// Gribouille is imported by the typst-render preamble (see assets/typst/_preamble.typ);
// do not import it here or the theme-* wrappers get rebound.
// #import "@preview/gribouille:0.3.0": *
// #import "@local/gribouille:0.0.0": *
// #set page(width: 18cm, height: 9.45cm, margin: 0cm)

// Treat every missing sentinel as `none` so a single guard filters them out.
#let num(s) = if s in ("NA", "N/A", "") { none } else { float(s) }

#let files = (
  ("England & Wales", "data/england_wales_names.csv"),
  ("Scotland", "data/scotland_names.csv"),
  ("Northern Ireland", "data/ni_names.csv"),
)

// Keep only years where all three nations publish data (E&W ends at 2024).
#let start_year = 1997
#let end_year = 2024
// A name counts as gender-neutral when the combined UK total for each sex
// reaches at least this many births; filters ONS-suppressed entries and noise.
#let min_n = 3

// Pass 1: sum births across nations per (year, name, sex) for shared years only.
#let year_name_sex = (:)
#for (_, path) in files {
  for r in csv(path, row-type: dictionary) {
    let yr = int(r.Year)
    if yr < start_year or yr > end_year { continue }
    let n = num(r.Number)
    if n == none { continue }
    let key = r.Year + "|" + r.Name + "|" + r.Sex
    year_name_sex.insert(key, year_name_sex.at(key, default: 0.0) + n)
  }
}

// Pass 2: apply threshold after combining; index which names qualify per sex.
#let year_name = (:)
#for (key, total_n) in year_name_sex {
  if total_n < min_n { continue }
  let parts = key.split("|")
  let name_key = parts.at(0) + "|" + parts.at(1)
  let sex = parts.at(2)
  let cur = year_name.at(name_key, default: (:))
  cur.insert(sex, true)
  year_name.insert(name_key, cur)
}

// Pass 3: count shared and total distinct names per year.
#let per_year = (:)
#for (key, counts) in year_name {
  let yr = key.split("|").at(0)
  let cur = per_year.at(yr, default: (shared: 0, total: 0))
  cur.insert("total", cur.total + 1)
  if "Boy" in counts and "Girl" in counts {
    cur.insert("shared", cur.shared + 1)
  }
  per_year.insert(yr, cur)
}

// Mean total distinct names across all years (used in y-axis "% (N)" label).
#let total_sum = 0.0
#let n_yrs = 0
#for (_, v) in per_year {
  total_sum += v.total
  n_yrs += 1
}
#let mean_total = total_sum / n_yrs

// Pass 4: find top gender-neutral name (by combined births) per 5-year interval.
// Intervals are half-open [start, end); labels placed at midpoint year.
#let intervals = (
  (start: 1997, end: 2000, mid: 1998),
  (start: 2000, end: 2005, mid: 2002),
  (start: 2005, end: 2010, mid: 2007),
  (start: 2010, end: 2015, mid: 2012),
  (start: 2015, end: 2020, mid: 2017),
  (start: 2020, end: 2025, mid: 2022),
)

#let interval_totals = (:)
#for (key, total_n) in year_name_sex {
  let parts = key.split("|")
  let yr = int(parts.at(0))
  let name = parts.at(1)
  let name_key = parts.at(0) + "|" + name
  let sexes = year_name.at(name_key, default: (:))
  if "Boy" not in sexes or "Girl" not in sexes { continue }
  for (i, iv) in intervals.enumerate() {
    if yr >= iv.start and yr < iv.end {
      let k = str(i) + "|" + name
      interval_totals.insert(k, interval_totals.at(k, default: 0.0) + total_n)
    }
  }
}

#let label_rows = ()
#for (i, iv) in intervals.enumerate() {
  let best_name = none
  let best_n = 0.0
  for (k, n) in interval_totals {
    if not k.starts-with(str(i) + "|") { continue }
    if n > best_n {
      best_n = n
      best_name = k.slice(str(i).len() + 1)
    }
  }
  if best_name == none { continue }
  let yr_data = per_year.at(str(iv.mid), default: (shared: 0, total: 1))
  let pct = yr_data.shared / yr_data.total * 100
  label_rows.push((year: iv.mid + 0.5, pct: pct, label: best_name))
}

