History

postings = d3.csv("data/postings.csv", d3.autoType)
fields = Array.from(new Set(postings.map((d) => d.field))).sort(d3.ascending)
seasons = Array.from(new Set(postings.map((d) => d.season))).sort(d3.ascending)

viewof selectedField = Inputs.select(
  ["All fields", ...fields],
  {label: "Field"}
)

viewof selectedType = Inputs.select(
  ["Tenure Track", "Postdoc", "Visiting", "All"],
  {label: "Position type", value: "Tenure Track"}
)

filteredPostings = postings.filter((d) =>
  (selectedField === "All fields" || d.field === selectedField) &&
  (selectedType === "All" || d.type === selectedType)
)

seasonStart = (season) => new Date(Date.UTC(+season.slice(0, 4), 5, 1))
anchorDate = new Date(Date.UTC(2000, 5, 1))

dayInSeason = (season, date) => d3.utcDay.count(
  seasonStart(season),
  new Date(Date.UTC(date.getUTCFullYear(), date.getUTCMonth(), date.getUTCDate()))
)

postingSeries = seasons.flatMap((season) => {
  const counts = d3.rollup(
    filteredPostings.filter((d) => d.season === season),
    (rows) => new Set(rows.map((d) => d.posting_id)).size,
    (d) => dayInSeason(season, d.posted_date)
  );
  const lastDay = d3.max(counts.keys()) ?? 0;
  let cumulative = 0;

  return d3.range(lastDay + 1).map((day) => {
    cumulative += counts.get(day) ?? 0;
    return {
      season,
      date: d3.utcDay.offset(anchorDate, day),
      cumulative
    };
  });
})

Plot.plot({
  y: {grid: true, label: "Cumulative postings"},
  x: {
    label: "Date in job-market season",
    tickFormat: d3.utcFormat("%b %d")
  },
  color: {legend: true},
  marks: [
    Plot.line(postingSeries, {
      x: "date",
      y: "cumulative",
      stroke: "season"
    })
  ]
})