Immigration or Abortion

immigration_abortion_white_evangelicals.R

calc_fun <- function(df, var, type){
  
  df %>% 
    mutate(nn = car::recode({{var}}, "1=5; 2=4; 3=3; 4=2; 5=1; else=99")) %>% 
    mutate(age = 2016 - birthyr) %>% 
    mutate(age2 = frcode(age >= 18 & age <= 35 ~ "18-35",
                         age >= 36 & age <= 44 ~ "36-44",
                         age >= 45 & age <= 54 ~ "45-54",
                         age >= 55 & age <= 64 ~ "55-64",
                         age >= 65 & age <= 74 ~ "65-74",
                         age >= 75             ~ "75 and Older")) %>% 
    filter(race ==1) %>% 
    filter(evangelical ==1) %>% 
    filter(nn != 99) %>% 
    group_by(age2) %>% 
    mean_ci(nn) %>% 
    mutate(issue = type)
    
}

ttt1 <- cces16 %>% calc_fun(CC16_301a, "Gun Control")
ttt2 <- cces16 %>% calc_fun(CC16_301b, "Abortion")
ttt3 <- cces16 %>% calc_fun(CC16_301c, "Taxes")
ttt4 <- cces16 %>% calc_fun(CC16_301d, "Immigration")
ttt5 <- cces16 %>% calc_fun(CC16_301e, "Deficit")
ttt6 <- cces16 %>% calc_fun(CC16_301f, "Defense")
ttt7 <- cces16 %>% calc_fun(CC16_301g, "Soc. Sec.")
ttt8 <- cces16 %>% calc_fun(CC16_301h, "Environment")
ttt9 <- cces16 %>% calc_fun(CC16_301i, "Jobs")
ttt10 <- cces16 %>% calc_fun(CC16_301j, "Crime")
ttt11 <- cces16 %>% calc_fun(CC16_301k, "Natl. Sec.")
ttt12 <- cces16 %>% calc_fun(CC16_301l, "Race Rel.")
ttt13 <- cces16 %>% calc_fun(CC16_301m, "Health Care")
ttt14 <- cces16 %>% calc_fun(CC16_301n, "Gay Marriage")
ttt15 <- cces16 %>% calc_fun(CC16_301o, "Corruption")


all <- bind_df("ttt") %>% 
  arrange(age2, -mean) %>% 
  mutate(rank = rep(1:15, times = 6))

rank <- all %>% 
  mutate(ag = case_when(age2 == "18-35" ~ 1,
                        age2 == "36-44" ~ 2,
                        age2 == "45-54" ~ 3,
                        age2 == "55-64" ~ 4,
                        age2 == "65-74" ~ 5,
                        age2 == "75 and Older" ~ 6))

ggplot(rank, aes(ag, rank, color = issue)) +
  geom_point(size=5, color="white") +
  geom_point(size=4, shape=1) +
  geom_point(size=3, shape=19) +
  geom_bump(aes(smooth = 8), size = 2) +
  scale_y_reverse(breaks = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14, 15)) +
  scale_x_continuous(breaks = c(1,2,3,4,5,6), labels = c("18-35", "36-44", "45-54", "55-64", "65-74", "75 and Older"), limits = c(-.8, 8)) +
  theme_gg("Abel") +
  geom_text(data = rank %>% filter(ag == min(ag)),
            aes(x = ag - .1, label = issue), size = 5, hjust = 1, family = "font") +
  geom_text(data = rank %>% filter(ag == max(ag)),
            aes(x = ag + .1, label = issue), size = 5, hjust = 0, family = "font") +
  scale_colour_tableau(palette = "Tableau 20") +
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
  labs(x = "Age Group", y = "Order of Importance", title = "Issue Ranks Among White Evangelicals", caption = "@ryanburge\nData: CCES 2016") +
  ggsave("E://bump_rank.png", type = "cairo-png", width = 7.75)





imm_fun <- function(df, var, name){
  
  df %>% 
    filter(pid7 == 5 | pid7 == 6 | pid7 == 7) %>% 
    filter(evangelical == 1 & race == 1) %>% 
    mutate(app = CC18_app_dtrmp_post) %>% 
    mutate(app = case_when(app == 1 | app == 2 ~ 1, TRUE ~ 0)) %>%
    mutate(qq = {{var}}) %>% 
    mutate(qq = frcode(qq == 1 ~ "Support", 
                       qq == 2 ~ "Oppose")) %>% 
    group_by(qq) %>% 
    mean_ci(app, ci = .84, wt = commonweight) %>% 
    mutate(type = name) %>% 
    na.omit()
  
}


aaa1 <- cces18 %>% imm_fun(CC18_322a, "Increase Border\nSecurity by $25 Billion")
aaa2 <- cces18 %>% imm_fun(CC18_322b, "DACA")
aaa3 <- cces18 %>% imm_fun(CC18_322c_new, "Eliminate Visa Lottery")
aaa4 <- cces18 %>% imm_fun(CC18_322c, "Withhold Fed. Funds\nfrom Sanctuary Cities")
aaa5 <- cces18 %>% imm_fun(CC18_322f, "Imprison Repeated\nBorder Crossers")

bbb1 <- cces18 %>% imm_fun(CC18_321a, "Allow Abortion\nas a Choice")
bbb2 <- cces18 %>% imm_fun(CC18_321b, "Permit Only In\nCase of Rape, \nIncest, Mother's Health")
bbb3 <- cces18 %>% imm_fun(CC18_321c, "Ban Abortion\nAfter 20 Weeks")
bbb4 <- cces18 %>% imm_fun(CC18_321d, "Allow Employers\nto Decline\nAbortion Coverage")
bbb5 <- cces18 %>% imm_fun(CC18_321f, "Make Illegal in\nAll Circumstances")

