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ryanburge/primary_general_election.R

Last active January 18, 2019 22:53
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Primary Voters to General Election
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primary_general_election.R
## First Graph ####
aaa <- cces08 %>%
mutate(att = recode(V217, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1; else = NA")) %>%
mutate(vote = recode(CC410, "3:9 = NA")) %>%
filter(vote != "NA") %>%
filter(evangelical ==1) %>%
filter(V211 ==1) %>%
group_by(att) %>%
ct(vote, wt = V201) %>%
mutate(year = 2008)
aaa1 <- cces12 %>%
mutate(att = recode(pew_churatd, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1; else = NA")) %>%
mutate(vote = recode(CC410a, "1=2; 2=1; 3:9 = NA")) %>%
filter(vote != "NA") %>%
filter(evangelical ==1) %>%
filter(race ==1) %>%
group_by(att) %>%
ct(vote, wt = V103) %>%
mutate(year = 2012)
aaa2 <- cces16 %>%
mutate(att = recode(pew_churatd, "1=6; 2=5; 3=4; 4=3; 5=2; 6=1; else = NA")) %>%
mutate(vote = recode(CC16_410a, "3:99 = NA")) %>%
filter(vote != "NA") %>%
filter(evangelical ==1) %>%
filter(race ==1) %>%
group_by(att) %>%
ct(vote, wt = commonweight_vv_post) %>%
mutate(year = 2016)
graph <- bind_df("aaa") %>%
filter(att != "NA") %>%
ungroup(att) %>%
mutate(att = frcode(att == 1 ~ "Never",
att == 2 ~ "Seldom",
att == 3 ~ "Yearly",
att == 4 ~ "Monthly",
att == 5 ~ "Weekly",
att == 6 ~ "Weekly+",
att == 7 ~ "Don`t Know",
TRUE ~ "REMOVE"))
graph %>%
mutate(vote = recode(vote, "1=2; 2=1")) %>%
ggplot(., aes(x=att, y = pct, fill = factor(vote))) +
geom_col(color = "black") +
facet_wrap(~ year) +
theme_gg("Abel") +
scale_y_continuous(labels = percent) +
scale_fill_manual(values = c("dodgerblue3","firebrick3")) +
geom_text(aes(label = paste0(pct*100, '%')), position = position_stack(vjust = 0.5), size = 14, family = "font") +
labs(x = "", y = "Share of Two Party Vote", title = "White Evangelicals Votes Choice", caption = "Data: CCES 2008-2016") +
ggsave("D://cces/images/vote_3waves.png", width = 10)
### Making Vote Choice Bar Graphs ####
graph <- vp %>%
filter(evangelical_baseline ==1) %>%
filter(race_baseline ==1) %>%
filter(pew_churatd_2016 < 7) %>%
group_by(pew_churatd_2016) %>%
filter(pp_repprim16_2016 <= 5) %>%
ct(pp_repprim16_2016, wt = weight_2016) %>%
ungroup(pew_churatd_2016) %>%
mutate(att = frcode(pew_churatd_2016 == 6 ~ "Never",
pew_churatd_2016 == 5 ~ "Seldom",
pew_churatd_2016 == 4 ~ "Yearly",
pew_churatd_2016 == 3 ~ "Monthly",
pew_churatd_2016 == 2 ~ "Weekly",
pew_churatd_2016 == 1 ~ "Weekly+")) %>%
mutate(cand = frcode(pp_repprim16_2016 == 1 ~ " Trump ",
pp_repprim16_2016 == 3 ~ " Cruz ",
pp_repprim16_2016 == 4 ~ " Rubio ",
pp_repprim16_2016 == 2 ~ " Kasich ",
pp_repprim16_2016 == 5 ~ " Someone Else "))
graph %>%
na.omit() %>%
ggplot(., aes(x = att, y = pct, fill = fct_rev(cand))) +
geom_col(color = "black", position = "dodge") +
scale_fill_lancet() +
