2021_03_23_tidy_tuesday.Rmd

---
title: "TidyTemplate"
date: 2021-03-25
output: html_output
---
[David Robinson](https://www.youtube.com/watch?v=WxKSauhOY4g&list=PL19ev-r1GBwkuyiwnxoHTRC8TTqP8OEi8&index=1) on his screencasts at first.

```{r setup, include=FALSE}

knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)
library(tidytuesdayR)
library(scales)
theme_set(theme_light())
```

# Load the weekly Data

Download the weekly data and make available in the `tt` object.

```{r Load}
tt <- tt_load("2021-03-23")

```
```{r mutate}
unvotes <- tt$unvotes %>% 
  mutate(vote_number = match(vote, c("no","abstain","yes")) -2) %>% 
  left_join(tt$roll_calls %>% 
              select(rcid, date, amend), by = "rcid")
  
unvotes %>% 
    count(country, sort = TRUE)
```


```{r votes}
summarise_votes <- function(tbl, min_votes = 10) {
  tbl %>% 
    summarise(n_votes =n(),
           n_yes = sum(vote == "yes"),
           pct_yes = n_yes / n_votes) %>% 
    filter(n_votes >= min_votes) %>% 
    arrange(desc(pct_yes)) 
}

by_country <- unvotes %>% 
  group_by(country) %>% 
  summarise_votes()

by_country %>% 
  slice(c(1:10, (n() - 10):n())) %>% 
  mutate(country = fct_reorder(country, pct_yes)) %>% 
  ggplot(aes(pct_yes, country)) +
  geom_point(aes(size = n_votes)) +
  scale_x_continuous(labels = percent) +
  labs(x = "% of yes votes in UN",
      title = "What countries voted yes the least")

```
```{r}
library(lubridate)
unvote

by_year <- unvotes %>% 
  group_by(year = year(date)) %>% 
  summarise_votes()

by_year %>% 
  ggplot(aes(year, pct_yes))+
  geom_line()+
  expand_limits(y = 0)

by_country_year <- unvotes %>% 
  group_by(year = year(date),
           country) %>% 
  summarise_votes()

by_country_year %>% 
  filter(country %in% c("United States", "Canada", "Mali", "Israel")) %>% 
  mutate(country = fct_reorder(country, pct_yes)) %>% 
  ggplot(aes(year, pct_yes, colour = country))+
  geom_line()+
  scale_color_discrete(guide = guide_legend(reverse = TRUE))+
  expand_limits(y = 0)
```

# Readme

Take a look at the readme for the weekly data to get insight on the dataset.
This includes a data dictionary, source, and a link to an article on the data.

```{r Readme, eval = interactive()}

tt

```


# Glimpse Data

Take an initial look at the format of the data available.

```{r Glimpse}

tt %>% 
  map(glimpse)

```

# Wrangle

Explore the data and process it into a nice format for plotting! Access each dataset by name by using a dollarsign after the `tt` object and then the name of the data set.

```{r Wrangle}



```


# Visualize

Using your processed dataset, create your unique visualization.

```{r Visualize}


  
```

# Save Image

Save your image for sharing. Be sure to use the `#TidyTuesday` hashtag in your post on twitter! 

```{r}

# This will save your most recent plot
ggsave(
  filename = "My TidyTuesday Plot.png",
  device = "png")

```