prepareSales() in booknest-prep.js gives the rest of the chapter one shape of the sample data: parsed dates, sales linked to book objects, revenue from catalog prices, and per-book and per-month totals:
// booknest-prep.js: prepareSales(raw) makes booknest-sales.json chart-ready (needs d3)
function prepareSales(raw) {
const parse = d3.utcParse('%Y-%m'), books = raw.books.map(b => ({ ...b }));
const byId = d3.index(books, b => b.id);
const sales = raw.sales.map(s => ({ date: parse(s.month), book: byId.get(s.book),
units: s.units, revenue: s.units * byId.get(s.book).price }));
for (const [book, rows] of d3.group(sales, s => s.book)) { // totals on each book
book.units = d3.sum(rows, s => s.units);
book.revenue = d3.sum(rows, s => s.revenue);
}
const months = d3.rollups(sales, v => ({ units: d3.sum(v, s => s.units),
revenue: d3.sum(v, s => s.revenue) }), s => s.date).map(([date, t]) => ({ date, ...t }));
const ratings = raw.ratings.map(r => ({ ...r, book: byId.get(r.book) }));
return { ...raw, books, sales, months, ratings };
}<script src="https://cdn.jsdelivr.net/npm/d3@7.9.0/dist/d3.min.js"></script>
<script src="booknest-sales.js"></script><script src="booknest-prep.js"></script>
<p id="kpi" style="font:15px Arial"></p>
<script>
const data = prepareSales(SALES), f = d3.format(','); // or d3.json(url).then(prepareSales)
const best = d3.greatest(data.months, d => d.units);
const earner = d3.greatest(data.books, b => b.revenue);
d3.select('#kpi').html(`${f(d3.sum(data.books, b => b.units))} units, `
+ `$${f(d3.sum(data.months, m => m.revenue))} revenue; best month `
+ `${d3.utcFormat('%b %Y')(best.date)} (${best.units});<br>`
+ `top earner <b>${earner.title}</b>`);
</script>
Rows point at shared book objects, so a chart reads d.book.color directly. The top earner, Patterns of the Deep Web, is only fourth in units; Sales Dashboard's dashboard builds on this function.
<!doctype html>
<style>
body { margin: 0; padding: 8px; background: #fafaf7; font: 12px system-ui, sans-serif; color: #263238; max-width: 600px; }
.kpis { display: flex; gap: 8px; }
.kpi { flex: 1; background: #fff; border: 1px solid #e0d8c8; border-radius: 6px; padding: 6px 8px; }
.kpi b { display: block; font-size: 18px; }
svg { width: 100%; display: block; margin-top: 6px; }
</style>
<script src="https://cdn.jsdelivr.net/npm/d3@7.9.0/dist/d3.min.js"></script>
<div class="kpis" id="kpis"></div>
<svg viewBox="0 0 600 190" font-size="11"></svg>
<script>
// BookNest sample data, invented for these examples (not real sales)
const SALES = (() => {
const books = [
{ id: 1, key: 'harbor', title: 'The Quiet Harbor', author: 'Elena Marsh', genre: 'Fiction', color: '#1f5f8b', price: 18.99, rating: 4.6 },
{ id: 2, key: 'deepweb', title: 'Patterns of the Deep Web', author: 'Ravi Menon', genre: 'Technology', color: '#5b3f99', price: 39.5, rating: 4.3 },
{ id: 3, key: 'saffron', title: 'Salt and Saffron', author: 'Leila Haddad', genre: 'Cooking', color: '#e09a10', price: 24, rating: 4.8 },
{ id: 4, key: 'summit', title: 'Small Steps to Big Summits', author: 'Tom Okafor', genre: 'Self-Help', color: '#3f7d3a', price: 14.99, rating: 4.1 },
{ id: 5, key: 'clock', title: "The Clockmaker's Paradox", author: 'Iris Vale', genre: 'Science Fiction', color: '#b5452f', price: 16.2, rating: 4.5 },
{ id: 6, key: 'glass', title: 'Gardens in Glass', author: 'June Park', genre: 'Gardening', color: '#2a9d8f', price: 22.5, rating: 4.4 },
];
const units = { // units sold per month, January 2025 to August 2026
1: [40, 47, 46, 47, 57, 51, 46, 54, 60, 60, 71, 111, 52, 42, 49, 56, 59, 48, 53, 51],
2: [40, 37, 35, 41, 48, 43, 40, 42, 50, 53, 53, 84, 42, 35, 38, 41, 45, 41, 46, 46],
3: [46, 45, 41, 46, 53, 49, 46, 45, 60, 51, 63, 100, 48, 48, 49, 48, 53, 49, 48, 52],
4: [100, 80, 72, 70, 66, 66], // released March 2026
5: [48, 47, 54, 52, 65, 55, 57, 58, 64, 66, 75, 124, 58, 49, 59, 56, 63, 57, 54, 57],
6: [31, 30, 28, 35, 31, 32, 29, 30, 41, 36, 45, 64, 31, 30, 30, 38, 40, 38, 36, 38],