scale_y_continuous(labels = percent) +
theme_gg("Abel") +
labs(x = "", y = "", title ="White Evangelicals Primary Vote Choice", caption = "Voter Study Group 2016") +
theme(legend.position = "bottom") +
geom_text(aes(y = pct + .025, label = paste0(pct*100, '%')), position = position_dodge(width = .9), size = 10, family = "font") +
ggsave("D://cces/images/vp_cand.png", width = 12)
## This is for White Born Agains ####
graph <- vp %>%
filter(pew_bornagain_2016 ==1) %>%
filter(race_baseline ==1) %>%
filter(pew_churatd_2016 < 7) %>%
group_by(pew_churatd_2016) %>%
filter(pp_repprim16_2016 <= 5) %>%
ct(pp_repprim16_2016, wt = weight_2016) %>%
ungroup(pew_churatd_2016) %>%
mutate(att = frcode(pew_churatd_2016 == 6 ~ "Never",
pew_churatd_2016 == 5 ~ "Seldom",
pew_churatd_2016 == 4 ~ "Yearly",
pew_churatd_2016 == 3 ~ "Monthly",
pew_churatd_2016 == 2 ~ "Weekly",
pew_churatd_2016 == 1 ~ "Weekly+")) %>%
mutate(cand = frcode(pp_repprim16_2016 == 1 ~ " Trump ",
pp_repprim16_2016 == 3 ~ " Cruz ",
pp_repprim16_2016 == 4 ~ " Rubio ",
pp_repprim16_2016 == 2 ~ " Kasich ",
pp_repprim16_2016 == 5 ~ " Someone Else "))
graph %>%
na.omit() %>%
ggplot(., aes(x = att, y = pct, fill = fct_rev(cand))) +
geom_col(color = "black", position = "dodge") +
scale_fill_lancet() +
scale_y_continuous(labels = percent) +
theme_gg("Abel") +
labs(x = "", y = "", title ="White Born Agains Primary Vote Choice", caption = "Voter Study Group 2016") +
theme(legend.position = "bottom") +
geom_text(aes(y = pct + .01, label = paste0(pct*100, '%')), position = position_dodge(width = .9), size = 10, family = "font") +
ggsave("D://cces/images/vp_cand_ba.png", width = 10)
## Catholic Rep Primary ####
graph <- vp %>%
filter(religpew_2016 ==2) %>%
filter(pew_churatd_2016 < 7) %>%
group_by(pew_churatd_2016) %>%
filter(pp_repprim16_2016 <= 5) %>%
ct(pp_repprim16_2016, wt = weight_2016) %>%
ungroup(pew_churatd_2016) %>%
mutate(att = frcode(pew_churatd_2016 == 6 ~ "Never",
pew_churatd_2016 == 5 ~ "Seldom",
pew_churatd_2016 == 4 ~ "Yearly",
pew_churatd_2016 == 3 ~ "Monthly",
pew_churatd_2016 == 2 ~ "Weekly",
pew_churatd_2016 == 1 ~ "Weekly+")) %>%
mutate(cand = frcode(pp_repprim16_2016 == 1 ~ " Trump ",
pp_repprim16_2016 == 3 ~ " Cruz ",
pp_repprim16_2016 == 4 ~ " Rubio ",
pp_repprim16_2016 == 2 ~ " Kasich ",
pp_repprim16_2016 == 5 ~ " Someone Else "))
graph %>%
na.omit() %>%
ggplot(., aes(x = att, y = pct, fill = fct_rev(cand))) +
geom_col(color = "black", position = "dodge") +
scale_fill_lancet() +
scale_y_continuous(labels = percent) +
theme_gg("Abel") +
labs(x = "", y = "", title ="Catholics Republican Primary Vote Choice", caption = "Voter Study Group 2016") +
theme(legend.position = "bottom") +
geom_text(aes(y = pct + .025, label = paste0(pct*100, '%')), position = position_dodge(width = .9), size = 10, family = "font") +
ggsave("D://cces/images/vp_cand_cath.png", width = 12)
## ProLife White Evangelicals Vote Choice ####
graph <- vp %>%
