ct_selection_untitled-49FE5171.R

all <- prri %>%
  select(weight, q28_a:q28_e) %>%  
  pivot_longer(cols = q28_a:q28_e, names_to = "topic", values_to = "qq") %>% 
  mutate(
    qq = frcode(qq == 1 ~ "Often", 
                qq == 2 ~ "Sometimes", 
                qq == 3 ~ "Rarely", 
                qq == 4 ~ "Never"),
    qq = fct_relevel(qq, "Never", "Rarely", "Sometimes", "Often"),  # Reorder factor levels
    type = dplyr::recode(topic, 
                         "q28_a" = "Abortion",
                         "q28_b" = "Donald Trump",
                         "q28_c" = "Election/Voter Fraud",
                         "q28_d" = "Racism",
                         "q28_e" = "Poverty and Inequality")
  ) %>%
  group_by(type) %>%  # Group by `type` so `ct()` retains it
  ct(qq, wt = weight, show_na = FALSE) %>% 
  ungroup()  # Ungroup for safety



# Reorder 'type' based on 'Never' percentage
never_order <- all %>%
  filter(qq == "Never") %>%  # Select only 'Never' responses
  arrange(desc(pct)) %>%  # Sort in descending order
  pull(type)  # Extract ordered factor levels

# Apply ordering to 'type' before plotting
all <- all %>%
  mutate(type = factor(type, levels = never_order))  # Reorder factor levels

# Now, plot using the new order
all %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(aes(x = 1, y = pct, fill = fct_rev(qq))) +
  geom_col(color = "black") + 
  coord_flip() +
  facet_wrap(~ type, ncol =1, strip.position = "left") +  # Now ordered by "Never"
  theme_rb() +
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("Never" = "#440154",
                               "Rarely" = "#31688E",
                               "Sometimes" = "#35B779",
                               "Often" = "#FDE725")) +
  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)) +
  geom_text(aes(label = ifelse(qq == "Never", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "white") +
  geom_text(aes(label = ifelse(qq == "Rarely", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "white") +
  labs(x = "", y = "", 
       title = "How often does the clergy at your church ever talk about...", 
       caption = "@ryanburge + @religiondata\nData: PRRI 2022 Health of Congregations Survey") 

# Save the plot
save("sermon_topics.png", wd = 9, ht = 4)

all <- prri %>%
  mutate(relig = frcode(q22 == 2 & q23 == 1 ~ "Evangelical", 
                        q22 == 2 & q23 == 2 ~ "Non-Evangelical", 
                        q22 == 1 ~ "Catholic", 
                        q22 == 4 ~ "LDS")) %>%
  select(weight, relig, q28_a:q28_e) %>%  # Include relig in selection
  pivot_longer(cols = q28_a:q28_e, names_to = "topic", values_to = "qq") %>% 
  mutate(
    qq = frcode(qq == 1 ~ "Often", 
                qq == 2 ~ "Sometimes", 
                qq == 3 ~ "Rarely", 
                qq == 4 ~ "Never"),
    qq_bin = case_when(qq %in% c("Never", "Rarely") ~ 0,   # Recode Never/Rarely as 0
                       qq %in% c("Sometimes", "Often") ~ 1), # Recode Sometimes/Often as 1
    type = dplyr::recode(topic, 
                         "q28_a" = "Abortion",
                         "q28_b" = "Donald Trump",
                         "q28_c" = "Election/Voter Fraud",
                         "q28_d" = "Racism",
                         "q28_e" = "Poverty and Inequality")
  ) %>%
  group_by(relig, type) %>%  # Now grouped by both `relig` and `type`
  mean_ci(qq_bin, wt = weight, ci = .84) %>% 
  ungroup()  



all %>%
  filter(relig != 'NA') %>% 
  ggplot(., aes(x = fct_rev(relig), y = mean, fill = fct_rev(relig))) +
  geom_col(color = "black", position = "dodge") +
  facet_wrap(~ type) +
  coord_flip() + 
  theme_rb() +
  scale_fill_manual(values = c("Evangelical Protestant" = "#1F77B4",
                               "Non-Evangelical" = "#FF7F0E",
                               "Catholic" = "#2CA02C",
                               "LDS" = "#D62728",
                               "Jewish" = "#9467BD")) +
  lab_bar(above = TRUE, type = mean, pos = .06, sz = 5) +
  y_pct() + 
  labs(
    title = "How often does the clergy talk about these topics?",
    subtitle = "Proportion of sometimes/often mentions by religious group",
    x = "",
    y = "",
    caption = "@ryanburge + @religiondata\nData: PRRI 2022 Health of Congregations Survey"
  ) 
save("sermon_topics_by_relig.png", ht = 5, wd = 12)


gg <- prri %>%
  mutate(relig = frcode(q22 == 2 & q23 == 1 ~ "Evangelical", 
                        q22 == 2 & q23 == 2 ~ "Non-Evangelical", 
                        q22 == 1 ~ "Catholic", 
                        q22 == 4 ~ "LDS")) %>%
  mutate(divide = q31_b) %>% 
  mutate(divide = frcode(divide == 1 ~ "Yes", 
                         divide == 2 ~ "No", 
                         divide == 3 ~ "Unsure")) %>% 
  group_by(relig) %>% 
  ct(divide, wt = weight, show_na = FALSE) %>% 
  filter(relig != "NA") %>% 
  mutate(divide = fct_rev(divide))



