ct_selection_untitled-78D9283D.R

aaa1 <- cces %>% 
  filter(race == 2) %>% 
  filter(religion == 1) %>% 
  filter(pew_attendance == 1 | pew_attendance == 2) %>% 
  filter(pid7 <= 7) %>% 
  group_by(year) %>% 
  mean_ci(pid7, wt = weight, ci = .84) %>% 
  select(year, pid = mean)

aaa2 <- cces %>% 
  filter(race == 2) %>% 
  filter(religion == 1) %>% 
  filter(pew_attendance == 1 | pew_attendance == 2) %>% 
  filter(ideo5 <= 5) %>% 
  group_by(year) %>% 
  mean_ci(ideo5, wt = weight, ci = .84) %>% 
  select(year, id = mean)

graph <- left_join(aaa1, aaa2) %>% mutate(year = as.factor(year)) %>%   filter(year == 2008 | year == 2012 | year == 2016 | year == 2020 | year == 2022)


library(ggrepel)

graph %>% 
  ggplot(., aes(x = pid, y = id, color = year, group = year)) + 
  geom_point() +
  geom_text_repel(data = graph, aes(x = pid, y = id, label = year), family = "font", size = 8) +
  scale_x_continuous(breaks = c(1,2,3,4,5,6,7), labels = c("Strong\nDem.", "Not Strong\nDemocrat", "Lean\nDemocrat", "Independent", "Lean\nRepublican", "Not Strong\nRepublican", "Strong\nRep."), limits = c(1, 7)) +
  scale_y_continuous(breaks = c(1,2,3,4,5), labels = c("Very Liberal", "Liberal", "Moderate", "Conservative", "Very Conservative"), limits = c(1, 5)) +
  scale_color_manual(values = c("#FF7F0E", "#1F77B4", "#2CA02C", "#D62728", "#9467BD")) +
  theme_rb() +
  labs(x = "Mean Political Partisanship", y = "Mean Political Ideology", title = "The Partisanship and Ideology of Weekly Attending Black Protestants", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022")
save("bprot_id_pid_scatter.png")


gg <- cces %>% 
  filter(year == 2008 | year == 2012 | year == 2016 | year == 2020 | year == 2022) %>% 
  filter(race == 2) %>% 
  filter(religion == 1) %>% 
  filter(pew_attendance == 1 | pew_attendance == 2) %>% 
  cces_pid7(pid7) %>% 
  group_by(year) %>% 
  ct(pid7, wt = weight, show_na = FALSE) 


gg %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x = 1, y = pct, fill = fct_rev(pid7))) +
  geom_col(color = "black") + 
  coord_flip() +
  facet_wrap(~ year, ncol =1, strip.position = "left") +
  theme_rb() +
  pid7_fill() +
  theme(legend.position = "bottom") +
  scale_y_continuous(labels = percent) +
  theme(strip.text.y.left = element_text(angle=0)) +
  guides(fill = guide_legend(reverse=T, nrow = 1)) +
  theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
  theme(panel.grid.minor.y=element_blank(), panel.grid.major.y=element_blank()) +
  geom_text(aes(label = ifelse(pct >.05, paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 7, family = "font", color = "black") +
  geom_text(aes(label = ifelse(pid7 == "Strong Dem.", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 7, family = "font", color = "white") +
  geom_text(aes(label = ifelse(pct >.05 & pid7 == "Strong Rep.", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 7, family = "font", color = "white") +
  theme(plot.title = element_text(size = 16)) +
  theme(strip.text.y.left = element_text(angle = 0, hjust = 1)) +
  labs(x = "", y = "", title = "The Political Partisanship of Black Protestants Who Attend Church Weekly", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022") 
save("bprot_pid7.png", wd = 9, ht = 4)




ggg1 <- cces08 %>% 
  filter(race == 2) %>%
  filter(religpew == 1) %>% 
  filter(pew_churatd == 1 | pew_churatd == 2) %>% 
  mutate(vote = CC410) %>% 
  mutate(vote = frcode(vote == 1 ~ "Republican",
                       vote == 2 ~ "Democrat",
                       vote == 3 | vote == 4 | vote == 5 | vote == 6 ~ "Third Party")) %>% 
  ct(vote, wt = V201, show_na = FALSE) %>% 
  mutate(year = 2008)

