1-map-cognitive-racism-cces-2016.R

library(RCurl)
library(tidyverse)
library(maps)
# library(fiftystater)

theme_steve <- function() {
  theme_bw() +
    theme(panel.border = element_blank(), 
          plot.caption=element_text(hjust=1, size=9,
                                    margin=margin(t=10),
                                    face="italic"),
          plot.title=element_text(hjust=0, size=18,
                                  margin=margin(b=10),
                                  face="bold"),
          axis.title.y=element_text(size=12,hjust=1,
                                    face="italic"),
          axis.title.x=element_text(hjust=1, size=12, face="italic"))
  
}

data <- getURL("https://raw.githubusercontent.com/svmiller/2016-cces-trump-vote/master/2016-cces-trump.csv")
Data <- read.csv(text = data) %>% tbl_df()


Data %>% 
  filter(lcograc >= .5) %>% 
  group_by(state) %>% 
  summarize(lcograc=n()) -> cogracists

Data %>%
  group_by(state) %>%
  summarize(population = n()) %>%
  left_join(cogracists, .) %>%
  mutate(`Percentage` = round((lcograc/population)*100, 2),
         region = tolower(state)) -> cogracists

cogracists <- left_join(states, cogracists, by="region") %>%
  arrange(order)

ggplot(cogracists, aes(x=long,y=lat,group=group))+
  geom_polygon(aes(fill=Percentage))+
  geom_path()+ 
  scale_fill_gradientn(colours=rev(heat.colors(10)),na.value="grey90")+
  coord_map() + theme_steve() + xlab("") + ylab("") +
  scale_x_continuous(breaks = NULL) + 
  scale_y_continuous(breaks = NULL) +
  ggtitle("Distribution of Cognitive Racists in the 2016 CCES Data") +
  labs(subtitle="'Cognitive racists' defined as those scoring above a standard deviation in the cognitive racism scale. Values communicate number of cognitive racists over number of state respondents in CCES data.",
       caption = "Questions comprising cognitive racism scale include whether whites have certain advantages because of skin color and whether racial problems are rare isolated situations. 
       Source: http://svmiller.com/blog/2017/04/age-income-racism-partisanship-trump-vote-2016/")

Data %>% 
  filter(lcograc >= .5 & dem == 1) %>% 
  group_by(state) %>% 
  summarize(lcograc=n()) -> cogracistsdem

Data %>%
  filter(dem == 1) %>%
  group_by(state) %>%
  summarize(population = n()) %>%
  left_join(cogracistsdem, .) %>%
  mutate(`Percentage` = round((lcograc/population)*100, 2),
         region = tolower(state)) -> cogracistsdem

cogracistsdem <- left_join(states, cogracistsdem, by="region") %>%
  arrange(order)

ggplot(cogracistsdem, aes(x=long,y=lat,group=group))+
  geom_polygon(aes(fill=Percentage))+
  geom_path()+ 
  scale_fill_gradientn(colours=rev(heat.colors(10)),na.value="grey90")+
  coord_map() + theme_steve() + xlab("") + ylab("") +
  scale_x_continuous(breaks = NULL) + 
  scale_y_continuous(breaks = NULL) +
  ggtitle("Distribution of Cognitive Racists among Democrats in the 2016 CCES Data") +
  labs(subtitle="Values communicate number of cognitive racist Democrats over number of state-level Democrat respondents in CCES data.",
       caption = "Questions comprising cognitive racism scale include whether whites have certain advantages because of skin color and whether racial problems are rare isolated situations. 
       Source: http://svmiller.com/blog/2017/04/age-income-racism-partisanship-trump-vote-2016/")