Trends between Democrats and Republicans

When we talk about Republicans and Democrats, it’s important to remember just how many more people are unaffiliated, and how many more don’t even register. Yes, there are more Republicans than Democrats, but a well-organized campaign in specific areas can reach a lot of people and have a big sway on the total votes.

Code

library(tidyverse)
df = read_csv("summary.csv") %>% 
  mutate(party_affiliation = case_when(party_affiliation == "R" ~ "Republican",
                                       party_affiliation == "D" ~ "Democrat",
                                       party_affiliation == "L" ~ "Libertarian",
                                       TRUE ~ "Independent"))

Code

voting_age_adults <- round(11756058*.728)

summary <- df %>%
  group_by(party_affiliation) %>%
  summarise(count = sum(sos_voter_id)) 

n_registered_voters = summary$count %>% sum

unregistered_voters = voting_age_adults - n_registered_voters


f = summary %>% 
  add_row(party_affiliation="Unregistered", count=unregistered_voters) %>% 
  ggplot(aes(x = count,
             y = reorder(party_affiliation,count),
             fill = party_affiliation)) +
  geom_col() +
  theme_minimal() +
  scale_fill_manual(values=c('Republican'='red',
                             'Democrat'='blue',
                             'Libertarian'='yellow',
                             'Independent'='purple',
                             'Unregistered'='darkgrey'),
                    na.value = 'grey')+
  theme(legend.position='none',
        panel.grid.minor = element_blank()) +
  labs(x='Total Voters (in thousands)',y='Party') +
  scale_x_continuous(labels = scales::comma)
  

f %>% plotly::ggplotly()

Most national trends apply to Ohio too. People turn more conservative as they get older, younger people are less likely to register to vote, etc.

Code

age_sum <- df %>%
  group_by(age,party_affiliation) %>%
  summarise(count = sum(sos_voter_id))

`summarise()` has grouped output by 'age'. You can override using the `.groups`
argument.

Code

party_order <- c("Libertarian", "Independent", "Republican", "Democrat")


# Set the Party variable as a factor with the desired order
age_sum$party_affiliation <- factor(age_sum$party_affiliation, levels = party_order)

f= ggplot(age_sum, aes(y=age,x=count,fill=party_affiliation,label=count)) +
  geom_col(position='dodge') +
  scale_fill_manual(values=c('Republican'='red','Democrat'='blue','Libertarian'='yellow','Unaffiliated'='purple'),na.value = 'grey') +
  theme_minimal() +
  labs(x='Number of Voters',y='Age Range',title='Affiliation by Age Range',
       fill='Party Affiliation') +
  scale_x_continuous(labels = scales::comma)


f %>% plotly::ggplotly()