ct_selection_untitled-A581048A.R

# Define the common pattern as a function to DRY
ab_summary <- function(df, var, label) {
  df %>%
    mutate(ab = case_when({{ var }} == 1 ~ 1,
                          {{ var }} == 2 ~ 0)) %>%
    group_by(year) %>%
    mean_ci(ab, wt = weight, ci = 0.84) %>%
    mutate(type = label)
}

# Generate each summary
ab_all <- ab_summary(cces, ab_choice, "Any Reason")
ab_rape <- ab_summary(cces, ab_rape, "Only In Cases of Rape,\nIncest, Life of Mother")
ab_late <- ab_summary(cces, ab_late, "Ban After 20 Weeks")
ab_ins <- ab_summary(cces, ab_ins, "Employers Insurance Can\nDecline Abortion Coverage")
ab_funds <- ab_summary(cces, ab_funds, "No Federal Funds for Abortion")
ab_never <- ab_summary(cces, ab_never, "Illegal in All Circumstances")

# Combine all
ab_combined <- bind_rows(ab_all, ab_rape, ab_late, ab_ins, ab_funds, ab_never) %>% na.omit()

ab_labels <- ab_combined %>%
  group_by(type) %>%
  filter(year == min(year) | year == max(year)) %>%
  mutate(label = scales::percent(mean, accuracy = 1))


# Facet line plot
ab_combined %>% 
  ggplot(., aes(x = year, y = mean, color = type)) +
  geom_line() +
  geom_point(stroke = 1 , shape = 21, fill = "white") +
  facet_wrap(~ type) +
  scale_color_manual(values = c("#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#66a61e", "#e6ab02")) +
  scale_x_continuous(limits = c(2013, 2025), breaks = c(2014, 2016, 2018, 2020, 2022, 2024)) +
  scale_y_continuous(labels = percent, limits = c(0, .8)) +
  theme_rb() +
  geom_text(data = ab_labels,
            aes(label = label),
            vjust = -0.5,
            size = 5,
            show.legend = FALSE, family = "font", color = "black") +
  theme(strip.text = element_text(size = 14)) +
  labs(x = "", y = "Share Agreeing", title = "Changes in Abortion Opinion, 2014-2024", caption = "@ryanburge\nData: Cooperative Election Study, 2024")
save("abortion_over_time24.png")



# Define the common pattern as a function to DRY
ab_summary <- function(df, var, label) {
  df %>%
    filter(year == 2016 | year == 2024) %>% 
    mutate(ab = case_when({{ var }} == 1 ~ 1,
                          {{ var }} == 2 ~ 0)) %>%
    group_by(year, trad2) %>%
    mean_ci(ab, wt = weight, ci = 0.84) %>%
    mutate(type = label)
}

# Generate each summary
ab_all <- ab_summary(cces, ab_choice, "Any Reason")
ab_never <- ab_summary(cces, ab_never, "Complete Ban")

# Combine all
both <- bind_rows(ab_all, ab_never) %>% na.omit()


# Filter for 2014 and 2024 only
ab_compare <- both %>%
  filter(year %in% c(2016, 2024)) %>%
  mutate(
    year = factor(year),
    trad2 = fct_reorder2(trad2, year, mean)  # orders within each type
  )

# Plot
ggplot(ab_compare, aes(x = mean, y = trad2, fill = year)) +
  geom_col(position = position_dodge(width = 0.7), width = 0.65, color = "black") +
  facet_wrap(~ type, ncol = 1) +
  x_pct() +
  scale_fill_manual(values = c("2016" = "#7570b3", "2024" = "#d95f02")) +
  labs(
    title = "Changes in Abortion Opinion by Religion (2016 vs 2024)",
    x = "Share Agreeing",
    y = NULL,
    fill = "Year",
    caption = "@ryanburge | Data: Cooperative Election Study"
  ) +
  theme_rb() +
  theme(strip.text = element_text(size = 14))



library(ggalt)  # for geom_dumbbell


# Prepare data for dumbbell plot
gap <- ab_compare %>%
  filter(year %in% c(2016, 2024)) %>%
  select(year, trad2, mean, type) %>%
  pivot_wider(names_from = year, values_from = mean) %>%
  mutate(
    diff = round((`2024` - `2016`) * 100),
    label = paste0(ifelse(diff > 0, "+", ""), diff, " pts."),
    trad2 = fct_reorder(trad2, `2024`)  # sort by most recent value
  )