// Build one row per year, sorted chronologically.
#let rows = ()
#let max_pct = 0.0
#for (yr, v) in per_year {
  let pct = v.shared / v.total * 100
  if pct > max_pct { max_pct = pct }
  rows.push((year: int(yr), pct: pct))
}
#let rows = rows.sorted(key: r => r.year)

// Okabe-Ito teal for the single series.
#let teal = rgb("#009e73")

#plot(
  data: rows,
  mapping: aes(x: "year", y: "pct"),
  layers: (
    geom-area(fill: teal, alpha: 0.25, stroke: none, position: "identity"),
    geom-line(colour: teal, stroke: 0.8pt),
    geom-label(
      data: label_rows,
      mapping: aes(label: "label"),
      size: 7pt,
      fill: teal,
      colour: white,
      stroke: 0.3pt,
      inset: 3pt,
      radius: 1pt,
      dy: 0.7cm,
    ),
  ),
  scales: (
    scale-x-continuous(breaks: (2000, 2005, 2010, 2015, 2020)),
    scale-y-continuous(
      name: "Share of All Distinct Names",
      limits: (0, max_pct + 0.5),
      // Labels show percentage and approximate count in parentheses.
      labels: p => {
        let n = int(calc.round(p / 100 * mean_total))
        str(calc.round(p, digits: 1)) + "% (" + str(n) + ")"
      },
    )
  ),
  labs: labs(
    title: "Britain's Gender-Neutral Baby Names",
    subtitle: [
      Names registered for both #strong[boys] and #strong[girls] across
      #text(fill: teal)[#strong[all three UK nations combined]] (_at least 3 births each sex, 1997–2024_). \
      Most-registered gender-neutral name labelled per five-year window.
    ],
    caption: [
      Source: UK baby names (TidyTuesday 2026-06-16). \
      Author: #link("https://mickael.canouil.fr")[Mickaël CANOUIL].
    ],
    x: none,
  ),
  theme: theme-minimal(),
  width: auto,
  height: auto,
)

// Gribouille is imported by the typst-render preamble (see assets/typst/_preamble.typ);
// do not import it here or the theme-* wrappers get rebound.
// #import "@preview/gribouille:0.2.0": *
// #import "@local/gribouille:0.0.0": *
// #set page(width: 18cm, height: 9.45cm, margin: 0cm)

// Treat every missing sentinel as `none` so a single guard filters them out.
#let num(s) = if s in ("NA", "N/A", "") { none } else { float(s) }

#let raw = csv("data/game_films.csv", row-type: dictionary)

// CinemaScore is an opening-night audience grade; map the letters onto a 0-100
// scale so it can be read against the critic percentage on the same ruler.
#let cinemascore = (
  "A+": 100, "A": 95, "A-": 90,
  "B+": 85, "B": 80, "B-": 75,
  "C+": 70, "C": 65, "C-": 60,
  "D+": 55, "D": 50, "D-": 45,
  "F": 0,
)

// Okabe-Ito accents (colourblind-safe). Vermillion = lost money, teal = profit;
// the diverging fill pivots at break-even (box office = budget). Accent matches
// the site theme so the inset border tracks the light / dark toggle.
#let loss = rgb("#d55e00")
#let profit = rgb("#009e73")
#let accent = rgb("#0f8b8d")