aaa <- bind_df("aaa")

aaa %>% 
  ggplot(., aes(x = qq, y = mean, fill = qq)) +
  geom_col(color = "black") +
  facet_wrap(~ type, nrow = 1) +
  error_bar() +
  theme_gg("Abel") +
  y_pct() + 
  scale_fill_paletteer_d("awtools::mpalette") +
  lab_bar(type = mean, pos = .025, sz = 4.25, top = FALSE) +
  labs(x = "", y = "", title = "Trump Approval Among White Evangelical Republicans", subtitle = "By Position on Immigration Policy", caption = "@ryanburge\nData: CCES 2018") +
  ggsave("E://immig_approval.png", type = "cairo-png", width = 8)


g2 <- bind_df("bbb")

g2 %>% 
  ggplot(., aes(x = qq, y = mean, fill = qq)) +
  geom_col(color = "black") +
  facet_wrap(~ type, nrow = 1) +
  error_bar() +
  theme_gg("Abel") +
  y_pct() + 
  scale_fill_paletteer_d("awtools::mpalette") +
  lab_bar(type = mean, pos = .025, sz = 4.25, top = FALSE) +
  labs(x = "", y = "", title = "Trump Approval Among White Evangelical Republicans", subtitle = "By Position on Abortion Policy", caption = "@ryanburge\nData: CCES 2018") +
  ggsave("E://ab_approval.png", type = "cairo-png", width = 8)




two_fun <- function(df, var, name){
  
  df %>% 
    filter(pid7 == 5 | pid7 == 6 | pid7 == 7) %>% 
    filter(evangelical == 1 & race == 1) %>% 
    mutate(qq = {{var}}) %>% 
    mutate(qq = case_when(qq == 1 ~ 1, 
                       qq == 2 ~ 0)) %>% 
    mean_ci(qq, ci = .84, wt = commonweight) %>% 
    mutate(type = name) %>% 
    na.omit()
  
}


bbb1 <- cces18 %>% two_fun(CC18_321a, "Allow Abortion\nas a Choice")
bbb2 <- cces18 %>% two_fun(CC18_321b, "Permit Only In\nCase of Rape, \nIncest, Mother's Health")
bbb3 <- cces18 %>% two_fun(CC18_321c, "Ban Abortion\nAfter 20 Weeks")
bbb4 <- cces18 %>% two_fun(CC18_321d, "Allow Employers\nto Decline\nAbortion Coverage")
bbb5 <- cces18 %>% two_fun(CC18_321f, "Make Illegal in\nAll Circumstances")



g3 <- bind_df("bbb")

g3 %>% 
  ggplot(., aes(x =type, y = mean, fill = mean)) +
  geom_col(color = "black") +
  error_bar() +
  theme_gg("Abel") +
  y_pct() + 
  coord_flip() +
  lab_bar(type = mean, pos = .05, sz = 4.25, top = FALSE) +
  labs(x = "", y = "", title = "Abortion Postions by White Evangelical Republicans", subtitle = "", caption = "@ryanburge\nData: CCES 2018") +
  ggsave("E://ab_pos.png", type = "cairo-png", width = 8, height = 3.5)


evan <- cces18 %>% 
  filter(race == 1 & evangelical == 1) %>% 
  filter(pid7 == 5 | pid7 == 6 | pid7 == 7) %>% 
  mutate(age = 2018 - birthyr) %>% 
  mutate(attend = 7 - pew_churatd) %>% 
  mutate(male = case_when(gender == 1 ~ 1, 
                          gender == 2 ~ 0)) %>% 
  mutate(ab = frcode(CC18_321a == 1 ~ "Support",
                     CC18_321a == 2 ~ "Oppose")) %>% 
  mutate(imm = frcode(CC18_322c_new == 1 ~ "Support",
                      CC18_322c_new == 2 ~ "Oppose")) %>% 
  mutate(income = car::recode(faminc_new, "97 = 0")) %>% 
  mutate(app = CC18_app_dtrmp_post) %>% 
  mutate(app = case_when(app == 1 | app == 2 ~ 1, TRUE ~ 0))

gg <- glm(app ~ ab*imm + attend + age + educ + male + income, data = evan, family = "binomial")

graph <- cat_plot(gg, pred = ab, modx = imm, interval = TRUE, int.width =  .76, errorbar.width = .3, legend.main = "End Visa Lottery")

graph +
  y_pct() +
  theme_gg("Abel", legend = FALSE) +
  # theme(legend.title = element_text(size = 12)) +
  add_text(x=.77, y = .885, word = "Pro-Choice\nAnti-Immigration", sz = 2.5) +
  add_text(x=1.22, y = .755, word = "Pro-Choice\nPro-Immigration", sz = 2.5) +
  add_text(x=1.77, y = .9185, word = "Pro-Life\nAnti-Immigration", sz = 2.5) +
  add_text(x=2.22, y = .8185, word = "Pro-Life\nPro-Immigration", sz = 2.5) +
  labs(x = "Allow Abortion as a Choice", y = "Pr(Trump Approval)", title = "Trump Approval among White\nEvangelical Republicans", caption = "@ryanburge\nData: CCES 2018") +
  ggsave("E://cat_plot_imm_abortion.png", type = "cairo-png", width = 4)