# Dumbbell plot
ggplot(gap, aes(x = `2016`, xend = `2024`, y = fct_rev(trad2))) +
  geom_dumbbell(
    color = "gray80",
    stroke = 1.5,
    shape = 21,
    size = 0.8,
    colour_x = "#7570b3",     # 2016
    colour_xend = "#d95f02"   # 2024
  ) +
  geom_text(
    aes(x = (`2016` + `2024`) / 2, label = label),
    vjust = -0.6, size = 3.5, color = "black", family = "font"
  ) +
  scale_x_continuous(labels = percent_format(accuracy = 1), limits = c(0, 1)) +
  facet_wrap(~ type, ncol = 1) +
  labs(
    title = "Change in Abortion Opinion by Religious Tradition (2016 to 2024)",
    subtitle = "Each dumbbell shows support in <span style='color:#7570b3;'>2016</span> and <span style='color:#d95f02;'>2024</span> for each group",
    x = NULL, y = NULL,
    caption = "@ryanburge\nData: Cooperative Election Study, 2016 + 2024"
  ) +
  theme_rb(legend = TRUE) +
  theme(strip.text = element_text(size = 25),
        plot.subtitle = ggtext::element_markdown())

# Save
save("dumbbell_abortion_2016_2024.png", ht = 10)


aaa1 <- cces24 %>% 
  mutate(ab = CC24_444c) %>% 
  mutate(ab = case_when(ab == 1 ~ 1, 
                        ab == 2 ~ 0)) %>% 
  mean_ci(ab, wt = commonweight, ci = .84) %>% 
  mutate(trad2 = "Entire Sample")


aaa2 <- cces24 %>% 
  cces_trad(religpew) %>% 
  mutate(ab = CC24_444c) %>% 
  mutate(ab = case_when(ab == 1 ~ 1, 
                        ab == 2 ~ 0)) %>% 
  group_by(trad2) %>% 
  mean_ci(ab, wt = commonweight, ci = .84) 

one <- bind_rows(aaa1, aaa2) %>% 
  mutate(type = "Prohibit Abortion Pills Through the Mail")


aaa1 <- cces24 %>% 
  mutate(ab = CC24_444d) %>% 
  mutate(ab = case_when(ab == 1 ~ 1, 
                        ab == 2 ~ 0)) %>% 
  mean_ci(ab, wt = commonweight, ci = .84) %>% 
  mutate(trad2 = "Entire Sample")


aaa2 <- cces24 %>% 
  cces_trad(religpew) %>% 
  mutate(ab = CC24_444d) %>% 
  mutate(ab = case_when(ab == 1 ~ 1, 
                        ab == 2 ~ 0)) %>% 
  group_by(trad2) %>% 
  mean_ci(ab, wt = commonweight, ci = .84) 

two <- bind_rows(aaa1, aaa2) %>% 
  mutate(type = "Prohibit Women Traveling Out of State to Receive an Abortion")


aaa1 <- cces24 %>% 
  mutate(ab = CC24_445b) %>% 
  mutate(ab = case_when(ab == 1 ~ 1, 
                        ab == 2 ~ 0)) %>% 
  mean_ci(ab, wt = commonweight, ci = .84) %>% 
  mutate(trad2 = "Entire Sample")


aaa2 <- cces24 %>% 
  cces_trad(religpew) %>% 
  mutate(ab = CC24_445b) %>% 
  mutate(ab = case_when(ab == 1 ~ 1, 
                        ab == 2 ~ 0)) %>% 
  group_by(trad2) %>% 
  mean_ci(ab, wt = commonweight, ci = .84) 

three <- bind_rows(aaa1, aaa2) %>% 
  mutate(type = "Constitution Doesn't Protect Right to an Abortion")


all <- bind_rows(one, two, three) %>% filter(trad2 != "Unclassified")


# Pull out the Entire Sample means for each type
issue_means <- all %>%
  filter(trad2 == "Entire Sample") %>%
  select(type, mean) %>%
  rename(avg = mean)

all_ordered <- all %>%
  filter(trad2 != "Entire Sample") %>%
  group_by(type) %>%
  mutate(trad2_reorder = fct_reorder(trad2, mean)) %>%
  ungroup()

religion_colors <- c(
  "#1b9e77",  # White Evangelical
  "#d95f02",  # Non-White Evangelical
  "#7570b3",  # Mainline
  "#e7298a",  # Black Protestant
  "#66a61e",  # White Catholic
  "#e6ab02",  # Non-White Catholic
  "#a6761d",  # Mormon
  "#666666",  # Orthodox
  "#1f78b4",  # Jewish
  "#b2df8a",  # Muslim
  "#33a02c",  # Buddhist
  "#fb9a99",  # Hindu
  "#e31a1c",  # Atheist
  "#ff7f00",  # Agnostic
  "#6a3d9a",  # Nothing in Particular
  "#b15928"   # Unclassified
)


