After the war, cities did the work of reconciliation

Europe dominates the global sister-city network — especially across Central and Eastern Europe

Using Wikidata’s sister-city network, this visualization reveals that Europe — particularly Germany and its Central and Eastern European neighbors — dominates global city twinning. Germany–Poland forms the largest bilateral corridor in the dataset with 344 links, part of a broader postwar reconciliation infrastructure built city by city across borders redrawn in living memory.

Figure 1: Two horizontal bar charts titled “After the war, cities did the work of reconciliation.” The left panel ranks the top 20 countries by international sister-city links, with reconciliation-bloc countries highlighted in burgundy. Germany ranks second with 1,646 links, nearly matching the United States at 1,757, despite being far smaller. Poland ranks fourth with 1,042. The right panel shows the strongest bilateral country-pair corridors, grouped by a dashed line into reconciliation corridors (burgundy) and global corridors (gray). Germany–Poland leads with 344 links — the largest bilateral corridor in the dataset — followed by Japan–United States at 213. The Central and Eastern European reconciliation cluster includes Germany–Hungary, Hungary–Romania, Czechia–Germany, Hungary–Poland, Czechia–Poland, Lithuania–Poland, and France–Germany, all in burgundy.

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load
#| warning: false
#| message: false      
#| results: "hide"     

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
    tidyverse, ggtext, showtext, janitor, ggrepel,      
    scales, glue, skimr, patchwork
    )
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 12,
  height = 8,
  units  = "in",
  dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

tt <- tidytuesdayR::tt_load(2026, week = 19)

cities <- tt$cities |> clean_names()
links <- tt$links |> clean_names()

rm(tt)
```

3. Examine the Data

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(cities)
glimpse(links)
```

4. Tidy Data

```{r}
#| label: tidy
#| warning: false

### |- enrich links with country/continent metadata ----
links_e <- links |>
  left_join(
    cities |> select(id, country_src = country, cont_src = continent),
    by = c("source" = "id")
  ) |>
  left_join(
    cities |> select(id, country_tgt = country, cont_tgt = continent),
    by = c("target" = "id")
  )

### |- international links only (filter domestic, 9.8% of total) ----
intl <- links_e |>
  filter(
    country_src != country_tgt,
    !is.na(country_src),
    !is.na(country_tgt)
  )

### |- PANEL A: top countries by international links ----
# Count cities per country for normalization denominator
cities_per_country <- cities |>
  count(country, name = "n_cities")

panel_a_data <- bind_rows(
  intl |> select(country = country_src, continent = cont_src),
  intl |> select(country = country_tgt, continent = cont_tgt)
) |>
  count(country, continent, name = "intl_links") |>
  left_join(cities_per_country, by = "country") |>
  mutate(
    links_per_city = intl_links / n_cities,
    # Highlight = postwar reconciliation bloc countries only
    # Russia excluded — current geopolitical context complicates the framing
    reconciliation_country = country %in% c(
      "Czechia", "France", "Germany", "Hungary", "Italy",
      "Lithuania", "Moldova", "Poland", "Portugal", "Romania",
      "Spain", "Ukraine", "United Kingdom"
    )
  ) |>
  arrange(desc(intl_links)) |>
  slice_head(n = 20) |>
  mutate(country = fct_reorder(country, intl_links))

### |- PANEL B: top bilateral corridors (replaces heatmap) ----
# Canonical pair: alphabetically sorted so each corridor appears once
country_continent <- cities |>
  distinct(country, continent)

panel_b_data <- intl |>
  mutate(
    country_a = if_else(country_src < country_tgt, country_src, country_tgt),
    country_b = if_else(country_src < country_tgt, country_tgt, country_src)
  ) |>
  count(country_a, country_b, name = "n_links") |>
  arrange(desc(n_links)) |>
  slice_head(n = 20) |>
  left_join(country_continent |> rename(country_a = country, cont_a = continent),
    by = "country_a"
  ) |>
  left_join(country_continent |> rename(country_b = country, cont_b = continent),
    by = "country_b"
  ) |>
  mutate(
    pair_label = glue("{country_a} \u2013 {country_b}"),
    pair_label = fct_reorder(pair_label, n_links),
    # Highlight = both countries in the postwar Central/Eastern European
    reconciliation_pair = country_a %in% c(
      "Czechia", "France", "Germany", "Hungary",
      "Lithuania", "Moldova", "Poland", "Romania", "Ukraine"
    ) & country_b %in% c(
      "Czechia", "France", "Germany", "Hungary",
      "Lithuania", "Moldova", "Poland", "Romania", "Ukraine"
    ),
    is_hero = country_a == "Germany" & country_b == "Poland"
  )
```