};
const months = d3.utcMonths(new Date('2025-01-01'), new Date('2026-09-01')).map(d3.utcFormat('%Y-%m'));
const sales = books.flatMap(b => units[b.id].map((n, i) =>
({ month: months[i + months.length - units[b.id].length], book: b.id, units: n })));
const ratings = [[4, 8, 20, 87, 293], [6, 10, 22, 95, 147], [2, 3, 8, 45, 342], [2, 3, 8, 24, 28],
[4, 8, 20, 90, 228], [4, 7, 20, 82, 147]].map((counts, i) => ({ book: i + 1, counts })); // 1 to 5 stars
const regions = [{ id: '826', name: 'United Kingdom', units: 1480 }, { id: '276', name: 'Germany', units: 1120 },
{ id: '250', name: 'France', units: 760 }, { id: '372', name: 'Ireland', units: 540 },
{ id: '528', name: 'Netherlands', units: 410 }, { id: '724', name: 'Spain', units: 320 }];
const links = [[5, 1, 38], [1, 3, 24], [2, 5, 18], [3, 6, 21], [4, 1, 12], [2, 6, 9], [4, 3, 15], [5, 6, 14]]
.map(([source, target, value]) => ({ source, target, value })); // customers who bought both
return { books, sales, ratings, regions, links };
})();
// prepareSales(raw): parsed dates, sales linked to book objects, revenue and totals
function prepareSales(raw) {
const parse = d3.utcParse('%Y-%m'), books = raw.books.map(b => ({ ...b }));
const byId = d3.index(books, b => b.id);
const sales = raw.sales.map(s => ({ date: parse(s.month), book: byId.get(s.book),
units: s.units, revenue: s.units * byId.get(s.book).price }));
for (const [book, rows] of d3.group(sales, s => s.book)) {
book.units = d3.sum(rows, s => s.units);
book.revenue = d3.sum(rows, s => s.revenue);
}
const months = d3.rollups(sales, v => ({ units: d3.sum(v, s => s.units),
revenue: d3.sum(v, s => s.revenue) }), s => +s.date).map(([t, v]) => ({ date: new Date(t), ...v }))
.sort((a, b) => a.date - b.date);
const ratings = raw.ratings.map(r => ({ ...r, book: byId.get(r.book) }));
return { ...raw, books, sales, months, ratings };
}
const data = prepareSales(SALES), f = d3.format(','), money = d3.format('$,.0f');
const best = d3.greatest(data.months, d => d.units);
const earner = d3.greatest(data.books, b => b.revenue);
const kpis = [
['units sold', f(d3.sum(data.books, b => b.units))],
['revenue', money(d3.sum(data.months, m => m.revenue))],
['best month', `${d3.utcFormat('%b %Y')(best.date)} (${best.units})`],
['top earner', earner.title],
];
d3.select('#kpis').selectAll('div').data(kpis).join('div').attr('class', 'kpi')
.html(([label, value]) => `${label}<b>${value}</b>`);
const svg = d3.select('svg');
// Monthly revenue sparkline, from data.months
const x = d3.scaleUtc(d3.extent(data.months, d => d.date), [10, 280]);
const y = d3.scaleLinear([0, d3.max(data.months, d => d.revenue)], [170, 20]);
svg.append('text').attr('x', 10).attr('y', 12).attr('font-weight', 'bold').text('revenue per month');
svg.append('path').datum(data.months).attr('fill', '#1f5f8b').attr('opacity', 0.15)
.attr('d', d3.area(d => x(d.date), 170, d => y(d.revenue)));
svg.append('path').datum(data.months).attr('fill', 'none').attr('stroke', '#1f5f8b').attr('stroke-width', 2)
.attr('d', d3.line(d => x(d.date), d => y(d.revenue)));
svg.append('g').attr('transform', 'translate(0,170)').call(d3.axisBottom(x).ticks(4));
// Per-book totals: rows point at shared book objects, so colors come straight from them
const by = d3.scaleBand(data.books.map(b => b.key), [20, 170]).padding(0.2);
const bx = d3.scaleLinear([0, d3.max(data.books, b => b.revenue)], [0, 130]);
const g = svg.append('g').attr('transform', 'translate(320,0)');
g.append('text').attr('y', 12).attr('font-weight', 'bold').text('revenue per book (units)');
g.selectAll('rect').data(data.books).join('rect').attr('y', b => by(b.key)).attr('height', by.bandwidth())
.attr('width', b => bx(b.revenue)).attr('fill', b => b.color);
g.selectAll('text.v').data(data.books).join('text').attr('class', 'v').attr('dy', '0.35em')
.attr('x', b => bx(b.revenue) + 4).attr('y', b => by(b.key) + by.bandwidth() / 2)
.text(b => `${b.key} ${money(b.revenue)} (${b.units})`);
</script>