filter(evangelical_baseline ==1) %>%
filter(race_baseline ==1) %>%
filter(abortview3_2016 ==3) %>%
# filter(gaymar_2016 ==2) %>%
filter(pew_churatd_2016 < 7) %>%
group_by(pew_churatd_2016) %>%
filter(pp_repprim16_2016 <= 5) %>%
ct(pp_repprim16_2016, wt = weight_2016) %>%
ungroup(pew_churatd_2016) %>%
mutate(att = frcode(pew_churatd_2016 == 6 ~ "Never",
pew_churatd_2016 == 5 ~ "Seldom",
pew_churatd_2016 == 4 ~ "Yearly",
pew_churatd_2016 == 3 ~ "Monthly",
pew_churatd_2016 == 2 ~ "Weekly",
pew_churatd_2016 == 1 ~ "Weekly+")) %>%
mutate(cand = frcode(pp_repprim16_2016 == 1 ~ " Trump ",
pp_repprim16_2016 == 3 ~ " Cruz ",
pp_repprim16_2016 == 4 ~ " Rubio ",
pp_repprim16_2016 == 2 ~ " Kasich ",
pp_repprim16_2016 == 5 ~ " Someone Else "))
graph %>%
na.omit() %>%
ggplot(., aes(x = att, y = pct, fill = fct_rev(cand))) +
geom_col(color = "black", position = "dodge") +
scale_fill_lancet() +
scale_y_continuous(labels = percent) +
theme_gg("Abel") +
labs(x = "", y = "", title ="White Pro-Life Evangelicals Primary Vote Choice", caption = "Voter Study Group 2016") +
theme(legend.position = "bottom") +
geom_text(aes(y = pct + .025, label = paste0(pct*100, '%')), position = position_dodge(width = .9), size = 10, family = "font") +
ggsave("D://cces/images/vp_cand_prolife_evan.png", width = 12)
## Issue Importance by Vote Choice ####
imp_fun <- function(df, var, var1){
var <- enquo(var)
df %>%
mutate(issue = recode(!! var, "1=4;2=3;3=2;4=1; else = NA")) %>%
filter(pp_repprim16_2016 <= 4) %>%
filter(evangelical_baseline ==1) %>%
filter(race_baseline ==1) %>%
group_by(pp_repprim16_2016) %>%
mean_ci(issue) %>%
mutate(issue = var1) %>%
ungroup(pp_repprim16_2016) %>%
mutate(cand = frcode(pp_repprim16_2016 == 1 ~ " Trump ",
pp_repprim16_2016 == 3 ~ " Cruz ",
pp_repprim16_2016 == 4 ~ " Rubio ",
pp_repprim16_2016 == 2 ~ " Kasich "))
}
ddd <- vp %>% imp_fun(imiss_a_2016, "Crime")
ddd1 <- vp %>% imp_fun(imiss_b_2016, "Economy")
ddd2 <- vp %>% imp_fun(imiss_c_2016, "Immigration")
ddd3 <- vp %>% imp_fun(imiss_d_2016, "Environment")
ddd5 <- vp %>% imp_fun(imiss_e_2016, "Religious liberty")
ddd6 <- vp %>% imp_fun(imiss_f_2016, "Terrorism")
ddd7 <- vp %>% imp_fun(imiss_g_2016, "Gay rights")
ddd8 <- vp %>% imp_fun(imiss_h_2016, "Education")
ddd9 <- vp %>% imp_fun(imiss_i_2016, "Family Leave")
ddd10 <- vp %>% imp_fun(imiss_j_2016, "Health Care")
ddd11 <- vp %>% imp_fun(imiss_k_2016, "Money in Politics")
ddd12 <- vp %>% imp_fun(imiss_l_2016, "Climate Change")
ddd13 <- vp %>% imp_fun(imiss_m_2016, "Social Security")
ddd14 <- vp %>% imp_fun(imiss_n_2016, "Infrastructure")
ddd15 <- vp %>% imp_fun(imiss_o_2016, "Jobs")
ddd16 <- vp %>% imp_fun(imiss_p_2016, "Budget Deficit")
ddd17 <- vp %>% imp_fun(imiss_q_2016, "Poverty")
ddd18 <- vp %>% imp_fun(imiss_r_2016, "Taxes")
ddd19 <- vp %>% imp_fun(imiss_s_2016, "Medicare")
ddd20 <- vp %>% imp_fun(imiss_t_2016, "Abortion")
ddd21 <- vp %>% imp_fun(imiss_u_2016, "Size of Govt.")
