Amount of Coverage

Rendered at 10:38 AM, Apr. 17, 2023

Ryan Thornburg (ryan.thornburg@unc.edu)

How much coverage did each incumbent receive?

Code

articles_per_prosecutor<- incumbents %>% 
  filter(duplicative_publication_of_previous_article == "0" |
           #Next line because of Devinney had no articles
           is.na(duplicative_publication_of_previous_article)) %>%
    mutate(prosecutor_name = case_when(
    prosecutor_name == "John J. Flynn" ~ "Flynn, John J.",
    prosecutor_name == "Devinney" ~ "Devinney, Darrin C.",
    TRUE ~ prosecutor_name)
    
    ) %>%
  group_by(state, contest, prosecutor_name) %>%
  summarize(articles = n()) %>%
  mutate(articles = case_when(
    prosecutor_name == "Devinney" ~ 0,
    prosecutor_name != "Devinney" ~ as.numeric(articles)
  )
  ) %>% 
  arrange(desc(articles))


g1 <- articles_per_prosecutor %>% 
  ggplot(aes(x = articles,
             y=reorder(prosecutor_name, articles), 
             text = paste("articles: ",articles)
             )
         ) +
  geom_col(fill="#4B9CD3") +
    scale_x_continuous(breaks=seq(0,350,50), limits=c(0,350)) +

  geom_vline(aes(xintercept = median(articles)), color="#952e46") +
  geom_text(aes(x=median(articles), 
                y=2,
                label= paste0("Median mentions per incumbent: ", median(articles))
                ),
            color="#952e46",
            nudge_x = 120, 
            show.legend = FALSE
            ) +
  labs(title = "Incumbent Media Mentions") + 
  xlab("Number of mentions") +
  ylab(NULL) +
  theme_classic() +
  theme(plot.margin = unit(c(0,0,0,0), "cm")) +
  theme(axis.ticks = element_blank() ) +
  theme(axis.line.y = element_blank()) +
  theme(text = element_text(family="sans", 
  size = 9, 
  color = "#3f2b56")) +
  theme(title = element_text(family = "serif", size = 16.2))+
  theme(axis.title.x = element_text(family = "sans", size = 12.15)) +
  theme(axis.text.y = element_text(family = "sans", size = 12.15))

g1

Code

datatable(articles_per_prosecutor,
          options = list(dom='t',
                         pageLength = 27),
            colnames = c('State', 'Contest', 'Prosecutor', 'Articles'),
          rownames = FALSE,
          filter = 'top') 

How much coverage did each non-incumbent receive?

Code

articles_per_ninc<- non_incumbents %>% 
  filter(duplicative_publication_of_previous_article == "0") %>%
  group_by(state, contest, candidate_name) %>% 
  summarize(articles = n()) %>% 
  arrange(desc(articles))

articles_per_ninc %>% 
  ggplot(aes(x = articles,
             y=reorder(candidate_name, articles))
  ) +
  geom_col(fill="#4B9CD3") +
    scale_x_continuous(breaks=seq(0,350,50), limits=c(0,350)) +

  geom_vline(aes(xintercept = median(articles)), color="#952e46") +
  geom_text(aes(x=median(articles), 
                y=2,
                label= paste0("Median mentions per non-incumbent: ", median(articles))
                ),
            color="#952e46",
            nudge_x = 120, 
            show.legend = FALSE
            ) +
  labs(title = "Non-incumbent Media Mentions") + 
  xlab("Number of mentions") +
  ylab(NULL) +
  theme_classic() +
  theme(plot.margin = unit(c(0,0,0,0), "cm")) +
  theme(axis.ticks = element_blank() ) +
  theme(axis.line.y = element_blank()) +
  theme(text = element_text(family="sans", 
  size = 9, 
  color = "#3f2b56")) +
  theme(title = element_text(family = "serif", size = 16.2))+
  theme(axis.title.x = element_text(family = "sans", size = 12.15)) +
  theme(axis.text.y = element_text(family = "sans", size = 12.15))

Code

datatable(articles_per_ninc,
          options = list(dom='t',
                         pageLength = 27),
            colnames = c('State', 'Contest', 'Candidate', 'Articles'),
          rownames = FALSE,
          filter='top') 

Comparing mentions of incumbents and challengers for each contest

Code

all_mentions %>%
  group_by(state, contest, candidate_status) %>% 
  summarize(mentions = n()) %>% 
  mutate(mentions = case_when(
    contest == "Butler" ~ 0,
    TRUE ~ as.numeric(mentions))
    ) %>% 
  pivot_wider(
              names_from = candidate_status,
              values_from = mentions) %>%
  replace_na(list(incumbent = 0, challenger = 0)) %>%
  datatable(
          options = list(dom='t',
                         pageLength = 27),
            colnames = c('State', 'Contest', 'Incumbent mentions', 'Challengers mentions'),
          rownames = FALSE) 

Median mentions for candidates, by incumbency and competitiveness

Code

nincs_competitive <- nincs_for_binding %>% 
  left_join(competitiveness, by=c("state", "contest")) %>%
  select(state, contest, candidate_status, candidate_name, type) 

incs_competitive <- incs_for_binding %>%
    left_join(competitiveness, by=c("state", "contest")) %>%
  select(state, contest, candidate_name, candidate_status, type)

bind_rows(nincs_competitive, incs_competitive) %>%
  group_by(state, contest, candidate_name, candidate_status, type) %>%
  summarize(mentions = n()) %>%
  as_tibble() %>%
  group_by(candidate_status, type) %>%
  summarize(median_mentions = median(mentions)) %>%
  as_tibble() %>%
  mutate(candidate_status = case_when(
    candidate_status == "challenger" ~ "All challengers in a contest",
    candidate_status == "incumbent" ~ "Incumbent"
  )
  ) %>% 
    pivot_wider(names_from = type,
                values_from = median_mentions) %>%
  
        replace_na(list(uncontested = 0)) %>% 
    datatable(options = list(dom='t',
                             order = list(1, 'desc')
                             ),
            colnames = c('Candidate Type', 'Contested','Open','Uncontested'),
          rownames = FALSE)