ct_selection_untitled-385CF27E.R

# Function to process each year's data
process_year <- function(df, year) {
  # Adjust variable names based on year
  prefix <- paste0("CC", substr(year, 3, 4), "_300b_")
  
  df %>%
    mutate(
      abc   = case_when(get(paste0(prefix, "1")) == 1 ~ 1, get(paste0(prefix, "1")) == 2 ~ 0),
      cbs   = case_when(get(paste0(prefix, "2")) == 1 ~ 1, get(paste0(prefix, "2")) == 2 ~ 0),
      nbc   = case_when(get(paste0(prefix, "3")) == 1 ~ 1, get(paste0(prefix, "3")) == 2 ~ 0),
      cnn   = case_when(get(paste0(prefix, "4")) == 1 ~ 1, get(paste0(prefix, "4")) == 2 ~ 0),
      fox   = case_when(get(paste0(prefix, "5")) == 1 ~ 1, get(paste0(prefix, "5")) == 2 ~ 0),
      msnbc = case_when(get(paste0(prefix, "6")) == 1 ~ 1, get(paste0(prefix, "6")) == 2 ~ 0),
      pbs   = case_when(get(paste0(prefix, "7")) == 1 ~ 1, get(paste0(prefix, "7")) == 2 ~ 0),
      other = case_when(get(paste0(prefix, "8")) == 1 ~ 1, get(paste0(prefix, "8")) == 2 ~ 0)
    ) %>%
    select(abc, cbs, nbc, cnn, fox, msnbc, pbs, other, commonweight) %>%
    na.omit() %>%
    mutate(year = year)
}

# Process all three years and combine
combined <- bind_rows(
  process_year(cces20, "2020"),
  process_year(cces22, "2022"),
  process_year(cces24, "2024")
)

# Calculate mean_ci for each network by year with weights
graph <- combined %>%
  pivot_longer(cols = abc:other, names_to = "network", values_to = "value") %>%
  group_by(year, network) %>%
  mean_ci(value, wt = commonweight, ci = .84) %>%
  mutate(
    network = frcode(
      network == "abc" ~ "ABC",
      network == "cbs" ~ "CBS",
      network == "nbc" ~ "NBC",
      network == "cnn" ~ "CNN",
      network == "fox" ~ "Fox News",
      network == "msnbc" ~ "MSNBC",
      network == "pbs" ~ "PBS",
      network == "other" ~ "Other"
    ),
    mean = round(mean, 2)
  )

graph %>% 
  ggplot(aes(x = fct_rev(network), y = mean, color = factor(year))) +
  geom_point(size = 3, position = position_dodge(width = 0.5)) +
  coord_flip() +
  scale_color_manual(values = c("2020" = "#2E86AB", 
                                "2022" = "#A23B72", 
                                "2024" = "#F18F01"),
                     name = "Year") +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(x = NULL,
       y = "",
       title = "In the past 24 Hours, which of these networks did you watch?",
       caption = "@ryanburge | Data: Cooperative Election Study, 2020-2024") +
  theme_rb(legend = TRUE) +
  theme(legend.text = element_text(size = 20)) 
  

save("avg_look_network.png", ht = 5)


# Process 2024 data with generation variable
gen24 <- cces24 %>%
  mutate(
    abc   = case_when(CC24_300b_1 == 1 ~ 1, CC24_300b_1 == 2 ~ 0),
    cbs   = case_when(CC24_300b_2 == 1 ~ 1, CC24_300b_2 == 2 ~ 0),
    nbc   = case_when(CC24_300b_3 == 1 ~ 1, CC24_300b_3 == 2 ~ 0),
    cnn   = case_when(CC24_300b_4 == 1 ~ 1, CC24_300b_4 == 2 ~ 0),
    fox   = case_when(CC24_300b_5 == 1 ~ 1, CC24_300b_5 == 2 ~ 0),
    msnbc = case_when(CC24_300b_6 == 1 ~ 1, CC24_300b_6 == 2 ~ 0),
    pbs   = case_when(CC24_300b_7 == 1 ~ 1, CC24_300b_7 == 2 ~ 0),
    other = case_when(CC24_300b_8 == 1 ~ 1, CC24_300b_8 == 2 ~ 0)
  ) %>%
  mutate(generation = frcode(
    birthyr >= 1997 ~ "Gen Z",
    birthyr >= 1981 & birthyr <= 1996 ~ "Millennial",
    birthyr >= 1965 & birthyr <= 1980 ~ "Gen X",
    birthyr >= 1946 & birthyr <= 1964 ~ "Boomer",
    birthyr <= 1945 ~ "Silent"
  )) %>%
  select(abc, cbs, nbc, cnn, fox, msnbc, pbs, other, generation, commonweight) %>%
  na.omit()

