ct_selection_untitled-1B359C64.R

graph <- cces %>% 
  filter(year >= 2020) %>% 
  mutate(rel = frcode(religion == 3 ~ "LDS", 
                      religion == 9 ~ "Atheist")) %>% 
  mutate(kids = case_when(havekids == 1 ~ 1, 
                          havekids == 2 ~ 0)) %>% 
  group_by(age, rel) %>% 
  mean_ci(kids, wt = weight) %>% 
  filter(rel != "NA")

graph %>% 
  filter(age <= 70) %>% 
  ggplot(., aes(x = age, y = mean, color = rel, group = rel)) +
  geom_point(stroke = 1, shape = 21, alpha = .45) + 
  geom_labelsmooth(aes(label = rel),  method = "loess", formula = y ~ x, family = "font", linewidth = 1, text_smoothing = 30, size = 4.5, linewidth = 1, boxlinewidth = 0.3, hjust = .35) +
  scale_color_calc() +
  theme_rb() +
  scale_y_continuous(labels = percent, limits = c(0, .85)) +
  labs(x = "", y = "", title = "Are you the parent or guardian of any children under the age of 18?", caption = "@ryanburge\nData: Cooperative Election Study, 2020-2022")
save("parent_ath_lds.png")

ggg <- cces %>% 
  filter(year >= 2020) %>% 
  filter(age >= 30 & age <= 45) %>% 
  cces_attend(pew_attendance) %>% 
  mutate(kids = case_when(havekids == 1 ~ 1, 
                          havekids == 2 ~ 0)) %>% 
  cces_pid3(pid7) %>% 
  group_by(pid3, att) %>% 
  mean_ci(kids, wt = weight, ci = .84) %>% 
  filter(att != "NA") %>% 
  filter(pid3 != "NA")

ggg %>% 
  filter(pid3 != "Independent") %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = att, y = mean, fill = pid3)) +
  geom_col(color = "black", position = "dodge") +
  scale_fill_manual(values = c("dodgerblue3", "firebrick3")) +
  theme_rb(legend = TRUE) +
  error_bar() +
  lab_bar(top = FALSE, type = lab, pos = .04, sz = 5.5) +
  y_pct() + 
  theme(plot.title = element_text(size = 15)) + 
  labs(x = "Religious Attendance", y = "", title = "Share Who Have Children by Worship Attendance and Partisanship", 
       subtitle = "Among those 30-45 Years Old", 
       caption = "@ryanburge\nData: Cooperative Election Study, 2020-2022") 
save("pid3_att_kids_ces.png", wd = 6)


ggg <- cces %>% 
  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")) %>% 
  mutate(yr = frcode(year == 2008 ~ "2008", 
                     year == 2022 ~ "2022")) %>% 
  mutate(factor(year)) %>% 
  mutate(kids = case_when(havekids == 1 ~ 1, 
                          havekids == 2 ~ 0)) %>% 
  group_by(age, yr, relig) %>% 
  mean_ci(kids, wt = weight) 


ggg %>% 
  filter(relig != 'NA') %>% 
  filter(yr != 'NA') %>% 
  filter(age <= 70) %>% 
  ggplot(., aes(x = age, y = mean, color = yr, group = yr)) +
  geom_smooth(se= FALSE) +
  scale_color_tableau() +
  theme_rb(legend = TRUE) +
  facet_wrap(~ relig) +
  scale_y_continuous(labels = percent, limits = c(0, .65)) +
  labs(x = "Age", y = "", title = "Are you the parent or guardian of any children under the age of 18?", caption = "@ryanburge\nData: Cooperative Election Study, 2008+2022")
save("yr_compare_relig.png")



