Network

For the network representation we first choose some countries. Then, the overall idea is to make a matrix of joint votes and construct the graph representation.

countries <- c( 
      "Argentina",
      "Brazil",
      "Canada",
      "Chile",
      "China",
      "France",
      "Italy",
      "Israel",
      "South Africa",
      "Germany",
      "Russia",
      "United States",
      "United Kingdom",
      "Venezuela",
      "Japan"
      )

# drop some unused columns
dat[, `:=` (importantvote = NULL,
            amend = NULL,
            para = NULL,
            short = NULL,
            descr = NULL)]

# make empty matrix
adj_mat <- matrix(rep(0,length(countries)^2),
       nrow = length(countries), 
       dimnames = list(countries,countries))

#function to increment link between countries every joint vote
make_link <- function(from, to){
   adj_mat[from,to] <<- adj_mat[from,to] + 1
   adj_mat[to, from] <<- adj_mat[to,from] + 1
}

# function to apply make_link to each resolution data.table
link_over_dt <- function(dt){
   yes <- dt[vote == 'yes', country]
   no <- dt[vote == 'no', country]
   abstain <- dt[vote == 'abstain', country]
   
   if (length(yes)>1) { 
         yes <- data.frame(from = t(combn(yes,2))[,1],
                           to = t(combn(yes,2))[,2])
         Map(make_link, yes$from, yes$to)
   }
   
   if (length(no)>1) { 
         no <- data.frame(from = t(combn(no,2))[,1],
                          to = t(combn(no,2))[,2])
         Map(make_link, no$from, no$to)
   }
   
   if (length(abstain)>1) { 
         abstain <- data.frame(from = t(combn(abstain,2))[,1],
                               to = t(combn(abstain,2))[,2])
         Map(make_link, abstain$from, abstain$to)
   }
   NULL
}

# apply to data.tables to increment matrix
dat[country %in% countries, link_over_dt(.SD), by = unres]

Now I make a scaled matrix of votes together to facilitate comparison among country pairs. So 1 is actually the the value for the pair that voted most times together.

adj_mat_prop <- adj_mat/ max(adj_mat)

Make graph:

un_graph <- as_tbl_graph(adj_mat_prop, directed = F) %E>% 
   filter(weight > .5) %>%
   mutate(weight = weight*3) %>% 
   ggraph(layout = "kk")+
   geom_edge_link0(aes(edge_width = weight,
                       edge_alpha = weight/3),
                   edge_colour = "#00acee")+
   coord_cartesian(clip = "off")+
   guides(edge_colour = F,
          edge_width = F,
          edge_alpha = F)

Add flags:

add_flag <- function(p, country){
   flag <- readPNG(paste0(here(),"/figs/",country,".png"), native = T)
   flag_x <- as.data.table(un_graph$data)[name == country, x]
   flag_y <- as.data.table(un_graph$data)[name == country, y]
   
   p+annotation_raster(flag, flag_x -.15, flag_x +.15, 
                        flag_y-.15, flag_y+.15)
}

un_graph <- Reduce(add_flag, countries,init = un_graph)

Add UN logo.

un_logo <- readJPEG(paste0(here(),"/figs/UN.jpg"), native = T)
un_graph +
   annotation_raster(un_logo, 1.3, 2, -1.3, -.5)+
   annotate("text", x = -1.5, y = -1.3,
            label = "Flag icons by Freepik ", size = 3)

There we have it! I am showing only links between countries that voted together at least half the number of times the pair that agreed more. It is nice to see the network shows the known polarization between USA and China, that Latin America seems to be pretty tight and that the long-standing alliance between Israel and USA also captured. A interesting fact is that not all G8 is always in agreement. The most curious feature of the network, I think, is Russia placed right in the center of the Latin-american cluster.

See you in the next post!