# Calculate by generation
gg <- gen24 %>%
  pivot_longer(cols = abc:other, names_to = "network", values_to = "value") %>%
  group_by(generation, network) %>%
  mean_ci(value, wt = commonweight, ci = .84) %>%
  mutate(
    network = frcode(
      network == "abc" ~ "ABC",
      network == "cbs" ~ "CBS",
      network == "nbc" ~ "NBC",
      network == "cnn" ~ "CNN",
      network == "fox" ~ "Fox News",
      network == "msnbc" ~ "MSNBC",
      network == "pbs" ~ "PBS",
      network == "other" ~ "Other",
      network == "none" ~ "None"
    )
  )

gg %>%
  filter(network != "None") %>%
  ggplot(aes(x = generation, y = mean, color = network, group = network)) +
  geom_line(linewidth = 1.2) +
  geom_point(stroke = 1, shape = 21, fill = 'white', size = 3) +
  scale_x_discrete(limits = c("Silent", "Boomer", "Gen X", "Millennial", "Gen Z")) +
  scale_color_manual(values = c(
    "ABC" = "#E63946",
    "CBS" = "#F77F00", 
    "NBC" = "#FCBF49",
    "CNN" = "#06A77D",
    "Fox News" = "#118AB2",
    "MSNBC" = "#073B4C",
    "PBS" = "#8338EC",
    "Other" = "#999999"
  ), name = NULL) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(x = NULL,
       y = "Share Who Watch",
       title = "Cable News Viewership by Generation",
       caption = "@ryanburge | Data: Cooperative Election Study 2024") +
  theme_rb(legend = TRUE) +
  theme(legend.text = element_text(size = 20)) 

save("gen_watch_networks.png")



small <- cces24 %>%
  mutate(abc   = case_when(CC24_300b_1 == 1 ~ 1, CC24_300b_1 == 2 ~ 0)) %>%
  mutate(cbs   = case_when(CC24_300b_2 == 1 ~ 1, CC24_300b_2 == 2 ~ 0)) %>%
  mutate(nbc   = case_when(CC24_300b_3 == 1 ~ 1, CC24_300b_3 == 2 ~ 0)) %>%
  mutate(cnn   = case_when(CC24_300b_4 == 1 ~ 1, CC24_300b_4 == 2 ~ 0)) %>%
  mutate(fox   = case_when(CC24_300b_5 == 1 ~ 1, CC24_300b_5 == 2 ~ 0)) %>%
  mutate(msnbc = case_when(CC24_300b_6 == 1 ~ 1, CC24_300b_6 == 2 ~ 0)) %>%
  mutate(pbs   = case_when(CC24_300b_7 == 1 ~ 1, CC24_300b_7 == 2 ~ 0)) %>%
  mutate(other = case_when(CC24_300b_8 == 1 ~ 1, CC24_300b_8 == 2 ~ 0)) %>%
  cces_trad(religpew) %>%
  select(abc, cbs, nbc, cnn, fox, msnbc, pbs, other, trad2) %>%
  as_tibble() %>%
  na.omit()

