Plot.plot({
y: { grid: true },
color: { legend: true },
marks: [
Plot.rectY(
crimea,
// Plot.stackY( // not sure why this doesn't work 🤔
{
x: "date",
y: "deaths",
interval: d3.utcMonth.every(4),
fill: "cause",
tip: true
}
// )
),
Plot.ruleY([0])
]
})
Plot.plot({
y: { grid: true },
color: { legend: true },
marks: [
Plot.rectY(
crimea,
Plot.stackY( // Plot.rectY calls Plot.stackY implicitly, so this is not required
Plot.binX(
{ y: "sum" },
{
x: "date",
y: "deaths",
interval: d3.utcMonth.every(4),
fill: "cause",
tip: true
}
)
)
),
Plot.ruleY([0])
]
})
{
const stackedValues = Plot.rectY(
crimea,
Plot.stackY(
Plot.binX(
{ y: "sum" },
{
x: "date",
y: "deaths",
interval: d3.utcMonth.every(4),
fill: "cause"
}
)
)
).initialize().channels;
return d3
.range(stackedValues.fill.value.length)
.map((d, i) => {
return {
cause: stackedValues.fill.value[i],
from: stackedValues.x1.value[i],
to: stackedValues.x2.value[i],
deaths: stackedValues.y2.value[i] - stackedValues.y1.value[i]
};
})
.sort((a, b) => d3.ascending(a.from, b.from));
}
d3
.bin()
.value((d) => d.date)
.thresholds(
d3.utcMonths(
d3.utcMonth.every(4).floor(d3.min(crimea, (d) => d.date)), // new Date("1854-01-01")
d3.max(crimea, (d) => d.date),
4 // every 4 months
)
)(crimea)
.flatMap((g) => {
return d3
.groups(g, (d) => d.cause)
.map(([cause, g2]) => ({
cause,
from: g.x0,
to: g.x1,
deaths: d3.sum(g2, (d) => d.deaths)
}));
})
{
const deathsByCauseAndMonth = d3.rollups(
crimea,
(v) => d3.sum(v, (d) => d.deaths),
(d) => d.cause,
(d) => d3.utcMonth(d.date)
);
const thresholds = d3.utcMonths(
d3.utcMonth.every(4).floor(d3.min(crimea, (d) => d.date)), // new Date("1854-01-01")
d3.max(crimea, (d) => d.date),
4 // every 4 months
);
return thresholds.flatMap((from, i) => {
const to =
i === thresholds.length - 1
? d3.utcMonth.offset(from, 4)
: thresholds[i + 1];
return deathsByCauseAndMonth.map(([cause, g]) => {
return {
cause,
from: from,
to: to,
deaths: d3.sum(
g.filter(([date, deaths]) => date >= from && date < to),
(d) => d[1]
)
};
});
});
}
crimea
Type Table, then Shift-Enter. Ctrl-space for more options.
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.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.