of U.N. calls:

roll_calls %>%
  pivot_longer(amend:para) %>%
  group_by(year = year(date), name) %>%
  summarize(call_count = sum(value, na.rm = T)) %>%
  ungroup() %>%
  mutate(name = fct_reorder(name, -call_count, sum)) %>%
  ggplot(aes(year, call_count, color = name)) +
  geom_line(size = 1) +
  labs(x = NULL,
       y = "call count",
       color = NULL,
       title = "U.N. Call Count") 

World map on coutries’ voting results:

map_joined <- unvotes %>%
  group_by(country, country_code) %>%
  summarize(pct_vote_yes = mean(vote == "yes"),
            n = n()) %>%
  ungroup() %>%
  right_join(
    worlddatajoin::world_data(2020) %>%
      select(long, lat, group, region, iso2c, continent),
    by = c("country_code" = "iso2c")
)

map_joined %>%
  ggplot(aes(long, lat, group = group, fill = pct_vote_yes)) +
  geom_polygon() +
  theme_void() +
  scale_fill_gradient2(high = "green",
                       low = "red",
                       mid = "pink",
                       midpoint = 0.5,
                       labels = percent) +
  labs(fill = "pct voting yes",
       title = "Average Pct of Voting Yes per Country")

map_joined %>%
  filter(!is.na(continent)) %>%
  distinct(country, pct_vote_yes, continent) %>%
  mutate(continent = fct_reorder(continent, pct_vote_yes, na.rm = T)) %>%
  ggplot(aes(pct_vote_yes, continent, fill = continent)) +
  geom_boxplot(show.legend = F) +
  scale_x_continuous(labels = percent) +
  labs(x = "pct voting yes",
       y = NULL,
       title = "Voting Yes per Continent")

Average voting yes per continent

unvotes %>%
  inner_join(issues, by = "rcid") %>%
  inner_join(map_joined %>% distinct(country_code, continent), by = c("country_code")) %>%
  group_by(issue, continent) %>%
  summarize(avg_yes = mean(vote == "yes")) %>%
  ungroup() %>%
  mutate(issue = reorder_within(issue, avg_yes, continent)) %>%
  ggplot(aes(avg_yes, issue, fill = issue)) +
  geom_col(show.legend = F) +
  scale_y_reordered() +
  scale_x_continuous(labels = percent) +
  facet_wrap(~continent, scales = "free_y") +
  labs(x = "voting yes",
       y = NULL,
       title = "Average Voting Yes on Issues per Continent")

Most voted issues:

unvotes %>%
  group_by(rcid) %>%
  summarize(voted_yes = sum(vote == "yes"),
            voted_no = sum(vote == "no")) %>%
  ungroup() %>%
  inner_join(
    roll_calls,
    by = "rcid"
) %>% 
  select(short, voted_yes, voted_no) %>%
  mutate(total = voted_yes + voted_no,
         pct_yes = voted_yes / total) %>%
  filter(!is.na(short)) %>%
  arrange(desc(total)) %>%
  distinct(short, .keep_all = T) %>%
  mutate(short = str_trunc(short, 30),
         short = paste0(short, "(", total, ")")) %>%
  head(20) %>% 
  mutate(short = fct_reorder(short, pct_yes)) %>%
  ggplot(aes(pct_yes, short, fill = short)) +
  geom_col(show.legend = F, position = "dodge") +
  scale_x_continuous(labels = percent) +
  labs(x = "pct voting yes",
       y = NULL,
       title = "Most Voted Issues",
       subtitle = "Numbers refers to the votes")

The following ideas are inspired by David Robinson from his code.

Correlations:

country_cor <- unvotes %>%
  mutate(vote_numeric = case_when(vote == "yes" ~ 1,
                                  vote == "no" ~ -1,
                                  TRUE ~ 0)) %>%
  pairwise_cor(country, rcid, vote_numeric, sort = T)

country_cor

## # A tibble: 39,800 x 3
##    item1     item2     correlation
##    <chr>     <chr>           <dbl>
##  1 Slovakia  Czechia         0.975
##  2 Czechia   Slovakia        0.975
##  3 Slovakia  Slovenia        0.965
##  4 Slovenia  Slovakia        0.965
##  5 Lithuania Estonia         0.962
##  6 Estonia   Lithuania       0.962
##  7 Lithuania Latvia          0.961
##  8 Latvia    Lithuania       0.961
##  9 Bulgaria  Hungary         0.957
## 10 Hungary   Bulgaria        0.957
## # ... with 39,790 more rows

country_cor %>%
  filter(item1 %in% c("United States", "China", "Japan", "Mexico", "France", "Israel")) %>%
  group_by(item1, correlation > 0) %>%
  slice_max(abs(correlation), n = 5) %>%
  ungroup() %>%
  mutate(item2 = reorder_within(item2, correlation, item1)) %>%
  ggplot(aes(correlation, item2, fill = item1)) +
  geom_col(show.legend = F) +
  scale_y_reordered() +
  facet_wrap(~item1, scales = "free_y") +
  labs(y = NULL,
       title = "How are contries correlated with others?") +
  theme(strip.text = element_text(size = 15),
        plot.title = element_text(size = 18))

Which countries are most disagreeable to other countries in the same continents?

country_cor %>%
  inner_join(map_joined %>%
               distinct(country, continent1 = continent),
             by = c("item1" = "country")) %>%
  inner_join(map_joined %>%
               distinct(country, continent2 = continent),
             by = c("item2" = "country")) %>%
  filter(continent1 == continent2) %>%
  group_by(item1, continent1) %>%
  summarize(mean_cor = mean(correlation, na.rm = T),
            median_cor = median(correlation)) %>%
  ungroup() %>%
  group_by(continent1) %>%
  slice_min(mean_cor, n = 5) %>%
  ungroup() %>%
  mutate(item1 = reorder_within(item1, mean_cor, continent1)) %>%
  ggplot(aes(mean_cor, item1, fill = continent1)) +
  geom_col(show.legend = F) +
  scale_y_reordered() +
  facet_wrap(~continent1, scales = "free") +
  theme(strip.text = element_text(size = 15),
        plot.title = element_text(size = 18)) +
  labs(x = "mean correlation",
       y = "",
       title = "5 Most Countries that Disagree with Neighbors")