# Now, plot using the new order
gg %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(aes(x = 1, y = pct, fill = fct_rev(divide))) +
  geom_col(color = "black") + 
  coord_flip() +
  facet_wrap(~ relig, ncol =1, strip.position = "left") +  # Now ordered by "Never"
  theme_rb() +
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("Yes" = "#E15759",
                               "No" = "#4E79A7",
                               "Unsure" = "#F28E2B")) +
  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)) +
  # geom_text(aes(label = ifelse(qq == "Never", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "white") +
  # geom_text(aes(label = ifelse(qq == "Rarely", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "white") +
  labs(x = "", y = "", 
       title = "Is your church more divided by politics than it was five years ago?", 
       caption = "@ryanburge + @religiondata\nData: PRRI 2022 Health of Congregations Survey") 

# Save the plot
save("politically_divided.png", wd = 9, ht = 4)


gg <- prri %>%
  mutate(relig = frcode(q22 == 2 & q23 == 1 ~ "Evangelical", 
                        q22 == 2 & q23 == 2 ~ "Non-Evangelical", 
                        q22 == 1 ~ "Catholic", 
                        q22 == 4 ~ "LDS")) %>%
  select(weight, relig, q30_a:q30_e) %>%  # Select relevant columns
  pivot_longer(cols = q30_a:q30_e, names_to = "topic", values_to = "issue") %>%
  mutate(
    issue = frcode(issue == 4 ~ "Not well at all", 
                   issue == 3 ~ "Not very well", 
                   issue == 2 ~ "Somewhat well", 
                   issue == 1 ~ "Very well"),
    type = dplyr::recode(topic,
                         "q30_a" = "Racial Justice",
                         "q30_b" = "LGBTQ+ Discrimination",
                         "q30_c" = "Hatred Toward Immigrants",
                         "q30_d" = "White Supremacy",
                         "q30_e" = "Abortion")
  ) %>%
  group_by(relig, type) %>% 
  ct(issue, wt = weight, show_na = FALSE) %>% 
  na.omit() %>% filter(issue == "Very well")


gg %>% 
  ggplot(., aes(x = type, y = pct, fill = type)) +
  geom_col(color = "black") +
  coord_flip() +
  lab_bar(type = pct, above = TRUE, pos = .06, sz = 6) +
  facet_wrap(~ relig) +
  theme_rb() +
  scale_fill_manual(values = c("Racial Justice" = "#1F77B4",
                               "LGBTQ+ Discrimination" = "#FF7F0E",
                               "Hatred Toward Immigrants" = "#2CA02C",
                               "White Supremacy" = "#D62728",
                               "Abortion" = "#9467BD")) +
  scale_y_continuous(labels = percent, limits = c(0, .75)) +
  labs(x = "", y = "", title = "Share Saying My Church is Handling the Following Issues Very Well", 
       caption = "@ryanburge + @religiondata\nData: PRRI 2022 Health of Congregations Survey") 
save("well_handled_church.png")




gg <- prri %>%
  mutate(relig = frcode(q22 == 2 & q23 == 1 ~ "Evangelical", 
                        q22 == 2 & q23 == 2 ~ "Non-Evangelical", 
                        q22 == 1 ~ "Catholic", 
                        q22 == 4 ~ "LDS")) %>%
  mutate(div = q32_b) %>% 
  mutate(div = frcode(div == 4 ~ "Completely Disagree", 
                      div == 3 ~ "Mostly Disagree", 
                      div == 2 ~ "Mostly Agree", 
                      div == 1 ~ "Completely Agree")) %>% 
  group_by(relig) %>% 
  ct(div, wt = weight, show_na = FALSE) %>% 
  na.omit()


# Now, plot using the new order
gg %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(aes(x = 1, y = pct, fill = fct_rev(div))) +
  geom_col(color = "black") + 
  coord_flip() +
  facet_wrap(~ relig, ncol =1, strip.position = "left") +  # Now ordered by "Never"
  theme_rb() +
  theme(legend.position = "bottom") +
  scale_fill_manual(values = c("Completely Disagree" = "#D62728",
                               "Mostly Disagree" = "#FF7F0E",
                               "Mostly Agree" = "#1F77B4",
                               "Completely Agree" = "#2CA02C")) +
  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)) +
  geom_text(aes(label = ifelse(div == "Completely Disagree", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "white") +
  # geom_text(aes(label = ifelse(qq == "Rarely", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "white") +
  labs(x = "", y = "", 
       title = "I wish my church talked more about political division in this country.", 
       caption = "@ryanburge + @religiondata\nData: PRRI 2022 Health of Congregations Survey") 

save("talk_about_political_division.png", ht = 4)