ggg2 <- cces12 %>% 
  filter(race == 2) %>%
  filter(religpew == 1) %>% 
  filter(pew_churatd == 1 | pew_churatd == 2) %>% 
  mutate(vote = CC410a) %>% 
  mutate(vote = frcode(vote == 1 ~ "Democrat",
                       vote == 2 ~ "Republican",
                       vote == 4 ~ "Third Party")) %>% 
  ct(vote, wt = weight_vv_post, show_na = FALSE) %>% 
  mutate(year = 2012)


ggg3 <- cces16 %>% 
  filter(race == 2) %>%
  filter(religpew == 1) %>% 
  filter(pew_churatd == 1 | pew_churatd == 2) %>% 
  mutate(vote = CC16_410a) %>% 
  mutate(vote = frcode(vote == 1 ~ "Republican",
                       vote == 2 ~ "Democrat",
                       vote == 3 | vote == 4 | vote == 5 | vote == 8 ~ "Third Party")) %>% 
  ct(vote, wt = commonweight_vv_post, show_na = FALSE) %>% 
  mutate(year = 2016)


ggg4 <- cces20 %>% 
  filter(race == 2) %>%
  filter(religpew == 1) %>% 
  filter(pew_churatd == 1 | pew_churatd == 2) %>% 
  mutate(vote = CC20_410) %>% 
  mutate(vote = frcode(vote == 1 ~ "Democrat", 
                       vote == 2 ~ "Republican", 
                       vote == 4 ~ "Third Party")) %>% 
  ct(vote, wt = commonpostweight, show_na = FALSE) %>% 
  mutate(year = 2020)

gg <- bind_rows(ggg1, ggg2, ggg3, ggg4)


gg %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x = 1, y = pct, fill = fct_rev(vote))) +
  geom_col(color = "black") + 
  coord_flip() +
  facet_wrap(~ year, ncol =1, strip.position = "left") +
  scale_fill_manual(values = c("darkorchid", "dodgerblue3", "firebrick3")) +
  theme_rb() + 
  theme(legend.position = "bottom") +
  scale_y_continuous(labels = percent) +
  theme(strip.text.y.left = element_text(angle = 0)) +
  guides(fill = guide_legend(reverse=T, nrow = 1)) +
  theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
  theme(panel.grid.minor.y=element_blank(), panel.grid.major.y=element_blank()) +
  geom_text(aes(label = ifelse(pct >.08, paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 8.5, family = "font", color = "black") +
  labs(x = "", y = "", title = "Vote Choice Among Weekly Attending Black Protestants", subtitle = "", caption = "@ryanburge\nData: Cooperative Election Study 2008-2020") 
save("bprot_wk_attend_vote.png", wd = 9, ht = 4)


cces <- read.fst("E://data/cces_abort21.fst")

ab22 <- cces22 %>% 
  cces_trad(religpew) %>% 
  mutate(age = 2022 - birthyr) %>% 
  select(trad2, pid3, pid7, race, age, income = faminc_new, educ, gender = gender4, weight = commonweight, pew_churatd, pew_bornagain, ab_choice = CC22_332a, ab_rape = CC22_332b, ab_late = CC22_332c, ab_ins = CC22_332d, ab_funds = CC22_332e, ab_never = CC22_332f) %>% 
  mutate(year = 2022)

cces <- bind_rows(cces, ab22)


yyy1 <- cces %>% 
  mutate(ab = case_when(ab_choice == 1 ~ 1, 
                        ab_choice == 2 ~ 0)) %>% 
  filter(trad2 == "Black Protestant") %>% 
  filter(pew_churatd == 1 | pew_churatd == 2) %>% 
  group_by(year) %>% 
  mean_ci(ab, wt = weight, ci = .84) %>% 
  mutate(grp = "Weekly Attending Black Protestants")


yyy2 <- cces %>% 
  mutate(ab = case_when(ab_choice == 1 ~ 1, 
                        ab_choice == 2 ~ 0)) %>% 
  filter(pid7 == 1 | pid7 == 2 | pid7 == 3) %>% 
  group_by(year) %>% 
  mean_ci(ab, wt = weight, ci = .84) %>% 
  mutate(grp = "All Democrats")

graph1 <- bind_rows(yyy1, yyy2) %>% 
  mutate(sit = "Allow For Any Reason")


