Mapping IGOs

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Maps are a powerful tool for presenting data. Because igoR focuses on intergovernmental organizations, mapping and IGOs are a natural fit.

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

The countrycode package is useful for translating between coding schemes (COW, ISO3, NUTS and FIPS) and country names.

library(igoR)

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

# Geospatial packages.
library(giscoR)
library(sf)

Evolution of UN membership

The following map shows the evolution of United Nations membership. First, extract the data.

# Extract shapes.
world <- gisco_get_countries()

# Extract three dates. Some errors occur because ISO does not include every COW
# code.
un_all <- igo_members("UN", c(1950, 1980, 2010), status = "Full Membership") %>%
  # Add the ISO3 code.
  mutate(ISO3_CODE = countrycode(ccode, "cown", "iso3c", warn = FALSE)) %>%
  select(year, orgname, ISO3_CODE, category)

# Build an auxiliary data frame to collect every ISO3-year pair.
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 information.
  left_join(un_all, by = c("ISO3_CODE", "year"))

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

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()
  )

Number of shared memberships

Shared memberships are useful for identifying regional patterns. The following code maps how many full memberships each country shared with Australia in 2014.

# Count IGOs shared in 2014.
# Find countries alive in 2014.
states2014 <- states2016 %>%
  filter(styear <= 2014 & endyear >= 2014)

# Find 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 the map.
sharedmap <- world %>%
  left_join(shared, by = "ISO3_CODE") %>%
  select(ISO3_CODE, shared)

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

# Plot the results.
ggplot(sharedmap) +
  geom_sf(aes(fill = shared), color = NA) +
  # Highlight 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")
  )

Cross-shared memberships

The following map shows how relationships between North American countries evolved over the last 90 years, with one year representing each decade.

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

# Find 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)

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

# Plot the 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 in 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)
  )

References

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They may not be fully stable and should be used with caution. We make no claims about them.
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