ct_selection_untitled-E5E26B1A.R

gg <- cces %>% 
  filter(year == 2022) %>% 
  cces_race(race) %>% 
  mutate(ev = case_when(pew_bornagain == 1 ~ 1, 
                        pew_bornagain == 2 ~ 0)) %>% 
  group_by(race) %>% 
  mean_ci(ev,  ci = .84)

gg %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = race, y = mean, fill = race)) +
  geom_col(color = "black") +
  theme_rb() + 
  error_bar() + 
  y_pct() + 
  scale_fill_manual(values = c(moma.colors("Panton", 5))) +
  lab_bar(top = FALSE, type = lab, pos = .03, sz = 11) +
  geom_text(aes(y = .03, label = ifelse(race == "Hispanic", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 11, family = "font", color = "white") +  
  geom_text(aes(y = .03, label = ifelse(race == "All Others", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 11, family = "font", color = "white") +   
  geom_text(aes(y = .03, label = ifelse(race == "Asian", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 11, family = "font", color = "white") +   
  labs(x = "Race", y = "", title = "Share Who Self-Identify as Evangelical by Race", caption = "@ryanburge\nData: Cooperative Election Study, 2022")
save("ev_race_ces22_new.png", wd = 5)

gg <- cces %>% 
  cces_race(race) %>% 
  mutate(ev = case_when(pew_bornagain == 1 ~ 1, 
                        pew_bornagain == 2 ~ 0)) %>% 
  group_by(race, year) %>% 
  mean_ci(ev,  ci = .84)

gg %>% 
  filter(year >= 2008) %>% 
  filter(race != "All Others") %>% 
  ggplot(., aes(x = year, y = mean, color = race, group = race)) +
  geom_line() +
  geom_point(stroke = 1, shape = 21, fill = "white") + 
  scale_color_manual(values = c(moma.colors("Panton", 4))) + 
  theme_rb() + 
  scale_y_continuous(labels = percent) +
  add_text(x = 2013, y = .505, sz = 7, word = "Black") +
  add_text(x = 2010, y = .325, sz = 7, word = "White") +
  add_text(x = 2010, y = .275, sz = 7, word = "Hispanic") +
  add_text(x = 2011, y = .21, sz = 7, word = "Asian") +
  add_text(x = 2008, y = .51, sz = 7, word = "49%") +
  add_text(x = 2008, y = .34, sz = 7, word = "33%") +
  add_text(x = 2008, y = .275, sz = 7, word = "26%") +
  add_text(x = 2008, y = .20, sz = 7, word = "18%") +
  add_text(x = 2022, y = .46, sz = 7, word = "44%") +
  add_text(x = 2022, y = .27, sz = 7, word = "25%") +
  add_text(x = 2022, y = .23, sz = 7, word = "25%") +
  add_text(x = 2022, y = .17, sz = 7, word = "15%") +
  labs(x = "Year", y = "", title = "Share Who Self-Identify as Evangelical by Race", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022") 
save("race_ev_year_ces22.png")

gg1 <- cces08 %>% 
  filter(V211 == 3) %>% 
  mutate(immstat = CC332) %>% 
  mutate(imm = frcode(immstat == 1 | immstat == 2 ~ "Immigrant", 
                      immstat == 3 ~ "First Generation", 
                      immstat == 4 ~ "Second Generation", 
                      immstat == 5 ~ "Third Generation or More")) %>% 
  mutate(religpew = V219) %>% 
  mutate(relig = frcode(religpew == 1 ~ "Protestant",
                        religpew == 2 ~ "Catholic",
                        religpew >= 3 & religpew <= 8 ~ "Other World Religion", 
                        religpew == 9 | religpew == 10 ~ "Atheist/Agnostic", 
                        religpew == 11 ~ "Nothing in Particular")) %>% 
  group_by(imm) %>% 
  ct(relig, wt = V201, show_na = FALSE) %>% 
  mutate(year = "2008")


gg2 <- cces22 %>% 
  filter(race == 3) %>% 
  mutate(imm = frcode(immstat == 1 | immstat == 2 ~ "Immigrant", 
                      immstat == 3 ~ "First Generation", 
                      immstat == 4 ~ "Second Generation", 
                      immstat == 5 ~ "Third Generation or More")) %>% 
  mutate(relig = frcode(religpew == 1 ~ "Protestant",
                        religpew == 2 ~ "Catholic",
                        religpew >= 3 & religpew <= 8 ~ "Other World Religion", 
                        religpew == 9 | religpew == 10 ~ "Atheist/Agnostic", 
                        religpew == 11 ~ "Nothing in Particular")) %>% 
  group_by(imm) %>% 
  ct(relig, wt = commonweight, show_na = FALSE) %>% 
  mutate(year = "2022")