yyy1 <- cces %>% 
  mutate(ab = case_when(ab_funds == 1 ~ 1, 
                        ab_funds == 2 ~ 0)) %>% 
  filter(trad2 == "Black Protestant") %>% 
  filter(pew_churatd == 1 | pew_churatd == 2) %>% 
  group_by(year) %>% 
  mean_ci(ab, wt = weight, ci = .84) %>% 
  mutate(grp = "Weekly Attending Black Protestants")


yyy2 <- cces %>% 
  mutate(ab = case_when(ab_funds == 1 ~ 1, 
                        ab_funds == 2 ~ 0)) %>% 
  filter(pid7 == 1 | pid7 == 2 | pid7 == 3) %>% 
  group_by(year) %>% 
  mean_ci(ab, wt = weight, ci = .84) %>% 
  mutate(grp = "All Democrats")

graph2 <- bind_rows(yyy1, yyy2) %>% 
  mutate(sit = "Prohibit Federal Funds")


yyy1 <- cces %>% 
  mutate(ab = case_when(ab_late == 1 ~ 1, 
                        ab_late == 2 ~ 0)) %>% 
  filter(trad2 == "Black Protestant") %>% 
  filter(pew_churatd == 1 | pew_churatd == 2) %>% 
  group_by(year) %>% 
  mean_ci(ab, wt = weight, ci = .84) %>% 
  mutate(grp = "Weekly Attending Black Protestants")


yyy2 <- cces %>% 
  mutate(ab = case_when(ab_late == 1 ~ 1, 
                        ab_late == 2 ~ 0)) %>% 
  filter(pid7 == 1 | pid7 == 2 | pid7 == 3) %>% 
  group_by(year) %>% 
  mean_ci(ab, wt = weight, ci = .84) %>% 
  mutate(grp = "All Democrats")

graph3 <- bind_rows(yyy1, yyy2) %>% 
  mutate(sit = "Prohibit After 20 Weeks")


yyy1 <- cces %>% 
  mutate(ab = case_when(ab_never == 1 ~ 1, 
                        ab_never == 2 ~ 0)) %>% 
  filter(trad2 == "Black Protestant") %>% 
  filter(pew_churatd == 1 | pew_churatd == 2) %>% 
  group_by(year) %>% 
  mean_ci(ab, wt = weight, ci = .84) %>% 
  mutate(grp = "Weekly Attending Black Protestants")


yyy2 <- cces %>% 
  mutate(ab = case_when(ab_never == 1 ~ 1, 
                        ab_never == 2 ~ 0)) %>% 
  filter(pid7 == 1 | pid7 == 2 | pid7 == 3) %>% 
  group_by(year) %>% 
  mean_ci(ab, wt = weight, ci = .84) %>% 
  mutate(grp = "All Democrats")

graph4 <- bind_rows(yyy1, yyy2) %>% 
  mutate(sit = "Never Permit Abortion")

all <- bind_rows(graph1, graph2, graph3, graph4)

all %>% 
  ggplot(., aes(x = year, y = mean, color = grp, group = grp)) +
  geom_point(stroke = .5, shape = 21, alpha = .45) + 
  geom_smooth(se = FALSE) +
  facet_wrap(~ sit) +
  theme_rb(legend = TRUE) +
  scale_color_calc() +
  scale_y_continuous(labels = percent) +
  labs(x = "", y = "", title = "Views of Abortion Among Black Protestants and All Democrats", caption = "@ryanburge\nData: Cooperative Election Study, 2014-2022")
save("ab_dems_bprot.png", ht = 7, wd = 6.5)


aaa1 <- cces22 %>% 
  filter(race == 2) %>% 
  filter(religpew == 1) %>% 
  filter(pew_churatd == 1 | pew_churatd == 2) %>% 
  mutate(imm = case_when(CC22_331a == 1 ~ 1, 
                         CC22_331a == 2 ~ 0)) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  mutate(grp = "Weekly Attending Black Protestants")

aaa2 <- cces22 %>% 
  filter(pid7 == 1 | pid7 == 2 | pid7 == 3) %>% 
  mutate(imm = case_when(CC22_331a == 1 ~ 1, 
                         CC22_331a == 2 ~ 0)) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  mutate(grp = "All Democrats")

tt1 <- bind_rows(aaa1, aaa2) %>% mutate(type = "Pathway to Legal Status")


