ct_selection_untitled-92385C4E.R

ggg1 <- cces08 %>% 
  filter(V219 == 3) %>% 
  mutate(vote = CC410) %>% 
  mutate(vote = frcode(vote == 1 ~ "Republican",
                       vote == 2 ~ "Democrat",
                       vote == 3 | vote == 4 | vote == 5 | vote == 6 ~ "Third Party")) %>% 
  ct(vote, wt = V201, show_na = FALSE) %>% 
  mutate(year = 2008)

ggg2 <- cces12 %>% 
  filter(religpew == 3) %>% 
  mutate(vote = CC410a) %>% 
  mutate(vote = frcode(vote == 1 ~ "Democrat",
                       vote == 2 ~ "Republican",
                       vote == 4 ~ "Third Party")) %>% 
  ct(vote, wt = weight_vv_post, show_na = FALSE) %>% 
  mutate(year = 2012)


ggg3 <- cces16 %>% 
  filter(CL_matched == "Y") %>% 
  
  filter(religpew == 3) %>% 
  mutate(vote = CC16_410a) %>% 
  mutate(vote = frcode(vote == 1 ~ "Republican",
                       vote == 2 ~ "Democrat",
                       vote == 3 | vote == 4 | vote == 5 | vote == 8 ~ "Third Party")) %>% 
  ct(vote, wt = commonweight_vv_post, show_na = FALSE) %>% 
  mutate(year = 2016)


ggg4 <- cces20 %>% 
  filter(CL_voter_status <= 5) %>% 
  filter(religpew == 3) %>% 
  mutate(vote = CC20_410) %>% 
  mutate(vote = frcode(vote == 1 ~ "Democrat", 
                       vote == 2 ~ "Republican", 
                       vote == 4 ~ "Third Party")) %>% 
  ct(vote, wt = vvweight_post, show_na = FALSE) %>% 
  mutate(year = 2020)


ggg5 <- cces24 %>% 
  filter(religpew == 3) %>% 
  mutate(vote = CC24_410) %>% 
  mutate(vote = frcode(vote == 1 ~ "Democrat", 
                       vote == 2 ~ "Republican", 
                       vote >= 3 & vote <= 9 ~ "Third Party")) %>% 
  ct(vote, wt = commonpostweight, show_na = FALSE) %>% 
  mutate(year = 2024)

all <-   bind_rows(ggg1, ggg2, ggg3, ggg4, ggg5) %>% mutate(year = as.factor(year))




all %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x = fct_rev(year), y = pct, fill = fct_rev(vote))) +
  geom_col(color = "black") + 
  coord_flip() +
  theme_rb() +
  scale_fill_manual(
    values = c(
      "Republican" = "#D73027",   # Strong red
      "Democrat"   = "#4575B4",   # Strong blue
      "Third Party" = "#F0E442"   # Bright gold/yellow
    )
  ) +
  theme(legend.position = "bottom") +
  scale_y_continuous(labels = percent) +
  theme(strip.text.y.left = element_text(angle=0)) +
  guides(fill = guide_legend(reverse=T, nrow = 1)) +
  # theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
  theme(panel.grid.minor.y=element_blank(), panel.grid.major.y=element_blank()) +
  geom_text(aes(label = ifelse(pct >.05, paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "white") +
  geom_text(aes(label = ifelse(pct >.05 & vote == "Third Party", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "black") +
  # geom_text(aes(label = ifelse(age2 == "36-44", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 4, family = "font", color = "white") +
  theme(plot.title = element_text(size = 16)) +
  theme(strip.text.y.left = element_text(angle = 0, hjust = 1)) +
  labs(x = "", y = "", title = "Presidential Vote Choice Among Latter-day Saints, 2008-2024", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2024") 
save("pres_vote_lds_24.png", wd = 7, ht = 4)

gg1 <- cces %>% 
  cces_pid3(pid7) %>% 
  filter(religion == 3) %>% 
  group_by(year) %>% 
  ct(pid3, wt = weight, show_na = FALSE) %>% 
  mutate(year = as.factor(year))



gg1 %>%
  filter(year == 2008 | year == 2012 | year == 2016 | year == 2020 | year == 2024) %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x = fct_rev(year), y = pct, fill = fct_rev(pid3))) +
  geom_col(color = "black") + 
  coord_flip() +
  theme_rb() +
  scale_fill_manual(
    values = c(
      "Republican" = "#D73027",   # Strong red
      "Democrat"   = "#4575B4",   # Strong blue
      "Independent" = "darkorchid"   # Bright gold/yellow
    )
  ) +
  theme(legend.position = "bottom") +
  scale_y_continuous(labels = percent) +
  theme(strip.text.y.left = element_text(angle=0)) +
  guides(fill = guide_legend(reverse=T, nrow = 1)) +
  # theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
  theme(panel.grid.minor.y=element_blank(), panel.grid.major.y=element_blank()) +
  geom_text(aes(label = ifelse(pct >.05, paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "white") +
  # geom_text(aes(label = ifelse(pct >.05 & vote == "Third Party", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "black") +
  # geom_text(aes(label = ifelse(age2 == "36-44", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 4, family = "font", color = "white") +
  theme(plot.title = element_text(size = 16)) +
  theme(strip.text.y.left = element_text(angle = 0, hjust = 1)) +
  labs(x = "", y = "", title = "The Partisanship of Latter-day Saints, 2008-2024", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2024") 
save("pid3_lds_24.png", wd = 7, ht = 4)


