analyze_british_election_2019.R

library(ggplot2)
library(ggthemes)
dat <- data.frame(
  party = c('con', 'lab', 'snp', 'lib', 'dup', 'sf', 'pc', 'green', 'ukip_brexit'),
  party_label = c('Conservative', 'Labour', 'Scottish National', 'Liberal Democrats', 
                  'Democratic Unionist', 'Sinn Fein', 'Plaid Cymru', 'Green', 'Brexit & UKIP'),
  vote_2017 = c(13636684, 12877918, 977568,  2371861, 292316, 238915, 164466, 525665, 594068),
  vote_2019 = c(13941200, 10292054, 1242372, 3675342, 244128, 181853, 153265, 864743, 665120),
  seats_2017 = c(317, 262, 35, 12, 10, 7, 4, 1, 0),
  seats_2019 = c(364, 203, 48, 11, 8,  7, 4, 1, 0),
  stringsAsFactors = FALSE
)
dat$seats_per_vote_2019 <- dat$seats_2019 / dat$vote_2019
dat$votes_per_seat_2019 <- ifelse(dat$seats_per_vote_2019 != 0, 1/dat$seats_per_vote_2019, NA)
dat$abs_change <- dat$vote_2019 - dat$vote_2017
dat$pct_change <- (dat$vote_2019 - dat$vote_2017) / dat$vote_2017
dat$seat_change_per_vote_change_2017_to_2019 <- (dat$seats_2019 - dat$seats_2017) / dat$abs_change
dat$party_label_factor <- factor(dat$party_label, levels = dat$party_label[order(dat$vote_2017)])
#
ggplot(dat) +
  geom_col(aes(y = abs_change, x = party_label_factor), size = 1.5) +
  geom_hline(yintercept = 0) +
  theme_economist() +
  scale_colour_fivethirtyeight() +
  theme(
    legend.position = "bottom", 
    legend.direction = "horizontal",
    legend.title = element_blank()
  ) +
  theme(axis.text.y = element_text(size = rel(1), face = 'bold')) +
  scale_y_continuous(
    labels = scales::unit_format(unit = 'K', scale = 1e-3, sep = ''),
    breaks = seq(-2.6e6, 1.4e6, 4e5)
  ) +
  labs(
    x = '', 
    y = '', 
    caption = 'By @johnlashlee. Sources: BBC (2017), Google (2019). Accessed 2019-12-13.'
  ) +
  ggtitle(
    'Votes Gained from 2017 to 2019 (Thousand)'
  ) +
  coord_flip()
ggplot(dat) +
  geom_col(aes(y = pct_change, x = party_label_factor), size = 1.5) +
  geom_hline(yintercept = 0) +
  theme_economist() +
  scale_colour_fivethirtyeight() +
  theme(
    legend.position = "bottom", 
    legend.direction = "horizontal",
    legend.title = element_blank()
  ) +
  theme(axis.text.y = element_text(size = rel(1), face = 'bold')) +
  scale_y_continuous(
    #labels = scales::unit_format(unit = 'K', scale = 1e-3, sep = ''),
    #breaks = seq(-2.6e6, 1.4e6, 4e5)
    labels = scales::percent
  ) +
  labs(
    x = '', 
    y = '', 
    caption = 'By @johnlashlee. Sources: BBC (2017), Google (2019). Accessed 2019-12-13.'
  ) +
  ggtitle(
    'Percentaged Change in Votes from 2017 to 2019'
  ) +
  coord_flip()
ggplot(dat[dat$party != 'ukip_brexit',]) +
  geom_col(aes(y = votes_per_seat_2019, x = party_label_factor), size = 1.5) +
  geom_hline(yintercept = 0) +
  theme_economist() +
  scale_colour_fivethirtyeight() +
  theme(
    legend.position = "bottom", 
    legend.direction = "horizontal",
    legend.title = element_blank()
  ) +
  theme(axis.text.y = element_text(size = rel(1), face = 'bold')) +
  scale_y_continuous(
    labels = scales::unit_format(unit = 'K', scale = 1e-3, sep = '')#,
    #breaks = seq(0, 340000, 20000)
  ) +
  labs(
    x = '', 
    y = '', 
    caption = 'By @johnlashlee. Sources: BBC (2017), Google (2019). Accessed 2019-12-13.'
  ) +
  ggtitle(
    'Votes Per Seat in 2019 (Thousands)'
  ) +
  coord_flip()
ggplot(dat[!(dat$party %in% c('ukip_brexit', 'green', 'lib')), ]) +
  geom_col(aes(y = votes_per_seat_2019, x = party_label_factor), size = 1.5) +
  geom_hline(yintercept = 0) +
  theme_economist() +
  scale_colour_fivethirtyeight() +
  theme(
    legend.position = "bottom", 
    legend.direction = "horizontal",
    legend.title = element_blank()
  ) +
  theme(axis.text.y = element_text(size = rel(1), face = 'bold')) +
  scale_y_continuous(
    labels = scales::unit_format(unit = 'K', scale = 1e-3, sep = '')
  ) +
  labs(
    x = '', 
    y = '', 
    caption = 'By @johnlashlee. Sources: BBC (2017), Google (2019). Accessed 2019-12-13.'
  ) +
  ggtitle(
    'Votes Per Seat in 2019 (Thousands)'
  ) +
  coord_flip()