Calendar
chart = {
const cellSize = 16; // height of a day
const height = cellSize * 7; // height of a week (5 days + padding)
const width = (cellSize + 1.5) * 53; // width of the chart
// Define formatting functions for the axes and tooltips.
const formatValue = d3.format("+.2%");
const formatClose = d3.format("$,.2f");
const formatDate = d3.utcFormat("%x");
const formatDay = i => "SMTWTFS"[i];
const formatMonth = d3.utcFormat("%b");
// Helpers to compute a day’s position in the week.
const timeWeek = d3.utcMonday;
const countDay = i => (i + 6) % 7;
// Compute the values used to color the cells: percent change is the difference between the day’s
// closing value and the previous day’s, as a fraction of the latter.
const data = d3.pairs(dji, ({Close: Previous}, {Date, Close}) => ({
date: Date,
value: (Close - Previous) / Previous,
close: Close
}));
// Compute the extent of the value, ignore the outliers
// and define a diverging and symmetric color scale.
const max = d3.quantile(data, 0.9975, d => Math.abs(d.value));
const color = d3.scaleSequential(d3.interpolatePiYG).domain([-max, +max]);
// Group data by year, in reverse input order. (Since the dataset is chronological,
// this will show years in reverse chronological order.)
const years = d3.groups(data, d => d.date.getUTCFullYear()).reverse();
// A function that draws a thin white line to the left of each month.
function pathMonth(t) {
const d = Math.max(0, Math.min(5, countDay(t.getUTCDay())));
const w = timeWeek.count(d3.utcYear(t), t);
return `${d === 0 ? `M${w * cellSize},0`
: d === 5 ? `M${(w + 1) * cellSize},0`
: `M${(w + 1) * cellSize},0V${d * cellSize}H${w * cellSize}`}V${5 * cellSize}`;
}
const svg = d3.create("svg")
.attr("width", width)
.attr("height", height * years.length)
.attr("viewBox", [0, 0, width, height * years.length])
.attr("style", "max-width: 100%; height: auto; font: 10px sans-serif;");
const year = svg.selectAll("g")
.data(years)
.join("g")
.attr("transform", (d, i) => `translate(40.5,${height * i + cellSize * 1.5})`);
year.append("text")
.attr("x", -5)
.attr("y", -5)
.attr("font-weight", "bold")
.attr("text-anchor", "end")
.text(([key]) => key);
year.append("g")
.attr("text-anchor", "end")
.selectAll()
.data(d3.range(1, 6))
.join("text")
.attr("x", -5)
.attr("y", i => (countDay(i) + 0.5) * cellSize)
.attr("dy", "0.31em")
.text(formatDay);
year.append("g")
.selectAll()
.data(([, values]) => values.filter(d => ![0, 6].includes(d.date.getUTCDay())))
.join("rect")
.attr("width", cellSize - 1)
.attr("height", cellSize - 1)
.attr("x", d => timeWeek.count(d3.utcYear(d.date), d.date) * cellSize + 0.5)
.attr("y", d => countDay(d.date.getUTCDay()) * cellSize + 0.5)
.attr("fill", d => color(d.value))
.append("title")
.text(d => `${formatDate(d.date)}
${formatValue(d.value)}${d.close === undefined ? "" : `
${formatClose(d.close)}`}`);
const month = year.append("g")
.selectAll()
.data(([, values]) => d3.utcMonths(d3.utcMonth(values[0].date), values.at(-1).date))
.join("g");
month.filter((d, i) => i).append("path")
.attr("fill", "none")
.attr("stroke", "#fff")
.attr("stroke-width", 3)
.attr("d", pathMonth);
month.append("text")
.attr("x", d => timeWeek.count(d3.utcYear(d), timeWeek.ceil(d)) * cellSize + 2)
.attr("y", -5)
.text(formatMonth);
return Object.assign(svg.node(), {scales: {color}});
}
dji = FileAttachment("^DJI.csv").csv({typed: true})
import {Legend} from "@d3/color-legend"
Plot.plot({
padding: 0,
x: {axis: null},
y: {tickFormat: Plot.formatWeekday("en", "narrow"), tickSize: 0},
fy: {tickFormat: "", reverse: true},
color: {scheme: "PiYG", legend: true, label: "Daily change", tickFormat: "+%", domain: [-0.06, 0.06]},
marks: [
Plot.cell(dji, {
x: (d) => d3.utcWeek.count(d3.utcYear(d.Date), d.Date),
y: (d) => d.Date.getUTCDay(),
fy: (d) => d.Date.getUTCFullYear(),
fill: (d, i) => i > 0 ? (d.Close - dji[i - 1].Close) / dji[i - 1].Close : NaN,
title: (d, i) => i > 0 ? ((d.Close - dji[i - 1].Close) / dji[i - 1].Close * 100).toFixed(1) : NaN,
inset: 0.5
})
]
})
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.