// Main frame: keep one currency ($) so the y axis reads in dollars, and only
// rows carrying budget + critic score so every channel (x, y, size, fill) is
// honest. All such rows happen to be theatrical releases; the split that earns
// the shape channel is therefore time, not category.
#let films = (
  raw
    .filter(r => (
      r.worldwide_box_office_currency == "\u{0024}"
        and num(r.worldwide_box_office) != none
        and num(r.budget_high) != none
        and num(r.rotten_tomatoes) != none
        and num(r.release_date.slice(0, 4)) != none
    ))
    .map(r => {
      let box = num(r.worldwide_box_office)
      let bud = num(r.budget_high)
      let yr = int(r.release_date.slice(0, 4))
      let era = if yr >= 2019 { "2019 onward" } else { "Before 2019" }
      (
        title: r.title,
        rt: num(r.rotten_tomatoes),
        box: box,
        budget: bud,
        // log10 of the profit multiple so the fill spreads evenly across orders
        // of magnitude and pivots at 0 = break-even (box office = budget).
        logmult: calc.log(box / bud, base: 2),
        era: era,
      )
    })
)

// Inset frame: every film scored by both critics and an opening audience. The
// two means carry the whole sub-story, so the inset only needs those numbers.
#let scored = raw.filter(r => (
  num(r.rotten_tomatoes) != none and r.cinema_score in cinemascore
))
#let critic-mean = scored.map(r => num(r.rotten_tomatoes)).sum() / scored.len()
#let audience-mean = scored.map(r => cinemascore.at(r.cinema_score)).sum() / scored.len()

// Hand-drawn dumbbell on a 0-100 track: the gap between the critic dot and the
// audience dot IS the disagreement. Built from std Typst primitives (no nested
// canvas) and reads `page.fill` so the card tracks the light / dark site toggle.
#let inset = context {
  let bg = if page.fill in (auto, none) { white } else { page.fill }
  let w = 4.6cm
  box(
    fill: bg,
    inset: 7pt,
    radius: 4pt,
    stroke: 0.5pt + accent,
  )[
    #set text(size: 7pt)
    #set par(leading: 4pt)
    #align(center)[#strong[Critics pan them; audiences don't]]
    #v(-5pt)
    #box(width: w, height: 8pt)[
      #place(left + horizon, line(length: w, stroke: 0.4pt + luma(75%)))
      #place(
        left + horizon,
        dx: critic-mean / 100 * w,
        line(
          length: (audience-mean - critic-mean) / 100 * w,
          stroke: 1.4pt + luma(55%),
        ),
      )
      #place(
        left + horizon,
        dx: critic-mean / 100 * w - 2.5pt,
        circle(radius: 2.5pt, fill: loss, stroke: none),
      )
      #place(
        left + horizon,
        dx: audience-mean / 100 * w - 2.5pt,
        circle(radius: 2.5pt, fill: profit, stroke: none),
      )
    ]
    #v(-7pt)
    #grid(
      columns: (1fr, 1fr),
      align: (left, right),
      text(fill: loss)[#strong[Critics] (_mean_): #calc.round(critic-mean)],
      text(fill: profit)[#strong[Audiences] (_mean_): #calc.round(audience-mean)],
    )
  ]
}

// Label with a translucent paper background so the text stays legible over the
// marker cloud; the background reads `page.fill` to track the light / dark site.
#let pill(body) = context {
  let bg = if page.fill in (auto, none) { white } else { page.fill }
  box(
    fill: bg.transparentize(12%),
    inset: (x: 2pt, y: 0.5pt),
    radius: 1.5pt,
  )[#text(size: 7.5pt)[#body]]
}

// Real dataset title -> the pill content to draw for it. Keys select which films
// are emphasised; coordinates come from the inherited frame, never hardcoded.
#let label-text = (
  "The Super Mario Bros. Movie": align(center)[*The \ Super Mario Bros. \ Movie*],
  "Pokémon Detective Pikachu": align(center)[*Detective \ Pikachu*],
  "Sonic the Hedgehog 3": align(center)[*Sonic \ the Hedgehog 3*],
  "Postal": align(center)[*Postal* \ (Uwe Boll)],
)

