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Edited
May 4, 2022
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Bandcamp Sales Data
Google Merchandise Sales DataDarkCideS: Bats in Caves Data
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Curated Datasets
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db
select *
from sales
-- lets grab 1 day worth of data (the entire dataset is a month's worth
WHERE utc_timestamp > TIMESTAMP '2020-09-10'
AND utc_timestamp < TIMESTAMP '2020-09-11'
-- lets limit to purchases made in US Dollars to simplify exchange rates
AND currency = 'USD'
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// the results are stored in the variable:
oneday
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db = DuckDBClient.of({
sales: FileAttachment("1000000-bandcamp-sales@1.parquet") ,
})
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db.describe("sales")
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db
select count(*)::INT as count
from sales;
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db
select * from sales
limit 1000;
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// when we save the result of a query in a named SQL Cell we can access it via JavaScript
sample[0]
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Plot.plot({
marks: [
Plot.barX(sample, Plot.groupY({x: "count"}, {y: "artist_name", sort: {y: "x", reverse: true, limit: 10}}))
],
marginLeft: 200
})
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getArtUrl = u => `https://f4.bcbits.com/img/${u}`
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getItemUrl = (u) => `https://${u.account}.bandcamp.com` + (u.type ? `/${u.type}` : "") + (u.slug ? `/${u.slug}` : "")
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db
select * from sales
where currency = 'USD'
order by amount_paid desc
limit 100
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topdollarUrls = topdollar.map(d => {
return {
...d,
url: getItemUrl(d),
art_url: getArtUrl(d.art_url)
}
})
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Inputs.table(topdollarUrls, {
columns: ["artist_name", "item_description", "amount_paid", "currency", "url"],
format: { url: r => html`<a href="${r}">${r}</a>` },
width: { amount_paid: "60px", currency: "40px", artist_name: "200px" },
})
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Plot.plot({
marks: [
Plot.ruleY([0]),
Plot.image(topdollarUrls, {
x: "utc_timestamp",
y: "amount_paid",
src: "art_url"
})
],
insetTop: 10,
insetLeft: 10,
})
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db
select type, physical, count(*)::INT as count
from sales
group by type, physical
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Plot.plot({
marks: [
Plot.barY(bytype, {x: "type", y: "count", fill: "physical"})
],
color: { legend: true, tickFormat: d => d ? "physical" : "digital" },
marginLeft: 60
})
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db
select currency, count(*)::INT as count, sum(item_price)::INT as prices, sum(amount_paid)::INT as revenue
from sales

group by currency
order by count desc
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Plot.plot({
marks: [
Plot.barX(bycurrency, {x: "count", y: "currency", sort: { "y": "x", reverse: true }})
]
})
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exchangeRates = d3.json("https://open.er-api.com/v6/latest/USD")
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bycurrencyUSD = bycurrency.map(d => {
return {
...d,
prices_usd: Math.round(d.prices / exchangeRates.rates[d.currency]),
revenue_usd: Math.round(d.revenue / exchangeRates.rates[d.currency]),
}
})
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Inputs.table(bycurrencyUSD)
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Now we can plot monetary values
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Plot.plot({
marks: [
Plot.barX(bycurrencyUSD, {x: "revenue_usd", y: "currency", fillOpacity: 0.55, fill: "green", sort: { "y": "x", reverse: true }}),
Plot.barX(bycurrencyUSD, {x: "prices_usd", y: "currency", fillOpacity: 0.55, fill: "steelblue", sort: { "y": "x", reverse: true }}),
]
})
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process = (data) => {
return data.map(d => {
return {
...d,
price_usd: d.item_price/exchangeRates.rates[d.currency], // item_price in USD
paid_usd: d.amount_paid/exchangeRates.rates[d.currency], // amount_paid in USD
url: getItemUrl(d), // full url to item
art_url: getArtUrl(d.art_url) // full url to artwork
}
})
}
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processed = process(sample)
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Inputs.table(processed)
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Plot.plot({
caption: "histogram of how much people paid",
marks: [
Plot.rectY(processed, Plot.binX({y: "sum"}, {x: "paid_usd"}))
],
})
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Plot.plot({
caption: "histogram of how much people paid",
facet: {
data: processed,
x: "type_label"
},
marks: [
Plot.frame(),
Plot.rectY(processed, Plot.binX({y: "sum"}, {x: "paid_usd"}))
],
grid: true,
inset: 5,
width: width
})
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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.
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