ct_selection_untitled-D4898E76.R

gg <- cces %>% 
  filter(year == 2008 | year == 2012 | year == 2016 | year == 2020 | year == 2022 | year == 2023) %>% 
  group_by(year) %>% 
  filter(race == 1) %>% 
  filter(pew_bornagain == 1) %>% 
  cces_id5(ideo5) %>% 
  ct(id5, wt = weight, show_na = FALSE)

colors <- c("Very Liberal" = "#B2182B",
            "Liberal" = "#EF8A62",
            "Moderate" = "azure4",
            "Conservative" = "#67A9CF",
            "Very Conservative" = "#2166AC")


gg %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x = 1, y = pct, fill = fct_rev(id5))) +
  geom_col(color = "black") + 
  coord_flip() +
  facet_wrap(~ year, ncol =1, strip.position = "left") +
  theme_rb() +
  scale_fill_manual(values = colors) +
  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 = "black") +
  theme(plot.title = element_text(size = 16)) +
  theme(strip.text.y.left = element_text(angle = 0, hjust = 1)) +
  labs(x = "", y = "", title = "The Ideological Composition of White Evangelicals", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2023") 
save("id5_wev.png", wd = 9, ht = 5)



cces_id5 <- function(df, var){
  
  df %>% 
    mutate(id5 = frcode({{var}} == 1 | {{var}} == 2 ~ "Liberal", 
                        {{var}} == 3 ~ "Moderate", 
                        {{var}} == 4 | {{var}} == 5 ~ "Conservative")) 
  
}


ggg1 <- cces08 %>% 
  filter(evangelical == 1) %>% 
  filter(race == 1) %>% 
  cces_id5(V243) %>% 
  mutate(vote = CC410) %>% 
  group_by(id5) %>% 
  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(evangelical == 1) %>% 
  filter(race == 1) %>% 
  cces_id5(ideo5) %>% 
  group_by(id5) %>% 
  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(evangelical == 1) %>% 
  filter(race == 1) %>% 
  cces_id5(ideo5) %>% 
  group_by(id5) %>% 
  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(race == 1) %>% 
  filter(pew_bornagain == 1) %>% 
  cces_id5(ideo5) %>% 
  group_by(id5) %>% 
  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)


all1 <- bind_rows(ggg1, ggg2, ggg3, ggg4) %>% filter(id5 != 'NA') %>% 
  mutate(type = "White Evangelicals")



cces_id5 <- function(df, var){
  
  df %>% 
    mutate(id5 = frcode({{var}} == 1 | {{var}} == 2 ~ "Liberal", 
                        {{var}} == 3 ~ "Moderate", 
                        {{var}} == 4 | {{var}} == 5 ~ "Conservative")) 
  
}


ggg1 <- cces08 %>% 
  filter(race == 1) %>% 
  cces_id5(V243) %>% 
  mutate(vote = CC410) %>% 
  group_by(id5) %>% 
  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(race == 1) %>% 
  cces_id5(ideo5) %>% 
  group_by(id5) %>% 
  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(race == 1) %>% 
  cces_id5(ideo5) %>% 
  group_by(id5) %>% 
  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(race == 1) %>% 
  cces_id5(ideo5) %>% 
  group_by(id5) %>% 
  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)


all2 <- bind_rows(ggg1, ggg2, ggg3, ggg4) %>% filter(id5 != 'NA') %>% 
  mutate(type = "All White Voters")


graph <- bind_rows(all1, all2)



c3 <- c("Republican" = "#B2182B",
            "Third Party" = "azure4",
            "Democrat" = "#2166AC")