aaa1 <- cces22 %>% 
  filter(race == 2) %>% 
  filter(religpew == 1) %>% 
  filter(pew_churatd == 1 | pew_churatd == 2) %>% 
  mutate(imm = case_when(CC22_331b == 1 ~ 1, 
                         CC22_331b == 2 ~ 0)) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  mutate(grp = "Weekly Attending Black Protestants")

aaa2 <- cces22 %>% 
  filter(pid7 == 1 | pid7 == 2 | pid7 == 3) %>% 
  mutate(imm = case_when(CC22_331b == 1 ~ 1, 
                         CC22_331b == 2 ~ 0)) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  mutate(grp = "All Democrats")

tt2 <- bind_rows(aaa1, aaa2) %>% mutate(type = "Increase Border Patrol Agents")

aaa1 <- cces22 %>% 
  filter(race == 2) %>% 
  filter(religpew == 1) %>% 
  filter(pew_churatd == 1 | pew_churatd == 2) %>% 
  mutate(imm = case_when(CC22_331c == 1 ~ 1, 
                         CC22_331c == 2 ~ 0)) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  mutate(grp = "Weekly Attending Black Protestants")

aaa2 <- cces22 %>% 
  filter(pid7 == 1 | pid7 == 2 | pid7 == 3) %>% 
  mutate(imm = case_when(CC22_331c == 1 ~ 1, 
                         CC22_331c == 2 ~ 0)) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  mutate(grp = "All Democrats")

tt3 <- bind_rows(aaa1, aaa2) %>% mutate(type = "Reduce Legal Immigration by 50%")


aaa1 <- cces22 %>% 
  filter(race == 2) %>% 
  filter(religpew == 1) %>% 
  filter(pew_churatd == 1 | pew_churatd == 2) %>% 
  mutate(imm = case_when(CC22_331d == 1 ~ 1, 
                         CC22_331d == 2 ~ 0)) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  mutate(grp = "Weekly Attending Black Protestants")

aaa2 <- cces22 %>% 
  filter(pid7 == 1 | pid7 == 2 | pid7 == 3) %>% 
  mutate(imm = case_when(CC22_331d == 1 ~ 1, 
                         CC22_331d == 2 ~ 0)) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  mutate(grp = "All Democrats")

tt4 <- bind_rows(aaa1, aaa2) %>% mutate(type = "Increased Border Funding by $25B")

graph <- bind_rows(tt1, tt2, tt3, tt4)

graph %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = grp, y = mean, fill = grp)) +
  geom_col(color = "black") +
  facet_wrap(~ type, nrow = 1) +
  theme_rb(legend = TRUE) +
  scale_fill_calc() +
  theme(axis.text.x = element_blank()) +
  error_bar() +
  scale_y_continuous(labels = percent) +
  lab_bar(top = FALSE, type = lab, pos = .06, sz = 11) +
  geom_text(aes(y = .06, label = ifelse(grp == "All Democrats", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 11, family = "font", color = "white") +
  labs(x = "", y = "", title = "Views of Immigration Among All Democrats and Black Protestants", caption = "@ryanburge\nData: Cooperative Election Study, 2022")
save("immi_bprot_dems.png")



aaa1 <- cces22 %>% 
  filter(race == 2) %>% 
  filter(religpew == 1) %>% 
  filter(pew_churatd == 1 | pew_churatd == 2) %>% 
  mutate(imm = case_when(CC22_334a == 1 ~ 1, 
                         CC22_334a == 2 ~ 0)) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  mutate(grp = "Weekly Attending Black Protestants")

aaa2 <- cces22 %>% 
  filter(pid7 == 1 | pid7 == 2 | pid7 == 3) %>% 
  mutate(imm = case_when(CC22_334a == 1 ~ 1, 
                         CC22_334a == 2 ~ 0)) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  mutate(grp = "All Democrats")

tt1 <- bind_rows(aaa1, aaa2) %>% mutate(type = "Eliminate Mandatory Minimums")


aaa1 <- cces22 %>% 
  filter(race == 2) %>% 
  filter(religpew == 1) %>% 
  filter(pew_churatd == 1 | pew_churatd == 2) %>% 
  mutate(imm = case_when(CC22_334b == 1 ~ 1, 
                         CC22_334b == 2 ~ 0)) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  mutate(grp = "Weekly Attending Black Protestants")