ddd22 <- vp %>% imp_fun(imiss_x_2016, "Racial Equality")
ddd23 <- vp %>% imp_fun(imiss_y_2016, "Gender Equality")
graph <- bind_df("ddd")
graph %>%
filter(cand != " Rubio ") %>%
ggplot(., aes(y=mean, x= fct_reorder(issue, mean), color = cand)) +
geom_point(position=position_dodge(width=0.5), size =4) +
geom_errorbar(aes(ymin = lower, ymax=upper), position=position_dodge(0.5), size = 1) +
coord_flip() +
theme_gg("Abel") +
labs(title = "Issue Importance Among White Evangelicals", x = "Issue Area", y = "Level of Importance", caption = "Data: Voter Study Group 2016") +
scale_y_continuous(limits = c(0.85,5.05), breaks = c(1,2,3,4,5), labels = c("No Importance", "Very Low", "Somewhat Low", "Somewhat High", "Very High")) +
scale_color_npg() +
theme(legend.position = "bottom") +
theme(legend.title=element_blank()) +
theme(text=element_text(size=64, family="font")) +
ggsave("D://cces/images/voter_imp_vp.png", height = 10, width =12)
### Issue Importance by Attendance ####
imp_fun <- function(df, var, var1){
var <- enquo(var)
df %>%
mutate(issue = recode(!! var, "1=4;2=3;3=2;4=1; else = NA")) %>%
filter(pew_churatd_2016 < 7) %>%
filter(evangelical_baseline ==1) %>%
filter(race_baseline ==1) %>%
group_by(pew_churatd_2016) %>%
mean_ci(issue) %>%
mutate(issue = var1) %>%
ungroup(pew_churatd_2016) %>%
mutate(att = frcode(pew_churatd_2016 == 6 ~ "Never",
pew_churatd_2016 == 5 ~ "Seldom",
pew_churatd_2016 == 4 ~ "Yearly",
pew_churatd_2016 == 3 ~ "Monthly",
pew_churatd_2016 == 2 ~ "Weekly",
pew_churatd_2016 == 1 ~ "Weekly+"))
}
ddd <- vp %>% imp_fun(imiss_a_2016, "Crime")
ddd1 <- vp %>% imp_fun(imiss_b_2016, "Economy")
ddd2 <- vp %>% imp_fun(imiss_c_2016, "Immigration")
ddd3 <- vp %>% imp_fun(imiss_d_2016, "Environment")
ddd5 <- vp %>% imp_fun(imiss_e_2016, "Religious liberty")
ddd6 <- vp %>% imp_fun(imiss_f_2016, "Terrorism")
ddd7 <- vp %>% imp_fun(imiss_g_2016, "Gay rights")
ddd8 <- vp %>% imp_fun(imiss_h_2016, "Education")
ddd9 <- vp %>% imp_fun(imiss_i_2016, "Family Leave")
ddd10 <- vp %>% imp_fun(imiss_j_2016, "Health Care")
ddd11 <- vp %>% imp_fun(imiss_k_2016, "Money in Politics")
ddd12 <- vp %>% imp_fun(imiss_l_2016, "Climate Change")
ddd13 <- vp %>% imp_fun(imiss_m_2016, "Social Security")
ddd14 <- vp %>% imp_fun(imiss_n_2016, "Infrastructure")
ddd15 <- vp %>% imp_fun(imiss_o_2016, "Jobs")
ddd16 <- vp %>% imp_fun(imiss_p_2016, "Budget Deficit")
ddd17 <- vp %>% imp_fun(imiss_q_2016, "Poverty")
ddd18 <- vp %>% imp_fun(imiss_r_2016, "Taxes")
ddd19 <- vp %>% imp_fun(imiss_s_2016, "Medicare")
ddd20 <- vp %>% imp_fun(imiss_t_2016, "Abortion")
ddd21 <- vp %>% imp_fun(imiss_u_2016, "Size of Govt.")
ddd22 <- vp %>% imp_fun(imiss_x_2016, "Racial Equality")
ddd23 <- vp %>% imp_fun(imiss_y_2016, "Gender Equality")
graph <- bind_df("ddd")
graph %>%
# filter(cand != " Rubio ") %>%
ggplot(., aes(y=mean, x= fct_reorder(issue, mean), color = att)) +
geom_point(position=position_dodge(width=0.5), size =4) +
geom_errorbar(aes(ymin = lower, ymax=upper), position=position_dodge(0.5), size = 1) +
coord_flip() +
theme_gg("Abel") +
labs(title = "Issue Importance Among White Evangelicals", x = "Issue Area", y = "Level of Importance", caption = "Data: Voter Study Group 2016") +
scale_y_continuous(limits = c(0.85,5.05), breaks = c(1,2,3,4,5), labels = c("No Importance", "Very Low", "Somewhat Low", "Somewhat High", "Very High")) +
scale_color_npg() +
theme(legend.position = "bottom") +
