UN Votes

Data visualization exercise

TidyTuesday week 13/2021: UN Votes, data comes from Harvard Dataverse by way of Mine Çetinkaya-Rundel, David Robinson, and Nicholas Goguen-Compagnoni.

Original data citation: Erik Voeten “Data and Analyses of Voting in the UN General Assembly” Routledge Handbook of International Organization, edited by Bob Reinalda (published May 27, 2013). Available at SSRN: http://ssrn.com/abstract=2111149

Data:
* unvotes:roll call id(rcid) and 3 variables
* roll_calls: rcid and 8 variables
* issues: rcid and 2 variables

# load library
library(tidyverse)
library(countrycode)
library(lubridate)
library(scales)
library(ggtext)
library(ggstream)
library(ggsci)
library(wesanderson)

# load data 
unvotes <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-03-23/unvotes.csv')

── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  rcid = col_double(),
  country = col_character(),
  country_code = col_character(),
  vote = col_character()
)

roll_calls <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-03-23/roll_calls.csv')

── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  rcid = col_double(),
  session = col_double(),
  importantvote = col_double(),
  date = col_date(format = ""),
  unres = col_character(),
  amend = col_double(),
  para = col_double(),
  short = col_character(),
  descr = col_character()
)

issues <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-03-23/issues.csv')

── Column specification ──────────────────────────────────────────────────────────────────────────────────────────────────────────────
cols(
  rcid = col_double(),
  short_name = col_character(),
  issue = col_character()
)

# get region and sub-region 
unvotes = as.data.frame(unvotes)
unvotes$region <- countrycode(sourcevar = unvotes[, "country"],
                            origin = "country.name",
                            destination = "un.region.name")
unvotes$sub_region <- countrycode(sourcevar = unvotes[, "country"],
                            origin = "country.name",
                            destination = "un.regionsub.name")
  

# join unvotes and roll_calls for year 
data = unvotes %>%
  left_join(roll_calls) %>%
  mutate(date2 = ymd(date)) %>%
  mutate(year = year(date2))
head(data)

Belgium: Percentage of UN Yes Votes

• shared on Twitter

# percentage of yes votes: Belgium compared to the rest of Western Europe
# Belgium/Western Europe
data1 = data %>%
  filter(sub_region=="Western Europe") %>%
  group_by(year, country, vote) %>%
  tally() %>% 
  mutate(pct = round(n/sum(n),3)) %>% 
  filter(vote=="yes") %>%
  drop_na() 

bel = data1 %>% filter(country=="Belgium") %>% ungroup()
belother = data1 %>% filter(country!="Belgium") %>% ungroup()

ggplot() + 
  geom_line(data= belother, aes(x=year, y=pct, group = country), colour = alpha("grey", 0.7)) + 
  geom_line(data=bel, aes(x=year, y=pct), color="#5e60ce") +
  theme_minimal(base_size = 10) + 
  theme(plot.margin=unit(c(1,2,1,1),"cm"),
        panel.grid.minor.x=element_blank(),
        plot.title.position = "plot",
        plot.title=element_markdown(face="bold"),
        axis.title=element_text(face="bold"),
        plot.subtitle=element_text(color="#495057")) + 
  scale_y_continuous(label=scales::percent) + 
  scale_x_continuous(breaks=seq(1950,2020,10)) + 
  labs(x="Year",y="Percent",
       caption="TidyTuesday Week 13 | Data from Harvard Dataverse",
       title="<span style = 'color:#5e60ce'><b>Belgium</b></span>: Percentage of UN Yes Votes",
       subtitle= "Compared to other countries in Western Europe, from 1946 to 2019")

Nuclear Disarmament UN Votes

• shared on Twitter

# map data 
world <- map_data("world") %>%   filter(region != "Antarctica") 
# join unvotes with map data
unvotes1 <- unvotes %>%
  filter(rcid==9110) %>%
  left_join(world, by = c('country'='region')) %>%
  rename("Vote"="vote") %>% 
  mutate(Vote= str_to_title(Vote))

# inspired by Juanma_MN (https://twitter.com/Juanma_MN/status/1374125991732334592/photo/1)

p2 <- ggplot() + 
  geom_map(data = world, map = world,
           aes(long, lat, group = group,  map_id = region),
           fill = "white", color = "#889696") +
  geom_map(data =unvotes1, map = world,
           aes(fill = Vote, map_id = country),
           color = "#889696", size = 0.1, alpha = .9) +
  scale_fill_manual(values = c( "No" = "#119da4",
                                "Yes" = "#ffc857",
                                "Abstain"= "#eb6424")) +
  scale_x_continuous(breaks = NULL) + 
  scale_y_continuous(breaks = NULL) +
  labs(x = "",y = "",
       title = "Nuclear Disarmament UN Votes on 2019-12-12",
       caption = "TidyTuesday Week 13 | Data from data from Harvard Dataverse") + 
  theme(legend.position="top", legend.box = "horizontal",
        legend.background = element_rect(fill="white"),
        panel.background=element_rect(fill="white"),
        plot.background=element_rect(fill="white"),
        legend.title = element_text(size=9, face="bold"),
        plot.title=element_text(face="bold"),
        plot.caption=element_text(color="#343a40", size=8),
        legend.justification = "left",
        plot.margin=unit(c(1,1.7,1,1.7),"cm"))

p2 

Issues

# unique issues
unique(issues$issue)

[1] "Palestinian conflict"                 "Nuclear weapons and nuclear material" "Arms control and disarmament"        
[4] "Human rights"                         "Colonialism"                          "Economic development"                

head(issues)

head(roll_calls)

