UN_voting_topics.Rmd

---
title: "UN_voting_topics"
output: html_document
date: "2022-12-14"
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r}
# Transform topic data into tidy data

topics <- UN_voting %>%
  gather(topic, has_topic, me:ec) %>%
  filter(has_topic == 1) %>%
  mutate(topic = recode(topic,
                        me = "Palestinian conflict",
                        nu = "Nuclear weapons and nuclear material",
                        di = "Arms control and disarmament",
                        hr = "Human rights",
                        co = "Colonialism",
                        ec = "Economic development"))

```


```{r}
# Clean Countrynames

unique(topics$Countryname)

topics$Countryname[topics$Countryname == "C\xf4te d\x92Ivoire"] <- "Cote dIvoire"
topics$Countryname[topics$Countryname == "S\xe3o Tom\xe9 & Pr\xedncipe"] <- "Sao Tome and Principe"
```


```{r}
# Plot "agreebleness" of topics

by_year_topic <- topics %>%
    group_by(year, topic) %>%
    summarize(total = n(),
              percent_yes = mean(vote == 1)) %>%
    ungroup()


ggplot(by_year_topic, aes(year, percent_yes)) +
  geom_line() +
  facet_wrap(~ topic)

```

```{r}
# Summarize the percentage "yes" per country-year-topic

by_country_year_topic <- topics %>%
    group_by(Countryname, year, topic) %>%
    summarize(total = n(),
              percent_yes = mean(vote == 1)) %>%
    ungroup()

```

```{r}
# Tidy modeling by country and topic

country_topic_coefficients <- by_country_year_topic %>%
  nest(-Countryname, -topic) %>%
  mutate(model = map(data, ~ lm(percent_yes ~ year, data = .)),
         tidied = map(model, tidy)) %>%
  unnest(tidied)

# Filter for slope and significant observations

country_topic_filtered <- country_topic_coefficients %>%
    filter(term == "year") %>%
    mutate(p.adjusted = p.adjust(p.value)) %>%
    filter(p.adjusted < .05)

```