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Sneaker Sales by State for Off-White and Yeezy from StockX
rawData = FileAttachment("StockX-Data-Contest-2019-3.csv").csv()
stateData = FileAttachment("state-abbrevs.csv").csv()
data = rawData.map(d => ({
buyer_region: d.Buyer_Region,
brand: d.Brand,
sneaker_name: d.Sneaker_Name
}))
viewof df = aq.from(data).view()
viewof dsf = aq.from(stateData).view()
viewof finalSneaker = df.join(dsf, ["buyer_region", "state"]).view()
columns = ["abbreviation", "brand", "sneaker_name"]
viewof groupKey = Select(["abbreviation"], { label: "Group by" })
viewof colorKey = Select(["brand"], { label: "Color by", value: ["brand"] })
viewof groupPivotedData = finalSneaker
.select(groupKey, colorKey)
.groupby(groupKey, colorKey)
.count()
.groupby(groupKey)
.pivot(colorKey, "count")
.view()
chartData = groupPivotedData.objects()
finalData = Object.assign(chartData, { y: "Number of Sneakers Bought" })
keys = df
.groupby(colorKey)
.count()
.column(colorKey).data
import { navio } from "@john-guerra/navio"
import { aq, op } from '@uwdata/arquero'
import { Select } from "@observablehq/inputs"
import { chart } with {
finalData as data,
groupKey,
keys
} from "@d3/grouped-bar-chart"
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.