# get dates of issues
issues1 = issues %>% left_join(roll_calls)

Joining, by = "rcid"

# get year 
issues1 %>%
  mutate(date2 = ymd(date)) %>%
  mutate(year = year(date2)) -> issues1
head(issues1)

NA

Number of UN Issues

# issues by year
pal <- wes_palette("Zissou1", 100, type = "continuous")

issues1 %>% 
  group_by(year) %>%
  tally() %>%
  ggplot(aes(x=year, y=n, fill=n)) + 
  #ggbump::geom_bump(aes(x=year, y=n)) + 
  geom_bar(stat="identity") + 
  scale_fill_gradientn(colours = pal) +
  theme_light(base_size=10) + 
  theme(plot.margin=unit(c(1,2,1,1),"cm"), 
        legend.position="none") + 
  labs(x= "Year", y="Count of Issues", 
       title= "Number of UN issues",
       subtitle="1946 to 2019")

UN Issues Categories Over The Years

issues1 %>% 
  group_by(year, issue) %>% 
  tally() %>% 
  rename("Issue"="issue") %>%
  ggplot(aes(year, n, fill=Issue)) + 
  geom_stream(bw=0.5) + 
  #geom_stream_label(aes(label = issue), size=3) +
  scale_fill_manual(values=c("#0091ad","#586ba4","#324376","#f5dd90","#f68e5f","#f65646")) +
  theme_minimal(base_size=10) +
  theme(legend.position="top",
        axis.text.y=element_blank(),
        axis.title=element_blank(),
        panel.grid.major.y=element_blank(),
        panel.grid.minor.y=element_blank(), 
        plot.title=element_text(hjust=0.5),
        plot.margin=unit(c(1,1,1,1),"cm")
        ) + 
  labs(title= "UN Issues Categories Over The Years")

NA
NA

Percentage of UN Issue Category by Year

# percentage
issues1 %>% 
  group_by(year, issue) %>% 
  tally() %>%
  mutate(pct = n/sum(n)) %>%
  rename("Issue"="issue") %>%
  ggplot(aes(x=year, y=pct, fill=Issue)) + 
  geom_bar(stat="identity", width=1, alpha=0.93) + 
  scale_fill_manual(values=c("#0091ad","#586ba4","#324376","#f5dd90","#f68e5f","#f65646")) +
  scale_y_continuous(labels=scales::percent) +
  theme_minimal(base_size=10) +
  theme(legend.position="top",
        plot.margin=unit(c(1,1,1,1),"cm"),
        #legend.title=element_blank(),
        plot.title=element_text(hjust=0.5),
        plot.title.position = "plot",
        panel.grid=element_blank()
        ) + 
  coord_cartesian(expand=FALSE, clip="off") + 
  labs(y="Percentage", x="Year", title="Percentage of UN Issue Category by Year")

Count of Votes from Belgium by Issue

# merge data 
bdata = unvotes %>%
 semi_join(roll_calls, by = "rcid") %>% 
 left_join(roll_calls, by = "rcid") %>%
  left_join(issues, by="rcid") %>%
  filter(country=="Belgium") %>%
  drop_na()

# plot
bdata %>% 
  mutate(issue = fct_lump_n(f = issue, n = 50)) %>% 
  filter(issue != "Other") %>% 
  count(issue, vote) %>%
  #mutate(vote = factor(vote, levels = c("yes", "abstain", "no"), ordered = TRUE)) %>%
  rename("Vote"="vote") %>% 
  mutate(Vote= str_to_title(Vote)) %>%
  ggplot(aes(y=fct_rev(issue), x=n)) + 
  geom_line(aes(group=issue), color="#393E41", alpha=0.9) +
  geom_point(aes(color=factor(Vote, levels = c("No", "Abstain", "Yes"), ordered = TRUE)), size=3) + 
  scale_color_manual(values = c( "Yes" = "#3F88C5",
                                "Abstain" = "#ff7d00",
                                "No"= "#44BBA4")) + 
  scale_x_continuous(breaks=c(75,100,125,150,175)) +
  scale_y_discrete(labels=function(x) str_wrap(x,19)) +
  theme_minimal(base_size=10) + 
  theme(legend.position="none",
        panel.grid.minor.x=element_blank(), 
        plot.margin=unit(c(1,1,1,1),"cm"),
        plot.title.position="plot",
        plot.title=element_text(hjust=0.5),
        plot.subtitle=element_markdown(hjust=0.5),
        axis.text=element_text(size=8.6)
        ) + 
  labs(color="Vote", y=NULL, x=NULL, title="Count of Votes from Belgium by Issue",
       subtitle="<span style = 'color:#44BBA4'><b>No</b></span> | 
       <span style = 'color:#ff7d00'><b>Abstain</b></span> | 
       <span style = 'color:#3F88C5'><b>Yes</b></span>") 

NA
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