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+# ============================================================
+#  PPT CONTENT — Empirical urban-rural typology of Swedish municipalities
+#  Correspondence Analysis + hierarchical clustering (2022 sampling)
+#  Six-cluster cut
+#
+#  Run from the project root (ruralitic-qrm/).
+#  Figures are saved to ppt/figures/. Slide text is in the
+#  comments below each section header.
+# ============================================================
+
+library(tidyverse)
+library(FactoMineR)
+library(factoextra)
+library(ggrepel)
+library(showtext)
+
+font_add_google("Source Sans 3", "source_sans_3")
+showtext_auto()
+
+theme_ppt <- theme_minimal(base_size = 14, base_family = "source_sans_3") +
+  theme(
+    panel.grid.minor  = element_blank(),
+    panel.grid.major  = element_line(colour = "grey92"),
+    legend.position   = "bottom",
+    legend.title      = element_text(face = "bold"),
+    plot.margin       = margin(8, 12, 8, 8)
+  )
+theme_set(theme_ppt)
+
+cluster_labels <- c(
+  "1" = "Remote & peripheral",
+  "2" = "Central industrial towns",
+  "3" = "Peri-rural commuter belt",
+  "4" = "Regional service centres",
+  "5" = "Affluent suburbs & university satellites",
+  "6" = "Inner Stockholm core"
+)
+cluster_palette <- c(
+  "1" = "#B07F4F",
+  "2" = "#D7A86E",
+  "3" = "#7CB07C",
+  "4" = "#6FA8DC",
+  "5" = "#C2738B",
+  "6" = "#7A3A4F"
+)
+
+county_names <- c(
+  "01" = "Stockholm",    "03" = "Uppsala",       "04" = "Södermanland",
+  "05" = "Östergötland", "06" = "Jönköping",     "07" = "Kronoberg",
+  "08" = "Kalmar",       "09" = "Gotland",       "10" = "Blekinge",
+  "12" = "Skåne",        "13" = "Halland",       "14" = "Västra Götaland",
+  "17" = "Värmland",     "18" = "Örebro",        "19" = "Västmanland",
+  "20" = "Dalarna",      "21" = "Gävleborg",     "22" = "Västernorrland",
+  "23" = "Jämtland",     "24" = "Västerbotten",  "25" = "Norrbotten"
+)
+
+# Load data
+afc      <- read_rds("data/processed/proportions_CA.rds")
+hcpc     <- read_rds("data/processed/proportions_HCPC_6.rds")
+clusters <- read_csv("data/processed/cluster_assignment_6.csv",
+                     show_col_types = FALSE) |>
+  rename(cluster = c6) |>
+  mutate(cluster_label = cluster_labels[as.character(cluster)])
+
+panel_raw <- readxl::read_excel("data/Municipalities_db_2.xlsx",
+                                col_types = "text", n_max = 290) |>
+  transmute(municipality, code = str_pad(code, 4, "left", "0"))
+
+dim1_pct <- round(afc$eig[1, 2], 1)
+dim2_pct <- round(afc$eig[2, 2], 1)
+
+row_df <- as.data.frame(afc$row$coord[, 1:2]) |>
+  rownames_to_column("municipality") |>
+  left_join(clusters, by = "municipality")
+
+
+# ============================================================
+#  SLIDE 1 — Data & motivation
+#  Title: "Building an urban–rural typology from the data"
+#
+#  Bullet points:
+#   • Sweden's 290 municipalities are routinely classified by
+#     administrative or population-size rules (SCB). These categories
+#     are imposed from outside the data.
+#   • This analysis asks instead: how do municipalities actually differ
+#     across structural dimensions?
+#   • Data source: Statistics Sweden, 2022 project sampling.
+#   • Six ACTIVE variable blocks:
+#       Education (4 attainment levels)
+#       Employment (16 activity sectors)
+#       Housing (rented / tenant-owned / owner-occupied)
+#       Workplace mobility (commuters in, commuters out, working locally)
+#       Migration (in- and outmigration)
+#       Demography (retirees, number of localities)
+#   • Two SUPPLEMENTARY blocks projected post-hoc:
+#       Educational provision (pre-school through HE, by ownership)
+#       Opinion (survey satisfaction with local schools)
+#   • Key pre-processing: block normalisation. Within each block,
+#     every municipality is rescaled to the same total, preventing
+#     Stockholm from dominating the analysis due to sheer size.
+#
+#  No figure needed — use the variable block list as a visual
+#  schematic or table in the slide.
+# ============================================================
+
+
+# ============================================================
+#  SLIDE 2 — Correspondence analysis: the space of municipalities
+#  Title: "Two dimensions capture two-thirds of the variation"
+#
+#  Bullet points:
+#   • CA places all 290 municipalities in a low-dimensional space
+#     where proximity = similarity on the active variables.
+#   • Dim 1 (rural–urban): Left pole → agriculture, mining &
+#     manufacturing, owner-occupied housing, upper-secondary education.
+#     Right pole → IT, finance, professional services, post-graduate
+#     attainment, apartment housing.
