ct_selection_untitled-513ED6FF.R

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


graph %>% 
  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") +
  scale_fill_manual(values = c(met.brewer("Johnson", 6))) +
  theme_rb() + 
  theme(legend.position = "bottom") +
  scale_y_continuous(labels = percent) +
  theme(strip.text.y.left = element_text(angle=0, hjust = 1)) +
  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 = 7, family = "font", color = "black") +
  geom_text(aes(label = ifelse(pct >.05 & att == "Never", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 7, family = "font", color = "white") +
  geom_text(aes(label = ifelse(pct >.05 & att == "Weekly+", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 7, family = "font", color = "white") +
  labs(x = "", y = "", title = "Religious Attendance Among Self-Identified Evangelicals", subtitle = "", caption = "@ryanburge\nData: Cooperative Election Study") 
save("ev_att2022.png", wd = 9, ht = 4)


gg1 <- cces %>% 
  mutate(vimp = case_when(pew_importance == 1 ~ 1, 
                          pew_importance == 2 | pew_importance == 3 | pew_importance == 4 ~ 0)) %>% 
  group_by(year) %>% 
  mean_ci(vimp, wt = weight, ci = .84) %>% 
  mutate(pid3 = "Entire Sample")


gg2 <- cces %>% 
  mutate(vimp = case_when(pew_importance == 1 ~ 1, 
                          pew_importance == 2 | pew_importance == 3 | pew_importance == 4 ~ 0)) %>% 
  cces_pid3(pid7) %>% 
  group_by(year, pid3) %>% 
  mean_ci(vimp, wt = weight, ci = .84) %>% 
  filter(pid3 != "NA")

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

graph$pid3 <- factor(graph$pid3, levels = c("Entire Sample", "Democrat", "Independent", "Republican"))


graph %>% 
  ggplot(., aes(x = year, y = mean, color = pid3, group = pid3)) +
  geom_point(stroke = .5, shape = 21, alpha = .45) +
  geom_labelsmooth(aes(label = pid3),  method = "loess", formula = y ~ x, family = "font", linewidth = 1, text_smoothing = 30, size = 6, linewidth = 1, boxlinewidth = 0.3, hjust = .85) +
  scale_color_manual(values = c("black", "dodgerblue3", "darkorchid", "firebrick3")) +
  y_pct() + 
  theme_rb() + 
  labs(x = "Year", y = "", title = "Share Saying Religion is Very Important", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022")
save("vimp_yrs_ces.png")


gg2 <- cces %>% 
  mutate(vimp = case_when(pew_importance == 1 ~ 1, 
                          pew_importance == 2 | pew_importance == 3 | pew_importance == 4 ~ 0)) %>% 
  cces_pid3(pid7) %>% 
  group_by(year, pid3) %>% 
  mean_ci(vimp, wt = weight, ci = .84) %>% 
  filter(pid3 != "NA") %>% 
  select(pid3, year, imp = mean)


gg3 <- cces %>% 
  filter(year >= 2008) %>% 
  mutate(wk = case_when(pew_attendance == 1 | pew_attendance == 2 ~ 1, 
                        pew_attendance <= 6 ~ 0)) %>% 
  cces_pid3(pid7) %>% 
  filter(pid3 != "NA") %>% 
  group_by(pid3, year) %>% 
  mean_ci(wk, wt = weight, ci = .84) %>% 
  select(pid3, year, wk = mean)


graph <- left_join(gg2, gg3) %>% na.omit() %>%   filter(pid3 != "Independent") 


library(ggrepel)

graph %>% 
  ggplot(., aes(x = imp, y = wk, color = pid3, group = pid3)) +
  geom_point(stroke = 1, shape = 21) +
  geom_smooth(se = FALSE, method = lm) +
  geom_text_repel(data = graph, aes(x = imp, y= wk, label = year), family = "font", size = 5) +
  theme_rb() +
  scale_color_manual(values = c("dodgerblue3", "firebrick3")) +
  scale_x_continuous(labels = percent, breaks = c(.25, .30, .35, .40, .45, .50, .55, .60)) +
  y_pct() + 
  add_text(x = .50, y = .43, word = "Republicans", sz = 8) +
  add_text(x = .33, y = .26, word = "Democrats", sz = 8) +
  labs(x = "Share Saying Religion is Very Important", y = "Share Attending Services Weekly", title = "Religion Importance and Religious Attendance among Democrats and Republicans", 
       caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022")
save("scatter_imp_att.png")


graph <- cces %>% 
  filter(year == 2008 | year == 2012 | year == 2016 | year == 2020 | year == 2022) %>% 
  filter(pew_attendance == 6) %>% 
  cces_pid3(pid7) %>% 
  mutate(imp = frcode(pew_importance == 4 ~ "Not at all important", 
                      pew_importance == 3 ~ "Not too important", 
                      pew_importance == 2 ~ "Somewhat important", 
                      pew_importance == 1 ~ "Very important")) %>% 
  group_by(year, pid3) %>% 
  ct(imp, wt = weight, show_na = FALSE) 



graph %>% 
  mutate(lab = round(pct, 2)) %>% 
  filter(pid3 != "Independent") %>% 
  ggplot(., aes(x = 1, y = pct, fill = fct_rev(imp))) +
  geom_col(color = "black") +
  coord_flip() +
  facet_grid(year ~ pid3, switch = "y") +
  scale_fill_manual(values = c(met.brewer("Homer1", 4))) +
  theme_rb() + 
  theme(legend.position = "bottom") +
  scale_y_continuous(labels = percent) +
  theme(strip.text.y.left = element_text(angle=0, hjust = 1)) +
  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 = 6, family = "font", color = "black") +
  geom_text(aes(label = ifelse(pct >.05 & imp == "Very important", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 6, family = "font", color = "white") +
  labs(x = "", y = "", title = "Religious Importance Among Never Attenders", subtitle = "", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022") 
save("imp_never__att2022.png", wd = 11, ht = 4)


graph <- cces %>% 
  filter(year >= 2008) %>% 
  cces_pid3(pid7) %>% 
  filter(pid3 == "Democrat" | pid3 == "Republican") %>% 
  mutate(ba = case_when(pew_bornagain == 1 ~ 1, 
                        pew_bornagain == 2 ~ 0)) %>% 
  cces_attend(pew_attendance) %>% 
  group_by(year, pid3, att) %>% 
  mean_ci(ba, wt = weight, ci = .84) %>% filter(att != "NA")

graph %>% 
  ggplot(., aes(x = year, y = mean, color = pid3, group = pid3)) +
  geom_point(stroke = .5, shape= 21, alpha = .45, show.legend = FALSE) + 
  geom_smooth(se = FALSE, method = lm) +
  facet_wrap(~ att) +
  scale_color_manual(values = c("dodgerblue3", "firebrick3")) +
  theme_rb(legend = TRUE) +
  y_pct() + 
  theme(strip.text = element_text(size = 20)) +
  labs(x = "Year", y = "", title = "Share Self-Identifying as Born-Again/Evangelical by Religious Attendance", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022")
save("ba_att_pid2.png", ht = 8)