graph <- small %>%
  group_by(trad2) %>%
  summarise(across(abc:other, ~ mean(.x, na.rm=TRUE))) %>%
  pivot_longer(!trad2) %>%
  mutate(name = frcode(name == "abc" ~ "ABC",
                       name == "cbs" ~ "CBS",
                       name == "nbc" ~ "NBC",
                       name == "cnn" ~ "CNN",
                       name == "fox" ~ "Fox\nNews",
                       name == "msnbc" ~ "MSNBC",
                       name == "pbs" ~ "PBS",
                       name == "other" ~ "Other")) %>%
  rename(mean = value) %>%
  mutate(mean = round(mean, 2))

graph %>%
  ggplot(., aes(x= name, y = fct_rev(trad2))) +
  geom_tile(aes(fill = mean), color = "black") +
  scale_fill_gradient2(low = "#3A506B", mid = "#F4E285", high = "#BC4B51", midpoint = .33) +
  theme_rb() +
  geom_text(aes(x= name, y = trad2, label = paste0(mean*100, '%')), size = 5, family = "bold") +
  labs(x= "News Network", y = "", 
       title = "Watched This Network in the Past 24 Hours", 
       caption = "@ryanburge | Data: Cooperative Election Study, 2024")

save("news_network_heat24.png", wd = 6)


######



# Process 2020 data
small20 <- cces20 %>%
  mutate(abc   = case_when(CC20_300b_1 == 1 ~ 1, CC20_300b_1 == 2 ~ 0)) %>%
  mutate(cbs   = case_when(CC20_300b_2 == 1 ~ 1, CC20_300b_2 == 2 ~ 0)) %>%
  mutate(nbc   = case_when(CC20_300b_3 == 1 ~ 1, CC20_300b_3 == 2 ~ 0)) %>%
  mutate(cnn   = case_when(CC20_300b_4 == 1 ~ 1, CC20_300b_4 == 2 ~ 0)) %>%
  mutate(fox   = case_when(CC20_300b_5 == 1 ~ 1, CC20_300b_5 == 2 ~ 0)) %>%
  mutate(msnbc = case_when(CC20_300b_6 == 1 ~ 1, CC20_300b_6 == 2 ~ 0)) %>%
  mutate(pbs   = case_when(CC20_300b_7 == 1 ~ 1, CC20_300b_7 == 2 ~ 0)) %>%
  mutate(other = case_when(CC20_300b_8 == 1 ~ 1, CC20_300b_8 == 2 ~ 0)) %>%
  cces_trad(religpew) %>%
  select(abc, cbs, nbc, cnn, fox, msnbc, pbs, other, trad2) %>%
  as_tibble() %>%
  na.omit()

graph20 <- small20 %>%
  group_by(trad2) %>%
  summarise(across(abc:other, ~ mean(.x, na.rm=TRUE))) %>%
  pivot_longer(!trad2) %>%
  mutate(name = frcode(name == "abc" ~ "ABC",
                       name == "cbs" ~ "CBS",
                       name == "nbc" ~ "NBC",
                       name == "cnn" ~ "CNN",
                       name == "fox" ~ "Fox\nNews",
                       name == "msnbc" ~ "MSNBC",
                       name == "pbs" ~ "PBS",
                       name == "other" ~ "Other")) %>%
  rename(mean20 = value)

# Process 2024 data
small24 <- cces24 %>%
  mutate(abc   = case_when(CC24_300b_1 == 1 ~ 1, CC24_300b_1 == 2 ~ 0)) %>%
  mutate(cbs   = case_when(CC24_300b_2 == 1 ~ 1, CC24_300b_2 == 2 ~ 0)) %>%
  mutate(nbc   = case_when(CC24_300b_3 == 1 ~ 1, CC24_300b_3 == 2 ~ 0)) %>%
  mutate(cnn   = case_when(CC24_300b_4 == 1 ~ 1, CC24_300b_4 == 2 ~ 0)) %>%
  mutate(fox   = case_when(CC24_300b_5 == 1 ~ 1, CC24_300b_5 == 2 ~ 0)) %>%
  mutate(msnbc = case_when(CC24_300b_6 == 1 ~ 1, CC24_300b_6 == 2 ~ 0)) %>%
  mutate(pbs   = case_when(CC24_300b_7 == 1 ~ 1, CC24_300b_7 == 2 ~ 0)) %>%
  mutate(other = case_when(CC24_300b_8 == 1 ~ 1, CC24_300b_8 == 2 ~ 0)) %>%
  cces_trad(religpew) %>%
  select(abc, cbs, nbc, cnn, fox, msnbc, pbs, other, trad2) %>%
  as_tibble() %>%
  na.omit()

