Clinton vs Obama vs Evanrate Mapping

clinton_evan_map.R

library(socsci)
library(urbnmapr)
library(urbnthemes)
library(viridis)
library(fst)
library(showtext)

### Data from here: https://www.kaggle.com/joelwilson/2012-2016-presidential-elections/

votes <- read_csv("D://votes1216.csv")

## Making Clinton vs Obama Share Map ####

vote <- votes %>% 
  select(fips, dem_diff = Clinton_Obama) %>% 
  arrange(-dem_diff)

territories_counties <- get_urbn_map(map = "territories_counties")

territories_counties <-  territories_counties %>% 
  mutate(fips =  as.numeric(county_fips)) %>% 
  select(-county_fips)

map <- left_join(territories_counties, vote)

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

map %>% 
  ggplot(aes(long, lat, group = group, fill = dem_diff)) +
  geom_polygon(color = "black", size = 0.05) +
  coord_map(projection = "albers", lat0 = 39, lat1 = 45) +
  scale_fill_gradient(low = "firebrick1", high = "dodgerblue1", labels = scales::percent) +
  # scale_fill_viridis(labels = scales::percent) +
  theme(legend.title = element_text(), legend.key.width = unit(.5, "in")) +
  labs(title  = "Clinton's Performance vs Obama in 2012", caption = "", fill = "") +
  urbnthemes::theme_urbn_map() +
  theme(plot.title = element_text(size=75, family= "font", face= "bold")) +
  theme(plot.caption = element_text(size = 36, family="font")) +
  theme(text=element_text(size=45, family="font")) +
  theme(legend.position = c(0.8, 0.2)) +
  ggsave("D://clinton_v_obama.png")

## Making Evangelical Map ####

set_urbn_defaults(style = "map")

census <- read.fst("D://census.fst")


census  <- census %>% 
  mutate(county_fips =  as.numeric(fips)) %>% 
  select(county_fips, evanrate)

territories_counties <- get_urbn_map(map = "territories_counties")

territories_counties <-  territories_counties %>% 
  mutate(county_fips =  as.numeric(county_fips))

joined <- left_join(territories_counties, census)

rm(territories_counties)

joined %>% 
  mutate(evanrate = recode(evanrate, "1000:2000 = 1000")) %>% 
  ggplot(aes(long, lat, group = group, fill = evanrate/1000)) +
  geom_polygon(color = "black", size = 0.05) +
  coord_map(projection = "albers", lat0 = 39, lat1 = 45) +
  labs(title  = "Percent Evangelical Per County", caption = "", fill = "") +
  scale_fill_viridis(labels = percent) +
  theme(legend.title = element_text(), legend.key.width = unit(.5, "in")) +
  urbnthemes::theme_urbn_map() +
  theme(plot.title = element_text(size=75, family= "font", face= "bold")) +
  theme(plot.caption = element_text(size = 36, family="font")) +
  theme(text=element_text(size=45, family="font")) +
  theme(legend.position = c(0.8, 0.2)) +
  ggsave("D://evan_map.png")

votes <- read_csv("D://votes1216.csv")

vote1 <- votes %>% 
  select(share = Obama, county_fips = fips) %>% 
  mutate(cand = "Obama")

vote2 <- votes %>% 
  select(share = Clinton, county_fips = fips) %>% 
  mutate(cand = "Clinton")

votes <- bind_rows(vote1, vote2)

votej <- left_join(census, votes) %>% as.tibble()


votej %>% 
  mutate(evanrate = recode(evanrate, "1000:2000 = 1000")) %>%
  na.omit() %>% 
  ggplot(., aes(x= evanrate, y = share, color = cand)) +
  geom_point(alpha = .2) +
  geom_smooth(method = lm) +
  theme_gg("Abel") +
  scale_y_continuous(labels = percent) +
  theme(legend.position = c(0.8, 0.7)) +
  theme(plot.title = element_text(size = 32)) +
  guides(color=guide_legend(override.aes=list(fill=NA))) +
  labs(x = "Rate of Evangelicals per 1000", y = "Vote Share", title = "The Relationship between Evangelicals and Vote Choice in 2012 and 2016") +
  ggsave("D://clinton_obama_scatter.png")

write_csv(votej, "D://djupe_data.csv")