aaa2 <- cces22 %>% 
  filter(pid7 == 1 | pid7 == 2 | pid7 == 3) %>% 
  mutate(imm = case_when(CC22_334b == 1 ~ 1, 
                         CC22_334b == 2 ~ 0)) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  mutate(grp = "All Democrats")

tt2 <- bind_rows(aaa1, aaa2) %>% mutate(type = "Require Body Cams for Police")

aaa1 <- cces22 %>% 
  filter(race == 2) %>% 
  filter(religpew == 1) %>% 
  filter(pew_churatd == 1 | pew_churatd == 2) %>% 
  mutate(imm = case_when(CC22_334c == 1 ~ 1, 
                         CC22_334c == 2 ~ 0)) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  mutate(grp = "Weekly Attending Black Protestants")

aaa2 <- cces22 %>% 
  filter(pid7 == 1 | pid7 == 2 | pid7 == 3) %>% 
  mutate(imm = case_when(CC22_334c == 1 ~ 1, 
                         CC22_334c == 2 ~ 0)) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  mutate(grp = "All Democrats")

tt3 <- bind_rows(aaa1, aaa2) %>% mutate(type = "Increase Police by 10%")


aaa1 <- cces22 %>% 
  filter(race == 2) %>% 
  filter(religpew == 1) %>% 
  filter(pew_churatd == 1 | pew_churatd == 2) %>% 
  mutate(imm = case_when(CC22_334d == 1 ~ 1, 
                         CC22_334d == 2 ~ 0)) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  mutate(grp = "Weekly Attending Black Protestants")

aaa2 <- cces22 %>% 
  filter(pid7 == 1 | pid7 == 2 | pid7 == 3) %>% 
  mutate(imm = case_when(CC22_334d == 1 ~ 1, 
                         CC22_334d == 2 ~ 0)) %>% 
  mean_ci(imm, wt = commonweight, ci = .84) %>% 
  mutate(grp = "All Democrats")

tt4 <- bind_rows(aaa1, aaa2) %>% mutate(type = "Decrease Police by 10%")

graph <- bind_rows(tt1, tt2, tt3, tt4)

graph %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = grp, y = mean, fill = grp)) +
  geom_col(color = "black") +
  facet_wrap(~ type, nrow = 1) +
  theme_rb(legend = TRUE) +
  scale_fill_calc() +
  theme(axis.text.x = element_blank()) +
  error_bar() +
  scale_y_continuous(labels = percent) +
  lab_bar(top = FALSE, type = lab, pos = .06, sz = 11) +
  geom_text(aes(y = .06, label = ifelse(grp == "All Democrats", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 11, family = "font", color = "white") +
  labs(x = "", y = "", title = "Views of Police Among All Democrats and Black Protestants", caption = "@ryanburge\nData: Cooperative Election Study, 2022")
save("police_bprot_dems.png")



fun <- function(df, var1, var2, var3, weight, yr) {
  
  aaa1 <- df %>% 
    filter({{var1}} <= 7) %>% 
    filter(race == 2) %>% 
    filter(religpew == 1) %>% 
    filter(pew_churatd == 1 | pew_churatd == 2) %>% 
    mean_ci({{var1}}, wt = {{weight}}) %>% 
    mutate(year = yr) %>% 
    mutate(type = "Yourself")
  
  aaa2 <- df %>% 
    filter({{var2}} <= 7) %>% 
    filter(race == 2) %>% 
    filter(religpew == 1) %>% 
    filter(pew_churatd == 1 | pew_churatd == 2) %>% 
    mean_ci({{var2}}, wt = {{weight}}) %>% 
    mutate(year = yr) %>% 
    mutate(type = "Democrats")
  
  aaa3 <- df %>% 
    filter({{var3}} <= 7) %>% 
    filter(race == 2) %>% 
    filter(religpew == 1) %>% 
    filter(pew_churatd == 1 | pew_churatd == 2) %>% 
    mean_ci({{var3}}, wt = {{weight}}) %>% 
    mutate(year = yr) %>% 
    mutate(type = "Republicans")
  
  bind_rows(aaa1, aaa2, aaa3)
  