aaa1 <- cces %>% 
  filter(year == 2016) %>% 
  filter(religion == 3) %>% 
  cces_pid3(pid7) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conserative")) %>% 
  filter(pid3 != "NA") %>% 
  group_by(pid3, year) %>% 
  ct(id3, wt = weight, show_na = FALSE) %>% 
  ungroup() %>% 
  mutate(pct = n/862) %>% 
  mutate(year = 2016)


aaa2 <- cces %>% 
  filter(year == 2020) %>% 
  filter(religion == 3) %>% 
  cces_pid3(pid7) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conserative")) %>% 
  filter(pid3 != "NA") %>% 
  group_by(pid3, year) %>% 
  ct(id3, wt = weight, show_na = FALSE) %>% 
  mutate(pct = n/716) %>% 
  mutate(year = 2020)


aaa3 <- cces %>% 
  filter(year == 2024) %>% 
  filter(religion == 3) %>% 
  cces_pid3(pid7) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conserative")) %>% 
  filter(pid3 != "NA") %>% 
  group_by(pid3, year) %>% 
  ct(id3, wt = weight, show_na = FALSE) %>% 
  mutate(pct = n/623) %>% 
  mutate(year = 2024)
  
  
heat <- bind_rows(aaa1, aaa2, aaa3)

heat <- heat %>% 
  mutate(
    bins = case_when(
      pct < 0.10 ~ "a",        # Low
      pct < 0.25 ~ "b",        # Medium
      TRUE ~ "e"               # High
    )
  )

custom_colors <- c(
  "a" = "#f0e5cf",  # light tan
  "b" = "#c2b280",  # muted khaki
  "e" = "#7c6f4f"   # deep olive brown
)

heat %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(aes(x = pid3, y = id3)) +
  geom_tile(aes(fill = bins), color = "black") +
  scale_fill_manual(values = custom_colors) +
  geom_text(
    aes(label = paste0(lab * 100, '%'), color = ifelse(bins == "e", "white", "black")),
    size = 11, family = "font"
  ) +
  scale_color_identity() +
  facet_wrap(~ year) +
  theme_rb() + 
  theme(
    plot.subtitle = element_text(size = 14),
    strip.text = element_text(size = 20)
  ) +
  labs(
    x = "Partisanship",
    y = "Ideology",
    title = "The Partisanship and Ideology of Latter-day Saints",
    caption = "@ryanburge\nData: Cooperative Election Study, 2016–2024"
  )

save("heat_lds_id3_pid3.png", wd = 10, ht = 5)





ggg3 <- cces16 %>% 
  filter(CL_matched == "Y") %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conserative")) %>% 
  filter(religpew == 3) %>% 
  mutate(vote = CC16_410a) %>% 
  mutate(vote = frcode(vote == 1 ~ "Republican",
                       vote == 2 ~ "Democrat",
                       vote == 3 | vote == 4 | vote == 5 | vote == 8 ~ "Third Party")) %>% 
  group_by(id3) %>% 
  ct(vote, wt = commonweight_vv_post, show_na = FALSE) %>% 
  mutate(year = 2016)


ggg4 <- cces20 %>% 
  filter(CL_voter_status <= 5) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conserative")) %>% 
  filter(religpew == 3) %>% 
  mutate(vote = CC20_410) %>% 
  mutate(vote = frcode(vote == 1 ~ "Democrat", 
                       vote == 2 ~ "Republican", 
                       vote == 4 ~ "Third Party")) %>% 
  group_by(id3) %>% 
  ct(vote, wt = vvweight_post, show_na = FALSE) %>% 
  mutate(year = 2020)


ggg5 <- cces24 %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conserative")) %>% 
  filter(religpew == 3) %>% 
  mutate(vote = CC24_410) %>% 
  mutate(vote = frcode(vote == 1 ~ "Democrat", 
                       vote == 2 ~ "Republican", 
                       vote >= 3 & vote <= 9 ~ "Third Party")) %>% 
  group_by(id3) %>% 
  ct(vote, wt = commonpostweight, show_na = FALSE) %>% 
  mutate(year = 2024)

all <-   bind_rows(ggg3, ggg4, ggg5) %>% filter(id3 == "Moderate") %>% mutate(year = as.factor(year))