theme(legend.title=element_blank()) +
theme(text=element_text(size=64, family="font")) +
ggsave("D://cces/images/voter_imp_vp_att.png", height = 10, width =12)
eee <- vp %>%
filter(evangelical_baseline ==1) %>%
filter(race_baseline ==1) %>%
filter(pp_repprim16_2016 ==3) %>%
filter(presvote16post_2016 <= 3) %>%
ct(presvote16post_2016, wt = weight_2017) %>%
mutate(vote = frcode(presvote16post_2016 == 1 ~ "Clinton",
presvote16post_2016 == 2 ~ "Trump",
presvote16post_2016 == 3 ~ "Johnson",
presvote16post_2016 == 4 ~ "Stein",
presvote16post_2016 == 5 ~ "McMullin",
presvote16post_2016 == 6 ~ "Other",
presvote16post_2016 == 7 ~ "No Vote")) %>%
select(vote, pct) %>%
mutate(prim = "Cruz")
## Mapping Primary Choice onto General Election Choice ####
eee1 <- vp %>%
filter(evangelical_baseline ==1) %>%
filter(race_baseline ==1) %>%
filter(pp_repprim16_2016 ==1) %>%
filter(presvote16post_2016 <= 3) %>%
ct(presvote16post_2016, wt = weight_2017) %>%
mutate(vote = frcode(presvote16post_2016 == 1 ~ "Clinton",
presvote16post_2016 == 2 ~ "Trump",
presvote16post_2016 == 3 ~ "Johnson",
presvote16post_2016 == 4 ~ "Stein",
presvote16post_2016 == 5 ~ "McMullin",
presvote16post_2016 == 6 ~ "Other",
presvote16post_2016 == 7 ~ "No Vote")) %>%
select(vote, pct) %>%
mutate(prim = "Trump")
eee2 <- vp %>%
filter(evangelical_baseline ==1) %>%
filter(race_baseline ==1) %>%
filter(pp_repprim16_2016 ==2) %>%
filter(presvote16post_2016 <= 3) %>%
ct(presvote16post_2016, wt = weight_2017) %>%
mutate(vote = frcode(presvote16post_2016 == 1 ~ "Clinton",
presvote16post_2016 == 2 ~ "Trump",
presvote16post_2016 == 3 ~ "Johnson",
presvote16post_2016 == 4 ~ "Stein",
presvote16post_2016 == 5 ~ "McMullin",
presvote16post_2016 == 6 ~ "Other",
presvote16post_2016 == 7 ~ "No Vote")) %>%
select(vote, pct) %>%
mutate(prim = "Kasich")
eee3 <- vp %>%
filter(evangelical_baseline ==1) %>%
filter(race_baseline ==1) %>%
filter(pp_repprim16_2016 ==4) %>%
filter(presvote16post_2016 <= 3) %>%
ct(presvote16post_2016, wt = weight_2017) %>%
mutate(vote = frcode(presvote16post_2016 == 1 ~ "Clinton",
presvote16post_2016 == 2 ~ "Trump",
presvote16post_2016 == 3 ~ "Johnson",
presvote16post_2016 == 4 ~ "Stein",
presvote16post_2016 == 5 ~ "McMullin",
presvote16post_2016 == 6 ~ "Other",
presvote16post_2016 == 7 ~ "No Vote")) %>%
select(vote, pct) %>%
mutate(prim = "Rubio")
graph <- bind_df("eee")
graph %>%
na.omit() %>%
ggplot(., aes(x = vote, y = pct, fill = vote)) +
geom_col(color = "black", position = "dodge") +
scale_fill_manual(values = c("dodgerblue3", "firebrick3", "goldenrod")) +
facet_wrap(~ prim, ncol =2) +
scale_y_continuous(labels = percent) +
theme_gg("Abel") +
labs(x = "", y = "", title ="Republican Primary Voters Choice in the General Election", subtitle = "Among White Evangelicals", caption = "Voter Study Group 2016") +
geom_text(aes(y = pct + .055, label = paste0(pct*100, '%')), position = position_dodge(width = .9), size = 10, family = "font") +
ggsave("D://cces/images/vp_primary_general.png", width = 6)
### This is the last graph - Primary Coalitions onto General ####
fff <- vp %>%
filter(evangelical_baseline ==1) %>%
filter(race_baseline ==1) %>%
filter(presvote16post_2016 != "NA") %>%
filter(pp_repprim16_2016 != "NA") %>%
group_by(pp_repprim16_2016) %>%
ct(presvote16post_2016) %>%
filter(presvote16post_2016 ==2) %>%
select(-pct, -presvote16post_2016) %>%
mutate(pct = n/720) %>%