graph24 <- small24 %>%
  group_by(trad2) %>%
  summarise(across(abc:other, ~ mean(.x, na.rm=TRUE))) %>%
  pivot_longer(!trad2) %>%
  mutate(name = frcode(name == "abc" ~ "ABC",
                       name == "cbs" ~ "CBS",
                       name == "nbc" ~ "NBC",
                       name == "cnn" ~ "CNN",
                       name == "fox" ~ "Fox\nNews",
                       name == "msnbc" ~ "MSNBC",
                       name == "pbs" ~ "PBS",
                       name == "other" ~ "Other")) %>%
  rename(mean24 = value)

# Calculate change
change <- graph20 %>%
  left_join(graph24, by = c("trad2", "name")) %>%
  mutate(diff = mean24 - mean20) %>%
  mutate(diff = round(diff, 3))

# Visualize the change
change %>%
  ggplot(aes(x = name, y = fct_rev(trad2))) +
  geom_tile(aes(fill = diff), color = "black") +
  scale_fill_gradient2(low = "#2E86AB", mid = "white", high = "#BC4B51", midpoint = 0) +
  theme_rb() +
  geom_text(aes(label = ifelse(diff > 0, paste0("+", diff*100), paste0(diff*100))), 
            size = 4, family = "font") +
  labs(x = "News Network", y = "Religious Tradition",
       title = "Change in Network Viewership: 2020 to 2024",
       subtitle = "Percentage Point Change",
       caption = "@ryanburge | Data: CCES 2020, 2024")

save("news_network_change_2024.png", wd = 6)



# Fox News vs MSNBC gap by religious tradition
comp <- small24 %>%
  select(trad2, fox, msnbc) %>%
  group_by(trad2) %>%
  summarise(
    fox = mean(fox, na.rm = TRUE),
    msnbc = mean(msnbc, na.rm = TRUE)
  ) %>%
  mutate(gap = fox - msnbc) %>%
  arrange(desc(gap))

comp %>%
  ggplot(aes(x = fct_reorder(trad2, gap), y = gap)) +
  geom_col(aes(fill = gap > 0), color = "black") +
  coord_flip() +
  scale_fill_manual(values = c("TRUE" = "#BC4B51", "FALSE" = "#2E86AB")) +
  geom_hline(yintercept = 0, linewidth = 1) +
  geom_text(aes(label = paste0(round(gap*100, 0), "%"), 
                hjust = ifelse(gap > 0, -0.1, 1.1)), 
            size = 4, family = "bold") +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1), limits = c(-.33, .5)) +
  theme_rb() +
  theme(legend.position = "none") +
  labs(x = NULL,
       y = "Fox News Advantage (Negative = MSNBC Advantage)",
       title = "Fox News vs MSNBC Viewership Gap by Religion",
       subtitle = "Difference in Share Who Watched Each Network",
       caption = "@ryanburge | Data: Cooperative Election Study, 2024")

save("fox_msnbc_gap_religion24.png")

