Abortion in 2020

abortion_2020.R

fun <- function(df, var, name)
  
{
  
  
  df %>% 
    mutate(xtn = case_when(religion == 1 | religion == 5 ~ 1, TRUE ~ 0)) %>% 
    mutate(white = case_when(race_ethnicity == 1 & hispanic == 1 ~ 1, TRUE ~ 0 )) %>% 
    mutate(trad = frcode(xtn == 1 & is_evangelical == 1 & white == 1 ~ "White Evangelical",
                         xtn == 1 & is_evangelical == 1 & white == 0 ~ "Non-White Evangelical",
                         xtn == 1 & is_evangelical == 2 ~ "Non-Evangelical Prot.",
                         religion == 2 ~ "Catholic",
                         religion == 3 ~ "Mormon",
                         religion == 4 ~ "Orthodox",
                         religion == 6 ~ "Jewish",
                         religion == 7 ~ "Muslim",
                         religion == 8 ~ "Buddhist",
                         religion == 9 ~ "Hindu",
                         religion == 10 ~ "Atheist",
                         religion == 11 ~ "Agnostic",
                         religion == 12 ~ "Nothing in Particular",
                         religion == 13 ~ "Something Else")) %>% 
    mutate(ab = case_when({{var}} == 1 ~ 1, 
                          {{var}} == 2 ~ 0)) %>% 
    group_by(trad) %>% 
    mean_ci(ab, wt = weight, ci = .84) %>% 
    mutate(type = name) %>% 
    na.omit()
    
    
}


yyy1 <- ns %>% fun(abortion_any_time, "Permit Abortion Anytime")
yyy2 <- ns %>% fun(abortion_never, "Never Permit Abortion")
yyy3 <- ns %>% fun(abortion_conditions, "Permit For Rape, Incest, Life of Mother")
yyy4 <- ns %>% fun(late_term_abortion, "Permit Late Term")
yyy5 <- ns %>% fun(abortion_insurance, "Permit Employers to\nNot Cover Abortion in Insurance")
yyy6 <- ns %>% fun(abortion_waiting, "Require Waiting Period/Ultrasound")

graph <- bind_df("yyy")


graph %>% 
  ggplot(., aes(x = trad, y = mean, fill = trad)) +
  geom_col(color = "black") +
  coord_flip() +
  facet_wrap(~ type) +
  theme_rb() + 
  y_pct() + 
  error_bar() +
  scale_fill_tableau(palette = "Tableau 20") +
  lab_bar(top = FALSE, type = mean, pos = .07, sz = 3) +
  labs(x = "", y = "Share in Favor", title = "Support for Abortion by Religious Tradition", caption = "@ryanburge\nData: Nationscape 2019-2020") +
  ggsave("E://ab_facets_ns.png", type = "cairo-png", height = 6)


regg <- ns %>% 
  mutate(xtn = case_when(religion == 1 | religion == 5 ~ 1, TRUE ~ 0)) %>% 
  mutate(white = case_when(race_ethnicity == 1 & hispanic == 1 ~ 1, TRUE ~ 0 )) %>% 
  mutate(trad = frcode(xtn == 1 & is_evangelical == 1 & white == 1 ~ "White Evangelical",
                       xtn == 1 & is_evangelical == 1 & white == 0 ~ "Non-White Evangelical",
                       xtn == 1 & is_evangelical == 2 ~ "Non-Evangelical Prot.",
                       religion == 2 ~ "Catholic",
                       religion == 3 ~ "Mormon",
                       religion == 4 ~ "Orthodox",
                       religion == 6 ~ "Jewish",
                       religion == 7 ~ "Muslim",
                       religion == 8 ~ "Buddhist",
                       religion == 9 ~ "Hindu",
                       religion == 10 ~ "Atheist",
                       religion == 11 ~ "Agnostic",
                       religion == 12 ~ "Nothing in Particular",
                       religion == 13 ~ "Something Else")) %>% 
  mutate(any = case_when(abortion_any_time == 1 ~ 1, abortion_any_time == 2 ~ 0)) %>% 
  mutate(never = case_when(abortion_never == 1 ~ 1, abortion_never == 2 ~ 0)) %>% 
  mutate(male = case_when(gender == 2 ~ 1, gender == 1 ~ 0)) %>% 
  cces_pid3(pid7) %>% 
  mutate(ed = frcode(education == 1 | education == 2 | education == 3 ~ "Less than HS",
                               education == 4 ~ "HS Grad",
                               education == 5 | education == 6 ~ "Some College",
                               education == 7 ~ "2 Yr",
                               education == 8 ~ "4 Yr",
                               education >= 9 ~ "Graduate")) %>% 
  mutate(education = case_when(education == 1 | education == 2 | education == 3 ~ 1,
                        education == 4 ~ 2,
                        education == 5 | education == 6 ~ 3,
                        education == 7 ~ 4,
                        education == 8 ~ 5,
                        education >= 9 ~ 6)) %>% 
  select(age, male, income = household_income, education, ed, white, trad, any, never, pid3) %>% 
  na.omit() %>% 
  as_tibble()

lab <- regg %>% ct(trad)


reg1 <- glm(any ~ education*trad*pid3 + age + male + income + white, data = regg, family = "binomial")

gg <- interact_plot(reg1, pred= education, modx = pid3, mod2 = trad, int.width = .76, interval = TRUE, mod2.labels = lab$trad) 

gg + 
  pid3_fill() +
  pid3_color() +
  scale_x_continuous(breaks = c(1,2,3,4,5,6), labels = c("Less\nthan\nHS", "HS\nGrad", "Some\nColl", "2 Yr.", "4 Yr.", "Grad.")) +
  y_pct() +
  theme_rb() + 
  theme(legend.position = c(.75, .05)) +
  labs(x = "", y = "Estimate of Support", title = "Interaction of Partisanship, Religion, and Education on Abortion Policy", 
       subtitle = "Permit abortion at any time during the pregnancy", caption = '@ryanburge\nData: Nationscape 2019-2020') +
  ggsave("E://abortion_any_interact.png", type = 'cairo-png', height = 8)



reg1 <- glm(never ~ education*trad*pid3 + age + male + income + white, data = regg, family = "binomial")

gg <- interact_plot(reg1, pred= education, modx = pid3, mod2 = trad, int.width = .76, interval = TRUE, mod2.labels = lab$trad) 

gg + 
  pid3_fill() +
  pid3_color() +
  scale_x_continuous(breaks = c(1,2,3,4,5,6), labels = c("Less\nthan\nHS", "HS\nGrad", "Some\nColl", "2 Yr.", "4 Yr.", "Grad.")) +
  y_pct() +
  theme_rb() + 
  theme(legend.position = c(.75, .05)) +
  labs(x = "", y = "Estimate of Support", title = "Interaction of Partisanship, Religion, and Education on Abortion Policy", 
       subtitle = "Never permit abortion", caption = '@ryanburge\nData: Nationscape 2019-2020') +
  ggsave("E://abortion_never_interact.png", type = 'cairo-png', height = 8)