mutate(cand = frcode(pp_repprim16_2016 == 1 ~ " Trump ",
pp_repprim16_2016 == 3 ~ " Cruz ",
pp_repprim16_2016 == 4 ~ " Rubio ",
pp_repprim16_2016 == 2 ~ " Kasich ",
pp_repprim16_2016 == 5 ~ " Someone Else ",
pp_repprim16_2016 == 6 ~ " Don`t Recall")) %>%
ungroup(pp_repprim16_2016) %>%
select(cand, pct)
fff2 <- vp %>%
filter(evangelical_baseline ==1) %>%
filter(race_baseline ==1) %>%
filter(presvote16post_2016 != "NA") %>%
filter(pp_demprim16_2016 != "NA") %>%
group_by(pp_demprim16_2016) %>%
ct(presvote16post_2016) %>%
filter(presvote16post_2016 ==2) %>%
select(-pct, -presvote16post_2016) %>%
mutate(pct = n/720) %>%
mutate(cand = frcode(pp_demprim16_2016 == 1 ~ " Clinton ",
pp_demprim16_2016 == 2 ~ " Sanders ",
pp_demprim16_2016 == 3 ~ " Someone Else ",
pp_demprim16_2016 == 4 ~ " Don't Recall ")) %>%
ungroup(pp_demprim16_2016) %>%
select(cand, pct)
graph <- bind_df("fff")
trump <- graph %>%
arrange(-pct) %>%
head(5) %>%
add_row(cand = " All Others ", pct = .075) %>%
mutate(group = "Trump")
fff <- vp %>%
filter(evangelical_baseline ==1) %>%
filter(race_baseline ==1) %>%
filter(presvote16post_2016 != "NA") %>%
filter(pp_repprim16_2016 != "NA") %>%
group_by(pp_repprim16_2016) %>%
ct(presvote16post_2016) %>%
filter(presvote16post_2016 ==1) %>%
select(-pct, -presvote16post_2016) %>%
mutate(pct = n/183) %>%
mutate(cand = frcode(pp_repprim16_2016 == 1 ~ " Trump ",
pp_repprim16_2016 == 3 ~ " Cruz ",
pp_repprim16_2016 == 4 ~ " Rubio ",
pp_repprim16_2016 == 2 ~ " Kasich ",
pp_repprim16_2016 == 5 ~ " Someone Else ",
pp_repprim16_2016 == 6 ~ " Don`t Recall")) %>%
ungroup(pp_repprim16_2016) %>%
select(cand, pct)
fff2 <- vp %>%
filter(evangelical_baseline ==1) %>%
filter(race_baseline ==1) %>%
filter(presvote16post_2016 != "NA") %>%
filter(pp_demprim16_2016 != "NA") %>%
group_by(pp_demprim16_2016) %>%
ct(presvote16post_2016) %>%
filter(presvote16post_2016 ==1) %>%
select(-pct, -presvote16post_2016) %>%
mutate(pct = n/183) %>%
mutate(cand = frcode(pp_demprim16_2016 == 1 ~ " Clinton ",
pp_demprim16_2016 == 2 ~ " Sanders ",
pp_demprim16_2016 == 3 ~ " Someone Else ",
pp_demprim16_2016 == 4 ~ " Don't Recall ")) %>%
ungroup(pp_demprim16_2016) %>%
select(cand, pct)
graph <- bind_df("fff")
clinton <- graph %>%
arrange(-pct) %>%
head(5) %>%
add_row(cand = " All Others ", pct = .055) %>%
mutate(group = "Clinton")
graph <- bind_rows(trump, clinton)
graph$cand <- factor(graph$cand, levels = c(" Trump ", " Cruz ", " Rubio ", " Kasich ", " Clinton ", " Sanders ", " All Others "))
graph %>%
mutate(pct = round(pct, 3)) %>%
ggplot(., aes(x = 1, y = pct, fill = fct_rev(cand))) +
geom_col(color = "black") +
coord_flip() +
facet_wrap(~ group, ncol =1) +
scale_fill_manual(values = c(' Trump ' = "#990000", ' Cruz ' = "#ff3232", ' Rubio ' = "#ff6666", ' Kasich ' = "#ff9999", ' Sanders ' = "#6666ff", ' Clinton ' = "#1919ff", ' All Others ' = "azure3")) +
theme_gg("Abel") +
theme(legend.position = "bottom") +
scale_y_continuous(labels = percent) +
theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
geom_text(aes(label = paste0(pct*100, '%')), position = position_stack(vjust = 0.5), size = 14, family = "font") +
labs(x = "", y = "", title = "Primary Vote Choice Translated to the General Election", subtitle = "Among White Evangelicals", caption = "Data: Voter Study Group") +
ggsave("D://cces/images/vp_horiz_bars.png", width = 12)
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