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 [@pevehouse2020] included in this package. Specific packages used for geospatial data:
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)
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() # Extract three dates - some errors given that ISO doesnt 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 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_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))
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 ) %>% 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), 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") )
The following map shows how the relationships between the countries of North America has flourished on 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 = 2163, 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_void() + theme( plot.title = element_text(face = "bold"), plot.subtitle = element_text(margin = margin(t = 3, b = 10)), plot.caption = element_text(face = "italic"), legend.box.margin = margin(l = 20), legend.title = element_text(size = 8), legend.key.height = unit(1.5, "lines"), legend.key.width = unit(1, "lines"), strip.background = element_rect(fill = "grey90", colour = NA) )
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