ct_selection_untitled-F22D61B7.R

gg <- cces %>% 
  filter(year == 2008 | year == 2012 | year == 2016 | year == 2020 | year == 2022 | year == 2023) %>% 
  filter(pew_bornagain == 1) %>% 
  cces_attend(pew_attendance) %>% 
  group_by(year) %>% 
  ct(att, wt = weight, show_na = FALSE)


gg %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x = 1, y = pct, fill = fct_rev(att))) +
  geom_col(color = "black") + 
  coord_flip() +
  facet_wrap(~ year, ncol =1, strip.position = "left") +
  theme_rb() +
  scale_fill_manual(values = c(moma.colors("OKeeffe", 6))) +
  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") +
  geom_text(aes(label = ifelse(att == "Seldom", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "white") +
  geom_text(aes(label = ifelse(att == "Yearly", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "white") +
  geom_text(aes(label = ifelse(att == "Never" & 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 Church Attendance of Self-Identified Evangelicals", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2023") 
save("att_evangelical_2023.png", wd = 9, ht = 4)

gg <- cces %>% 
  filter(year >= 2008) %>% 
  mutate(ba = case_when(pew_bornagain == 1 ~ 1, 
                        pew_bornagain == 2 ~ 0)) %>% 
  mutate(low = case_when(pew_attendance == 6 | pew_attendance == 5 ~ 1, 
                         pew_attendance <= 4 ~ 0)) %>% 
  mutate(both = ba + low) %>% 
  mutate(both = case_when(both == 2 ~ 1, 
                          both == 1 | both == 0 ~ 0)) %>% 
  group_by(year) %>% 
  mean_ci(both, wt = weight, ci = .84)

gg %>% 
  mutate(lab = round(mean, 3)) %>% 
  ggplot(., aes(x = year, y = mean, fill = mean)) +
  geom_col(color = "black") +
  theme_rb() +
  error_bar() +
  scale_fill_gradient(low = "#009FFF", high = "#ec2F4B") +
  scale_y_continuous(labels = percent) +
  lab_bar(top = FALSE, type = lab, pos = .0075, sz = 5.25) +
  labs(x = "", y = "", title = "Share of the Public Who Are Self-Identified Evangelicals Who Attend Church Seldom or Never", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2023")
save("low_attend_evangelical_over_time.png")



gg <- cces %>% 
  filter(year >= 2008) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conservative")) %>% 
  mutate(ba = case_when(pew_bornagain == 1 ~ 1, 
                        pew_bornagain == 2 ~ 0)) %>% 
  mutate(low = case_when(pew_attendance == 6 | pew_attendance == 5 ~ 1, 
                         pew_attendance <= 4 ~ 0)) %>% 
  mutate(both = ba + low) %>% 
  mutate(both = case_when(both == 2 ~ 1, 
                          both == 1 | both == 0 ~ 0)) %>% 
  group_by(year,  id3) %>% 
  mean_ci(both, wt = weight, ci = .84) %>% filter(id3 != 'NA')


gg %>% 
  mutate(lab = round(mean, 3)) %>% 
  ggplot(., aes(x = year, y = mean, color = id3)) +
  geom_line() + 
  geom_point(stroke = 1, shape = 21, fill = "white") + 
  geom_text(data = . %>% filter(year == 2023),
            aes(label = scales::percent(mean, accuracy = 0.1)),
            vjust = -0.5, size = 5, family = "font", show.legend = FALSE) +  
  geom_text(data = . %>% filter(year == 2008),
            aes(label = scales::percent(mean, accuracy = 0.1)),
            hjust = 1.1, vjust = -.1, size = 5, family = "font", show.legend = FALSE) +
  theme_rb(legend = TRUE) +
  pid3_color() + 
  scale_y_continuous(labels = scales::percent, limits = c(0, .14)) +
  labs(x = "", y = "", title = "Share of the Public Who Are Self-Identified Evangelicals Who Attend Church Seldom or Never", 
       caption = "@ryanburge\nData: Cooperative Election Study, 2008-2023") 
save("low_attend_evangelical_over_time_id3.png")



gg <- cces %>% 
  filter(year >= 2008) %>% 
  mutate(ed2 = frcode(educ == 1 | educ == 2 ~ "HS or Less", 
                      educ == 5 | educ == 6 ~ "College Grad.")) %>% 
  mutate(ba = case_when(pew_bornagain == 1 ~ 1, 
                        pew_bornagain == 2 ~ 0)) %>% 
  mutate(low = case_when(pew_attendance == 6 | pew_attendance == 5 ~ 1, 
                         pew_attendance <= 4 ~ 0)) %>% 
  mutate(both = ba + low) %>% 
  mutate(both = case_when(both == 2 ~ 1, 
                          both == 1 | both == 0 ~ 0)) %>% 
  group_by(year,  ed2) %>% 
  mean_ci(both, wt = weight, ci = .84) %>% filter(ed2 != 'NA')