#plot(
  data: films,
  mapping: aes(
    x: "rt",
    y: "box",
    fill: "logmult",
    size: "budget",
    shape: "era",
  ),
  layers: (
    // Shade the "panned but paid" zone: rotten with critics (left of 60) yet
    // grossing past $100M. Painted first so points sit on top of it. A direct
    // geom-rect with a fixed fill keeps it off the continuous fill scale.
    geom-rect(
      data: ((xmin: 0, xmax: 60, ymin: 1e8, ymax: 3e9),),
      mapping: aes(xmin: "xmin", xmax: "xmax", ymin: "ymin", ymax: "ymax"),
      fill: accent.transparentize(90%),
      colour: accent,
      stroke: 0.5pt,
      inherit-aes: false,
    ),
    // The Rotten Tomatoes "fresh" threshold.
    annotate(
      "vline",
      xintercept: 60,
      colour: luma(55%),
      stroke: 0.6pt,
      linetype: "dashed",
    ),
    geom-point(alpha: 0.95, stroke: 0.4pt, colour: luma(45%)),
    // Accent outline on the labelled films so the eye finds them in the cloud;
    // inherits the plot mapping, so the ring matches each film's shape and size.
    geom-point(
      data: d => d.filter(r => r.title in label-text),
      fill: none,
      colour: accent,
      stroke: 1.2pt,
    ),
    // Direct labels instead of a per-film legend; the data function filters the
    // inherited frame and attaches each film's pill, so coordinates come from films.
    geom-typst(
      data: d => d
        .filter(r => r.title in label-text)
        .map(r => (..r, lab: pill(label-text.at(r.title)))),
      mapping: aes(x: "rt", y: "box", label: "lab"),
      anchor: "south",
      dy: 0.35,
      inherit-aes: false,
    ),
    // Inset dumbbell parked in the sparse top-left corner above the cloud.
    annotate("typst", x: 25, y: 1.75e6, label: inset, anchor: "north-west"),
  ),
  scales: (
    scale-x-continuous(
      name: "Rotten Tomatoes Critics' Score (%)",
      limits: (0, 100),
      breaks: (0, 20, 40, 60, 80, 100),
    ),
    scale-y-continuous(
      name: "Worldwide Box Office (US$)",
      transform: "log10",
      // limits: (8e4, 3e9),
      breaks: (1e5, 1e6, 1e7, 1e8, 1e9),
      labels: ("$100K", "$1M", "$10M", "$100M", "$1B"),
      // expand: (5%, 25%),
    ),
    scale-fill-gradient2(
      name: "Box Office / Budget Ratio",
      low: loss, mid: rgb("#cfcfcf"), high: profit,
      midpoint: 0,
      breaks: (-6, -4, -2, 0, 2, 4),
      labels: ([$1/64$×], [$1/16$×], [$1/4$×], [$1$×], [$4$×], [$16$×]),
    ),
    scale-size-continuous(
      name: "Production Budget",
      range: (3pt, 10pt),
      breaks: (50e6, 100e6, 200e6),
      labels: ("$50M", "$100M", "$200M"),
    ),
    scale-shape(
      name: "Release Era",
      limits: ("Before 2019", "2019 onward"),
    ),
  ),

  guides: guides(
    shape: none,
    fill: guide-legend(direction: "horizontal"),
    size: guide-legend(direction: "horizontal"),
  ),
  labs: labs(
    title: "Panned by Critics, Paid by Audiences",
    subtitle: [
      A poor critics' score has rarely stopped a theatrical video-game adaptation
      from making money: most films land #text(fill: profit, weight: "bold")[in profit]
      rather than at a #text(fill: loss, weight: "bold")[loss], the biggest grossers
      carry the biggest budgets, and the runaway hits now belong to the
      post-#emph[Detective Pikachu] boom \
      (#box(rect(width: 4.6pt, height: 4.6pt, fill: luma(55%), stroke: 0.4pt + luma(40%)))~2019 onward,
      not #box(circle(radius: 2.6pt, fill: luma(55%), stroke: 0.4pt + luma(40%)))~before).
    ],
    caption: typst([
      Source: Films and series based on video games (TidyTuesday 2026-06-09). \
      Author: #link("https://mickael.canouil.fr")[Mickaël CANOUIL].
    ]),
  ),
  theme: theme-minimal(),
  width: auto,
  height: auto,
)