all %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x = fct_rev(year), y = pct, fill = fct_rev(vote))) +
  geom_col(color = "black") + 
  coord_flip() +
  theme_rb() +
  scale_fill_manual(
    values = c(
      "Republican" = "#D73027",   # Strong red
      "Democrat"   = "#4575B4",   # Strong blue
      "Third Party" = "#F0E442"   # Bright gold/yellow
    )
  ) +
  theme(legend.position = "bottom") +
  scale_y_continuous(labels = percent) +
  theme(strip.text.y.left = element_text(angle=0)) +
  guides(fill = guide_legend(reverse=T, nrow = 1)) +
  # theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
  theme(panel.grid.minor.y=element_blank(), panel.grid.major.y=element_blank()) +
  geom_text(aes(label = ifelse(pct >.05, paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "white") +
  geom_text(aes(label = ifelse(pct >.05 & vote == "Third Party", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "black") +
  # geom_text(aes(label = ifelse(age2 == "36-44", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 4, family = "font", color = "white") +
  theme(plot.title = element_text(size = 16)) +
  theme(strip.text.y.left = element_text(angle = 0, hjust = 1)) +
  labs(x = "", y = "", title = "Presidential Vote Choice Among Ideologically Moderate LDS, 2016-2024", caption = "@ryanburge\nData: Cooperative Election Study, 2016-2024") 
save("pres_vote_lds_moderate_24.png", wd = 7, ht = 3)

gg1 <- cces %>% 
  filter(year >= 2020) %>% 
  filter(religion == 3) %>% 
  group_by(age) %>% 
  cces_pid3(pid7) %>% 
  ct(pid3, wt = weight, show_na = FALSE)


gg1 %>% 
  filter(age <= 75) %>% 
  ggplot(., aes(x = age, y = pct, color = pid3)) +
  geom_point(stroke = 1, shape = 21, alpha = .20) +
  geom_labelsmooth(aes(label = pid3, linetype = pid3),  method = "loess", formula = y ~ x, family = "font", linewidth = 1, text_smoothing = 30, size = 6, linewidth = 1, boxlinewidth = 0.3) +
  pid3_color() +
  y_pct() + 
  theme_rb() +
  labs(x ="Age", y = "", title = "Partisanship Composition of Latter-day Saints by Age", caption = "@ryanburge\nData: Cooperative Election Study, 2022-2024")
save("ces_lds_age_pid3.png")



library(socsci)

ggg3 <- cces16 %>% 
  mutate(age = 2016 - birthyr) %>% 
  mutate(age2 = frcode(age <= 35 ~ "18-35", 
                       age >= 36 & age <= 50 ~ "36-50", 
                       age >= 51 & age <= 64 ~ "51-64", 
                       age >= 65 ~ "65+")) %>% 
  filter(CL_matched == "Y", religpew == 3) %>% 
  mutate(vote = case_when(CC16_410a == 1 ~ 1,  # Republican
                       CC16_410a %in% c(2, 3, 4, 5, 8) ~ 0)) %>%  # Democrat, Third Party
  group_by(age2) %>% 
  mean_ci(vote, wt = commonweight_vv_post, ci = 0.84) %>% 
  mutate(year = 2016)

ggg4 <- cces20 %>% 
  filter(CL_voter_status <= 5, religpew == 3) %>% 
  mutate(age = 2020 - birthyr) %>% 
  mutate(age2 = frcode(age <= 35 ~ "18-35", 
                       age >= 36 & age <= 50 ~ "36-50", 
                       age >= 51 & age <= 64 ~ "51-64", 
                       age >= 65 ~ "65+")) %>% 
  mutate(vote = case_when(CC20_410 == 2 ~ 1,  # Republican
                       CC20_410 %in% c(1, 4) ~ 0)) %>%  # Democrat, Third Party
  group_by(age2) %>% 
  mean_ci(vote,  ci = 0.84) %>% 
  mutate(year = 2020)

ggg5 <- cces24 %>% 
  filter(religpew == 3) %>% 
  mutate(age = 2024 - birthyr) %>% 
  mutate(age2 = frcode(age <= 35 ~ "18-35", 
                       age >= 36 & age <= 50 ~ "36-50", 
                       age >= 51 & age <= 64 ~ "51-64", 
                       age >= 65 ~ "65+")) %>% 
  mutate(vote = case_when(CC24_410 == 2 ~ 1,  # Republican
                       CC24_410 %in% c(1, 3:9) ~ 0)) %>%  # Democrat, Third Party
  group_by(age2) %>% 
  mean_ci(vote, wt = commonpostweight, ci = 0.84) %>% 
  mutate(year = 2024)


all <-   bind_rows(ggg3, ggg4, ggg5) %>%  mutate(year = as.factor(year))

all %>% 
  ggplot(., aes(x = year, y = mean, fill = mean)) +
  geom_col(color = "black") +
  facet_wrap(~ age2) +
  scale_fill_gradient2(low = "dodgerblue3", mid = "azure1", high = "firebrick3", midpoint = .65) +
  theme_rb() + 
  error_bar() +
  y_pct() + 
  lab_bar(above = FALSE, pos = .065, sz = 8, type = mean) +
  labs(x = "", y = "", title = "Vote Share for Trump among Latter-day Saints", caption = "@ryanburge\nData: Cooperative Election Study, 2016-2024")
save("lds_age2_votes.png", ht = 8, wd = 5)