Brexit Vote Analysis

Import and Inspect

brexit_results <- read_csv(here::here("data","brexit_results.csv"))

glimpse(brexit_results)

## Rows: 632
## Columns: 11
## $ Seat        <chr> "Aldershot", "Aldridge-Brownhills", "Altrincham and Sale W…
## $ con_2015    <dbl> 50.592, 52.050, 52.994, 43.979, 60.788, 22.418, 52.454, 22…
## $ lab_2015    <dbl> 18.333, 22.369, 26.686, 34.781, 11.197, 41.022, 18.441, 49…
## $ ld_2015     <dbl> 8.824, 3.367, 8.383, 2.975, 7.192, 14.828, 5.984, 2.423, 1…
## $ ukip_2015   <dbl> 17.867, 19.624, 8.011, 15.887, 14.438, 21.409, 18.821, 21.…
## $ leave_share <dbl> 57.89777, 67.79635, 38.58780, 65.29912, 49.70111, 70.47289…
## $ born_in_uk  <dbl> 83.10464, 96.12207, 90.48566, 97.30437, 93.33793, 96.96214…
## $ male        <dbl> 49.89896, 48.92951, 48.90621, 49.21657, 48.00189, 49.17185…
## $ unemployed  <dbl> 3.637000, 4.553607, 3.039963, 4.261173, 2.468100, 4.742731…
## $ degree      <dbl> 13.870661, 9.974114, 28.600135, 9.336294, 18.775591, 6.085…
## $ age_18to24  <dbl> 9.406093, 7.325850, 6.437453, 7.747801, 5.734730, 8.209863…

Our main outcome variable (or y) is leave_share, which is the percent of votes cast in favour of Brexit, or leaving the EU. Each row is a UK parliament constituency.

Explore

A histogram, a density plot, and the empirical cumulative distribution function of the leave % in all constituencies was plotted to get a sense of the spread, or distribution of the data.

# histogram
ggplot(brexit_results, aes(x = leave_share)) +
  geom_histogram(binwidth = 2.5) + 
  labs(title = "Histogram of Constituency Leave Votes",
       subtitle = "Vote spread uneven across constituencies",
       x = "Leave votes in constituency (%)",
       y = "Number of constituencies") + 
  theme_bw()

# density plot
ggplot(brexit_results, aes(x = leave_share)) +
  geom_density() +
  labs(title = "Density Plot of Constituency Leave Votes",
       subtitle = "The peak is just over 55%", 
       x = "Leave votes in constituency (%)", 
       y = "Density") +
  theme_bw()

# The empirical cumulative distribution function (ECDF) 
ggplot(brexit_results, aes(x = leave_share)) +
  stat_ecdf(geom = "step", pad = FALSE) +
  scale_y_continuous(labels = scales::percent) +
  labs(title = "ECDF Plot of Constituency Leave Votes",
       subtitle = "About 37% of constituencies had less than 50% leave votes",
       x = "Leave votes in constituency (%)",
       y = "% of constituencies") +
  theme_bw()

One common explanation for the Brexit outcome was fear of immigration and opposition to the EU’s more open border policy. We can check the relationship (or correlation) between the proportion of native born residents (born_in_uk) in a constituency and its leave_share by getting the correlation between the two variables.

brexit_results %>% 
  select(leave_share, born_in_uk) %>% 
  cor() 

##             leave_share born_in_uk
## leave_share   1.0000000  0.4934295
## born_in_uk    0.4934295  1.0000000

The correlation is almost 0.5, which shows that the two variables are positively correlated. A scatter plot was also created between these two variables using geom_point.

ggplot(brexit_results, aes(x = born_in_uk, y = leave_share)) +
  geom_point(alpha=0.3) +
  geom_smooth(method = "lm") + 
  theme_bw() +
  labs(title = "Brexit leave votes vs native born residents",
       subtitle = "Leave votes higher in areas with low immigration",
       x = "% of UK-born residents",
       y = "% of leave votes") +
  
  NULL