ct_selection_untitled-3A854576.R

library(rio)
library(janitor)

pew <- import("E://data/rls24.sav")

pew <- pew %>% clean_names()

gg1 <- pew %>% 
  mutate(moral = qb2d) %>% 
  mutate(moral = frcode(moral == 1 ~ "There are clear absolue standards", 
                        moral == 2 ~ "It depends on the situation", 
                        moral == 99 ~ "Don't Know/Refused")) %>% 
  ct(moral, wt = weight, show_na = FALSE)


gg1 %>% 
  ggplot(., aes(x = reorder(moral, pct), y = pct, fill = moral)) +
  geom_col(color = "black") +
  coord_flip()+
  theme_rb() +
  scale_fill_manual(values = c(
    "There are clear absolute standards" = "#4E79A7",  # slate blue
    "It depends on the situation"    = "#F28E2B",  # orange
    "Don't Know/Refused"              = "#A0CBE8"  # light blue
  )) +
  scale_y_continuous(labels = percent, limits = c(0, .65)) +
  lab_bar(above = TRUE, pos = .045, sz = 9, type = pct) +
  labs(x = "", y = "", title = "Are There Clear Standards or Is it Situational?", caption = "@ryanburge | Data: Pew Religious Landscape Survey, 2023-2024")
save("pew_clear_morals.png", ht = 2.5, wd = 8)


gg1 <- pew %>% 
  mutate(trad = frcode(
    reltrad == 1100 ~ "Evangelical",
    reltrad == 1200 ~ "Mainline",
    reltrad == 1300 ~ "Black Protestant",
    reltrad == 10000 ~ "Catholic",
    reltrad >= 20000 & reltrad <= 99999 ~ "Other World Religions",
    reltrad == 100000 & unaffildetail %in% c(1, 2) ~ "Atheist/Agnostic",
    reltrad == 100000 & unaffildetail == 3 ~ "Nothing in Particular")) %>% 
  mutate(moral = qb2d) %>% 
  mutate(moral = frcode(moral == 1 ~ "There are clear absolute standards", 
                        moral == 2 ~ "It depends on the situation", 
                        moral == 99 ~ "Don't Know/Refused")) %>% 
  group_by(trad) %>% 
  ct(moral, wt = weight, show_na = FALSE)



gg1 %>% 
  filter(trad != "NA") %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x = 1, y = pct, fill = fct_rev(moral))) +
  geom_col(color = "black") + 
  coord_flip() +
  facet_wrap(~ trad, ncol =1, strip.position = "left") +
  theme_rb() +
  scale_fill_manual(values = c(
    "There are clear absolute standards" = "#4E79A7",  # slate blue
    "It depends on the situation"    = "#F28E2B",  # orange
    "Don't Know/Refused"              = "#A0CBE8"  # light blue
  )) +
  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 = 7, family = "font", color = "black") +
  # geom_text(aes(label = ifelse(age2 == "18-35", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 4, 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 = "Are There Clear Standards or Is it Situational?", caption = "@ryanburge | Data: Pew Religious Landscape Survey, 2023-2024")
save("clear_moral_reltrad.png", wd = 9, ht = 4)


gg1 <- pew %>% 
  mutate(decade = frcode(
    birthdecade == 1 ~ "1940s",
    birthdecade == 2 ~ "1950s",
    birthdecade == 3 ~ "1960s",
    birthdecade == 4 ~ "1970s",
    birthdecade == 5 ~ "1980s",
    birthdecade == 6 ~ "1990s",
    birthdecade == 7 ~ "2000s"
  )) %>% 
  mutate(moral = qb2d) %>% 
  mutate(moral = frcode(moral == 1 ~ "There are clear absolute standards", 
                        moral == 2 ~ "It depends on the situation", 
                        moral == 99 ~ "Don't Know/Refused")) %>% 
  group_by(decade) %>% 
  ct(moral, wt = weight, show_na = FALSE)


gg1 %>% 
  filter(decade != "NA") %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x = 1, y = pct, fill = fct_rev(moral))) +
  geom_col(color = "black") + 
  coord_flip() +
  facet_wrap(~ decade, ncol =1, strip.position = "left") +
  theme_rb() +
  scale_fill_manual(values = c(
    "There are clear absolute standards" = "#4E79A7",  # slate blue
    "It depends on the situation"    = "#F28E2B",  # orange
    "Don't Know/Refused"              = "#A0CBE8"  # light blue
  )) +
  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 = 7, family = "font", color = "black") +
  # geom_text(aes(label = ifelse(age2 == "18-35", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 4, 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 = "Are There Clear Standards or Is it Situational?", caption = "@ryanburge | Data: Pew Religious Landscape Survey, 2023-2024")
save("clear_moral_decades.png", wd = 9, ht = 4.5)