// Gribouille comes from the typst-render preamble; do not re-import (rebinds the
// theme-* wrappers).
// #import "@preview/gribouille:0.2.0": *
// #import "@local/gribouille:0.0.0": *
// #set page(width: 18cm, height: 9.45cm, margin: 0cm)

// EPLP missing sentinels all mean "no leave", so map them to 0; this keeps every
// year balanced at 21 countries for an honest mean.
#let num(s) = if s in ("NA", "Not applicable", "-98", "-99", "") { 0.0 } else {
  float(s)
}

#let raw = csv("data/eplp.csv", row-type: dictionary)

// Each series bound to one Okabe-Ito hue (colourblind-safe), reused by strokes,
// labels, and boxes; insertion order fixes the scale order. Both series share one
// months axis so the gap between the lines is the story (Wilke).
#let series-colours = (
  "Mothers": rgb("#0072b2"),
  "Co-parents": rgb("#d55e00"),
)
#let series = series-colours.keys()

// Boxed callout that lifts prose off the lines. Background reads the ambient
// `page.fill`, so the box tracks the light / dark site toggle.
#let accent = rgb("#0f8b8d")
#let callout(body) = context {
  let bg = if page.fill in (auto, none) { white } else { page.fill }
  box(
    fill: bg,
    inset: (x: 5pt, y: 4pt),
    radius: 3pt,
    stroke: 0.5pt + accent,
  )[#body]
}

// Maternity leave spans four columns; sum them per country-year.
#let mat-cols = ("mat_m_ld_bb", "mat_m_ld_ab", "mat_v_ld_bb", "mat_v_ld_ab")
#let mother-leave(r) = mat-cols.fold(0.0, (a, c) => a + num(r.at(c)))

// Long format, one row per country x year x series; the layers mean these via
// stat-summary.
#let obs = {
  let acc = ()
  for r in raw {
    let y = float(r.year)
    acc.push((year: y, series: "Mothers", months: mother-leave(r)))
    acc.push((year: y, series: "Co-parents", months: num(r.co_ld)))
  }
  acc
}

// ISO-2 codes to full names for the secondary panel's y axis.
#let country-names = (
  AT: "Austria", BE: "Belgium", CZ: "Czechia", DE: "Germany", DK: "Denmark",
  EE: "Estonia", ES: "Spain", FI: "Finland", FR: "France", GR: "Greece",
  HU: "Hungary", IE: "Ireland", IT: "Italy", LT: "Lithuania", NL: "Netherlands",
  NO: "Norway", PL: "Poland", SE: "Sweden", SI: "Slovenia", SK: "Slovakia",
  UK: "United Kingdom",
)

// Secondary panel data: where each country stands in 2024. One row per country
// with its mother and co-parent months and the gap between them, sorted so the
// closest-to-parity sits at the top of the dumbbell.
#let gap-2024 = (
  raw
    .filter(r => float(r.year) == 2024)
    .map(r => {
      let m = mother-leave(r)
      let co = num(r.co_ld)
      let name = country-names.at(r.country, default: r.country)
      (country: name, mother: m, coparent: co, spread: m - co)
    })
    .sorted(key: row => row.at("spread"))
)

// y order for the discrete country axis. gap-2024 is sorted parity-first; the
// discrete y axis draws its first level at the bottom, so reverse to put parity
// (smallest gap) at the top of the dumbbell.
#let country-order = gap-2024.map(row => row.country).rev()

// The two dots per country, long format, coloured by series like the main panel.
#let gap-dots = {
  let acc = ()
  for row in gap-2024 {
    acc.push((country: row.country, series: "Mothers", months: row.mother))
    acc.push((country: row.country, series: "Co-parents", months: row.coparent))
  }
  acc
}