graph <- bind_rows(gg1, gg2) %>% filter(imm != "NA")


graph %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x = fct_rev(year), y = pct, fill = fct_rev(relig))) +
  geom_col(color = "black") + 
  coord_flip() +
  facet_wrap(~ imm, ncol =1, strip.position = "top") +
  theme_rb() +
  scale_fill_manual(values = c(moma.colors("OKeeffe", 5))) +
  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 =11, family = "font", color = "black") +
  geom_text(aes(label = ifelse(relig == "Protestant", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 11, family = "font", color = "white") +
  # 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 Religious Composition of Hispanics by Immigrant Status", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022") 
save("hisp_imm_stat.png", wd = 9, ht = 8)


gg1 <- cces08 %>% 
  filter(V211 == 3) %>% 
  mutate(immstat = CC332) %>% 
  mutate(ba = case_when(V215 == 1 ~ 1, 
                        V215 == 2 ~ 0)) %>% 
  mutate(imm = frcode(immstat == 1 | immstat == 2 ~ "Immigrant", 
                      immstat == 3 ~ "First Generation", 
                      immstat == 4 ~ "Second Generation", 
                      immstat == 5 ~ "Third Generation or More")) %>% 
  group_by(imm) %>% 
  mean_ci(ba, wt = V201, ci = .84) %>% 
  mutate(year = "2008")


gg2 <- cces22 %>% 
  filter(race == 3) %>% 
  mutate(ba = case_when(pew_bornagain == 1 ~ 1, 
                        pew_bornagain == 2 ~ 0)) %>% 
  mutate(imm = frcode(immstat == 1 | immstat == 2 ~ "Immigrant", 
                      immstat == 3 ~ "First Generation", 
                      immstat == 4 ~ "Second Generation", 
                      immstat == 5 ~ "Third Generation or More")) %>% 
  group_by(imm) %>% 
  mean_ci(ba, wt = commonweight, ci = .84) %>% 
  mutate(year = "2022")

graph <- bind_rows(gg1, gg2) %>% filter(imm != "NA")

graph %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = fct_rev(year), y = mean, fill = year)) +
  geom_col(color = "black") +
  facet_wrap(~ imm, ncol = 1) +
  coord_flip() +
  theme_rb() +
  y_pct() +
  scale_fill_calc() +
  theme(plot.title = element_text(size = 16)) +
  geom_text(aes(y = mean + .018, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 7, family = "font")   +
  labs(x = "",y = "", title = "Share of Hispanics Who Self-Identify as Evangelical by Immigrant Status", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022")
save("imm_ba_hispanic.png", wd = 7)

gg1 <- cces %>% 
  filter(race == 3) %>% 
  mutate(ba = case_when(pew_bornagain == 1 ~ 1, 
                        pew_bornagain == 2 ~ 0)) %>% 
  filter(religion == 1) %>% 
  group_by(year) %>% 
  mean_ci(ba,  ci = .84) %>% 
  mutate(type = "Protestants")

gg2 <- cces %>% 
  filter(race == 3) %>% 
  mutate(ba = case_when(pew_bornagain == 1 ~ 1, 
                        pew_bornagain == 2 ~ 0)) %>% 
  filter(religion != 1) %>% 
  group_by(year) %>% 
  mean_ci(ba,  ci = .84) %>% 
  mutate(type = "Non-Protestants")

both <- bind_rows(gg1, gg2) %>% filter(year >= 2008)

both %>% 
  ggplot(., aes(x = year, y = mean, fill = type, group = type, color = type)) +
  geom_line() + 
  geom_point(stroke = 1, shape = 21, fill = "white") + 
  geom_smooth(se = FALSE, method = lm) +
  scale_color_jama() +
  theme_rb() +
  scale_y_continuous(labels = percent) +
  add_text(x = 2010, y = .76, word = "Protestants", sz = 6) +
  add_text(x = 2012, y = .18, word = "Non-Protestants", sz = 6) +
  labs(x = "Year", y = "", title = "Share of Hispanics Who Self-ID as Evangelical by Religion", caption = '@ryanburge\nData: Cooperative Election Study, 2008-2022')
save("ev_status_hisp_prot_nonprot.png", wd = 6)


cces22 %>% 
  cces_race(race) %>% 
  mutate(imm = frcode(immstat == 1 | immstat == 2 ~ "Immigrant", 
                      immstat == 3 ~ "First Generation", 
                      immstat == 4 ~ "Second Generation", 
                      immstat == 5 ~ "Third Generation or More")) %>% 
  group_by(race) %>% 
  ct(imm, wt = commonweight, show_na = FALSE)