UN Votes

Introduction

How do various countries vote in the United Nations General Assembly, how have their voting patterns evolved throughout time, and how similarly or differently do they view certain issues? Answering these questions (at a high level) is the focus of this analysis.

Packages

We will use the tidyverse, lubridate, and scales packages for data wrangling and visualization, and the DT package for interactive display of tabular output, and the unvotes package for the data.

library(tidyverse)
library(lubridate)
library(scales)
library(DT)
library(unvotes)

Data

The data we’re using originally come from the unvotes package. In the chunk below we modify the data by joining the various data frames provided in the package to help you get started with the analysis.

unvotes <- un_votes %>%
  inner_join(un_roll_calls, by = "rcid") %>%
  inner_join(un_roll_call_issues, by = "rcid")

Warning in inner_join(., un_roll_call_issues, by = "rcid"): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 382 of `x` matches multiple rows in `y`.
ℹ Row 3009 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
  "many-to-many"` to silence this warning.

UN voting patterns

Let’s create a data visualisation that displays how the voting record of the UK & NI changed over time on a variety of issues, and compares it to two other countries: US and Turkey.

We can easily change which countries are being plotted by changing which countries the code above filters for. Note that the country name should be spelled and capitalized exactly the same way as it appears in the data. See the Appendix for a list of the countries in the data.

unvotes %>%
  filter(country %in% c("United Kingdom", "United States", "Turkey")) %>%
  mutate(year = year(date)) %>%
  group_by(country, year, issue) %>%
  summarize(percent_yes = mean(vote == "yes")) %>%
  ggplot(mapping = aes(x = year, y = percent_yes, color = country)) +
  geom_point(alpha = 0.4) +
  geom_smooth(method = "loess", se = FALSE) +
  facet_wrap(~issue) +
  scale_y_continuous(labels = percent) +
  labs(
    title = "Percentage of 'Yes' votes in the UN General Assembly",
    subtitle = "1946 to 2019",
    y = "% Yes",
    x = "Year",
    color = "Country"
  )

References

1. David Robinson (2017). unvotes: United Nations General Assembly Voting Data. R package version 0.2.0.
2. 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).
3. Much of the analysis has been modeled on the examples presented in the unvotes package vignette.

Appendix

Below is a list of countries in the dataset:

UN Votes Extra Credit - Emilio S.

Using the unvotes data set, I will continue to explore the same stat’s from above using different countries: Chile, Bolivia, and Argentina. (I chose these countries because I know a lot about them)

unvotes %>%
  filter(country %in% c("Chile", "Bolivia", "Argentina")) %>%
  mutate(year = year(date)) %>%
  group_by(country, year, issue) %>%
  summarize(percent_yes = mean(vote == "yes")) %>%
  ggplot(mapping = aes(x = year, y = percent_yes, color = country)) +
  geom_point(alpha = 0.4) +
  geom_smooth(method = "loess", se = FALSE) +
  facet_wrap(~issue) +
  scale_y_continuous(labels = percent) +
  geom_point(alpha = 0.2, size = 1) + #did this for better visibility
  labs(
    title = "Percentage of 'Yes' votes in the UN General Assembly",
    subtitle = "1946 to 2019",
    y = "% Yes",
    x = "Year",
    color = "Country"
  )

`summarise()` has grouped output by 'country', 'year'. You can override using
the `.groups` argument.
`geom_smooth()` using formula = 'y ~ x'

As you can see, the countries have different voting patterns on different issues. For example, Chile and Bolivia have similar voting patterns on the issue of “Nuclear weapons and nuclear material”. Bolivia had a VERY different Palestinian conflict back in 1970.