United Nations General Assembly Voting Data
This package provides the voting history of countries in the United Nations General Assembly, along with information such as date, description, and topics for each vote.
These come from the dataset found here:
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)
This raw data, and the processing script, can be found in the data-raw folder.
Installation
Install the package with:
install.packages("unvotes")You can also install the development version of the package using devtools:
devtools::install_github("dgrtwo/unvotes")Datasets
The package contains three datasets. First is the history of each country's vote. These are represented in the un_votes dataset, with one row for each country/vote pair:
library(dplyr)
library(unvotes)
un_votes
#> # A tibble: 738,764 x 4
#> rcid country country_code vote
#> <int> <chr> <chr> <fctr>
#> 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 rowsThe package also contains a dataset of information about each roll call vote, including the date, description, and relevant resolution that was voted on:
un_roll_calls
#> # A tibble: 5,429 x 9
#> rcid session importantvote date unres amend para
#> <int> <dbl> <dbl> <date> <chr> <dbl> <dbl>
#> 1 3 1 0 1946-01-01 R/1/66 1 0
#> 2 4 1 0 1946-01-02 R/1/79 0 0
#> 3 5 1 0 1946-01-04 R/1/98 0 0
#> 4 6 1 0 1946-01-04 R/1/107 0 0
#> 5 7 1 0 1946-01-02 R/1/295 1 0
#> 6 8 1 0 1946-01-05 R/1/297 1 0
#> 7 9 1 0 1946-02-05 R/1/329 0 0
#> 8 10 1 0 1946-02-05 R/1/361 1 1
#> 9 11 1 0 1946-02-05 R/1/376 0 0
#> 10 12 1 0 1946-02-06 R/1/394 1 1
#> # ... with 5,419 more rows, and 2 more variables: short <chr>, descr <chr>Finally, the un_roll_call_issues dataset shows relationships betwen each vote and 6 issues:
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 rows
count(un_roll_call_issues, issue, sort = TRUE)
#> # A tibble: 6 x 2
#> issue n
#> <chr> <int>
#> 1 Palestinian conflict 1104
#> 2 Colonialism 991
#> 3 Human rights 986
#> 4 Arms control and disarmament 956
#> 5 Nuclear weapons and nuclear material 762
#> 6 Economic development 482(Use help() to get information and documentation about each dataset).
Example analysis
Many useful analyses will first involve joining the vote and roll call datasets by the shared rcid (roll call ID) column:
library(dplyr)
joined <- un_votes %>%
inner_join(un_roll_calls, by = "rcid")
#> Warning: Column `rcid` has different attributes on LHS and RHS of join
joined
#> # A tibble: 738,764 x 12
#> rcid country country_code vote session
#> <int> <chr> <chr> <fctr> <dbl>
#> 1 3 United States of America US yes 1
#> 2 3 Canada CA no 1
#> 3 3 Cuba CU yes 1
#> 4 3 Haiti HT yes 1
#> 5 3 Dominican Republic DO yes 1
#> 6 3 Mexico MX yes 1
#> 7 3 Guatemala GT yes 1
#> 8 3 Honduras HN yes 1
#> 9 3 El Salvador SV yes 1
#> 10 3 Nicaragua NI yes 1
#> # ... with 738,754 more rows, and 7 more variables: importantvote <dbl>,
#> # date <date>, unres <chr>, amend <dbl>, para <dbl>, short <chr>,
#> # descr <chr>One could then count how often each country votes "yes" on a resolution in each year:
library(lubridate)
by_country_year <- joined %>%
group_by(year = year(date), country) %>%
summarize(votes = n(),
percent_yes = mean(vote == "yes"))
by_country_year
#> # A tibble: 9,689 x 4
#> # Groups: year [?]
#> year country votes percent_yes
#> <dbl> <chr> <int> <dbl>
#> 1 1946 Afghanistan 17 0.4117647
#> 2 1946 Argentina 43 0.6976744
#> 3 1946 Australia 43 0.5581395
#> 4 1946 Belarus 43 0.4418605
#> 5 1946 Belgium 43 0.6046512
#> 6 1946 Bolivia (Plurinational State of) 43 0.6976744
#> 7 1946 Brazil 43 0.6046512
#> 8 1946 Canada 42 0.6428571
#> 9 1946 Chile 43 0.6046512
#> 10 1946 Colombia 42 0.3095238
#> # ... with 9,679 more rowsAfter which this can be visualized for one or more countries:
library(ggplot2)
theme_set(theme_bw())
countries <- c("United States of America", "India", "France")
# there were fewer votes in 2013
by_country_year %>%
filter(country %in% countries, year <= 2013) %>%
ggplot(aes(year, percent_yes, color = country)) +
geom_line() +
ylab("% of votes that are 'Yes'")Similarly, we could look at how the voting record of the United States has changed on each of the issues by joining with the un_roll_call_issues dataset:
joined %>%
filter(country == "United States of America") %>%
inner_join(un_roll_call_issues, by = "rcid") %>%
group_by(year = year(date), issue) %>%
summarize(votes = n(),
percent_yes = mean(vote == "yes")) %>%
filter(votes > 5) %>%
ggplot(aes(year, percent_yes)) +
geom_point() +
geom_smooth(se = FALSE) +
facet_wrap(~ issue)
#> Warning: Column `rcid` has different attributes on LHS and RHS of joinCode of Conduct
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