UN Votes
Your Name
2020-03-04
Let’s take a look at the voting history of countries in the United Nations General Assembly. We will be using data from the unvotes package. Additionally, we will make use of the tidyverse and lubridate packages for the analysis, and the DT package for interactive display of tabular output.
library(unvotes)
library(tidyverse)
library(lubridate)
library(DT)Data
The unvotes package provides three datasets we can work with: un_roll_calls, un_roll_call_issues, and un_votes. Each of these datasets contains a variable called rcid, the roll call id, which can be used as a unique identifier to join them with each other.
- The
un_votesdataset provides information on the voting history of the United Nations General Assembly. It contains one row for each country-vote pair.
un_votes## # A tibble: 738,764 x 4
## rcid country country_code vote
## <int> <chr> <chr> <fct>
## 1 3 United States of America US yes
## 2 3 Canada CA no
## 3 3 Cuba CU yes
## 4 3 Haiti HT yes
## 5 3 Dominican Republic DO yes
## 6 3 Mexico MX yes
## 7 3 Guatemala GT yes
## 8 3 Honduras HN yes
## 9 3 El Salvador SV yes
## 10 3 Nicaragua NI yes
## # … with 738,754 more rows- The
un_roll_callsdataset contains information on each roll call vote of the United Nations General Assembly.
un_roll_calls## # A tibble: 5,429 x 9
## rcid session importantvote date unres amend para short descr
## <int> <dbl> <dbl> <date> <chr> <dbl> <dbl> <chr> <chr>
## 1 3 1 0 1946-01-01 R/1/66 1 0 AMENDM… "TO ADOPT …
## 2 4 1 0 1946-01-02 R/1/79 0 0 SECURI… "TO ADOPT …
## 3 5 1 0 1946-01-04 R/1/98 0 0 VOTING… "TO ADOPT …
## 4 6 1 0 1946-01-04 R/1/1… 0 0 DECLAR… "TO ADOPT …
## 5 7 1 0 1946-01-02 R/1/2… 1 0 GENERA… "TO ADOPT …
## 6 8 1 0 1946-01-05 R/1/2… 1 0 ECOSOC… "TO ADOPT …
## 7 9 1 0 1946-02-05 R/1/3… 0 0 POST-W… "TO OPEN T…
## 8 10 1 0 1946-02-05 R/1/3… 1 1 U.N. M… "TO ADOPT …
## 9 11 1 0 1946-02-05 R/1/3… 0 0 TRUSTE… "TO ADOPT …
## 10 12 1 0 1946-02-06 R/1/3… 1 1 COUNCI… "TO ADOPT …
## # … with 5,419 more rows- The
un_roll_call_issuesdataset contains (topic) classifications of roll call votes of the United Nations General Assembly. Many votes had no topic, and some have more than one.
un_roll_call_issues## # A tibble: 5,281 x 3
## rcid short_name issue
## <int> <chr> <chr>
## 1 3372 me Palestinian conflict
## 2 3658 me Palestinian conflict
## 3 3692 me Palestinian conflict
## 4 2901 me Palestinian conflict
## 5 3020 me Palestinian conflict
## 6 3217 me Palestinian conflict
## 7 3298 me Palestinian conflict
## 8 3429 me Palestinian conflict
## 9 3558 me Palestinian conflict
## 10 3625 me Palestinian conflict
## # … with 5,271 more rowsAnalysis
First, let’s take a look at how often each country voted “Yes” on a resolution in each year. We’ll visualize the results, so let’s pick a few countries of interest first,
country_list <- c("United States of America",
"Russian Federation",
"Australia",
"New Zealand",
"China")and focus our analysis on them.
un_votes %>%
filter(country %in% country_list) %>%
inner_join(un_roll_calls, by = "rcid") %>%
group_by(year = year(date), country) %>%
summarize(
votes = n(),
percent_yes = mean(vote == "yes")
) %>%
ggplot(mapping = aes(x = year, y = percent_yes, color = country)) +
geom_line() +
labs(
title = "Percentage of 'Yes' votes in the UN General Assembly",
subtitle = "1946 to 2015",
y = "% Yes",
x = "Year",
color = "Country"
)
Next, let’s create a visualization that displays how the voting record of the United States changed over time on a variety of issues, and compares it to another country. Let’s compare Australia and the USA.
un_votes %>%
filter(country %in% c("Australia",
"United States of America")) %>%
inner_join(un_roll_calls, by = "rcid") %>%
inner_join(un_roll_call_issues, by = "rcid") %>%
group_by(country, year = year(date), issue) %>%
summarize(
votes = n(),
percent_yes = mean(vote == "yes")
) %>%
filter(votes > 5) %>% # only use records where there are more than 5 votes
ggplot(mapping = aes(x = year, y = percent_yes, color = country)) +
geom_point() +
geom_smooth(method = "loess", se = FALSE) +
facet_wrap(~ issue) +
labs(
title = "Percentage of 'Yes' votes in the UN General Assembly",
subtitle = "1946 to 2015",
y = "% Yes",
x = "Year",
color = "Country"
)
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.
References
- David Robinson (2017). unvotes: United Nations General Assembly Voting Data. R package version 0.2.0. https://CRAN.R-project.org/package=unvotes.
- 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).
- 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 %>%
select(country) %>%
arrange(country) %>%
distinct() %>%
datatable()Acknowledgements
This example was originally created by Mine Çetinkaya-Rundel in her course, “Data Science in a Box” https://datasciencebox.org/