gg %>% 
  mutate(lab = round(mean, 3)) %>% 
  ggplot(., aes(x = year, y = mean, color = ed2)) +
  geom_line() + 
  scale_color_calc() + 
  geom_point(stroke = 1, shape = 21, fill = "white") + 
  geom_text(data = . %>% filter(year == 2008 | year == 2023),
            aes(label = scales::percent(mean, accuracy = 0.1)),
            vjust = -0.5, size = 5, family = "font", show.legend = FALSE) +  
  theme_rb(legend = TRUE) +
  scale_y_continuous(labels = scales::percent, limits = c(0, .16)) +
  labs(x = "", y = "", title = "Share of the Public Who Are Self-Identified Evangelicals Who Attend Church Seldom or Never", 
       caption = "@ryanburge\nData: Cooperative Election Study, 2008-2023") 
save("low_attend_evangelical_over_time_ed2.png")



gg <- cces %>% 
  filter(year >= 2008) %>% 
  cces_race(race) %>% 
  mutate(ba = case_when(pew_bornagain == 1 ~ 1, 
                        pew_bornagain == 2 ~ 0)) %>% 
  mutate(low = case_when(pew_attendance == 6 | pew_attendance == 5 ~ 1, 
                         pew_attendance <= 4 ~ 0)) %>% 
  mutate(both = ba + low) %>% 
  mutate(both = case_when(both == 2 ~ 1, 
                          both == 1 | both == 0 ~ 0)) %>% 
  group_by(year,  race) %>% 
  mean_ci(both, wt = weight, ci = .84) %>% filter(race != 'All Others')


gg %>% 
  mutate(lab = round(mean, 3)) %>% 
  ggplot(., aes(x = year, y = mean, color = race)) +
  geom_line() + 
  scale_color_gdocs() + 
  geom_point(stroke = 1, shape = 21, fill = "white") + 
  geom_text(data = . %>% filter(year == 2008 | year == 2023),
            aes(label = scales::percent(mean, accuracy = 0.1)),
            vjust = -0.5, size = 5, family = "font", show.legend = FALSE) +  
  theme_rb(legend = TRUE) +
  scale_y_continuous(labels = scales::percent, limits = c(0, .12)) +
  labs(x = "", y = "", title = "Share of the Public Who Are Self-Identified Evangelicals Who Attend Church Seldom or Never", 
       caption = "@ryanburge\nData: Cooperative Election Study, 2008-2023") 
save("low_attend_evangelical_over_time_race.png")


regg <- cces %>% 
  filter(income <= 16) %>% 
  filter(year >= 2020) %>% 
  mutate(ba = case_when(pew_bornagain == 1 ~ 1, 
                        pew_bornagain == 2 ~ 0)) %>% 
  mutate(low = case_when(pew_attendance == 6 | pew_attendance == 5 ~ 1, 
                         pew_attendance <= 4 ~ 0)) %>% 
  mutate(both = ba + low) %>% 
  mutate(both = case_when(both == 2 ~ 1, 
                          both == 1 | both == 0 ~ 0)) %>% 
  mutate(white = case_when(race == 1 ~ 1, 
                           TRUE ~ 0)) %>% 
  mutate(male = case_when(gender == 1 ~ 1, 
                          gender == 2 ~ 0)) %>% 
  mutate(cons = case_when(ideo5 == 4 | ideo5 == 5 ~ 1, 
                          ideo5 == 3 | ideo5 == 2 | ideo5 == 1 ~ 0)) %>% 
  select(both, white, male, cons, educ, income, age)

out <- glm(both ~ ., data = regg, family = "binomial")


coef_names <- c("White" = "white", 
                "Income" = "income", 
                "Education" = "educ", 
                "Age" = "age", 
                "Male" = "male", 
                "Conservative" = "cons")


library(jtools)

gg <- plot_summs(out, scale = TRUE, robust = "HC3", coefs = coef_names, colors = "firebrick3")

  

gg + 
  theme_rb() +
  # # add_text(x = -.5, y = 3.5, word = "Less Agreement", sz = 9) +
  # add_text(x = .85, y = 11.5, word = "More Agreement", sz = 9) +
  labs(x = "", y = "", title = "Predicting the Likelihood of Being a Low Attending Evangelical", 
       caption = "@ryanburge\nData: Cooperative Election Study, 2020-2023")
save("reg_ev_low_attend.png", ht = 4)



regg <- cces %>% 
  filter(income <= 16) %>% 
  filter(year >= 2020) %>% 
  mutate(ba = case_when(pew_bornagain == 1 ~ 1, 
                        pew_bornagain == 2 ~ 0)) %>% 
  mutate(low = case_when(pew_attendance == 6 | pew_attendance == 5 ~ 1, 
                         pew_attendance <= 4 ~ 0)) %>% 
  mutate(both = ba + low) %>% 
  mutate(both = case_when(both == 2 ~ 1, 
                          both == 1 | both == 0 ~ 0)) %>% 
  cces_race(race) %>% 
  mutate(male = case_when(gender == 1 ~ 1, 
                          gender == 2 ~ 0)) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conservative")) %>% 
  select(both, race, male, id3, educ, income, age)

out <- glm(both ~ race*id3 + ., data = regg, family = "binomial")

library(interactions)


gg <- cat_plot(out, pred = race, modx = id3, interval = TRUE, int.width =  .76, errorbar.width = .1, geom = "bar")



gg + 
  theme_rb(legend = TRUE) +
  scale_y_continuous(labels = percent) +
  pid3_fill() +
  pid3_color() +
  labs(x = "", y = "", title = "Predicting the Likelihood of Being a Low Attending Evangelical by Political Ideology", 
       caption = "@ryanburge\nData: Cooperative Election Study, 2020-2023")
save("reg_ev_low_attend_id3.png", ht = 6)