#let main = defer(
  plot,
  data: obs,
  mapping: aes(x: "year", y: "months", colour: "series"),
  layers: (
    // +/-1 SE band per series via stat-summary(mean-se); drawn first so it sits
    // under the line. fill: "series" tints it in each line's hue.
    geom-ribbon(
      mapping: aes(fill: "series"),
      stat: stat-summary(fun: "mean-se", axis: "y"),
      alpha: 0.2,
    ),
    // Per-year mean line: stat-summary buckets each series by year, fun: "mean".
    geom-line(stroke: 1.6pt, stat: stat-summary(fun: "mean", axis: "y")),
    // Direct labels instead of a legend: bold series name at a hand-placed point,
    // coloured by series via the shared scale.
    geom-typst(
      data: (
        (year: 1983, months: 19.4, series: "Mothers", label: "*Mothers*"),
        (year: 2010, months: 0.75, series: "Co-parents", label: "*Co-parents*"),
      ),
      mapping: aes(x: "year", y: "months", label: "label", colour: "series"),
      anchor: "west",
      inherit-aes: false,
    ),
    // End-value labels: `data` filters the inherited frame to the final year, then
    // stat-summary means it like the line; the box sits at that mean and after-stat
    // binds its text to the same value, so the number can never drift from the line.
    geom-label(
      data: d => d.filter(o => o.year == 2024),
      mapping: aes(
        x: "year",
        y: "months",
        fill: "series",
        label: after-stat((row, ctx) => str(calc.round(row.y)) + " months"),
      ),
      stat: stat-summary(fun: "mean", axis: "y"),
      size: 9pt,
      colour: white,
      dx: 0,
      dy: 1,
      segment: true,
      inherit-aes: false,
    ),
    // Callout in the empty wedge between the lines.
    annotate(
      "typst",
      x: 1977,
      y: 9,
      label: callout[
        Leave reserved for the
        #text(fill: series-colours.at("Co-parents"), weight: "semibold")[co-parent] \
        barely existed until 2000. \
        *4* countries offered it in *1980*; *19* do in *2024*.
      ],
      size: 8pt,
      anchor: "west",
    ),
  ),
  scales: (
    scale-x-continuous(
      breaks: (1970, 1980, 1990, 2000, 2010, 2024),
      expand: (1%, 10%),
    ),
    scale-y-continuous(limits: (0, 24)),
    scale-colour-discrete(
      limits: series,
      palette: series-colours.values(),
    ),
    scale-fill-discrete(
      limits: series,
      palette: series-colours.values(),
    ),
  ),
  guides: guides(colour: none, fill: none),
  labs: labs(x: none, y: "Months of Leave"),
  theme: theme-minimal(),
)

// Secondary panel: a 2024 snapshot of all 21 countries as a dumbbell. A grey
// segment per country spans its mother dot to its co-parent dot, so the bar
// length IS the gap; sorted with parity at the top. Different axes (months by
// country) make this read as a different question from the time series, not a
// lookalike. Colour still means series, tying it to the main panel.
#let secondary = defer(
  plot,
  data: gap-dots,
  mapping: aes(x: "months", y: "country", fill: "series"),
  layers: (
    geom-segment(
      data: gap-2024,
      mapping: aes(x: "coparent", y: "country", xend: "mother", yend: "country"),
      stroke: 2pt,
      colour: luma(70%),
      inherit-aes: false,
    ),
    geom-point(size: 3.4pt, alpha: 0.5),
  ),
  scales: (
    scale-x-continuous(limits: (0, 44), breaks: (0, 12, 24, 36)),
    scale-y-discrete(limits: country-order),
    scale-fill-discrete(limits: series, palette: series-colours.values()),
  ),
  guides: guides(fill: none),
  labs: labs(x: "Months of Leave", y: none),
  theme: theme-minimal(
    axis-text-y: element-text(font: "DejaVu Sans Mono", size: 7pt),
    tick-length-y: 0.08cm,
  ),
)