+#   • Dim 2 (labour-market self-containment): Top → residents work
+#     where they live (Göteborg, Malmö, Umeå). Bottom → outbound
+#     commuters, residential satellites (Knivsta, Salem, Staffanstorp).
+#   • Together Dim 1 + Dim 2 account for ~65% of total variability.
+#
+#  Figure: slide2_biplot.png
+# ============================================================
+
+label_munis <- c("Stockholm", "Göteborg", "Malmö", "Uppsala", "Lund", "Umeå",
+                 "Linköping", "Solna", "Danderyd", "Kiruna", "Gotland",
+                 "Knivsta", "Falköping", "Tomelilla", "Skellefteå", "Piteå",
+                 "Partille", "Sundbyberg", "Lindesberg")
+
+contribs <- as.data.frame(afc$col$contrib) |>
+  rownames_to_column("variable") |>
+  mutate(total = `Dim 1` + `Dim 2`) |>
+  arrange(desc(total)) |>
+  head(15)
+
+col_df <- as.data.frame(afc$col$coord[, 1:2]) |>
+  rownames_to_column("variable") |>
+  filter(variable %in% contribs$variable)
+
+fig_biplot <- ggplot() +
+  geom_hline(yintercept = 0, linetype = "dashed", colour = "grey60") +
+  geom_vline(xintercept = 0, linetype = "dashed", colour = "grey60") +
+  geom_point(data = row_df,
+             aes(`Dim 1`, `Dim 2`, colour = cluster_label),
+             alpha = 0.75, size = 2.2) +
+  geom_text_repel(
+    data    = row_df |> filter(municipality %in% label_munis),
+    aes(`Dim 1`, `Dim 2`, label = municipality),
+    size = 3.5, colour = "grey20", family = "source_sans_3",
+    max.overlaps = 30, segment.size = 0.25
+  ) +
+  geom_point(data = col_df, aes(`Dim 1`, `Dim 2`),
+             shape = 17, colour = "firebrick", size = 3) +
+  geom_text_repel(
+    data = col_df, aes(`Dim 1`, `Dim 2`, label = variable),
+    colour = "firebrick", size = 3, family = "source_sans_3",
+    max.overlaps = 30, segment.size = 0.25
+  ) +
+  scale_colour_manual(values = cluster_palette |> set_names(cluster_labels),
+                      name = NULL) +
+  labs(
+    x = paste0("Dim 1 — rural–urban (", dim1_pct, "%)"),
+    y = paste0("Dim 2 — labour-market self-containment (", dim2_pct, "%)")
+  ) +
+  guides(colour = guide_legend(nrow = 2))
+
+ggsave("ppt/figures/slide2_biplot.png", fig_biplot,
+       width = 11, height = 7, dpi = 150)
+message("Saved: ppt/figures/slide2_biplot.png")
+
+
+# ============================================================
+#  SLIDE 3 — Six empirical types of municipality
+#  Title: "Six coherent types emerge from Ward clustering"
+#
+#  Cluster descriptions:
+#
+#  Cl 1 — Remote & peripheral
+#    Most rural extreme. Agriculture/forestry/fishing, sparsely
+#    populated, own full educational infrastructure at every age
+#    (komvux, preschool). Examples: Kiruna, Piteå, Skellefteå, Gotland.
+#
+#  Cl 2 — Central industrial towns
+#    Rural-industrial, but not as remote. Mining & manufacturing
+#    dominant. Owner-occupied housing, upper-secondary ceiling.
+#    Examples: Falköping, Lindesberg, Hedemora.
+#
+#  Cl 3 — Peri-rural commuter belt
+#    Small southern and central rural municipalities. Many residents
+#    commute out. Owner-occupied, construction and agriculture visible.
+#    Below-average satisfaction with local high schools.
+#    Examples: Tomelilla, Osby, Klippan, Sölvesborg.
+#
+#  Cl 4 — Regional service centres
+#    Mid-sized cities with self-contained labour markets. Rented and
+#    tenant-owned housing, public administration, post-secondary
+#    attainment. Examples: Göteborg, Malmö, Umeå, Linköping, Kalmar.
+#
+#  Cl 5 — Affluent suburbs & university satellites
+#    Outbound commuters, post-secondary attainment, tenant-owned
+#    housing. Residential satellites whose labour markets sit elsewhere.
+#    Examples: Lund, Mölndal, Partille, Huddinge, Knivsta, Kungsbacka.
+#
+#  Cl 6 — Inner Stockholm core
+#    Inbound commuting, IT and finance employment, apartment housing,
+#    post-graduate attainment at extreme levels. These are destinations
+#    in the commuting network, not origins.
+#    Examples: Stockholm, Solna, Sundbyberg, Danderyd, Lidingö, Täby.
