ryanburge/ct_selection_untitled-0ACC1244.R Secret

## ct_selection_untitled-0ACC1244.R

gg <- cces %>%
  mutate(religpew = religion) %>%
  mutate(trad = frcode(religpew == 1 ~ "Protestant",
                       religpew == 2 ~ "Catholic",
                       religpew == 3 ~ "Mormon",
                       religpew == 4 ~ "Orthodox",
                       religpew == 5 ~ "Jewish",
                       religpew == 6 ~ "Muslim",
                       religpew == 7 ~ "Buddhist",
                       religpew == 8 ~ "Hindu",
                       religpew == 9 ~ "Atheist",
                       religpew == 10 ~ "Agnostic",
                       religpew == 11 ~ "Nothing in Particular",
                       religpew == 12 ~ "Something Else"))  %>%
  group_by(year) %>%
  ct(trad, wt = weight, show_na = FALSE) %>% filter(trad == "Atheist" | trad == "Agnostic" | trad == "Nothing in Particular")


gg %>%
  mutate(lab = round(pct, 2)) %>%
  ggplot(., aes(x = year, y = pct, fill = trad)) +
  geom_col(color = "black") +
  theme_rb(legend = TRUE) +
  scale_fill_gdocs() +
  y_pct() +
  geom_text(aes(label = paste0(lab*100, '%')), position = position_stack(vjust = 0.5), size = 6.5, family = "font") +
  labs(x = "", y = "", title = "The Rise of the Nones, 2008-2023", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2023")
save("nones_ces23.png", wd = 9)


gg <- cces %>%
  filter(year == 2023) %>%
  mutate(relig = frcode(religion == 1 ~ "Protestant",
                        religion == 2 ~ "Catholic",
                        religion >= 3 & religion <= 8 ~ "Other World Religions",
                        religion == 9 | religion == 10 ~ "Atheist/Agnostic",
                        religion == 11 ~ "Nothing in Particular", TRUE ~ "All Others")) %>%
  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')) %>%
  group_by(gen) %>%
  ct(relig, wt = weight, show_na = FALSE)


gg %>%
  mutate(lab = round(pct, 2)) %>%
  ggplot(., aes(x = 1, y = pct, fill = fct_rev(relig))) +
  geom_col(color = "black") +
  coord_flip() +
  facet_wrap(~ gen, ncol =1, strip.position = "left") +
  scale_fill_paletteer_d("ggthemes::excel_Berlin") +
  theme_rb() +
  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()) +
  theme(plot.title = element_text(size = 20)) +
  geom_text(aes(label = ifelse(pct >.05, paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "black") +
  labs(x = "", y = "", title = "Religious Composition of Each Generation", caption = "@ryanburge\nData: Cooperative Election Study, 2023")
save("gens_relig_2023.png", wd = 9, ht = 4)


gg <- cces %>%
  filter(year == 2023) %>%
  cces_attend(pew_attendance) %>%
  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')) %>%
  group_by(gen) %>%
  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(~ gen, ncol =1, strip.position = "left") +
  scale_fill_manual(values=met.brewer("Egypt", 6)) +
  theme_rb() +
  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()) +
  theme(plot.title = element_text(size = 20)) +
  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 == "Weekly", 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 Service Attendance by Generation", caption = "@ryanburge\nData: Cooperative Election Study, 2023")
save("gens_att_2023.png", wd = 9, ht = 4)


gg1 <- cces %>%
  filter(year == 2023) %>%
  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(gen == "Gen Z") %>%
  mutate(religpew = religion) %>%
  mutate(trad = frcode(religpew == 1 ~ "Protestant",
                       religpew == 2 ~ "Catholic",
                       religpew == 3 ~ "Mormon",
                       religpew == 4 ~ "Orthodox",
                       religpew == 5 ~ "Jewish",
                       religpew == 6 ~ "Muslim",
                       religpew == 7 ~ "Buddhist",
                       religpew == 8 ~ "Hindu",
                       religpew == 9 ~ "Atheist",
                       religpew == 10 ~ "Agnostic",
                       religpew == 11 ~ "Nothing in Particular",
                       religpew == 12 ~ "Something Else"))  %>%
  ct(trad, wt = weight, show_na = FALSE) %>%
  mutate(type = "Gen Z")


gg2 <- cces %>%
  filter(year == 2023) %>%
  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(gen != "Gen Z") %>%
  mutate(religpew = religion) %>%
  mutate(trad = frcode(religpew == 1 ~ "Protestant",
                       religpew == 2 ~ "Catholic",
                       religpew == 3 ~ "Mormon",
                       religpew == 4 ~ "Orthodox",
                       religpew == 5 ~ "Jewish",
                       religpew == 6 ~ "Muslim",
                       religpew == 7 ~ "Buddhist",
                       religpew == 8 ~ "Hindu",
                       religpew == 9 ~ "Atheist",
                       religpew == 10 ~ "Agnostic",
                       religpew == 11 ~ "Nothing in Particular",
                       religpew == 12 ~ "Something Else"))  %>%
  ct(trad, wt = weight, show_na = FALSE) %>%
  mutate(type = "Everyone Else")

