Maps are a powerful tool to show data. As the scope of igoR
are the
InterGovermental Organizations, mapping and IGOs are a perfect match.
This vignette provides some geospatial visualizations using the IGO datasets [@pevehouse2020] 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)
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(year = "2010") un <- igo_search("UN", exact = TRUE) # Extract three dates - some errors given that ISO doesnt have every COW Code # Also join with world sf UN1950 <- igo_members("UN", 1950) %>% mutate(ISO3_CODE = countrycode(ccode, "cown", "iso3c", warn = FALSE )) %>% left_join(world, .) %>% mutate(year = 1950) %>% select(year, orgname) UN1980 <- igo_members("UN", 1980) %>% mutate(ISO3_CODE = countrycode(ccode, "cown", "iso3c", warn = FALSE )) %>% left_join(world, .) %>% mutate(year = 1980) %>% select(year, orgname) UN2010 <- 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(UN1950, UN1980, UN2010)
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(vars(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"))
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, guide = "legend" ) + 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) )
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.
# Get shapes countries.sf <- gisco_get_countries(country = c("USA", "MEX", "CAN")) # Select years years <- seq(1930, 2010, 10) # Shared memberships USA <- igo_dyadic("USA", c("MEX", "CAN"), years) CAN <- igo_dyadic("CAN", c("USA", "MEX"), years) MEX <- igo_dyadic("MEX", c("CAN", "USA"), years) USA$value <- rowSums(USA == 1) CAN$value <- rowSums(CAN == 1) MEX$value <- rowSums(MEX == 1) # Long data Final <- USA %>% rbind(CAN) %>% rbind(MEX) %>% select(ccode1, year, value) %>% mutate(ISO3_CODE = countrycode(ccode1, "cown", "iso3c")) # Create map map <- left_join(countries.sf, Final) # Map ggplot(map) + geom_sf(aes(fill = value), color = NA) + coord_sf( crs = 2163, xlim = c(-3200000, 3333018) ) + facet_wrap(vars(year), ncol = 3 ) + scale_fill_gradientn( colors = hcl.colors(10, "YlGn", rev = TRUE), breaks = seq(0, 100, 10), guide = "legend" ) + guides(fill = guide_legend(keyheight = 1.5)) + 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", hjust = 0.05 ), legend.box.margin = margin(l = 20), legend.title = element_text(size = 8), strip.background = element_rect(fill = "grey90", colour = NA) )
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