#compose(
  main,
  secondary,
  columns: 2,
  widths: (1.4, 1),
  tag-levels: "A",
  tag-prefix: "(",
  tag-suffix: ")",
  align-panels: true,
  labs: labs(
    title: "Europe Still Reserves Far More Leave for Mothers Than for the Co-Parent",
    subtitle: [*(A)* the gap between #text(fill: series-colours.at("Mothers"), weight: "bold")[mothers] and the #text(fill: series-colours.at("Co-parents"), weight: "bold")[co-parent] has narrowed since 2000 yet mothers still get far more; \ *(B)* in 2024 it ranges from parity (Spain, Slovakia, UK) to roughly 40 months (Ireland).],
    caption: typst([
      Source: European Parenting Leave Policies (TidyTuesday 2026-06-02). \
      Author: #link("https://mickael.canouil.fr")[Mickaël CANOUIL].
    ]),
  ),
)

// Gribouille is imported by the typst-render preamble (see assets/typst/_preamble.typ);
// do not import it here or the theme-* wrappers get rebound.
// #import "@preview/gribouille:0.1.0": *
// #import "@local/gribouille:0.0.0": *
// #set page(width: 18cm, height: 9.45cm, margin: 0cm)

#let num(s) = if s == "NA" or s == "" { 0.0 } else { float(s) }

#let raw = csv("data/energy_cleaned.csv", row-type: dictionary)

// Four mutually exclusive, exhaustive slices of the renewable total (% of final
// energy). One dictionary keeps each category bound to its Okabe-Ito colour, so
// labels, the stacking order, and the legend can never drift out of sync.
// Okabe-Ito is colourblind-safe; warm "old" biomass through cool "new" turbines.
#let cat-colours = (
  "Traditional biomass": rgb("#e69f00"),
  "Modern bioenergy": rgb("#009e73"),
  "Hydropower": rgb("#56b4e9"),
  "Wind & solar": rgb("#0072b2"),
)
#let cats = cat-colours.keys()

// One-line definition per category, aligned with `cats`. Each rolls up the
// raw columns summed in `reshape`, so the legend names what every band is.
#let cat-labels = (
  [#strong[Traditional biomass] \ wood, charcoal & dung for cooking],
  [#strong[Modern bioenergy] \ biogas, biofuels & waste-to-energy],
  [#strong[Hydropower] \ electricity from flowing water],
  [#strong[Wind & solar] \ plus geothermal & marine],
)

#let accent = rgb("#0f8b8d")

// Box-styled callout: a translucent paper background plus a hairline accent
// border lifts the prose off the stacked bands so it stays legible. The paper
// colour comes from the ambient `page.fill` (typst-render sets the page fill to
// the document background), mirroring how ink is read from `text.fill`, so the
// boxes track the light / dark site toggle.
#let callout(body) = context {
  // `page.fill` is `auto` when the document sets no page fill (standalone
  // compile); typst-render sets it to the background, so fall back to white.
  let bg = if page.fill in (auto, none) { white } else { page.fill }
  box(
    fill: bg.transparentize(12%),
    inset: (x: 5pt, y: 3pt),
    radius: 3pt,
    stroke: 0.5pt + accent.transparentize(45%),
  )[#body]
}

// One row per (year, category) carrying the stacked band bounds (ymin, ymax).
// geom-area draws every band from y = 0, so stacked areas would paint over one
// another; geom-ribbon with explicit bounds is the honest stacking primitive.
#let reshape(country) = {
  raw
    .filter(row => row.country_name == country)
    .map(row => {
      let y = float(row.yr)
      let shares = (
        "Traditional biomass": num(row.traditional_biomass_consumption_tfec_pct),
        "Modern bioenergy": num(row.modern_biomass_energy_consumption_tfec_pct)
          + num(row.biogas_consumption_tfec_pct)
          + num(row.liquid_biofuels_energy_consumption_tfec_pct)
          + num(row.waste_energy_consumption_tfec_pct),
        "Hydropower": num(row.hydro_energy_consumption_tfec_pct),
        "Wind & solar": num(row.wind_energy_consumption_tfec_pct)
          + num(row.solar_energy_consumption_tfec_pct)
          + num(row.geothermal_energy_consumption_tfec_pct)
          + num(row.marine_energy_consumption_tfec_pct),
      )
      cats
        .fold((acc: 0.0, out: ()), (st, c) => {
          let hi = st.acc + shares.at(c)
          (
            acc: hi,
            out: st.out + ((year: y, category: c, ymin: st.acc, ymax: hi),),
          )
        })
        .out
    })
    .flatten()
}