both <- bind_rows(gg1, gg2)

both %>%
  mutate(lab = round(pct, 2)) %>%
  ggplot(., aes(x = reorder(trad, pct), y = pct, fill = type)) +
  geom_col(color = "black", position = "dodge") +
  coord_flip() +
  geom_text(aes(y = pct + .0095, label = ifelse(pct < .10, paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size =6, family = "font") +
  geom_text(aes(y = pct + .0105, label = ifelse(pct > .10, paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 6, family = "font") +
  theme_rb(legend = TRUE) +
  y_pct() +
  scale_fill_calc() +
  guides(fill = guide_legend(reverse = TRUE)) +
  theme(legend.text = element_text(size = 20)) +
  labs(x= "", y = "", title = "The Religious Breakdown of Generation Z vs Everyone Else", caption = "@ryanburge\nData: Cooperative Election Study, 2023")
save("gen_z_relig23_compare.png", ht = 9)


gg <- cces %>%
  mutate(gen = frcode(birthyr>= 1901 & birthyr <= 1924 ~ 'Greatest Generation',
                      birthyr>= 1925 & birthyr <= 1945 ~ 'Silent, 1925-1945',
                      birthyr>= 1946 & birthyr <= 1964 ~ 'Boomers, 1946-1964',
                      birthyr>= 1965 & birthyr <= 1976 ~ 'Gen X, 1965-1976',
                      birthyr>= 1977 & birthyr <= 1995 ~ 'Millennials, 1977-1995',
                      birthyr>= 1996 & birthyr <= 2019 ~ 'Gen Z, 1996-')) %>%
  mutate(nones = case_when(religion == 9 | religion == 10 | religion == 11 ~ 1, TRUE ~ 0)) %>%
  group_by(year, gen) %>%
  mean_ci(nones, wt = weight, ci = .84)

gg %>%
  mutate(lab = round(mean, 2)) %>%
  filter(gen != "Greatest Generation") %>%
  filter(year == 2008 | year == 2012 | year == 2016 | year == 2020 | year == 2022| year == 2023) %>%
  ggplot(., aes(x = factor(year), y = mean, fill = factor(year))) +
  geom_col(color = "black", position = "dodge") +
  facet_wrap(~ gen) +
  geom_text(aes(y = .03, label = ifelse(gen != "Gen Z, 1996-", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 5.5, family = "font")+
  geom_text(aes(y = .03, label = ifelse(gen != "Gen Z, 1996-" & year == 2008, paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 5.5, family = "font", color = "white")+
  geom_text(aes(y = .03, label = ifelse(gen != "Gen Z, 1996-" & year == 2012, paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 5.5, family = "font", color = "white")+
  geom_text(aes(y = .035, label = ifelse(gen == "Gen Z, 1996-", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 6.5, family = "font")+
  theme_rb() +
  error_bar() +
  scale_fill_manual(values=met.brewer("Renoir", 6)) +
  y_pct() +
  theme(strip.text = element_text(size = 20)) +
  theme(plot.title =  element_text(size = 26)) +
  labs(x = "", y = "", title = "Share Who Have No Religious Affiliation by Generation", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2023")
save("none_years_gen23.png", wd = 10, ht = 8)


gg <- cces %>%
  filter(birthyr > 1940) %>%
  filter(year == 2020 | year == 2021 | year == 2022 | year == 2023) %>%
  mutate(none = case_when(religion == 9 | religion == 10 | religion == 11 ~ 1,
                           TRUE ~ 0)) %>%
  group_by(birthyr, year) %>%
  mean_ci(none, wt = weight) %>%
  mutate(year = as.factor(year))

gg %>%
  # filter(birthyr >= 1980) %>%
  ggplot(., aes(x = birthyr, y = mean, color = year, group = year)) +
  geom_point(stroke = .25, shape = 21, alpha = .6) +
  geom_smooth(se = FALSE) +
  theme_rb(legend = TRUE) +
  scale_color_manual(values = c("#ff5964", "#00b259", "#35a7ff", "#ffcc00")) +
  scale_y_continuous(labels = percent, limits = c(0, .51)) +
  labs(x = "Birth Year", y = "", title = "Share Who Have No Religious Affiliation by Birth Year, Among Those Born in 1980 or Later", caption = "@ryanburge\nData: Cooperative Election Study, 2020-2023")
save("nones_birthyr23.png")

