Intro to the unvotes package
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.
Datasets
The unvotes contains three datasets, each data frames (specifically
tbl_dfs, which are more convenient to print). 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:
## # A tibble: 870,109 × 4
## rcid country country_code vote
## <dbl> <chr> <chr> <fct>
## 1 3 United States 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
## # ℹ 870,099 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:
## # A tibble: 6,203 × 9
## rcid session importantvote date unres amend para short descr
## <int> <dbl> <int> <date> <chr> <int> <int> <chr> <chr>
## 1 3 1 0 1946-01-01 R/1/66 1 0 AMENDMENTS,… "TO …
## 2 4 1 0 1946-01-02 R/1/79 0 0 SECURITY CO… "TO …
## 3 5 1 0 1946-01-04 R/1/98 0 0 VOTING PROC… "TO …
## 4 6 1 0 1946-01-04 R/1/107 0 0 DECLARATION… "TO …
## 5 7 1 0 1946-01-02 R/1/295 1 0 GENERAL ASS… "TO …
## 6 8 1 0 1946-01-05 R/1/297 1 0 ECOSOC POWE… "TO …
## 7 9 1 0 1946-02-05 R/1/329 0 0 POST-WAR RE… "TO …
## 8 10 1 0 1946-02-05 R/1/361 1 1 U.N. MEMBER… "TO …
## 9 11 1 0 1946-02-05 R/1/376 0 0 TRUSTEESHIP… "TO …
## 10 12 1 0 1946-02-06 R/1/394 1 1 COUNCIL MEM… "TO …
## # ℹ 6,193 more rowsFinally, the un_roll_call_issues dataset shows
relationships betwen each vote and 6 issues:
## # A tibble: 5,745 × 3
## rcid short_name issue
## <int> <chr> <fct>
## 1 77 me Palestinian conflict
## 2 9001 me Palestinian conflict
## 3 9002 me Palestinian conflict
## 4 9003 me Palestinian conflict
## 5 9004 me Palestinian conflict
## 6 9005 me Palestinian conflict
## 7 9006 me Palestinian conflict
## 8 128 me Palestinian conflict
## 9 129 me Palestinian conflict
## 10 130 me Palestinian conflict
## # ℹ 5,735 more rows## # A tibble: 6 × 2
## issue n
## <fct> <int>
## 1 Arms control and disarmament 1092
## 2 Palestinian conflict 1061
## 3 Human rights 1015
## 4 Colonialism 957
## 5 Nuclear weapons and nuclear material 855
## 6 Economic development 765(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:
## # A tibble: 870,109 × 12
## rcid country country_code vote session importantvote date unres amend
## <dbl> <chr> <chr> <fct> <dbl> <int> <date> <chr> <int>
## 1 3 United… US yes 1 0 1946-01-01 R/1/… 1
## 2 3 Canada CA no 1 0 1946-01-01 R/1/… 1
## 3 3 Cuba CU yes 1 0 1946-01-01 R/1/… 1
## 4 3 Haiti HT yes 1 0 1946-01-01 R/1/… 1
## 5 3 Domini… DO yes 1 0 1946-01-01 R/1/… 1
## 6 3 Mexico MX yes 1 0 1946-01-01 R/1/… 1
## 7 3 Guatem… GT yes 1 0 1946-01-01 R/1/… 1
## 8 3 Hondur… HN yes 1 0 1946-01-01 R/1/… 1
## 9 3 El Sal… SV yes 1 0 1946-01-01 R/1/… 1
## 10 3 Nicara… NI yes 1 0 1946-01-01 R/1/… 1
## # ℹ 870,099 more rows
## # ℹ 3 more variables: para <int>, 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: 10,633 × 4
## # Groups: year [74]
## year country votes percent_yes
## <dbl> <chr> <int> <dbl>
## 1 1946 Afghanistan 17 0.412
## 2 1946 Argentina 43 0.698
## 3 1946 Australia 43 0.558
## 4 1946 Belarus 43 0.442
## 5 1946 Belgium 43 0.605
## 6 1946 Bolivia 43 0.698
## 7 1946 Brazil 43 0.605
## 8 1946 Canada 42 0.643
## 9 1946 Chile 43 0.605
## 10 1946 Colombia 42 0.310
## # ℹ 10,623 more rowsAfter which this can be visualized for one or more countries:
library(ggplot2)
theme_set(theme_bw())
countries <- c("United States", "United Kingdom", "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") %>%
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)