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UN Votes

The data this week comes from Harvard’s Dataverse by way of Mine Çetinkaya-Rundel, David Robinson, and Nicholas Goguen-Compagnoni.

Original Data citation:
> Citation: 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). Available at SSRN: http://ssrn.com/abstract=2111149

The {unvotes} R package is on CRAN! Github link

This is a wrapper around the datasets, although I’ve included them as CSVs in the TidyTuesday repo.

Get package from CRAN:

install.packages("unvotes")

Mine Çetinkaya-Rundel wrote about the process of updating the package on the Citizen Statistician blog.

Get the data here

# Get the Data

# Read in with tidytuesdayR package 
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest

# Either ISO-8601 date or year/week works!

tuesdata <- tidytuesdayR::tt_load('2021-03-23')
tuesdata <- tidytuesdayR::tt_load(2021, week = 13)

unvotes <- tuesdata$unvotes

# Or read in the data manually

unvotes <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2021/2021-03-23/unvotes.csv')
roll_calls <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2021/2021-03-23/roll_calls.csv')
issues <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2021/2021-03-23/issues.csv')

Data Dictionary

unvotes.csv

variable	class	description
rcid	double	The roll call id; used to join with un_votes and un_roll_call_issues
country	character	Country name, by official English short name
country_code	character	2-character ISO country code
vote	integer	Vote result as a factor of yes/abstain/no

roll_calls.csv

variable	class	description
rcid	integer	.
session	double	Session number. The UN holds one session per year; these started in 1946
importantvote	integer	Whether the vote was classified as important by the U.S. State Department report “Voting Practices in the United Nations”. These classifications began with session 39
date	double	Date of the vote, as a Date vector
unres	character	Resolution code
amend	integer	Whether the vote was on an amendment; coded only until 1985
para	integer	Whether the vote was only on a paragraph and not a resolution; coded only until 1985
short	character	Short description
descr	character	Longer description

issues.csv

variable	class	description
rcid	integer	The roll call id; used to join with unvotes and un_roll_calls
short_name	character	Two-letter issue codes
issue	integer	Descriptive issue name

Cleaning Script

The data cleaning can be found in the data-raw folder of the package.

library(dplyr)
library(readxl)
library(countrycode)
library(tidyr)
library(forcats)

vlevels <- c("yes", "abstain", "no")

load("data-raw/UNVotes2021.RData")

un_votes <- completeVotes %>%
  filter(vote <= 3) %>%
  mutate(
    country = countrycode(ccode, "cown", "country.name"),
    country_code = countrycode(ccode, "cown", "iso2c"),
    # Match based on old version of data from unvotes package
    country_code = case_when(
      country == "Czechoslovakia" ~ "CS",
      country == "Yugoslavia" ~ "YU",
      country == "German Democratic Republic" ~ "DD",
      country == "Yemen People's Republic" ~ "YD",
      TRUE ~ country_code
    ),
    country = if_else(!is.na(Countryname) & Countryname == "German Federal Republic", "Federal Republic of Germany", country)
  ) %>%
  select(rcid, country, country_code, vote) %>%
  mutate(vote = as.character(vote)) %>%
  mutate(vote = fct_recode(vote, yes = "1", abstain = "2", no = "3"))

descriptions <- completeVotes %>%
  select(session, rcid, abstain, yes, no, importantvote, date, unres, amend, para, short, descr, me, nu, di, hr, co, ec) %>%
  distinct(rcid, .keep_all = TRUE)

un_roll_calls <- descriptions %>%
  select(rcid, session, importantvote:descr) %>%
  mutate(rcid = as.integer(rcid),
         date = as.Date(date)) %>%
  arrange(rcid)

un_roll_call_issues <- descriptions %>%
  select(rcid, me:ec) %>%
  gather(short_name, value, me:ec) %>%
  mutate(rcid = as.integer(rcid),
         value = as.numeric(value)) %>%
  filter(value == 1) %>%
  select(-value) %>%
  mutate(issue = fct_recode(short_name,
                            "Palestinian conflict" = "me",
                            "Nuclear weapons and nuclear material" = "nu",
                            "Colonialism" = "co",
                            "Human rights" = "hr",
                            "Economic development" = "ec",
                            "Arms control and disarmament" = "di"))

See Mine Çetinkaya-Rundel who wrote about the process of updating the package on the Citizen Statistician blog.