	gg <- cces %>%
	mutate(religpew = religion) %>%
	mutate(trad = frcode(religpew == 1 ~ "Protestant",
	religpew == 2 ~ "Catholic",
	religpew == 3 ~ "Mormon",
	religpew == 4 ~ "Orthodox",
	religpew == 5 ~ "Jewish",
	religpew == 6 ~ "Muslim",
	religpew == 7 ~ "Buddhist",
	religpew == 8 ~ "Hindu",
	religpew == 9 ~ "Atheist",
	religpew == 10 ~ "Agnostic",
	religpew == 11 ~ "Nothing in Particular",
	religpew == 12 ~ "Something Else")) %>%
	group_by(year) %>%
	ct(trad, wt = weight, show_na = FALSE) %>% filter(trad == "Atheist" \| trad == "Agnostic" \| trad == "Nothing in Particular")


	gg %>%
	mutate(lab = round(pct, 2)) %>%
	ggplot(., aes(x = year, y = pct, fill = trad)) +
	geom_col(color = "black") +
	theme_rb(legend = TRUE) +
	scale_fill_gdocs() +
	y_pct() +
	geom_text(aes(label = paste0(lab*100, '%')), position = position_stack(vjust = 0.5), size = 6.5, family = "font") +
	labs(x = "", y = "", title = "The Rise of the Nones, 2008-2023", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2023")
	save("nones_ces23.png", wd = 9)


	gg <- cces %>%
	filter(year == 2023) %>%
	mutate(relig = frcode(religion == 1 ~ "Protestant",
	religion == 2 ~ "Catholic",
	religion >= 3 & religion <= 8 ~ "Other World Religions",
	religion == 9 \| religion == 10 ~ "Atheist/Agnostic",
	religion == 11 ~ "Nothing in Particular", TRUE ~ "All Others")) %>%
	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')) %>%
	group_by(gen) %>%
	ct(relig, wt = weight, show_na = FALSE)



	gg %>%
	mutate(lab = round(pct, 2)) %>%
	ggplot(., aes(x = 1, y = pct, fill = fct_rev(relig))) +
	geom_col(color = "black") +
	coord_flip() +
	facet_wrap(~ gen, ncol =1, strip.position = "left") +
	scale_fill_paletteer_d("ggthemes::excel_Berlin") +
	theme_rb() +
	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()) +
	theme(plot.title = element_text(size = 20)) +
	geom_text(aes(label = ifelse(pct >.05, paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8, family = "font", color = "black") +
	labs(x = "", y = "", title = "Religious Composition of Each Generation", caption = "@ryanburge\nData: Cooperative Election Study, 2023")
	save("gens_relig_2023.png", wd = 9, ht = 4)




	gg <- cces %>%
	filter(year == 2023) %>%
	cces_attend(pew_attendance) %>%
	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')) %>%
	group_by(gen) %>%
	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(~ gen, ncol =1, strip.position = "left") +
	scale_fill_manual(values=met.brewer("Egypt", 6)) +
	theme_rb() +
	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()) +
	theme(plot.title = element_text(size = 20)) +
	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 == "Weekly", 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 Service Attendance by Generation", caption = "@ryanburge\nData: Cooperative Election Study, 2023")
	save("gens_att_2023.png", wd = 9, ht = 4)




	gg1 <- cces %>%
	filter(year == 2023) %>%
	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(gen == "Gen Z") %>%
	mutate(religpew = religion) %>%
	mutate(trad = frcode(religpew == 1 ~ "Protestant",
	religpew == 2 ~ "Catholic",
	religpew == 3 ~ "Mormon",
	religpew == 4 ~ "Orthodox",
	religpew == 5 ~ "Jewish",
	religpew == 6 ~ "Muslim",
	religpew == 7 ~ "Buddhist",
	religpew == 8 ~ "Hindu",
	religpew == 9 ~ "Atheist",
	religpew == 10 ~ "Agnostic",
	religpew == 11 ~ "Nothing in Particular",
	religpew == 12 ~ "Something Else")) %>%
	ct(trad, wt = weight, show_na = FALSE) %>%
	mutate(type = "Gen Z")



	gg2 <- cces %>%
	filter(year == 2023) %>%
	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(gen != "Gen Z") %>%
	mutate(religpew = religion) %>%
	mutate(trad = frcode(religpew == 1 ~ "Protestant",
	religpew == 2 ~ "Catholic",
	religpew == 3 ~ "Mormon",
	religpew == 4 ~ "Orthodox",
	religpew == 5 ~ "Jewish",
	religpew == 6 ~ "Muslim",
	religpew == 7 ~ "Buddhist",
	religpew == 8 ~ "Hindu",
	religpew == 9 ~ "Atheist",
	religpew == 10 ~ "Agnostic",
	religpew == 11 ~ "Nothing in Particular",
	religpew == 12 ~ "Something Else")) %>%
	ct(trad, wt = weight, show_na = FALSE) %>%
	mutate(type = "Everyone Else")