graph %>% 
  filter(id5 == "Moderate") %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x = fct_rev(type), y = pct, fill = fct_rev(vote))) +
  geom_col(color = "black") + 
  coord_flip() +
  facet_wrap(~ year, ncol =1, strip.position = "top") +
  theme_rb() +
  scale_fill_manual(values = c3) +
  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)) +
  geom_text(aes(label = ifelse(pct >.05, paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "white") +
  theme(plot.title = element_text(size = 16)) +
  theme(strip.text.y.left = element_text(angle = 0, hjust = 1)) +
  theme(strip.text.x.top = element_text(size = 22)) +
  labs(x = "", y = "", title = "Vote Choice Among Ideologically Moderate White Respondents", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2020") 
save("moderate_wev_presvote20.png", wd = 9, ht = 7)



gg <- cces %>% 
  filter(year == 2008 | year == 2012 | year == 2016 | year == 2020 | year == 2022 | year == 2023) %>% 
  group_by(year) %>% 
  filter(pew_bornagain == 1) %>% 
  filter(race == 1) %>% 
  filter(ideo5 == 3) %>% 
  cces_pid3(pid7) %>% 
  ct(pid3, show_na = FALSE, wt = weight)

c3 <- c("Republican" = "#B2182B",
            "Independent" = "azure4",
            "Democrat" = "#2166AC")


gg %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x = 1, y = pct, fill = fct_rev(pid3))) +
  geom_col(color = "black") + 
  coord_flip() +
  facet_wrap(~ year, ncol =1, strip.position = "left") +
  theme_rb() +
  scale_fill_manual(values = c3) +
  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") +
  theme(plot.title = element_text(size = 16)) +
  theme(strip.text.y.left = element_text(angle = 0, hjust = 1)) +
  labs(x = "", y = "", title = "The Partisan Composition of Moderate White Evangelicals", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2023") 
save("id3_moderate_wev.png", wd = 9, ht = 5)



cces_id3 <- function(df, var){
  
  df %>% 
    mutate(id3 = frcode({{var}} == 1 | {{var}} == 2 ~ "Liberal", 
                        {{var}} == 3 ~ "Moderate", 
                        {{var}} == 4 | {{var}} == 5 ~ "Conservative")) 
  
}

aaa1 <- cces23 %>% 
  filter(pew_bornagain == 1) %>% 
  filter(race == 1) %>% 
  cces_id3(ideo5) %>% 
  group_by(id3) %>% 
  mutate(imm = CC23_323a) %>% 
  mutate(imm = case_when(imm == 1 ~ 1, 
                         imm == 2 ~ 0)) %>% 
  group_by(id3) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  filter(id3 != "NA") %>% 
  mutate(type = "White Evangelicals")


aaa2 <- cces23 %>% 
  filter(race == 1) %>% 
  cces_id3(ideo5) %>% 
  group_by(id3) %>% 
  mutate(imm = CC23_323a) %>% 
  mutate(imm = case_when(imm == 1 ~ 1, 
                         imm == 2 ~ 0)) %>% 
  group_by(id3) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  filter(id3 != "NA") %>% 
  mutate(type = "All White Sample")

yyy1 <- bind_rows(aaa1, aaa2) %>% mutate(ques = "Pathway to Citizenship")


aaa1 <- cces23 %>% 
  filter(pew_bornagain == 1) %>% 
  filter(race == 1) %>% 
  cces_id3(ideo5) %>% 
  group_by(id3) %>% 
  mutate(imm = CC23_324a) %>% 
  mutate(imm = case_when(imm == 1 ~ 1, 
                         imm == 2 ~ 0)) %>% 
  group_by(id3) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  filter(id3 != "NA") %>% 
  mutate(type = "White Evangelicals")


aaa2 <- cces23 %>% 
  filter(race == 1) %>% 
  cces_id3(ideo5) %>% 
  group_by(id3) %>% 
  mutate(imm = CC23_324a) %>% 
  mutate(imm = case_when(imm == 1 ~ 1, 
                         imm == 2 ~ 0)) %>% 
  group_by(id3) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  filter(id3 != "NA") %>% 
  mutate(type = "All White Sample")

yyy2 <- bind_rows(aaa1, aaa2) %>% mutate(ques = "Allow Abortion as a Choice")