# Plot
all_ordered %>%
  filter(trad2 != "Entire Sample") %>%  # omit the overall bar if not wanted
  ggplot(aes(x = trad2_reorder, y = mean, fill = trad2)) +
  geom_col(color = "black") +
  facet_wrap(~ type, scales = "free_y", ncol = 1) +
  geom_hline(data = issue_means, aes(yintercept = avg), 
             linetype = "dashed", color = "black", linewidth = 0.7) +
  coord_flip() +
  theme_rb() +
  scale_fill_manual(values = religion_colors) +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  labs(
    x = NULL, y = "Share Agreeing",
    title = "Religious Group Support for Anti-Abortion Policies",
    caption = "@ryanburge\nData: Cooperative Election Study 2024"
  ) +
  lab_bar(above = TRUE, pos = .025, sz = 5, type = mean) +
  theme(strip.text = element_text(size = 13), legend.position = "none")

save('ces24_abortion_facets.png', ht = 10)


gg1 <- cces24 %>% 
  cces_trad(religpew) %>% 
  mutate(age = 2024 - birthyr) %>% 
  mutate(age2 = frcode(age <= 35 ~ "18-35", 
                       age >= 36 & age <= 49 ~ "36-49", 
                       age >= 50 & age <= 64 ~ "50-64", 
                       age >= 65 ~ "65+")) %>% 
  mutate(ab = CC24_324d) %>% 
  mutate(ab = case_when(ab == 1 ~ 1, 
                        ab == 2 ~ 0)) %>% 
  group_by(trad2, age2) %>% 
  mean_ci(ab, wt = commonweight, ci = .84)

age4_colors <- c(
  "18-35" = "#1b9e77",  # teal
  "36-49" = "#d95f02",  # orange
  "50-64" = "#7570b3",  # purple
  "65+"   = "#e7298a"   # pink
)


gg1 %>% 
  ggplot(., aes(x = age2, y = mean, fill = age2)) +
  geom_col(color = "black") +
  facet_wrap(~ trad2) +
  error_bar() +
  theme_rb() +
  y_pct() + 
  scale_fill_manual(values = age4_colors) +
  lab_bar_white(above = FALSE, type = mean, sz = 5, pos = .075) +
  theme(strip.text = element_text(size = 14)) +
  labs(x = "", y = "", title = "Expand access to abortion, including making it more affordable, broadening the\ntypes of providers who can offer care, and protecting access to abortion clinics", 
       caption = "@ryanburge\nData: Cooperative Election Study, 2024")
save("expand_access24_trad2_age2.png", ht = 12, wd = 8)



# Define the common pattern as a function to DRY
ab_summary <- function(df, var, label) {
  df %>%
    cces_pid3(pid7) %>% 
    mutate(ab = case_when({{ var }} == 1 ~ 1,
                          {{ var }} == 2 ~ 0)) %>%
    group_by(year, pid3) %>%
    mean_ci(ab, wt = weight, ci = 0.84) %>%
    mutate(type = label)
}

# Generate each summary
ab_all <- ab_summary(cces, ab_choice, "Allow for Any Reason")
ab_never <- ab_summary(cces, ab_never, "Illegal in All Circumstances")

both <- bind_rows(ab_all, ab_never) %>% filter(pid3 != "NA") 

label_data <- both %>%
  filter(!is.na(mean)) %>%  # only keep actual data rows
  group_by(type, pid3) %>%
  slice_min(order_by = year, n = 1, with_ties = FALSE) %>%
  bind_rows(
    both %>%
      filter(!is.na(mean)) %>%
      group_by(type, pid3) %>%
      slice_max(order_by = year, n = 1, with_ties = FALSE)
  ) %>%
  mutate(
    label = percent(mean, accuracy = 1),
    hjust = ifelse(year == min(year), 1.1, -0.1)
  )

type_colors <- c(
  "Allow for Any Reason" = "#1b9e77",  # Deep teal
  "Illegal in All Circumstances" = "#d95f02"  # Burnt orange
)


both %>% 
  ggplot(aes(x = year, y = mean, color = type, group = type)) +
  geom_line() + 
  geom_point(stroke = 1, shape = 21, fill = 'white') + 
  geom_text(data = label_data,
            aes(label = label, hjust = hjust),
            vjust = -0.3, size = 4.2, show.legend = FALSE,
            family = "font") +
  facet_wrap(~ pid3) +
  y_pct() + 
  scale_color_manual(values = type_colors) +
  scale_x_continuous(limits = c(2013, 2025), breaks = c(2014, 2016, 2018, 2020, 2022, 2024)) +
  theme_rb(legend = TRUE) +
  theme(legend.text = element_text(size = 20)) +
  labs(
    x = "", y = "", 
    title = "Abortion Opinion by Political Partisanship Over the Last Decade", 
    caption = "@ryanburge\nData: Cooperative Election Study, 2014–2024"
  )

save("ab_pid3_2scenarios.png")