	both <- bind_rows(gg1, gg2)

	both %>%
	mutate(lab = round(pct, 2)) %>%
	ggplot(., aes(x = reorder(trad, pct), y = pct, fill = type)) +
	geom_col(color = "black", position = "dodge") +
	coord_flip() +
	geom_text(aes(y = pct + .0095, label = ifelse(pct < .10, paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size =6, family = "font") +
	geom_text(aes(y = pct + .0105, label = ifelse(pct > .10, paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 6, family = "font") +
	theme_rb(legend = TRUE) +
	y_pct() +
	scale_fill_calc() +
	guides(fill = guide_legend(reverse = TRUE)) +
	theme(legend.text = element_text(size = 20)) +
	labs(x= "", y = "", title = "The Religious Breakdown of Generation Z vs Everyone Else", caption = "@ryanburge\nData: Cooperative Election Study, 2023")
	save("gen_z_relig23_compare.png", ht = 9)




	gg <- cces %>%
	mutate(gen = frcode(birthyr>= 1901 & birthyr <= 1924 ~ 'Greatest Generation',
	birthyr>= 1925 & birthyr <= 1945 ~ 'Silent, 1925-1945',
	birthyr>= 1946 & birthyr <= 1964 ~ 'Boomers, 1946-1964',
	birthyr>= 1965 & birthyr <= 1976 ~ 'Gen X, 1965-1976',
	birthyr>= 1977 & birthyr <= 1995 ~ 'Millennials, 1977-1995',
	birthyr>= 1996 & birthyr <= 2019 ~ 'Gen Z, 1996-')) %>%
	mutate(nones = case_when(religion == 9 \| religion == 10 \| religion == 11 ~ 1, TRUE ~ 0)) %>%
	group_by(year, gen) %>%
	mean_ci(nones, wt = weight, ci = .84)

	gg %>%
	mutate(lab = round(mean, 2)) %>%
	filter(gen != "Greatest Generation") %>%
	filter(year == 2008 \| year == 2012 \| year == 2016 \| year == 2020 \| year == 2022\| year == 2023) %>%
	ggplot(., aes(x = factor(year), y = mean, fill = factor(year))) +
	geom_col(color = "black", position = "dodge") +
	facet_wrap(~ gen) +
	geom_text(aes(y = .03, label = ifelse(gen != "Gen Z, 1996-", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 5.5, family = "font")+
	geom_text(aes(y = .03, label = ifelse(gen != "Gen Z, 1996-" & year == 2008, paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 5.5, family = "font", color = "white")+
	geom_text(aes(y = .03, label = ifelse(gen != "Gen Z, 1996-" & year == 2012, paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 5.5, family = "font", color = "white")+
	geom_text(aes(y = .035, label = ifelse(gen == "Gen Z, 1996-", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 6.5, family = "font")+
	theme_rb() +
	error_bar() +
	scale_fill_manual(values=met.brewer("Renoir", 6)) +
	y_pct() +
	theme(strip.text = element_text(size = 20)) +
	theme(plot.title = element_text(size = 26)) +
	labs(x = "", y = "", title = "Share Who Have No Religious Affiliation by Generation", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2023")
	save("none_years_gen23.png", wd = 10, ht = 8)


	gg <- cces %>%
	filter(birthyr > 1940) %>%
	filter(year == 2020 \| year == 2021 \| year == 2022 \| year == 2023) %>%
	mutate(none = case_when(religion == 9 \| religion == 10 \| religion == 11 ~ 1,
	TRUE ~ 0)) %>%
	group_by(birthyr, year) %>%
	mean_ci(none, wt = weight) %>%
	mutate(year = as.factor(year))

	gg %>%
	# filter(birthyr >= 1980) %>%
	ggplot(., aes(x = birthyr, y = mean, color = year, group = year)) +
	geom_point(stroke = .25, shape = 21, alpha = .6) +
	geom_smooth(se = FALSE) +
	theme_rb(legend = TRUE) +
	scale_color_manual(values = c("#ff5964", "#00b259", "#35a7ff", "#ffcc00")) +
	scale_y_continuous(labels = percent, limits = c(0, .51)) +
	labs(x = "Birth Year", y = "", title = "Share Who Have No Religious Affiliation by Birth Year, Among Those Born in 1980 or Later", caption = "@ryanburge\nData: Cooperative Election Study, 2020-2023")
	save("nones_birthyr23.png")