))
.call(g => g.select(".domain").remove())
.call(g => g.select(".tick:last-of-type text").clone()
.attr("x", 3)
.attr("text-anchor", "start")
.attr("font-weight", "bold")
.text(data.y))
data = Object.assign(d3.csvParse(await FileAttachment("Transit - Sheet1.csv").text(), d3.autoType), {y: "Percentage"})
groupKey = data.columns[0]
keys = data.columns.slice(1)
margin = ({top: 10, right: 10, bottom: 20, left: 40})
height = 500
width = 700
d3 = require("d3@5")
var margin = {top: 20, right: 20, bottom: 30, left: 40},
width = 660 - margin.left - margin.right,
height = 500 - margin.top - margin.bottom;
/*
* value accessor - returns the value to encode for a given data object.
* scale - maps value to a visual display encoding, such as a pixel position.
* map function - maps from data value to display value
* axis - sets up axis
*/
// setup x
var xValue = function(d) { return d.Calories;}, // data -> value
xScale = d3.scale.linear().range([0, width]), // value -> display
xMap = function(d) { return xScale(xValue(d));}, // data -> display
xAxis = d3.svg.axis().scale(xScale).orient("bottom");
// setup y
var yValue = function(d) { return d["Protein (g)"];}, // data -> value
yScale = d3.scale.linear().range([height, 0]), // value -> display
yMap = function(d) { return yScale(yValue(d));}, // data -> display
yAxis = d3.svg.axis().scale(yScale).orient("left");
// setup fill color
var cValue = function(d) { return d.Manufacturer;},
color = d3.scale.category10();
// add the graph canvas to the body of the webpage
var svg = d3.select("body").append("svg")
.attr("width", width + margin.left + margin.right)
.attr("height", height + margin.top + margin.bottom)
.append("g")
.attr("transform", "translate(" + margin.left + "," + margin.top + ")");
// add the tooltip area to the webpage
var tooltip = d3.select("body").append("div")
.attr("class", "tooltip")
.style("opacity", 0);
// load data
d3.csv("cereal.csv", function(error, data) {
// change string (from CSV) into number format
data.forEach(function(d) {
d.Calories = +d.Calories;
d["Protein (g)"] = +d["Protein (g)"];
// console.log(d);
});
// don't want dots overlapping axis, so add in buffer to data domain
xScale.domain([d3.min(data, xValue)-1, d3.max(data, xValue)+1]);
yScale.domain([d3.min(data, yValue)-1, d3.max(data, yValue)+1]);
// x-axis
svg.append("g")
.attr("class", "x axis")
.attr("transform", "translate(0," + height + ")")
.call(xAxis)
.append("text")
.attr("class", "label")
.attr("x", width)
.attr("y", -6)
.style("text-anchor", "end")
.text("Calories");
// y-axis
svg.append("g")
.attr("class", "y axis")
.call(yAxis)
.append("text")
.attr("class", "label")
.attr("transform", "rotate(-90)")
.attr("y", 6)
.attr("dy", ".71em")
.style("text-anchor", "end")
.text("Protein (g)");
// draw dots
svg.selectAll(".dot")
.data(data)
.enter().append("circle")
.attr("class", "dot")
.attr("r", 3.5)
.attr("cx", xMap)
.attr("cy", yMap)
.style("fill", function(d) { return color(cValue(d));})
.on("mouseover", function(d) {
tooltip.transition()
.duration(200)
.style("opacity", .9);
tooltip.html(d["Cereal Name"] + "<br/> (" + xValue(d)
+ ", " + yValue(d) + ")")
.style("left", (d3.event.pageX + 5) + "px")
.style("top", (d3.event.pageY - 28) + "px");
})
.on("mouseout", function(d) {
tooltip.transition()
.duration(500)
.style("opacity", 0);
});
// draw legend
var legend = svg.selectAll(".legend")
.data(color.domain())
.enter().append("g")
.attr("class", "legend")
.attr("transform", function(d, i) { return "translate(0," + i * 20 + ")"; });
// draw legend colored rectangles
legend.append("rect")
.attr("x", width - 18)
.attr("width", 18)
.attr("height", 18)
.style("fill", color);
// draw legend text
legend.append("text")
.attr("x", width - 24)
.attr("y", 9)
.attr("dy", ".35em")
.style("text-anchor", "end")
.text(function(d) { return d;})
});
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.
