Intro to the unvotes package

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:

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 rows

The 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

library(dplyr)
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")

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 rows

After which this can be visualized for one or more countries:

library(ggplot2)
theme_set(theme_bw())

countries <- c("United States of America", "United Kingdom of Great Britain and Northern Ireland", "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)