Maps are a powerful tool for showing data. Because igoR focuses on Intergovernmental Organizations, mapping and IGOs are a natural fit.
This vignette provides geospatial visualizations using the IGO datasets (Pevehouse et al. 2020) included in this package. It uses these packages for geospatial data:
- giscoR for extracting the shapefiles of the countries.
- ggplot2 for plotting.
The countrycode package is useful 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 United Nations membership. First, 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 because the base shapefile contains 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 how many full memberships each country shared with Australia in 2014:
# 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 relationships between North American countries have evolved over the last 90 years, with one year representing 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)