United Nations Voting Correlations

Pairwise correlations

Notice that the vote column is a factor, with levels (in order) “yes”, “abstain”, and “no”:

levels(un_votes$vote)

## [1] "yes"     "abstain" "no"

We may then be interested in obtaining a measure of country-to-country agreement for each vote, using the pairwise_cor function.

library(widyr)

cors <- un_votes %>%
  mutate(vote = as.numeric(vote)) %>%
  pairwise_cor(country, rcid, vote, use = "pairwise.complete.obs", sort = TRUE)

cors

## # A tibble: 39,800 × 3
##    item1     item2     correlation
##    <chr>     <chr>           <dbl>
##  1 Slovakia  Czechia         0.989
##  2 Czechia   Slovakia        0.989
##  3 Lithuania Germany         0.978
##  4 Germany   Lithuania       0.978
##  5 Lithuania Estonia         0.975
##  6 Estonia   Lithuania       0.975
##  7 Lithuania Latvia          0.973
##  8 Latvia    Lithuania       0.973
##  9 Slovakia  Slovenia        0.972
## 10 Slovenia  Slovakia        0.972
## # … with 39,790 more rows

We could, for example, find the countries that the US is most and least in agreement with:

US_cors <- cors %>%
  filter(item1 == "United States")

# Most in agreement
US_cors

## # A tibble: 199 × 3
##    item1         item2          correlation
##    <chr>         <chr>                <dbl>
##  1 United States United Kingdom       0.575
##  2 United States Canada               0.570
##  3 United States Israel               0.546
##  4 United States Australia            0.514
##  5 United States Netherlands          0.513
##  6 United States Luxembourg           0.504
##  7 United States Belgium              0.498
##  8 United States Italy                0.470
##  9 United States Japan                0.462
## 10 United States France               0.459
## # … with 189 more rows

# Least in agreement
US_cors %>%
  arrange(correlation)

## # A tibble: 199 × 3
##    item1         item2          correlation
##    <chr>         <chr>                <dbl>
##  1 United States Belarus             -0.331
##  2 United States Cuba                -0.313
##  3 United States Czechoslovakia      -0.275
##  4 United States Egypt               -0.262
##  5 United States Russia              -0.261
##  6 United States Syria               -0.261
##  7 United States India               -0.241
##  8 United States Afghanistan         -0.191
##  9 United States Iraq                -0.189
## 10 United States Indonesia           -0.188
## # … with 189 more rows

This can be particularly useful when visualized on a map.

if (require("maps", quietly = TRUE) &&
    require("fuzzyjoin", quietly = TRUE) &&
    require("countrycode", quietly = TRUE) &&
    require("ggplot2", quietly = TRUE)) {
  world_data <- map_data("world") %>%
    regex_full_join(iso3166, by = c("region" = "mapname")) %>%
    filter(region != "Antarctica")
  
  US_cors %>%
    mutate(a2 = countrycode(item2, "country.name", "iso2c")) %>%
    full_join(world_data, by = "a2") %>%
    ggplot(aes(long, lat, group = group, fill = correlation)) +
    geom_polygon(color = "gray", size = .1) +
    scale_fill_gradient2() +
    coord_quickmap() +
    theme_void() +
    labs(title = "Correlation of each country's UN votes with the United States",
         subtitle = "Blue indicates agreement, red indicates disagreement",
         fill = "Correlation w/ US")
}

Visualizing clusters in a network

Another useful kind of visualization is a network plot, which can be created with Thomas Pedersen’s ggraph package. We can filter for pairs of countries with correlations above a particular threshold.

if (require("ggraph", quietly = TRUE) &&
    require("igraph", quietly = TRUE) &&
    require("countrycode", quietly = TRUE)) {
  cors_filtered <- cors %>%
    filter(correlation > .6)
  
  continents <- tibble(country = unique(un_votes$country)) %>%
    filter(country %in% cors_filtered$item1 |
             country %in% cors_filtered$item2) %>%
    mutate(continent = countrycode(country, "country.name", "continent"))
  
  set.seed(2017)
  
  cors_filtered %>%
    graph_from_data_frame(vertices = continents) %>%
    ggraph() +
    geom_edge_link(aes(edge_alpha = correlation)) +
    geom_node_point(aes(color = continent), size = 3) +
    geom_node_text(aes(label = name), check_overlap = TRUE, vjust = 1, hjust = 1) +
    theme_void() +
    labs(title = "Network of countries with correlated United Nations votes")
}

Choosing the threshold for filtering correlations (or other measures of similarity) typically requires some trial and error. Setting too high a threshold will make a graph too sparse, while too low a threshold will make a graph too crowded.