reg <- cces22 %>% 
  filter(faminc_new <= 16) %>% 
  mutate(trad = frcode(evangelical == 1 & race == 1 ~ "White Evangelical",
                       evangelical == 1 & race != 1 ~ "Non-White Evangelical",
                       mainline == 1 ~ "Mainline", 
                       bprot == 1 ~ "Black Prot.",
                       religpew == 2 & race == 1 ~ "White Catholic",
                       religpew == 2 & race != 1 ~ "Non-White 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", 
                       TRUE ~ "Unclassified")) %>% 
  mutate(trad2 = frcode(religpew == 1 & pew_bornagain == 1 ~ "Evangelical Prot.",
                        religpew == 1 & pew_bornagain == 2 ~ "Non-Evangelical Prot.", 
                        religpew == 2 ~ "Catholic",
                        religpew == 9 | religpew == 10 ~ "Atheist/Agnostic")) %>% 
  mutate(male = car::recode(gender4, "1=1; else =0")) %>% 
  mutate(white = car::recode(race, "1=1; else =0")) %>% 
  mutate(income = faminc_new) %>% 
  mutate(pid2 = frcode(pid3 == 1 ~ "Democrat",
                       pid3 == 2 ~ "Republican")) %>% 
  mutate(age = 2022 - birthyr) %>% 
  mutate(kids = case_when(child18 == 1 ~ 1, 
                          child18 == 2 ~ 0)) %>% 
  filter(age >= 30 & age <= 45) %>% 
  select(trad, trad2, male, white, age, income, pid2, educ, kids)


regg <- glm(kids ~ income*trad2*pid2 + male + white + educ, family = "binomial", data = reg)

int <- interact_plot(regg, pred= income, modx = trad2, mod2 = pid2, int.width = .76, interval = TRUE, mod2.labels = c("Democrat", "Republican")) 

int +
  theme_rb(legend = TRUE) +
  y_pct() +
  theme(strip.text = element_text(size = 16)) +
  scale_x_continuous(breaks = c(4, 8, 12, 16), labels = c("$30K-$40K", "$70K-$80K", "$150K-$200K", "$500K+")) +
  labs(x = "Household Income", y = "", title = "Regression Predicting Likelihood of Being a Parent",
       subtitle = "Controls for Gender, Race, and Education - Sample Restricted to Those 30-45 Years Old",
       caption = "@ryanburge\nData: Cooperative Election Study 2022")
save("regress_parent_ces22.png")




ggg <- cces %>% 
  filter(age >= 30 & age <= 45) %>% 
  filter(year >= 2020) %>% 
  mutate(relig = frcode(religion == 1 ~ "Protestant",
                        religion == 2 ~ "Catholic",
                        religion >= 3 & religion <= 8 ~ "Other\nWorld Religions", 
                        religion == 9 | religion == 10 ~ "Atheist/Agnostic", 
                        religion == 11 ~ "Nothing\nin Particular")) %>% 
  mutate(pid2 = frcode(pid3 == 1 ~ "Democrat",
                       pid3 == 2 ~ "Republican")) %>% 
  mutate(numkids = replace_na(numkids, 0)) %>% 
  group_by(relig, pid2) %>% 
  mean_ci(numkids, wt = weight, ci = .84) %>% filter(pid2 != "NA") %>% filter(relig != "NA")


ggg %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = relig, y = mean, fill = pid2)) +
  geom_col(color = "black", position = "dodge") +
  scale_fill_manual(values = c("dodgerblue3", "firebrick3")) +
  theme_rb(legend = TRUE) +
  error_bar() +
  geom_text(aes(y = .15, label = lab), position = position_dodge(width = .9), size = 6, family = "font") +
  theme(plot.title = element_text(size = 15)) + 
  labs(x = "Religious Tradition", y = "", title = "Number of Children Per Adult by Partisanship and Religion", 
       subtitle = "Among those 30-45 Years Old - Having No Children Was Counted as Zero", 
       caption = "@ryanburge\nData: Cooperative Election Study, 2020-2022") 
save("pid2_rel_numkids_ces.png", wd = 6)