ct_selection_untitled-8A654EDC.R

gg <- cces %>% 
  filter(year == 2008 | year == 2022) %>% 
  mutate(cath = case_when(religion == 2 ~ 1, 
                          TRUE ~ 0)) %>% 
  group_by(birthyr, year) %>% 
  mean_ci(cath, wt = weight, ci = .84) %>% 
  na.omit() %>% 
  filter(n > 25)  %>% 
  mutate(year = as.factor(year))

gg %>% 
  filter(birthyr >= 1930) %>% 
  ggplot(., aes(x = birthyr, y = mean, color = year, group = year)) +
  geom_point(stroke = .5, shape = 21) + 
  scale_color_manual(values = c("#0D84A5", "#CA472F")) +
  theme_rb() + 
  scale_y_continuous(labels = percent, limits = c(.1, .3)) +
  geom_labelsmooth(aes(label = year),  method = "loess", formula = y ~ x, family = "font", linewidth = 1, text_smoothing = 30, size = 7, linewidth = 1, boxlinewidth = 0.3) +
  labs(x = "Birth Year", y = "", title = "Share Identifying as Catholic by Birth Year and Survey Year", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022")
save("cath_birth_year_ces.png")
  
gg1 <- gss %>%
  filter(year <= 2018) %>% 
  mutate(birthyr = year - age) %>% 
  mutate(gen = frcode(birthyr>= 1901 & birthyr <= 1924 ~ 'Greatest Generation',
                      birthyr>= 1925 & birthyr <= 1945 ~ 'Silent Generation',
                      birthyr>= 1946 & birthyr <= 1964 ~ 'Boomers',
                      birthyr>= 1965 & birthyr <= 1976 ~ 'Gen X',
                      birthyr>= 1977 & birthyr <= 1995 ~ 'Millennials',
                      birthyr>= 1996 & birthyr <= 2019 ~ 'Gen Z')) %>% 
  filter(reltrad == 4) %>% 
  mutate(wk = case_when(attend == 6 | attend == 7 | attend == 8 ~ 1, 
                        attend <= 5 ~ 0)) %>% 
  group_by(gen, year) %>% 
  mean_ci(wk, wt = wtssall)

gg2 <- gss %>%
  filter(year > 2018) %>% 
  mutate(birthyr = year - age) %>% 
  mutate(gen = frcode(birthyr>= 1901 & birthyr <= 1924 ~ 'Greatest Generation',
                      birthyr>= 1925 & birthyr <= 1945 ~ 'Silent Generation',
                      birthyr>= 1946 & birthyr <= 1964 ~ 'Boomers',
                      birthyr>= 1965 & birthyr <= 1976 ~ 'Gen X',
                      birthyr>= 1977 & birthyr <= 1995 ~ 'Millennials',
                      birthyr>= 1996 & birthyr <= 2019 ~ 'Gen Z')) %>% 
  filter(reltrad == 4) %>% 
  mutate(wk = case_when(attend == 6 | attend == 7 | attend == 8 ~ 1, 
                        attend <= 5 ~ 0)) %>% 
  group_by(gen, year) %>% 
  mean_ci(wk, wt = wtssnrps)

all <- bind_rows(gg1, gg2) %>% filter(n > 50) %>% filter(gen != "Gen Z") %>% filter(gen != "NA")

all %>% 
  ggplot(., aes(x = year, y = mean, color = gen, group = gen)) +
  geom_point(stroke = .56, shape = 21) + 
  geom_labelsmooth(aes(label = gen),  method = "loess", formula = y ~ x, family = "font", linewidth = 1, text_smoothing = 30, size = 6, linewidth = 1, boxlinewidth = 0.3) +
  theme_rb() + 
  scale_color_gdocs() +
  scale_y_continuous(labels = percent, limits = c(0, .7)) +
  labs(x = "", y = "", title = "Share of Catholics Attending Mass Nearly Every Week or More", caption = "@ryanburge\nData: General Social Survey, 1972-2022")
save("cath_mass_attend_gens.png")

gg <- cces %>% 
  mutate(gen = frcode(birthyr>= 1925 & birthyr <= 1945 ~ 'Silent Generation',
                      birthyr>= 1946 & birthyr <= 1964 ~ 'Boomers',
                      birthyr>= 1965 & birthyr <= 1976 ~ 'Gen X',
                      birthyr>= 1977 & birthyr <= 1995 ~ 'Millennials',
                      birthyr>= 1996 & birthyr <= 2019 ~ 'Gen Z')) %>% 
  mutate(wk = case_when(pew_attendance == 1 | pew_attendance == 2 ~ 1, 
                        pew_attendance <= 6 ~ 0)) %>% 
  group_by(year, gen) %>% 
  mean_ci(wk, wt = weight, ci = .84) %>% filter(n > 50)

gg %>% 
  ggplot(., aes(x = year, y = mean, color = gen, group = gen)) +
  geom_point(stroke = .56, shape = 21) + 
  geom_labelsmooth(aes(label = gen),  method = "loess", formula = y ~ x, family = "font", linewidth = 1, text_smoothing = 30, size = 6, linewidth = 1, boxlinewidth = 0.3) +
  theme_rb() + 
  scale_y_continuous(labels = percent, limits = c(.2, .43)) +
  scale_color_manual(values = c("#DC3912", "#FF9900", "#119618", "#980799", "azure4")) +
  labs(x = "", y = "", title = "Share of Catholics Attending Mass Nearly Every Week or More", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022")
save("cath_mass_attend_gens_ces.png")

