White Evangelical Women with College Degrees

wht_wmn_college.R

## Ridgeline Graph ####

font_add_google("Abel", "font")
showtext_auto()

cces16 %>% 
  filter(pid7 <= 7) %>% 
  filter(race ==1) %>% 
  filter(gender ==2)  %>%
  filter(educ ==5 | educ ==6) %>% 
  mutate(evangelical = recode(evangelical, "1 = 'Evangelical'; 0 = 'Non-Evangelical'")) %>% 
  ggplot(., aes(x = pid7, y = factor(evangelical), fill = ..x..)) +
  geom_density_ridges_gradient(rel_min_height=.02) +
  scale_fill_gradient2(low="dodgerblue3", high="firebrick3", mid="white", midpoint=4) +
  theme_minimal() +
  theme(text=element_text(size=44, family="font")) +
  scale_x_continuous(limits = c(-.5,9), breaks = c(1,2,3,4,5,6,7), labels = c("Strong Democrat", "Not Strong Democrat", "Lean Democrat", "Independent", "Lean Republican", "Not Strong Republican", "Strong Republican")) +
  labs(x="Party Identification", y="", title="Distribution of Partisanship", subtitle = "Among White College-Educated Women", caption="Data: CCES 2016") + 
  theme(legend.position = "none") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  ggsave("D://pid_ridges_cces.png")

## Bar Graph ####

evan <- cces16 %>% 
  filter(evangelical ==1) %>% 
  filter(race ==1) %>% 
  filter(gender ==2) %>% 
  filter(educ ==5 | educ ==6) %>% 
  filter(CC16_410a < 3) %>% 
  ct(CC16_410a, wt = commonweight_vv_post) %>% 
  mutate(group = "Evangelicals")

not_evan <- cces16 %>% 
  filter(evangelical !=1) %>% 
  filter(race ==1) %>% 
  filter(gender ==2) %>% 
  filter(educ ==5 | educ ==6) %>% 
  filter(CC16_410a < 3) %>% 
  ct(CC16_410a, wt = commonweight_vv_post) %>% 
  mutate(group = "Not Evangelical")

graph <- bind_df("evan")

graph <- graph %>% 
  mutate(CC16_410a =  recode(CC16_410a, "1 = 'Trump'; 2= 'Clinton'; 3 = 'Johnson'; 4 = 'Stein'; 5:8 = NA"))



font_add_google("Abel", "font")
showtext_auto()

graph %>% 
  filter(CC16_410a != "NA") %>% 
  ggplot(., aes(x= CC16_410a, y = pct, fill = CC16_410a)) +
  geom_col(color = "black") +
  facet_grid(. ~ group) +
  geom_text(aes(y = pct + .025, label = paste0(pct*100, '%')), position = position_dodge(width = .9), size = 14, family = "font") +
  theme_minimal() +
  scale_fill_manual(values = c("dodgerblue3", "firebrick3")) +
  theme(legend.position = "none") +
  scale_y_continuous(labels = percent) +
  labs(x = "", y = "", title = "Vote Choice for College-Educated White Women", caption = "Data: CCES 2016") +
  theme(text=element_text(size=44, family="font")) +
  ggsave("D://college_white_wmn_vote16_2party.png")


## Ribbon Chart ####

female_eee <- gss %>% 
  filter(evangelical == 1) %>% 
  filter(race ==1) %>% 
  filter(sex ==2) %>% 
  filter(educ >=16) %>% 
  filter(partyid <=6) %>% 
  group_by(year) %>% 
  mean_ci(partyid, wt = wtssall) %>% 
  mutate(type = "Evangelical") %>% 
  mutate(gender = "Female")

female_not_eee <- gss %>% 
  filter(evangelical != 1) %>% 
  filter(race ==1) %>% 
  filter(sex ==2) %>% 
  filter(educ >=16) %>% 
  filter(partyid <=6) %>% 
  group_by(year) %>% 
  mean_ci(partyid, wt = wtssall) %>% 
  mutate(type = "Not Evangelical")  %>% 
  mutate(gender = "Female")


male_eee <- gss %>% 
  filter(evangelical == 1) %>% 
  filter(race ==1) %>% 
  filter(sex ==1) %>% 
  filter(educ >=16) %>% 
  filter(partyid <=6) %>% 
  group_by(year) %>% 
  mean_ci(partyid, wt = wtssall) %>% 
  mutate(type = "Evangelical")  %>% 
  mutate(gender = "Male")


male_not_eee <- gss %>% 
  filter(evangelical != 1) %>% 
  filter(race ==1) %>% 
  filter(sex ==1) %>% 
  filter(educ >=16) %>% 
  filter(partyid <=6) %>% 
  group_by(year) %>% 
  mean_ci(partyid, wt = wtssall) %>% 
  mutate(type = "Not Evangelical")  %>% 
  mutate(gender = "Male")

graph <- bind_df("eee")

graph <- graph %>% 
  mutate(new = paste(type, gender, sep = " - "))

font_add_google("Abel", "font")
showtext_auto()

graph %>% 
  ggplot(., aes(x= year, y = mean, group = new, color = new)) +
  geom_line() +
  geom_ribbon(aes(ymin=lower, ymax=upper, color = new, fill = new), alpha = .05, size = .005,  show.legend = FALSE) +
  theme_minimal() +
  theme(text=element_text(size=45, family="font")) +
  scale_color_manual(values=c("#EE82EE", "dodgerblue3", "forestgreen", "firebrick1")) +
  scale_fill_manual(values=c("#EE82EE", "dodgerblue3", "forestgreen", "firebrick1")) +
  labs(x ="", y = "", title = "Change in Party Identification", subtitle = "For White Respondents with a College Education", caption = "Data: GSS (1972-2016)") +
  theme(legend.position = c(0.8, 0.25)) +
  scale_y_continuous(limits = c(0,6), breaks = c(0,1,2,3,4,5,6), labels = c("Strong Democrat", "Not Strong Democrat", "Ind., near Dem.",  "Independent", "Ind., near Rep.", "Not Strong Republican", "Strong Republican")) +
  theme(legend.title=element_blank()) +
  ggsave("D://pid_men_gss_long_together.png")

## Waffle ####


parts <- c(`White Evangelical Women w/College Degrees   `= 3 , `White Not Evangelical Women w/College Degrees   `= 14, `White Evangelical Men w/College Degrees   `= 3 , `White Not Evangelical Men w/College Degrees   `= 14, `Rest of the Electorate` = 166)

ev <- waffle(parts, legend_pos = "bottom", rows = 5) + 
  scale_fill_manual(values = c('White Evangelical Women w/College Degrees   ' = "darkorchid", 'White Not Evangelical Women w/College Degrees   ' = "dodgerblue3", 'White Evangelical Men w/College Degrees   ' = "forestgreen", 'White Not Evangelical Men w/College Degrees   ' = "firebrick1", 'Rest of the Electorate' = "azure3")) +
  theme(text=element_text(size=84, family="font")) + theme(legend.title=element_blank()) +
  labs(title = "Relative Size of the White College Educated Population", caption = "Data: CCES 2016") + 
  theme(plot.title = element_text(family = "font", size = 120, vjust =2, face = "bold")) +
  guides(fill=guide_legend(nrow=2,byrow=TRUE)) +
  theme(plot.margin=unit(c(2,1,2,1),"cm"))

ggsave(file="D://waffles_wmn_men.png", type = "cairo-png",  width = 18, height = 12, ev)