+#
+#  Figures: slide3_clusters.png (main) · slide3_dendrogram.png (inset)
+# ============================================================
+
+centroids <- row_df |>
+  group_by(cluster_label) |>
+  summarise(`Dim 1` = mean(`Dim 1`), `Dim 2` = mean(`Dim 2`), .groups = "drop")
+
+fig_clusters <- ggplot(row_df, aes(`Dim 1`, `Dim 2`, colour = cluster_label)) +
+  geom_hline(yintercept = 0, linetype = "dashed", colour = "grey70") +
+  geom_vline(xintercept = 0, linetype = "dashed", colour = "grey70") +
+  geom_point(alpha = 0.5, size = 2) +
+  stat_ellipse(level = 0.68, linewidth = 0.7) +
+  geom_point(data = centroids, size = 5, shape = 18, colour = "grey15") +
+  geom_label_repel(
+    data  = centroids, aes(label = cluster_label),
+    fill  = "white", colour = "black",
+    family = "source_sans_3", size = 3.5,
+    label.size = 0.25, label.padding = unit(0.3, "lines"),
+    min.segment.length = 0, max.overlaps = 20
+  ) +
+  scale_colour_manual(values = cluster_palette |> set_names(cluster_labels),
+                      guide = "none") +
+  labs(
+    x = paste0("Dim 1 — rural–urban (", dim1_pct, "%)"),
+    y = paste0("Dim 2 — labour-market self-containment (", dim2_pct, "%)")
+  )
+
+ggsave("ppt/figures/slide3_clusters.png", fig_clusters,
+       width = 11, height = 7, dpi = 150)
+message("Saved: ppt/figures/slide3_clusters.png")
+
+# Dendrogram with 6-cluster cut
+tree  <- hcpc$call$t$tree
+h_max <- max(tree$height)
+
+fig_dendro <- fviz_dend(
+  tree, k = 6, show_labels = FALSE,
+  rect = TRUE,
+  rect_border = unname(cluster_palette),
+  rect_fill   = TRUE,
+  k_colors    = unname(cluster_palette),
+  main = "", ylab = "Merge distance (Ward)"
+) +
+  coord_cartesian(ylim = c(0, h_max * 1.05)) +
+  guides(linewidth = "none") +
+  theme(plot.title = element_blank(),
+        text = element_text(family = "source_sans_3", size = 13))
+
+ggsave("ppt/figures/slide3_dendrogram.png", fig_dendro,
+       width = 11, height = 4.5, dpi = 150)
+message("Saved: ppt/figures/slide3_dendrogram.png")
+
+
+# ============================================================
+#  SLIDE 4 — Geography & key takeaways
+#  Title: "The typology maps coherently onto Swedish geography"
+#
+#  Bullet points:
+#   • Clusters 1 & 2 (both rural types) dominate almost everywhere
+#     outside the metropolitan areas — the rural majority.
+#   • Cluster 6 (inner Stockholm core) is confined to Stockholm and
+#     Uppsala counties; cluster 5 (affluent suburbs) spreads into
+#     Skåne (Lund) and Västra Götaland (Mölndal, Partille).
+#   • Cluster 4 (regional centres) appears thinly but consistently
+#     across most counties — one or two per county.
+#
+#  Key takeaways:
+#   • Biggest empirical break: NOT metro vs. non-metro, but between
+#     two kinds of rural — remote & peripheral (Cl. 1), central
+#     industrial towns (Cl. 2), and peri-rural commuters (Cl. 3).
+#   • At the urban end, two kinds of city: self-contained regional
+#     centres (Cl. 4) vs. the metropolitan region (Cl. 5 & 6).
+#   • Within the metropolitan region, the data cleanly separate
+#     residential suburbs that commute IN (Cl. 5) from the inner core
+#     that receives commuters (Cl. 6).
+#
+#  Figure: slide4_county.png
+# ============================================================
+
+county_order <- c(
+  "Skåne", "Blekinge", "Halland", "Kronoberg", "Kalmar", "Gotland",
+  "Jönköping", "Östergötland", "Södermanland", "Västra Götaland",
+  "Örebro", "Västmanland", "Stockholm", "Uppsala", "Dalarna", "Värmland",
+  "Gävleborg", "Västernorrland", "Jämtland", "Västerbotten", "Norrbotten"
+)
+
+clusters_geo <- clusters |>
+  left_join(panel_raw, by = "municipality") |>
+  mutate(county = county_names[str_sub(code, 1, 2)]) |>
+  filter(!is.na(county))
+
+fig_county <- clusters_geo |>
+  count(county, cluster_label) |>
+  mutate(
+    county        = factor(county, levels = county_order),
+    cluster_label = factor(cluster_label, levels = cluster_labels)
+  ) |>
+  ggplot(aes(county, n, fill = cluster_label)) +
+  geom_col(position = "fill") +
+  scale_fill_manual(values = cluster_palette |> set_names(cluster_labels),
+                    name = NULL) +
+  scale_y_continuous(labels = scales::percent_format(), expand = c(0, 0)) +
+  labs(x = NULL, y = "Share of municipalities") +
+  theme(
+    axis.text.x     = element_text(angle = 45, hjust = 1, size = 11),
+    legend.position = "bottom",
+    legend.text     = element_text(size = 10)
+  ) +
+  guides(fill = guide_legend(nrow = 2))
+
+ggsave("ppt/figures/slide4_county.png", fig_county,
+       width = 12, height = 6.5, dpi = 150)
+message("Saved: ppt/figures/slide4_county.png")
+
+message("\nAll figures written to ppt/figures/. Ready to paste into the slide deck.")
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