5. Visualization Parameters

```{r}
#| label: params
#| include: true
#| warning: false

### |- plot aesthetics ----
colors <- get_theme_colors(
    palette = list(
        bar_europe  = "#722F37",   
        bar_other   = "gray70",    
        bar_germany = "#722F37",   
        bar_europe_b = "#722F37",  
        bar_mixed    = "gray70",   
        annotation   = "#3d3d3d",  
        bg          = "#FAFAF8"    
    )
)

### |- titles and caption ----
title_text    <- str_glue("After the war, cities did the work of reconciliation")

subtitle_text <- str_glue(
    "Europe dominates the global sister-city network — especially across Central and Eastern Europe."
)

caption_text <- create_social_caption(
    tt_year = 2026,
    tt_week = 19,
    source_text  = "Wikidata via Wikipedia"
)

### |- fonts ----
setup_fonts()
fonts <- get_font_families()

### |- base theme ----
base_theme  <- create_base_theme(colors)

weekly_theme <- extend_weekly_theme(
    base_theme,
    theme(
        # Panel
        panel.background = element_rect(fill = colors$palette$bg, color = NA),
        plot.background = element_rect(fill = colors$palette$bg, color = NA),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        
        # Axes
        axis.ticks = element_blank(),
        axis.title = element_blank(),
        axis.text.x = element_text(
            family = fonts$text, size = 7.5, color = "gray40"
        ),
        axis.text.y = element_text(
            family = fonts$text, size = 8, color = "gray25", hjust = 1
        ),
        
        # Title / subtitle / caption
        plot.title = element_text(
            family = fonts$title,
            size = 18,
            face = "bold",
            color = "gray10",
            margin = margin(b = 6)
        ),
        plot.subtitle = element_markdown(
            family = 'sans',
            size = 10,
            color = "gray35",
            lineheight = 1.4,
            margin = margin(b = 16)
        ),
        plot.caption = element_markdown(
            family = fonts$text,
            size = 7,
            color = "gray55",
            hjust = 0,
            margin = margin(t = 12)
        ),
        plot.margin = margin(16, 20, 12, 16)
    )
)

theme_set(weekly_theme)
```

6. Plot

```{r}
#| label: plot
#| warning: false

### |- PANEL A: horizontal bar chart ----
p_a <- panel_a_data |>
    ggplot(aes(
        x    = intl_links,
        y    = country,
        fill = reconciliation_country
    )) +
    # Geoms
    geom_col(width = 0.72, show.legend = FALSE) +
    geom_text(
        aes(label = comma(intl_links, accuracy = 1)),
        hjust = -0.15,
        family = fonts$text,
        size = 2.8,
        color = "gray35"
    ) +
    # Scalea
    scale_fill_manual(values = c(
        "TRUE" = colors$palette$bar_europe,
        "FALSE" = colors$palette$bar_other
    )) +
    scale_x_continuous(
        expand = expansion(mult = c(0, 0.18)),
        labels = comma_format(accuracy = 1)
    ) +
    # Labs
    labs(
        title    = "International sister-city links by country",
        subtitle = "Top 20 countries · **European** countries highlighted · *Hungary, Lithuania & Czechia rank highest per city*"
    ) +
    # Theme
    theme(
        plot.title = element_text(
            family = fonts$title, size = 13, face = "bold", color = "gray15",
            margin = margin(b = 3)
        ),
        plot.subtitle = element_markdown(
            family = 'sans', size = 8.5, color = "gray45",
            margin = margin(b = 10)
        ),
        plot.margin = margin(8, 12, 8, 8)
    )

### |- PANEL B: top bilateral corridors ----
p_b <- panel_b_data |>
    ggplot(aes(
        x    = n_links,
        y    = pair_label,
        fill = reconciliation_pair
    )) +
    # Geoms
    geom_col(width = 0.72, show.legend = FALSE) +
    geom_hline(
        yintercept = 14.5,
        color      = "gray80",
        linewidth  = 0.4,
        linetype   = "dashed"
    ) +
    geom_text(
        aes(label = comma(n_links, accuracy = 1)),
        hjust = -0.15,
        family = fonts$text,
        size = 2.8,
        color = "gray35"
    ) +
    # Annotate
    annotate(
        "text",
        x = 380, y = 15.1,
        label = "Reconciliation corridors",
        family = fonts$text, size = 2.4,
        color = colors$palette$bar_europe_b,
        hjust = 1, fontface = "italic"
    ) +
    annotate(
        "text",
        x = 380, y = 14.0,
        label = "Global corridors",
        family = fonts$text, size = 2.4,
        color = "gray55",
        hjust = 1, fontface = "italic"
    ) +
    # Scales
    scale_fill_manual(
        values = c(
            "TRUE" = colors$palette$bar_europe_b,
            "FALSE" = colors$palette$bar_mixed
        )
    ) +
    scale_x_continuous(
        expand = expansion(mult = c(0, 0.05)),
        labels = comma_format(accuracy = 1)
    ) +
    # Labw
    labs(
        title    = "Strongest bilateral sister-city corridors",
        subtitle = "Top 20 country pairs · **Reconciliation corridors** highlighted · Germany appears in 8 of the top 13"
    ) +
    # Theme
    theme(
        plot.title = element_text(
            family = fonts$title, size = 13, face = "bold", color = "gray15",
            margin = margin(b = 3)
        ),
        plot.subtitle = element_markdown(
            family = 'sans', size = 8.5, color = "gray45",
            margin = margin(b = 10)
        ),
        panel.grid.major.x = element_line(color = "gray92", linewidth = 0.3),
        plot.margin = margin(8, 16, 8, 8)
    )

### |- combine plots ----
combined_plot <- p_a + plot_spacer() + p_b +
    plot_layout(widths = c(1, 0.01, 1)) +
    plot_annotation(
        title = title_text,
        subtitle = subtitle_text,
        caption = caption_text,
        theme = theme(
            plot.title = element_text(
                family = fonts$title,
                size = 24,
                face = "bold",
                color = "gray10",
                margin = margin(b = 6)
            ),
            plot.subtitle = element_markdown(
                family = 'sans',
                size = 12,
                color = "gray35",
                lineheight = 1.4,
                margin = margin(b = 4)
            ),
            plot.caption = element_markdown(
                family = fonts$caption,
                size = 7,
                color = "gray40",
                hjust = 0,
                margin = margin(t = 10)
            ),
            plot.background = element_rect(fill = colors$palette$bg, color = NA),
            plot.margin = margin(16, 20, 12, 16),
        )
    )
```