aaa1 <- cces23 %>% 
  filter(pew_bornagain == 1) %>% 
  filter(race == 1) %>% 
  cces_id3(ideo5) %>% 
  group_by(id3) %>% 
  mutate(imm = CC23_328f) %>% 
  mutate(imm = case_when(imm == 1 ~ 1, 
                         imm == 2 ~ 0)) %>% 
  group_by(id3) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  filter(id3 != "NA") %>% 
  mutate(type = "White Evangelicals")


aaa2 <- cces23 %>% 
  filter(race == 1) %>% 
  cces_id3(ideo5) %>% 
  group_by(id3) %>% 
  mutate(imm = CC23_328f) %>% 
  mutate(imm = case_when(imm == 1 ~ 1, 
                         imm == 2 ~ 0)) %>% 
  group_by(id3) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  filter(id3 != "NA") %>% 
  mutate(type = "All White Sample")

yyy3 <- bind_rows(aaa1, aaa2) %>% mutate(ques = "Forgive $20K/Person in Student Loans")



aaa1 <- cces23 %>% 
  filter(pew_bornagain == 1) %>% 
  filter(race == 1) %>% 
  cces_id3(ideo5) %>% 
  group_by(id3) %>% 
  mutate(imm = CC23_343a) %>% 
  mutate(imm = case_when(imm == 1 ~ 1, 
                         imm == 2 ~ 0)) %>% 
  group_by(id3) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  filter(id3 != "NA") %>% 
  mutate(type = "White Evangelicals")


aaa2 <- cces23 %>% 
  filter(race == 1) %>% 
  cces_id3(ideo5) %>% 
  group_by(id3) %>% 
  mutate(imm = CC23_343a) %>% 
  mutate(imm = case_when(imm == 1 ~ 1, 
                         imm == 2 ~ 0)) %>% 
  group_by(id3) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  filter(id3 != "NA") %>% 
  mutate(type = "All White Sample")

yyy4 <- bind_rows(aaa1, aaa2) %>% mutate(ques = "Ban Gender Transition for Children")

all <- bind_rows(yyy1, yyy2, yyy3, yyy4)

all %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = id3, y = mean, fill = type)) +
  geom_col(color = "black", position = "dodge") +
  facet_wrap(~ ques) +
  theme_rb(legend = TRUE) +
  y_pct() + 
  scale_fill_calc() + 
  geom_text(aes(y = .07, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 5, family = "font", color = 'white') +   
  error_bar() + 
  labs(x = "", y = "", title = "How Do White Evangelicals Differ from All White Americans?", caption = "@ryanburge\nData: Cooperative Election Study, 2023")
save("white_ev_public_opinion.png", wd = 6.25)



graph <- cces20 %>% 
  filter(CL_voter_status <= 5) %>% 
  filter(race == 1) %>% 
  filter(pew_bornagain == 1) %>% 
  cces_id3(ideo5) %>% 
  group_by(id3) %>% 
  cces_attend(pew_churatd) %>% 
  mutate(vote = CC20_410) %>% 
  mutate(vote = case_when(vote == 2 ~ 1, 
                          vote == 1 ~ 0)) %>% 
  group_by(att, id3) %>% 
  mean_ci(vote, wt = commonweight, ci = .84)  %>% filter(id3 != 'NA') %>% filter(att != "NA")

graph %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = att, y = mean, fill = id3)) +
  geom_col(color = "black", position = "dodge") +
  theme_rb(legend = TRUE) +
  error_bar() +
  pid3_fill() +
  y_pct() + 
  geom_text(aes(y = .05, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 5, family = "font", color = "white") +
  geom_text(aes(y = .05, label = ifelse(id3 == "Liberal", paste0(lab*100, '%'), '')), position = position_dodge(width = .9), size = 5, family = "font", color = "black") +
  labs(x = "Church Attendance", y = "", title = "Two Party Vote for Trump by Ideology and Attendance - Among White Evangelicals", 
       caption = "@ryanburge\nData: Cooperative Election Study, 2020")
save("trump_vote_wev_id3_att.png")