Brexit vote analysis

We will have a look at the results of the 2016 Brexit vote in the UK. First we read the data using read_csv() and have a quick glimpse at the data

# read data directly off github repo
brexit_results <- read_csv("https://raw.githubusercontent.com/kostis-christodoulou/am01/master/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…

The data comes from Elliott Morris, who cleaned it and made it available through his DataCamp class on analysing election and polling data in R.

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.

To get a sense of the spread, or distribution, of the data, we can plot a histogram, a density plot, and the empirical cumulative distribution function of the leave % in all constituencies.

# histogram
ggplot(brexit_results, aes(x = leave_share)) +
  geom_histogram(binwidth = 2.5)+
labs(title = "2016 UK Brexit Vote Results",subtitle="histogram",
       x = "leave_share(the percent of votes cast in favour of Brexit)",
       y = "count(the number of constituencies)") +
   NULL

# density plot-- think smoothed histogram
ggplot(brexit_results, aes(x = leave_share)) +
  geom_density()+
labs(title = "2016 UK Brexit Vote Results",subtitle="Probability Density Function",
       x = "leave_share(the percent of votes cast in favour of Brexit)",
       y = "PDF") +
   NULL

# 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 = "2016 UK Brexit Vote Results",subtitle="Empirical Cumulative Distribution Function",
       x = "leave_share(the percent of votes cast in favour of Brexit)",
       y = "ECDF") +
   NULL

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. To do this, let us get 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.

We can also create a scatterplot between these two variables using geom_point. We also add the best fit line, using geom_smooth(method = "lm").

ggplot(brexit_results, aes(x = born_in_uk, y = leave_share)) +
  geom_point(alpha=0.3) +
  
  # add a smoothing line, and use method="lm" to get the best straight-line
  geom_smooth(method = "lm") + 
  
  # use a white background and frame the plot with a black box
  theme_bw() +labs(title = "2016 UK Brexit Vote Results by Constituency",subtitle="Correlation between proportion of native born residents(born_in_uk) & leave_share",
       x = "born_in_uk",
       y = "leave_share") +
  NULL

## `geom_smooth()` using formula 'y ~ x'