About Me

My name is Landon and I am good at drawing. I co-own a company that makes video games. I am currently taking data science.

My Favorite Websites Are:

These are my favorite websites: CoolMathGames Sheep Studios CrazyGames

My Favorite Animal Is

A Red Eared Slider Turtle

Watch Me Do Math!

2+2
## [1] 4

We can write this in LaTeX

\[2+2=4\]

I am looking forward to learning about

I am looking forward to learning about data science. I don’t know a whole lot about the class so I’m not sure everything I will learn. I am looking forward to helping out companies.

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Introduction

How do various countries vote in the United Nations General Assembly, how have their voting patterns evolved throughout time, and how similarly or differently do they view certain issues? Answering these questions (at a high level) is the focus of this analysis.

Packages

We will use the tidyverse, lubridate, and scales packages for data wrangling and visualization, and the DT package for interactive display of tabular output, and the unvotes package for the data.

library(tidyverse)
library(lubridate)
library(scales)
library(DT)
library(unvotes)

Data

The data we’re using originally come from the unvotes package. In the chunk below we modify the data by joining the various data frames provided in the package to help you get started with the analysis.

unvotes <- un_votes %>%
  inner_join(un_roll_calls, by = "rcid") %>%
  inner_join(un_roll_call_issues, by = "rcid")

UN voting patterns

Let’s create a data visualisation that displays how the voting record of the UK & NI changed over time on a variety of issues, and compares it to two other countries: US and Turkey.

We can easily change which countries are being plotted by changing which countries the code above filters for. Note that the country name should be spelled and capitalized exactly the same way as it appears in the data. See the Appendix for a list of the countries in the data.

unvotes %>%
  filter(country %in% c("Canada", "Italy", "Germany")) %>%
  mutate(year = year(date)) %>%
  group_by(country, year, issue) %>%
  summarize(percent_yes = mean(vote == "yes")) %>%
  ggplot(mapping = aes(x = year, y = percent_yes, color = country)) +
  geom_point(alpha = 0.4) +
  geom_smooth(method = "loess", se = FALSE) +
  facet_wrap(~issue) +
  scale_y_continuous(labels = percent) +
  labs(
    title = "Percentage of 'Yes' votes in the UN General Assembly",
    subtitle = "1946 to 2019",
    y = "% Yes",
    x = "Year",
    color = "Country"
  )

References

  1. David Robinson (2017). unvotes: United Nations General Assembly Voting Data. R package version 0.2.0.
  2. 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).
  3. Much of the analysis has been modeled on the examples presented in the unvotes package vignette.

Appendix

Below is a list of countries in the dataset: