Maps are a powerful tool to show data. As the scope of igoR is the Intergovernmental Organizations, mapping and IGOs are a perfect match.

This vignette provides some geospatial visualizations using the IGO data sets (Pevehouse et al. 2020) included in this package. Specific packages used for geospatial data:

- giscoR for extracting the shapefiles of the countries.
- ggplot2 for plotting.

Also countrycode is a very handy package for translating between coding schemes (CoW, ISO3, NUTS, FIPS) and country names.

    library(igoR)

    # Helper packages
    library(dplyr)
    library(ggplot2)
    library(countrycode)

    # Geospatial packages
    library(giscoR)
    library(sf)

## Evolution of the composition of UN

The following map shows the evolution of countries that are members of the United Nations. First we should extract the data:

    # Extract shapes
    world <- gisco_get_countries()

    # Extract three dates - some errors given that ISO doesn't have every COW Code
    un_all <- igo_members("UN", c(1950, 1980, 2010), status = "Full Membership") %>%
      # Add ISO3 Code
      mutate(ISO3_CODE = countrycode(ccode, "cown", "iso3c", warn = FALSE)) %>%
      select(year, orgname, ISO3_CODE, category)

    # Auxiliar data.frame to collect every ISO3-year pairs

    base_df <- expand.grid(
      ISO3_CODE = unique(world$ISO3_CODE),
      year = unique(un_all$year),
      stringsAsFactors = FALSE
    ) %>%
      as_tibble()

    # Merge everything with the spatial object
    un_all_sf <- world %>%
      # Expand to all cases
      left_join(base_df, by = "ISO3_CODE") %>%
      # Add info
      left_join(un_all, by = c("ISO3_CODE", "year"))

Note that the map is not completely accurate, as the base shapefile contains the countries that existed in 2016. Some countries, such as Czechoslovakia, East Germany and West Germany are not included.

Now we are ready to plot with ggplot2:

    ggplot(un_all_sf) +
      geom_sf(aes(fill = category), color = NA, show.legend = FALSE) +
      # Robinson
      coord_sf(crs = "ESRI:54030") +
      facet_wrap(~year, ncol = 1, strip.position = "left") +
      scale_fill_manual(
        values = c("Full Membership" = "#74A9CF"),
        na.value = "#E0E0E0",
      ) +
      labs(
        title = "UN Members",
        caption = gisco_attributions(),
      ) +
      theme_minimal() +
      theme(
        plot.caption = element_text(face = "italic", hjust = 0.15),
        axis.line = element_blank(),
        axis.text = element_blank()
      )

UN Members (1950, 1980, 2010)

## Number of shared memberships

Shared memberships are useful for identifying regional patterns. The following code produces a map showing the number of full memberships shared with Australia for each country in the world:

    # Number of igos shared - 2014
    # Countries alive in 2014
    states2014 <- states2016 %>%
      filter(styear <= 2014 & endyear >= 2014)

    # Shared memberships with Australia
    shared <- igo_dyadic("AUL", as.character(states2014$statenme), year = 2014) %>%
      rowwise() %>%
      mutate(shared = sum(c_across(aaaid:wassen) == 1)) %>%
      mutate(ISO3_CODE = countrycode(ccode2, "cown", "iso3c", warn = FALSE)) %>%
      select(ISO3_CODE, shared)

    # Merge with map
    sharedmap <- world %>%
      left_join(shared, by = "ISO3_CODE") %>%
      select(ISO3_CODE, shared)

    # Plot with custom palette
    pal <- hcl.colors(10, palette = "Lajolla")

    # Plot
    ggplot(sharedmap) +
      geom_sf(aes(fill = shared), color = NA) +
      # Australia
      geom_sf(
        data = sharedmap %>% filter(ISO3_CODE == "AUS"),
        fill = "black",
        color = NA,
      ) +
      # Robinson
      coord_sf(crs = "ESRI:54030") +
      scale_fill_gradientn(colours = pal, n.breaks = 10) +
      guides(fill = guide_legend(nrow = 1)) +
      labs(
        title = "Shared Full Memberships with Australia (2014)",
        fill = "Number of IGOs shared",
        caption = gisco_attributions()
      ) +
      theme_minimal() +
      theme(
        plot.title = element_text(face = "bold", hjust = 0.5),
        plot.caption = element_text(face = "italic", size = 7, hjust = 0.15),
        axis.line = element_blank(),
        axis.text = element_blank(),
        legend.title = element_text(size = 7),
        legend.text = element_text(size = 8),
        legend.position = "bottom",
        legend.direction = "horizontal",
        legend.title.position = "top",
        legend.text.position = "bottom",
        legend.key.width = unit(1.5, "lines"),
        legend.key.height = unit(0.5, "lines")
      )

Shared Full Memberships with Australia (2014)

## Cross-shared memberships

The following map shows how the relationships between the countries of North America have flourished over the last 90 years, using a year as representative of each decade.

    # Select years
    years <- seq(1930, 2010, 10)

    # Shared memberships
    cntries <- c("USA", "CAN", "MEX")
    all <- igo_dyadic(cntries, cntries, years) %>%
      rowwise() %>%
      mutate(value = sum(c_across(aaaid:wassen) == 1)) %>%
      mutate(ISO3_CODE = countrycode(ccode1, "cown", "iso3c")) %>%
      select(ISO3_CODE, year, value)

    # Create map
    # Get shapes
    countries_sf <- gisco_get_countries(country = c("USA", "MEX", "CAN")) %>%
      left_join(all, by = "ISO3_CODE")

    # Map
    ggplot(countries_sf) +
      geom_sf(aes(fill = value), color = NA) +
      coord_sf(crs = 9311, xlim = c(-3200000, 3333018)) +
      facet_wrap(~year, ncol = 3) +
      scale_fill_gradientn(
        colors = hcl.colors(10, "YlGn", rev = TRUE),
        breaks = seq(0, 100, 5)
      ) +
      guides(fill = guide_legend(reverse = TRUE)) +
      labs(
        title = "Shared Full Memberships on North America",
        subtitle = "(1930-2010)",
        fill = "Shared IGOs",
        caption = gisco_attributions()
      ) +
      theme_minimal() +
      theme(
        panel.grid = element_blank(),
        axis.line = element_blank(),
        axis.text = element_blank(),
        strip.background = element_rect(fill = "grey90", colour = NA)
      )

Shared Full Memberships on North America (1930 - 2010)

## References

Pevehouse, Jon CW, Timothy Nordstrom, Roseanne W McManus, and Anne Spencer Jamison. 2020. “Tracking Organizations in the World: The Correlates of War IGO Version 3.0 Datasets.” Journal of Peace Research 57 (3): 492–503. https://doi.org/10.1177/0022343319881175.