######


# Process 2020 data with generation
gen20 <- cces20 %>%
  mutate(abc   = case_when(CC20_300b_1 == 1 ~ 1, CC20_300b_1 == 2 ~ 0)) %>%
  mutate(cbs   = case_when(CC20_300b_2 == 1 ~ 1, CC20_300b_2 == 2 ~ 0)) %>%
  mutate(nbc   = case_when(CC20_300b_3 == 1 ~ 1, CC20_300b_3 == 2 ~ 0)) %>%
  mutate(cnn   = case_when(CC20_300b_4 == 1 ~ 1, CC20_300b_4 == 2 ~ 0)) %>%
  mutate(fox   = case_when(CC20_300b_5 == 1 ~ 1, CC20_300b_5 == 2 ~ 0)) %>%
  mutate(msnbc = case_when(CC20_300b_6 == 1 ~ 1, CC20_300b_6 == 2 ~ 0)) %>%
  mutate(pbs   = case_when(CC20_300b_7 == 1 ~ 1, CC20_300b_7 == 2 ~ 0)) %>%
  mutate(other = case_when(CC20_300b_8 == 1 ~ 1, CC20_300b_8 == 2 ~ 0)) %>%
  cces_trad(religpew) %>%
  mutate(generation = frcode(
    birthyr >= 1997 ~ "Gen Z",
    birthyr >= 1981 & birthyr <= 1996 ~ "Millennial",
    birthyr >= 1965 & birthyr <= 1980 ~ "Gen X",
    birthyr >= 1946 & birthyr <= 1964 ~ "Boomer",
    birthyr <= 1945 ~ "Silent"
  )) %>%
  select(fox, msnbc, cnn, trad2, generation) %>%
  na.omit()

# Process 2024 data with generation
gen24 <- cces24 %>%
  mutate(abc   = case_when(CC24_300b_1 == 1 ~ 1, CC24_300b_1 == 2 ~ 0)) %>%
  mutate(cbs   = case_when(CC24_300b_2 == 1 ~ 1, CC24_300b_2 == 2 ~ 0)) %>%
  mutate(nbc   = case_when(CC24_300b_3 == 1 ~ 1, CC24_300b_3 == 2 ~ 0)) %>%
  mutate(cnn   = case_when(CC24_300b_4 == 1 ~ 1, CC24_300b_4 == 2 ~ 0)) %>%
  mutate(fox   = case_when(CC24_300b_5 == 1 ~ 1, CC24_300b_5 == 2 ~ 0)) %>%
  mutate(msnbc = case_when(CC24_300b_6 == 1 ~ 1, CC24_300b_6 == 2 ~ 0)) %>%
  mutate(pbs   = case_when(CC24_300b_7 == 1 ~ 1, CC24_300b_7 == 2 ~ 0)) %>%
  mutate(other = case_when(CC24_300b_8 == 1 ~ 1, CC24_300b_8 == 2 ~ 0)) %>%
  cces_trad(religpew) %>%
  mutate(generation = frcode(
    birthyr >= 1997 ~ "Gen Z",
    birthyr >= 1981 & birthyr <= 1996 ~ "Millennial",
    birthyr >= 1965 & birthyr <= 1980 ~ "Gen X",
    birthyr >= 1946 & birthyr <= 1964 ~ "Boomer",
    birthyr <= 1945 ~ "Silent"
  )) %>%
  select(fox, msnbc, cnn, trad2, generation) %>%
  na.omit()

# Calculate Fox-MSNBC gap by generation and religion
gap20 <- gen20 %>%
  group_by(generation, trad2) %>%
  summarise(gap20 = mean(fox) - mean(msnbc), .groups = "drop")

gap24 <- gen24 %>%
  group_by(generation, trad2) %>%
  summarise(gap24 = mean(fox) - mean(msnbc), .groups = "drop")

# Combine and calculate change
combined <- gap20 %>%
  left_join(gap24, by = c("generation", "trad2")) %>%
  mutate(change = gap24 - gap20)

# Visualize - focus on key religious groups
combined %>%
  filter(trad2 %in% c("White Evangelical", "Mainline", "White Catholic", 
                      "Atheist", "Agnostic", "Nothing in Particular")) %>%
  ggplot(aes(x = generation, y = change, fill = trad2)) +
  geom_col(position = "dodge", color = "black") +
  scale_x_discrete(limits = c("Gen Z", "Millennial", "Gen X", "Boomer", "Silent")) +
  scale_fill_manual(values = c(
    "White Evangelical" = "#BC4B51",
    "Mainline" = "#F77F00",
    "White Catholic" = "#FCBF49",
    "Atheist" = "#2E86AB",
    "Agnostic" = "#06A77D",
    "Nothing in Particular" = "#8338EC"
  ), name = NULL) +
  geom_hline(yintercept = 0, linewidth = 1) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(x = NULL,
       y = "Change in Fox-MSNBC Gap",
       title = "Change in Fox News Advantage by Generation & Religion",
       subtitle = "2020 to 2024 (Positive = More Fox Advantage)",
       caption = "@ryanburge | Data: CCES 2020, 2024") +
  theme_rb(legend = TRUE)

save("fox_gap_change_gen_religion.png")



