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Clinton vs Obama vs Evanrate Mapping
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")
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