ruralitic-qrm/ppt/content.R

# ============================================================
#  PPT CONTENT: Empirical urban-rural typology of Swedish municipalities
#  Correspondence Analysis + hierarchical clustering (2022 sampling)
#  Six-cluster cut
# ============================================================

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 = 18, 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")

label_munis <- c("Stockholm", "Göteborg", "Malmö", "Uppsala", "Lund", "Umeå",
                 "Linköping", "Solna", "Danderyd", "Kiruna", "Gotland",
                 "Knivsta", "Falköping", "Tomelilla", "Skellefteå", "Piteå",
                 "Partille", "Sundbyberg", "Lindesberg", "Gävle")

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)

highlight_munis <- c("Gävle")

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_point(data = row_df |> filter(municipality %in% highlight_munis),
             aes(`Dim 1`, `Dim 2`),
             shape = 21, size = 5, stroke = 1.5,
             fill = NA, colour = "black") +
  geom_text_repel(
    data    = row_df |> filter(municipality %in% label_munis),
    aes(`Dim 1`, `Dim 2`, label = municipality,
        fontface = if_else(municipality %in% highlight_munis, "bold", "plain")),
    size = 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 = 3.5) +
  geom_text_repel(
    data = col_df, aes(`Dim 1`, `Dim 2`, label = variable),
    colour = "firebrick", size = 4.5, 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, "%)")
  ) +
  theme(legend.position = "none")
  #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")

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 = 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 = 17))

ggsave("ppt/figures/slide3_dendrogram.png", fig_dendro,
       width = 11, height = 4.5, dpi = 150)
message("Saved: ppt/figures/slide3_dendrogram.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"
)

# Population per municipality (2022)
pop_raw <- readxl::read_excel("data/Municipalities_db_2.xlsx",
                              col_types = "text") |>
  filter(year == "2022") |>
  transmute(municipality, pop = as.numeric(Population))

clusters_geo <- clusters |>
  left_join(panel_raw, by = "municipality") |>
  left_join(pop_raw,   by = "municipality") |>
  mutate(county = county_names[str_sub(code, 1, 2)]) |>
  filter(!is.na(county))

county_pop <- clusters_geo |>
  group_by(county, cluster_label) |>
  summarise(pop = sum(pop, na.rm = TRUE), .groups = "drop") |>
  group_by(county) |>
  mutate(
    share     = pop / sum(pop),
    pop_label = ifelse(pop >= 1e6,
                       paste0(round(pop / 1e6, 1), "M"),
                       paste0(round(pop / 1e3), "k")),
    county        = factor(county, levels = county_order),
    cluster_label = factor(cluster_label, levels = cluster_labels)
  ) |>
  ungroup()

# Combined faceted figure: municipalities (left) and population (right)
county_munis <- clusters_geo |>
  count(county, cluster_label) |>
  group_by(county) |>
  mutate(share = n / sum(n)) |>
  ungroup() |>
  mutate(
    county        = factor(county, levels = county_order),
    cluster_label = factor(cluster_label, levels = cluster_labels),
    facet         = "Share of municipalities",
    pop_label     = NA_character_
  )

county_pop_f <- county_pop |>
  mutate(facet = "Share of population")

combined <- bind_rows(
  county_munis |> select(county, cluster_label, share, facet, pop_label),
  county_pop_f |> select(county, cluster_label, share, facet, pop_label)
) |>
  mutate(facet = factor(facet, levels = c("Share of municipalities",
                                          "Share of population")))

fig_county <- ggplot(combined, aes(x = share, y = county, fill = cluster_label)) +
  geom_col(position = "stack", width = 0.8) +
  geom_text(
    aes(label = pop_label),
    position = position_stack(vjust = 0.5),
    size = 3, family = "source_sans_3", colour = "grey20",
    check_overlap = TRUE, na.rm = TRUE
  ) +
  facet_wrap(~facet) +
  scale_fill_manual(values = cluster_palette |> set_names(cluster_labels),
                    name = NULL) +
  scale_x_continuous(labels = scales::percent_format(), expand = c(0, 0)) +
  labs(y = NULL, x = NULL) +
  theme(
    axis.text.y     = element_text(size = 13),
    strip.text      = element_text(size = 14, face = "bold"),
    legend.position = "bottom",
    legend.text     = element_text(size = 12)
  ) +
  guides(fill = guide_legend(nrow = 3))

ggsave("ppt/figures/slide4_county.png", fig_county,
       width = 14, height = 9, dpi = 150)
message("Saved: ppt/figures/slide4_county.png")

# Standalone: absolute population stacked bars (bar length = total population)
fig_county_abs <- ggplot(county_pop,
                         aes(x = pop, y = county, fill = cluster_label)) +
  geom_col(width = 0.8) +
  geom_text(
    aes(label = pop_label),
    position = position_stack(vjust = 0.5),
    size = 3.3, family = "source_sans_3", colour = "grey20",
    check_overlap = TRUE
  ) +
  scale_fill_manual(values = cluster_palette |> set_names(cluster_labels),
                    name = NULL) +
  scale_x_continuous(labels = \(x) paste0(round(x / 1e6, 1), "M"),
                     expand = c(0, 0)) +
  labs(y = NULL, x = "Total population") +
  theme(
    axis.text.y     = element_text(size = 14),
    legend.position = "bottom",
    legend.text     = element_text(size = 13)
  ) +
  guides(fill = guide_legend(nrow = 3))

ggsave("ppt/figures/slide4_county_abs.png", fig_county_abs,
       width = 9, height = 9, dpi = 150)
message("Saved: ppt/figures/slide4_county_abs.png")

# Clusters detail
cl_lb <- as.data.frame(cluster_labels) |> 
  rowid_to_column("c6")

cl <- read.csv("data/processed/cluster_assignment_6.csv") |> 
  arrange(-2) |> 
  left_join(
    cl_lb
  ) |> 
  select(-c6)