gg1 <- pew %>% 
  mutate(trad = frcode(
    reltrad == 1100 ~ "Evangelical",
    reltrad == 1200 ~ "Mainline",
    reltrad == 1300 ~ "Black Protestant",
    reltrad == 10000 ~ "Catholic",
    reltrad >= 20000 & reltrad <= 99999 ~ "Other World Religions",
    reltrad == 100000 ~ "Non-\nReligious")) %>% 
  mutate(decade = frcode(
    birthdecade == 1 ~ "1940s",
    birthdecade == 2 ~ "1950s",
    birthdecade == 3 ~ "1960s",
    birthdecade == 4 ~ "1970s",
    birthdecade == 5 ~ "1980s",
    birthdecade == 6 ~ "1990s",
    birthdecade == 7 ~ "2000s"
  )) %>% 
  filter(trad == "Evangelical" | trad == "Catholic" | trad == "Mainline" | trad == "Non-\nReligious") %>% 
  group_by(trad, decade) %>% 
  mutate(moral = qb2d) %>% 
  mutate(moral = case_when(moral == 1 ~ 1, 
                           moral == 2 | moral == 3 ~ 0)) %>% 
  mean_ci(moral, wt = weight, ci = .84) %>% filter(decade != 'NA')

gg1 %>% 
  ggplot(., aes(x = trad, y = mean, fill = trad)) +
  geom_col(color = "black") +
  facet_wrap(~ decade, ncol = 2) +
  theme_rb() +
  error_bar() + 
  y_pct() + 
  theme(axis.text.x = element_text(size = 10)) +
  scale_fill_manual(values = c(
    "Evangelical"       = "#1b9e77",  # green
    "Catholic"          = "#d95f02",  # orange
    "Mainline"          = "#7570b3",  # purple
    "Non-\nReligious"   = "#e7298a"   # pink
  )) +
  lab_bar(above = FALSE, type = mean, pos = .065, sz = 7) +
  labs(x = "", y = "", title = "Share Saying There are Clear Standards\nof Right and Wrong", caption = "@ryanburge | Data: Pew Religious Landscape Survey, 2023-2024")
save('clear_moral_decade_reltrad.png', ht = 10, wd = 5.5)





gg1 <- pew %>% 
  mutate(ideo = frcode(ideo == 1 ~ "Very\nConservative", 
                       ideo == 2 ~ "Conservative", 
                       ideo == 3 ~ "Moderate", 
                       ideo == 4 ~ "Liberal", 
                       ideo == 5 ~ "Very\nLiberal")) %>% 
  mutate(moral = qb2d) %>% 
  mutate(moral = case_when(moral == 1 ~ 1, 
                           moral == 2 | moral == 3 ~ 0)) %>% 
  mutate(relig = frcode(reltrad <= 99999 ~ "Religious", 
                        reltrad == 100000 ~ "Non-Religious")) %>% 
  group_by(ideo, relig) %>% 
mean_ci(moral, wt = weight, ci = .84) %>% na.omit()



gg1 %>%
  ggplot(aes(x = ideo, y = mean, fill = relig)) +
  geom_col(position = position_dodge(width = 0.9), color = "black") +
  error_bar() +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c(
    "Religious" = "#264653",
    "Non-Religious" = "#E76F51"
  )) +
  lab_bar_white(above = FALSE, type = mean, pos = .04, sz = 6.25) +
  labs(
    x = "Political Ideology",
    y = "",
    title = "Share Saying There are Clear Standards of Right and Wrong",
    caption = "@ryanburge | Data: Pew Religious Landscape Survey, 2023–2024",
    fill = ""
  ) +
  theme_rb(legend = TRUE)
save("ideo_clear_moral_relig2.png", wd = 6)





regg <- pew %>% 
  filter(birthdecade != 99) %>% 
  mutate(moral = qb2d) %>% 
  mutate(moral = case_when(moral == 1 ~ 1, 
                           moral == 2 | moral == 3 ~ 0)) %>% 
  mutate(white = case_when(
    hisp == 2 & racecmb == 1 ~ 1,
    TRUE ~ 0
  )) %>% 
  mutate(male = case_when(gender == 1 ~ 1, 
                          TRUE ~ 0)) %>% 
  mutate(educ = educrec) %>% 
  mutate(age = 8 - birthdecade) %>% 
  mutate(income = inc_sdt1) %>% 
  mutate(cons = case_when(ideo == 1 | ideo == 2 ~ 1, 
                          ideo <= 5 ~ 0)) %>% 
  mutate(relig = case_when(reltrad <= 99999 ~ 1, 
                        reltrad == 100000 ~ 0)) %>% 
  mutate(attend = 7 - attndperrls) %>% 
  select(moral, white, male, educ, age, income, cons, relig, attend, weight)


fit <- glm(moral ~ white + male + educ + relig + attend + income + age + cons,
           data = regg, family = binomial())


coef_or <- tidy(fit, conf.int = TRUE, conf.level = 0.95, exponentiate = TRUE) %>%
  filter(term != "(Intercept)") %>%
  mutate(term = dplyr::recode(term,
                       white = "White",
                       male  = "Male",
                       relig = "Religious",
                       attend = "Religious Attendance", 
                       cons  = "Conservative",
                       age   = "Age",
                       educ  = "Education",
                       income= "Income"
  ))



ggplot(coef_or, aes(x = estimate, y = reorder(term, estimate))) +
  geom_vline(xintercept = 1, linetype = 2) +
  geom_errorbarh(aes(xmin = conf.low, xmax = conf.high), height = 0.18) +
  geom_point(size = 2.8, stroke = 1, shape = 21, fill = "white", color = "firebrick3") +
  scale_x_log10() +
  labs(
    x = "Odds ratio (log scale, 95% CI)",
    y = "",
    title = "What Factors Predict a More Black and White Worldview?",
    caption = "@ryanburge | Pew Religious Landscape Survey, 2023–2024"
  ) +
  theme_rb()
save("coef_clear_moral.png", ht = 3)