## Attendance ScatterPlot ####



vote1 <- votes %>% 
  group_by(state_fips) %>% 
  mean_ci(Obama) %>% 
  select(inputstate = state_fips, share = mean) %>% 
  mutate(cand = "Obama")
  

cces16 <- read.fst("C://cces16.fst")

att16 <- cces16 %>% 
  mutate(att = recode(pew_churatd, "1:2=1; else =0")) %>% 
  group_by(inputstate) %>% 
  mean_ci(att) %>% 
  select(inputstate, att = mean)

v16 <- left_join(vote1, att16)

cces12 <- read.fst("C://cces12.fst")

att12 <- cces12 %>% 
  mutate(att = recode(pew_churatd, "1:2=1; else =0")) %>% 
  group_by(inputstate) %>% 
  mean_ci(att) %>% 
  select(inputstate, att = mean)

vote2 <- votes %>% 
  group_by(state_fips) %>% 
  mean_ci(Clinton) %>% 
  select(inputstate = state_fips, share = mean) %>% 
  mutate(cand = "Clinton")

v12 <- left_join(vote2, att12)

graph <- bind_rows(v12, v16) %>% 
  rename(state = inputstate)

library(haven)
library(labelled)

cc <- read_dta("D://cces/data/cces16.dta")

tt <- cc %>% 
  mutate(stname = to_factor(inputstate)) %>% 
  mutate(state = as.numeric(inputstate)) %>% 
  select(state, stname) %>% 
  distinct(state, stname)

graph <- merge(graph, tt) %>% as.tibble()


graph %>% 
  na.omit() %>% 
  ggplot(., aes(x= att, y = share, color = cand)) +
  geom_point(alpha = .2, show.legend = FALSE) +
  geom_smooth(method = lm, alpha = .2) +
  theme_gg("Abel") +
  geom_text_repel(data = graph, aes(att,share,label=stname), size = 6, show.legend = FALSE) +
  scale_y_continuous(labels = percent) +
  scale_x_continuous(labels = percent) +
  theme(legend.position = c(0.8, 0.7)) +
  theme(plot.title = element_text(size = 46)) +
  guides(color=guide_legend(override.aes=list(fill=NA))) +
  labs(x = "Percent Who Attend Weekly", y = "Vote Share", title = "The Relationship between Church Attendance and Vote Choice in 2012 and 2016") +
  ggsave("D://clinton_obama_scatter_weekly.png")

## Field Offices ####

darr <- read_csv("D://darr.csv") %>% 
  rename(stname = state)

obama <- darr %>% 
  select(stname, num =obama) %>% 
  mutate(cand = "Obama")

clinton <- darr %>% 
  select(stname, num = clinton) %>% 
  mutate(cand = "Clinton")

gg1 <- graph %>% 
  filter(cand == "Clinton") %>% 
  select(cand, stname, att) %>% 
  left_join(clinton)

gg2 <- graph %>% 
  filter(cand == "Obama") %>% 
  select(cand, stname, att) %>% 
  left_join(obama)


graph <- bind_rows(gg1, gg2)


graph %>% 
  na.omit() %>% 
  filter(num > 0)  %>% 
  ggplot(., aes(x= att, y = num, color = cand)) +
  geom_point(alpha = .2, show.legend = FALSE) +
  geom_smooth(alpha = .2) +
  theme_gg("Abel") +
  geom_text_repel(data = graph, aes(att,num,label=stname), size = 6, show.legend = FALSE) +
  # scale_y_continuous(labels = percent) +
  scale_x_continuous(labels = percent) +
  theme(legend.position = c(0.8, 0.7)) +
  theme(plot.title = element_text(size = 46)) +
  guides(color=guide_legend(override.aes=list(fill=NA))) +
  labs(x = "Percent Who Attend Weekly", y = "Number of Field Offices", title = "The Relationship between Church Attendance and Field Offices in 2012 and 2016") +
  ggsave("D://clinton_obama_scatter_offices.png")