#let vn = reshape("Vietnam")
#let de = reshape("Germany")

#let panel(rows, country, annotations) = defer(
  plot,
  data: rows,
  mapping: aes(x: "year", ymin: "ymin", ymax: "ymax", fill: "category"),
  layers: (geom-ribbon(alpha: 1),) + annotations,
  scales: (
    scale-x-continuous(breaks: (1990, 2000, 2010)),
    scale-fill-discrete(limits: cats, palette: cat-colours.values(), labels: cat-labels),
  ),
  labs: labs(title: country, x: none, y: "% Final Energy", fill: none),
  theme: theme-minimal(),
)

// In-panel callouts: the total renewable share at each end, plus a one-line
// "why" sitting in the empty corner of each panel. Text colour is left to the
// theme ink so the callouts track the light / dark site toggle.
#let vn-annot = (
  annotate("label", x: 1990.2, y: 79, label: "76%", size: 9pt, anchor: "west"),
  annotate("label", x: 2009.8, y: 42, label: "35%", size: 9pt, anchor: "east"),
  annotate(
    "typst",
    x: 2009.6,
    y: 70,
    label: callout[
      #text(
        fill: cat-colours.at("Traditional biomass"),
        weight: "semibold"
      )[Traditional biomass] #emph[for cooking] \
      #emph[fades as fossil fuels spread]
    ],
    size: 6.5pt,
    anchor: "east",
  ),
)
#let de-annot = (
  annotate("label", x: 1990.2, y: 2.5, label: "2%", size: 9pt, anchor: "west"),
  annotate("label", x: 2009.8, y: 11.4, label: "11%", size: 9pt, anchor: "east"),
  annotate(
    "typst",
    x: 1990.2,
    y: 9.6,
    label: callout[
      #text(
        fill: cat-colours.at("Modern bioenergy"),
        weight: "semibold"
      )[Modern bioenergy],
      #emph[then]
      #text(
        fill: cat-colours.at("Wind & solar"),
        weight: "semibold"
      )[wind & solar], \
      #emph[built from almost nothing]
    ],
    size: 6.5pt,
    anchor: "west",
  ),
)

#let panels = compose(
  panel(vn, "Vietnam", vn-annot),
  panel(de, "Germany", de-annot),
  layout: "grid",
  columns: 2,
  collect: ("fill",),
  guides: guides(default: guide-legend(position: "bottom")),
  gutter: 1cm,
  labs: labs(
    title: "What \"Renewable Energy\" Means Depends on How Rich a Country Is",
    subtitle: "Renewable share of final energy use, by source, 1990 to 2010.",
    caption: typst([
      Source: Sustainable Energy for All (TidyTuesday 2026-05-26). \
      Author: #link("https://mickael.canouil.fr")[Mickaël CANOUIL].
    ])
  ),
)

#box[
  #panels
  #place(top + center, dx: 0pt, dy: 2cm)[
    #set align(center)
    #set text(size: 8pt, style: "italic", fill: accent)
    As incomes rise, \ two roads diverge
    #v(2pt)
    #box(inset: (x: 2pt))[
      #text(size: 40pt, fill: cat-colours.at("Traditional biomass"))[↓]
      #h(4pt)
      #text(size: 40pt, fill: cat-colours.at("Wind & solar"))[↑]
    ]
  ]
]