# Calculate Fox-MSNBC gap by generation and religion
gap20 <- gen20 %>%
  group_by(generation, trad2) %>%
  summarise(gap20 = mean(fox) - mean(msnbc), .groups = "drop")

gap24 <- gen24 %>%
  group_by(generation, trad2) %>%
  summarise(gap24 = mean(fox) - mean(msnbc), .groups = "drop")

# Combine and calculate change
combined <- gap20 %>%
  left_join(gap24, by = c("generation", "trad2")) %>%
  mutate(change = gap24 - gap20) %>% filter(generation != "Silent")

# Visualize - focus on key religious groups
combined %>%
  filter(trad2 %in% c("White Evangelical", "Mainline", "White Catholic", 
                      "Atheist", "Agnostic", "Nothing in Particular")) %>%
  ggplot(aes(x = fct_reorder(trad2, change), y = change, color = change > 0)) +
  geom_segment(aes(xend = trad2, y = 0, yend = change), linewidth = 1.5) +
  geom_point(size = 4) +
  geom_text(aes(label = paste0(round(change*100, 0), "%"), 
                hjust = ifelse(change > 0, -0.5, 1.5)), 
            size = 3, family = "bold", color = "black") +
  coord_flip() +
  facet_wrap(~ factor(generation, levels = c("Silent", "Boomer", "Gen X", "Millennial", "Gen Z"))) +
  scale_color_manual(values = c("TRUE" = "#BC4B51", "FALSE" = "#2E86AB")) +
  geom_hline(yintercept = 0, linewidth = 1) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1), limits = c(-.1, .1)) +
  labs(x = NULL,
       y = "Change in Fox-MSNBC Gap",
       title = "Change in Fox News Advantage by Generation & Religion",
       subtitle = "2020 to 2024 (Positive = More Fox Advantage)",
       caption = "@ryanburge | Data: Cooperative Election Study, 2020 vs 2024") +
  theme_rb() +
  theme(legend.position = "none")

save("fox_gap_change_gen_religion.png")



# Fox vs MSNBC by generation and religion for 2024
gen24 %>%
  filter(trad2 %in% c("White Evangelical", "Mainline", "White Catholic", 
                      "Atheist", "Agnostic", "Nothing in Particular")) %>%
  group_by(generation, trad2) %>%
  summarise(gap = mean(fox) - mean(msnbc), .groups = "drop") %>%
  ggplot(aes(x = fct_reorder(trad2, gap), y = gap, color = gap > 0)) +
  geom_segment(aes(xend = trad2, y = 0, yend = gap), linewidth = 1.5) +
  geom_point(size = 4) +
  geom_text(aes(label = paste0(round(gap*100, 0), "%"), 
                hjust = ifelse(gap > 0, -0.5, 1.5)), 
            size = 3, family = "bold", color = "black") +
  coord_flip() +
  facet_wrap(~ factor(generation, levels = c("Silent", "Boomer", "Gen X", "Millennial", "Gen Z"))) +
  scale_color_manual(values = c("TRUE" = "#BC4B51", "FALSE" = "#2E86AB")) +
  geom_hline(yintercept = 0, linewidth = 1) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1), limits = c(-.60, .75)) +
  labs(x = NULL,
       y = "Fox News Advantage (Negative = MSNBC Advantage)",
       title = "Fox News vs MSNBC Gap by Generation & Religion",
       subtitle = "Difference in Share Who Watched Each Network",
       caption = "@ryanburge | Data: Cooperative Election Study, 2024") +
  theme_rb() +
  theme(legend.position = "none")

save("fox_msnbc_gap_gen_religion24.png")