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

## Source: local data frame [711,275 x 3]
## 
##     rcid     country    vote
##    <dbl>       <chr>  <fctr>
## 1      3       Egypt abstain
## 2      3    Honduras     yes
## 3      3  Costa Rica     yes
## 4      3 El Salvador     yes
## 5      3      France      no
## 6      3     Uruguay     yes
## 7      3       Chile     yes
## 8      3     Ecuador     yes
## 9      3   Argentina     yes
## 10     3       Haiti     yes
## ..   ...         ...     ...

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

## Source: local data frame [5,356 x 9]
## 
##     rcid session importantvote       date   unres amend  para
##    <dbl>   <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
## ..   ...     ...           ...        ...     ...   ...   ...
## Variables not shown: short <chr>, descr <chr>.

Finally, the un_roll_call_issues dataset shows relationships betwen each vote and 6 issues:

un_roll_call_issues

## Source: local data frame [4,951 x 3]
## 
##     rcid short_name                issue
##    <dbl>      <chr>                <chr>
## 1     30         me Palestinian conflict
## 2     34         me Palestinian conflict
## 3     77         me Palestinian conflict
## 4   9002         me Palestinian conflict
## 5   9003         me Palestinian conflict
## 6   9004         me Palestinian conflict
## 7   9005         me Palestinian conflict
## 8   9006         me Palestinian conflict
## 9    128         me Palestinian conflict
## 10   129         me Palestinian conflict
## ..   ...        ...                  ...

library(dplyr)
count(un_roll_call_issues, issue, sort = TRUE)

## Source: local data frame [6 x 2]
## 
##                                  issue     n
##                                  <chr> <int>
## 1                 Palestinian conflict  1047
## 2                          Colonialism   971
## 3                         Human rights   901
## 4         Arms control and disarmament   859
## 5 Nuclear weapons and nuclear material   712
## 6                 Economic development   461

(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

## Source: local data frame [711,275 x 11]
## 
##     rcid     country    vote session importantvote       date  unres amend
##    <dbl>       <chr>  <fctr>   <dbl>         <dbl>     <date>  <chr> <dbl>
## 1      3       Egypt abstain       1             0 1946-01-01 R/1/66     1
## 2      3    Honduras     yes       1             0 1946-01-01 R/1/66     1
## 3      3  Costa Rica     yes       1             0 1946-01-01 R/1/66     1
## 4      3 El Salvador     yes       1             0 1946-01-01 R/1/66     1
## 5      3      France      no       1             0 1946-01-01 R/1/66     1
## 6      3     Uruguay     yes       1             0 1946-01-01 R/1/66     1
## 7      3       Chile     yes       1             0 1946-01-01 R/1/66     1
## 8      3     Ecuador     yes       1             0 1946-01-01 R/1/66     1
## 9      3   Argentina     yes       1             0 1946-01-01 R/1/66     1
## 10     3       Haiti     yes       1             0 1946-01-01 R/1/66     1
## ..   ...         ...     ...     ...           ...        ...    ...   ...
## Variables not shown: 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

## Source: local data frame [9,496 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
## ..   ...                             ...   ...         ...

After 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)