7. Save

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plot, 
  type = "tidytuesday", 
  year = 2026, 
  week = 19, 
  width  = 12,
  height = 8
  )
```

8. Session Info

TipExpand for Session Info

R version 4.5.3 (2026-03-11 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.2      patchwork_1.3.2 skimr_2.2.2     glue_1.8.0     
 [5] scales_1.4.0    ggrepel_0.9.8   janitor_2.2.1   showtext_0.9-8 
 [9] showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.5
[13] forcats_1.0.1   stringr_1.6.0   dplyr_1.2.1     purrr_1.2.2    
[17] readr_2.2.0     tidyr_1.3.2     tibble_3.3.1    ggplot2_4.0.3  
[21] tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   farver_2.1.2       S7_0.2.1           fastmap_1.2.0     
 [5] gh_1.5.0           digest_0.6.39      timechange_0.4.0   lifecycle_1.0.5   
 [9] rsvg_2.7.0         magrittr_2.0.5     compiler_4.5.3     rlang_1.2.0       
[13] tools_4.5.3        yaml_2.3.12        knitr_1.51         labeling_0.4.3    
[17] htmlwidgets_1.6.4  bit_4.6.0          curl_7.0.0         xml2_1.5.2        
[21] camcorder_0.1.0    repr_1.1.7         RColorBrewer_1.1-3 tidytuesdayR_1.3.2
[25] withr_3.0.2        grid_4.5.3         gitcreds_0.1.2     cli_3.6.6         
[29] rmarkdown_2.31     crayon_1.5.3       generics_0.1.4     otel_0.2.0        
[33] rstudioapi_0.18.0  tzdb_0.5.0         commonmark_2.0.0   parallel_4.5.3    
[37] ggplotify_0.1.3    base64enc_0.1-6    vctrs_0.7.3        yulab.utils_0.2.4 
[41] jsonlite_2.0.0     litedown_0.9       gridGraphics_0.5-1 hms_1.1.4         
[45] bit64_4.6.0-1      systemfonts_1.3.2  magick_2.9.1       gifski_1.32.0-2   
[49] codetools_0.2-20   stringi_1.8.7      gtable_0.3.6       pillar_1.11.1     
[53] rappdirs_0.3.4     htmltools_0.5.9    R6_2.6.1           httr2_1.2.2       
[57] textshaping_1.0.5  rprojroot_2.1.1    vroom_1.7.1        evaluate_1.0.5    
[61] markdown_2.0       gridtext_0.1.6     snakecase_0.11.1   Rcpp_1.1.1        
[65] svglite_2.2.2      xfun_0.57          fs_2.0.1           pkgconfig_2.0.3   

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in tt_2026_19.qmd.

For the full repository, click here.

10. References

TipExpand for References

1. Data Source:
   • TidyTuesday 2026 Week 19: Twinned Cities

11. Custom Functions Documentation

Note📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

• fonts.R: setup_fonts(), get_font_families() - Font management with showtext
• social_icons.R: create_social_caption() - Generates formatted social media captions
• image_utils.R: save_plot() - Consistent plot saving with naming conventions
• base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils