cd_race_long <- left_join(cd_st, race_by_cd) %>%
mutate(maj_frac = replace(frac, frac < 0.5, 0),
maj_race = replace(frac, frac < 0.5, NA_real_))
cd_race_long %>%
filter(!is.na(maj_race),
race != "Asian") %>%
mutate(race = fct_relevel(race, "White", "Hispanic", "Black")) %>%
ggplot() +
facet_wrap(~ race) +
geom_sf(data = st_shp, fill = "white", size = 0.1) +
geom_sf(aes(alpha = maj_frac, fill = race),
size = 0.5) +
theme_map() +
scale_alpha_continuous() +
guides(fill = FALSE, alpha = FALSE) +
theme(legend.position = "bottom",
plot.title = element_text(face = "bold", hjust = 0.5)) +
labs(title = "Majority Race",
caption = "Source: ACS 2018. \nACS respondents who identify with multiple races are not counted towards the proportion.")
ggsave("figures/majority-race.pdf")
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