Mapping IGOs

Maps are a powerful tool to show data. As the scope of igoR are 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 maps shows the evolution of countries that are members of the United Nations.

First we should extract the data:

# Extract shapes
world <- gisco_get_countries()
un <- igo_search("UN", exact = TRUE)

# Extract three dates - some errors given that ISO doesnt have every COW Code
# Also join with world sf
un_1950 <- igo_members("UN", 1950) %>%
  mutate(ISO3_CODE = countrycode(ccode, "cown", "iso3c", warn = FALSE)) %>%
  left_join(world, .) %>%
  mutate(year = 1950) %>%
  select(year, orgname)

un_1989 <- igo_members("UN", 1980) %>%
  mutate(ISO3_CODE = countrycode(ccode, "cown", "iso3c", warn = FALSE)) %>%
  left_join(world, .) %>%
  mutate(year = 1980) %>%
  select(year, orgname)

un_2010 <- igo_members("UN", 2010) %>%
  mutate(ISO3_CODE = countrycode(ccode, "cown", "iso3c", warn = FALSE)) %>%
  left_join(world, .) %>%
  mutate(year = 2010) %>%
  select(year, orgname)

# Join all
un_all <- bind_rows(un_1950, un_1989, un_2010)

Note that the map is not completely accurate, as the base shapefile contains the countries that exists on 2016. Some countries, as Czechoslovakia, East or West Germany are not included.

Now we are ready to plot with ggplot2:

ggplot(un_all) +
  geom_sf(aes(fill = orgname), color = NA, show.legend = FALSE) +
  # Robinson
  coord_sf(crs = "ESRI:54030") +
  facet_wrap(~year, ncol = 1, strip.position = "left") +
  scale_fill_manual(values = "#74A9CF", na.value = "#E0E0E0") +
  theme_void() +
  labs(title = "UN Members", caption = gisco_attributions()) +
  theme(plot.caption = element_text(face = "italic"))

UN Members

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 on 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$statenme),
  year = 2014
)
shared$shared <- rowSums(shared == 1)

# ISO3 Code
shared$ISO3_CODE <- countrycode(shared$ccode2, "cown", "iso3c", warn = FALSE)

# Merge with map
sharedmap <- world %>%
  left_join(shared) %>%
  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(
    direction = "horizontal",
    title.position = "top",
    label.position = "bottom",
    nrow = 1,
    keyheight = 0.5,
    keywidth = 1.5
  )) +
  labs(
    title = "Shared Full Memberships with Australia (2014)",
    fill = "Number of IGOs shared",
    caption = gisco_attributions()
  ) +
  theme_void() +
  theme(
    legend.position = "bottom",
    plot.title = element_text(face = "bold", hjust = 0.5),
    plot.caption = element_text(
      face = "italic",
      size = 8,
      hjust = 0.05
    ),
    legend.title = element_text(size = 7),
    legend.text = element_text(size = 8)
  )