gg <- cces %>% 
  mutate(gen = frcode(birthyr>= 1925 & birthyr <= 1945 ~ 'Silent Generation',
                      birthyr>= 1946 & birthyr <= 1964 ~ 'Boomers',
                      birthyr>= 1965 & birthyr <= 1976 ~ 'Gen X',
                      birthyr>= 1977 & birthyr <= 1995 ~ 'Millennials',
                      birthyr>= 1996 & birthyr <= 2019 ~ 'Gen Z')) %>% 
  mutate(race2 = frcode(race == 1 ~ "White", TRUE ~ "Non-White")) %>% 
  mutate(wk = case_when(pew_attendance == 1 | pew_attendance == 2 ~ 1, 
                        pew_attendance <= 6 ~ 0)) %>% 
  group_by(year, gen, race2) %>% 
  mean_ci(wk, wt = weight, ci = .84) %>% filter(n > 50) %>% filter(gen != 'NA')

gg %>% 
  ggplot(aes(x = year, y = mean, color = race2, group = race2)) +
  geom_point(stroke = .56, shape = 21, show.legend = FALSE) + 
  geom_smooth(se = FALSE) +
  facet_wrap(~ gen) +
  theme_rb(legend = TRUE) + 
  scale_color_calc() +
  theme(legend.position = c(.85, .16)) +
  scale_y_continuous(labels = percent) +
  labs(x = "", y = "", title = "Share of Catholics Attending Mass Nearly Every Week or More",
       caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022") +
  guides(color = guide_legend(reverse = TRUE)) +
  theme(legend.text = element_text(size = 20)) +
  theme(strip.text = element_text(size = 20)) +
save("cath_mass_attend_gens_ces_race.png")

cces_attend2 <- function(df, var){
  
  var <- enquo(var)
  
  df %>% 
    mutate(att = car::recode(!! var, "6=1; 5=2; 4=3; 3=4; 2=5; 1=6; else = NA")) %>% 
    mutate(att = frcode(att == 1 ~ "Never",
                        att == 2 ~ "Seldom",
                        att == 3 ~ "Yearly",
                        att == 4 ~ "Monthly",
                        att == 5 | att == 6 ~ "Weekly")) 
  
}

gg1 <- cces %>%
  filter(year == 2008) %>%
  filter(religion == 2) %>% 
  mutate(pew_religimp = pew_importance) %>% 
  mutate(imp = frcode(pew_religimp == 4 ~ "Not\nat All",
                      pew_religimp == 3 ~ "Not too",
                      pew_religimp == 2 ~ "Somewhat",
                      pew_religimp == 1 ~ "Very")) %>% 
  cces_attend2(pew_attendance) %>% 
  group_by(imp) %>% 
  ct(att, wt = weight, show_na = FALSE) %>% 
  na.omit() %>% 
  mutate(pct = n/6725) %>% 
  mutate(pct = round(pct, 2)) %>% 
  mutate(year = 2008)


gg2 <- cces %>%
  filter(year == 2022) %>%
  filter(religion == 2) %>% 
  mutate(pew_religimp = pew_importance) %>% 
  mutate(imp = frcode(pew_religimp == 4 ~ "Not\nat All",
                      pew_religimp == 3 ~ "Not too",
                      pew_religimp == 2 ~ "Somewhat",
                      pew_religimp == 1 ~ "Very")) %>% 
  cces_attend2(pew_attendance) %>% 
  group_by(imp) %>% 
  ct(att, wt = weight, show_na = FALSE) %>% 
  na.omit() %>% 
  mutate(pct = n/10219) %>% 
  mutate(pct = round(pct, 2)) %>% 
  mutate(year = 2022)

graph <- bind_rows(gg1, gg2)


graph <- graph %>% 
  mutate(bins = frcode(pct <= .01 ~ "a", 
                       pct > .01 & pct <= .05 ~ "b", 
                       pct > .05 & pct <= .10 ~ "c", 
                       pct > .10 & pct <= .15 ~ "d", 
                       pct > .15 ~ "e"))

graph %>% 
  ggplot(., aes(x= imp, y = att)) +
  geom_tile(aes(fill = bins), color = "black") +
  scale_fill_manual(values = c("#d6f2f7", "#42b1ce", "#237997", "#22627c", '#112c3b')) +
  theme_rb() + 
  facet_wrap(~ year) +
  theme(plot.subtitle = element_text(size = 24)) +
  geom_text(aes(x= imp, y = att, label = paste0(pct*100, '%')), size = 11, family = "font") +
  geom_text(aes(x= imp, y = att, label = ifelse(bins == 'e', paste0(pct*100, '%'), "")), size = 11, family = "font", color = "white") +
  geom_text(aes(x= imp, y = att, label = ifelse(bins == 'd', paste0(pct*100, '%'), "")), size = 11, family = "font", color = "white") +
  theme(strip.text = element_text(size = 20)) +
  labs(x= "Religious Importance", y = "Religious Attendance", title = "Religious Importance and Religious Attendance among Catholics", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022") 
save("heat_imp_att2022_caths.png", wd = 8, ht = 6)


gg <- cces %>% 
  filter(year == 2008 | year == 2022) %>% 
  cces_attend(pew_attendance) %>% 
  filter(religion == 2) %>% 
  group_by(year) %>% 
  ct(att, wt = weight, show_na = FALSE)