shippedOrders = data.filter(
(d) =>
d.shipDate &&
d.shipDate >= new Date("2022-04-10") &&
d.shipDate <= new Date()
)
shippingProjection = d3
// y = mx + b mapping of
.scaleLinear()
// shipped order number
.domain(d3.extent(shippedOrders, (d) => d.orderID).reverse())
// to shipping dates
.range(d3.extent(shippedOrders, (d) => d.shipDate))
predictionLine = [
d3.least(shippedOrders, (d) => d.orderID),
{
shipDate: shippingProjection(finalOrderToShip.orderID),
orderID: finalOrderToShip.orderID
}
]
shippingGroupsPoints = d3
// take the point with the highest order number in each group
.flatRollup( data, (g) => d3.least(g, (d) => d.orderID), (d) => d.group)
// and generate a shipping projection
.map(([groupNumber, datum]) => ({
...datum,
projectedShipDate: shippingProjection.copy()(datum.orderID),
label: `group ${groupNumber}`
}))
finalOrderToShip = d3.least(data, (d) => d.orderID)
data = {
const cleaned = massaged.map((d) => ({
...d,
shipDate: isNaN(d.shipDate)
? null
: new Date(new Date(d.shipDate).setFullYear(2022))
}));
return cleaned.filter((d) => !(d[col.orderID] === "448XX" && d.group === 2));
}
// clean up raw data and assign reasonable column names
massaged = raw
.filter((d) => d["What is your order ID?"] !== "")
.map((d) => ({
...d,
status: d[col.status],
isShipping: d[col.isShipping] === "Yes",
shipDate: new Date(d[col.shipDate]),
consoleOrCombo: d[col.consoleOrCombo],
orderID: +d[col.orderID].replace(/[xX#\*]/g, "0").replace(/\D/g, "") * -1,
group: +d[col.group]
}))
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.
