guns.R

library(readr)
library(dplyr)
df <- read_csv('~/desktop/guns.csv')

library(ggplot2)
library(ggthemes)
df %>%
  arrange(desc(guns_per_100_people)) %>% 
  top_n(n = 20, wt = guns_per_100_people) %>% 
  ggplot(aes(x = country, 
             y = guns_per_100_people)) +
    geom_bar(stat = 'identity') +
    xlab('') +
    ylab('Guns per 100 people') +
    coord_flip() +
    theme_tufte()
    
us_guns <- df$guns_per_100_people[df$country == 'United States'])

df %>% 
  select(guns_per_100_people,
         country) %>% 
  na.omit() %>% 
  ggplot(aes(x = guns_per_100_people)) +
    geom_histogram(aes(y = ..density..), 
                   binwidth = 2.5,
                   colour = 'black',
                   fill= 'white') +
    geom_density(alpha = .2,
                 fill= '#FF6666') +
    geom_vline(aes(xintercept = mean(guns_per_100_people),
               color= 'red',
               linetype= 'dashed',
               size = 0.5) +
    geom_vline(aes(xintercept = us_guns,
               color= 'blue',
               linetype= 'dashed',
               size = 0.5) +
    scale_x_continuous() +
    expand_limits(x = c(0,100)) +
    xlab('Guns per 100 people') +
    ylab('') +
    theme_tufte()
    
US <- df$guns_per_100_people[df$country == 'United States']
mu <- mean(df$guns_per_100_people, na.rm = T)
s <- sd(df$guns_per_100_people, na.rm = T)
(US - mu)/s

df %>%
  select(homicides_by_guns_per_100k_people,
         country) %>% 
  na.omit() %>% 
  arrange(desc(homicides_by_guns_per_100k_people)) %>% 
  top_n(n = 20,
        wt = homicides_by_guns_per_100k_people) %>% 
  ggplot(aes(x = country,
             y = homicides_by_guns_per_100k_people)) +
    geom_bar(stat = 'identity') +
    xlab('') +
    ylab('Homicides by guns per 100k people') +
    coord_flip() +
    theme_tufte()
    
oecd_list <- data.frame(country = c(
   'Australia', 'Austria', 'Belgium', 'Canada',
   'Chile', 'Czech Republic', 'Denmark', 'Estonia',
   'Finland', 'France', 'Germany', 'Greece',
   'Hungary', 'Iceland', 'Ireland', 'Israel',
   'Italy', 'Japan', 'South Korea', 'Luxembourg', 'Mexico',
   'Netherlands', 'New Zealand', 'Norway', 'Poland',
   'Portugal', 'Slovakia', 'Slovenia',
   'Spain', 'Sweden', 'Switzerland', 'Turkey', 
   'England and Wales', 'Scotland', 'United States'))
df %>% 
  inner_join(oecd_list) %>% 
  select(homicides_by_guns_per_100k_people,
         country) %>% 
  na.omit() %>% 
  arrange(desc(homicides_by_guns_per_100k_people)) %>% 
  top_n(n = 20,
        wt = homicides_by_guns_per_100k_people) %>% 
  ggplot(aes(x = country,
             y = homicides_by_guns_per_100k_people)) +
    geom_bar(stat = 'identity') +
    xlab('') +
    ylab('Homicides by guns per 100k people') +
    coord_flip() +
    theme_tufte()
    
oecd_list <- filter(oecd_list, country != 'Mexico')
us_homicides <- df$homicides_by_guns_per_100k_people[df$country == 'United States']

df %>% 
  inner_join(oecd_list) %>% 
  select(homicides_by_guns_per_100k_people,
         country) %>% 
  na.omit() %>% 
  ggplot(aes(x = homicides_by_guns_per_100k_people)) +
    geom_histogram(aes(y = ..density..), 
                   binwidth = 0.2,
                   colour = 'black',
                   fill= 'white') +
    geom_density(alpha = .2,
                 fill= '#FF6666') +
    geom_vline(aes(xintercept = mean(homicides_by_guns_per_100k_people,
                                     na.rm = T)),
               color = 'red',
               linetype = 'dashed',
               size = 0.5) +
    geom_vline(aes(xintercept=us_homicides,
               color = 'blue',
               linetype = 'dashed',
               size = 0.5) +
    scale_x_continuous() +
    ylab('') + 
    expand_limits(x = c(0,4)) +
    xlab('Homicides by guns per 100k people') +
    theme_tufte()
    
df %>% 
  select(guns_per_100_people, homicides_by_guns_per_100k_people,
         country) %>% 
  na.omit() %>% 
  mutate(us = ifelse(country == 'United States', 1, 0)) %>% 
  ggplot(aes(x = guns_per_100_people,
             y = homicides_by_guns_per_100k_people,
             colour = factor(us))) + 
    geom_point() + 
    xlab('Guns per 100 people') +
    ylab('Homicides by guns per 100k people') +
    scale_color_manual(values = c('black', 'red'),
                       guide = FALSE) +
    theme_tufte()

df %>% 
  inner_join(oecd_list) %>% 
  select(guns_per_100_people,
         homicides_by_guns_per_100k_people,
         country) %>% 
  na.omit() %>% 
  mutate(us = ifelse(country == 'United States', 1, 0)) %>% 
  ggplot(aes(x = guns_per_100_people,
             y = homicides_by_guns_per_100k_people,
             colour = factor(us))) + 
    geom_point() + 
    xlab('Guns per 100 people') +
    ylab('Homicides by guns per 100k people') +
    scale_color_manual(values = c('black', 'red'),guide=FALSE) +
    expand_limits(x = c(0,100), y = c(0,4)) +
    theme_tufte()

df %>% 
  select(guns_per_100_people,
         percent_homicides_by_guns,
         country) %>% 
  na.omit() %>% 
  mutate(us = ifelse(country == 'United States', 1, 0)) %>% 
  ggplot(aes(x = guns_per_100_people,
             y = percent_homicides_by_guns,
             colour = factor(us))) + 
    geom_point() + 
    xlab('Guns per 100 people') +
    ylab('% of total homicides by guns') +
    scale_color_manual(values = c('black', 'red'),
                       guide = FALSE) +
    theme_tufte()
    
df %>% 
  inner_join(oecd_list) %>% 
  select(guns_per_100_people,
         percent_homicides_by_guns,
         country) %>% 
  na.omit() %>% 
  mutate(us = ifelse(country == 'United States', 1, 0)) %>% 
  ggplot(aes(x = guns_per_100_people,
             y = percent_homicides_by_guns,
             colour = factor(us))) + 
    geom_point() + 
    xlab('Guns per 100 people') +
    ylab('% of total homicides by guns') +
    scale_color_manual(values = c('black', 'red'),guide=FALSE) +
    expand_limits(x = c(0,100), y = c(0,4)) +
    theme_tufte()