}

yyy1 <- cces12 %>% fun(CC334A, CC334E, CC334F, weight_vv, yr = 2012)
yyy2 <- cces14 %>% fun(CC334A, CC334K, CC334L, weight, yr = 2014)
yyy3 <- cces16 %>% fun(CC16_340a, CC16_340g, CC16_340h, commonweight, yr = 2016)
yyy4 <- cces17 %>% fun(CC17_350a, CC17_350d, CC17_350e, weights_common, yr = 2017)
yyy5 <- cces18 %>% fun(CC18_334A, CC18_334D, CC18_334E, commonweight, yr = 2018)
yyy6 <- cces19 %>% fun(CC19_334a, CC19_334d, CC19_334e, commonweight, yr = 2019)
yyy7 <- cces20 %>% fun(CC20_340a, CC20_340e, CC20_340f, commonweight, yr = 2020) 
yyy8 <- cces21 %>% fun(CC21_330a, CC21_330e, CC21_330f, commonweight, yr = 2021)
yyy9 <- cces22 %>% fun(CC22_340a, CC22_340e, CC22_340f, commonweight, yr = 2022)

qqq1 <- bind_df("yyy") %>% mutate(grp = "Weekly Attending Black Protestants")



fun <- function(df, var1, var2, var3, weight, yr) {
  
  aaa1 <- df %>% 
    filter({{var1}} <= 7) %>% 
    filter(pid7 == 1 | pid7 == 2 | pid7 == 3) %>% 
    mean_ci({{var1}}, wt = {{weight}}) %>% 
    mutate(year = yr) %>% 
    mutate(type = "Yourself")
  
  aaa2 <- df %>% 
    filter({{var2}} <= 7) %>% 
    filter(pid7 == 1 | pid7 == 2 | pid7 == 3) %>% 
    mean_ci({{var2}}, wt = {{weight}}) %>% 
    mutate(year = yr) %>% 
    mutate(type = "Democrats")
  
  aaa3 <- df %>% 
    filter({{var3}} <= 7) %>% 
    filter(pid7 == 1 | pid7 == 2 | pid7 == 3) %>% 
    mean_ci({{var3}}, wt = {{weight}}) %>% 
    mutate(year = yr) %>% 
    mutate(type = "Republicans")
  
  bind_rows(aaa1, aaa2, aaa3)
  
}

yyy1 <- cces12 %>% fun(CC334A, CC334E, CC334F, weight_vv, yr = 2012)
yyy2 <- cces14 %>% fun(CC334A, CC334K, CC334L, weight, yr = 2014)
yyy3 <- cces16 %>% fun(CC16_340a, CC16_340g, CC16_340h, commonweight, yr = 2016)
yyy4 <- cces17 %>% fun(CC17_350a, CC17_350d, CC17_350e, weights_common, yr = 2017)
yyy5 <- cces18 %>% fun(CC18_334A, CC18_334D, CC18_334E, commonweight, yr = 2018)
yyy6 <- cces19 %>% fun(CC19_334a, CC19_334d, CC19_334e, commonweight, yr = 2019)
yyy7 <- cces20 %>% fun(CC20_340a, CC20_340e, CC20_340f, commonweight, yr = 2020) 
yyy8 <- cces21 %>% fun(CC21_330a, CC21_330e, CC21_330f, commonweight, yr = 2021)
yyy9 <- cces22 %>% fun(CC22_340a, CC22_340e, CC22_340f, commonweight, yr = 2022)

qqq2 <- bind_df("yyy") %>% mutate(grp = "All Democrats")

graph <- bind_rows(qqq1, qqq2)

graph$type <- factor(graph$type, levels = c("Democrats", "Yourself", "Republicans"))


graph %>% 
  mutate(year = as.factor(year)) %>% 
  ggplot(., aes(x = fct_rev(year), y = mean, color = type, group = type)) +
  geom_hline(yintercept =  4, linetype = "twodash") +
  geom_line() + 
  geom_errorbar(aes(ymin = lower, ymax=upper), size = 1, width = 0) +
  geom_point(shape = 21, stroke = 2, fill = "white") +
  coord_flip() +
  facet_wrap(~ grp, ncol = 1) +
  scale_y_continuous(breaks = c(1,2,3,4,5,6,7), labels = c("Very\nLiberal", "Lib.", "", "Middle of\nthe Road", "", "Cons.", "Very\nConservative")) +
  scale_color_manual(values = c("dodgerblue3", "azure4", "firebrick3")) +
  theme_rb(legend = TRUE) +
  theme(strip.text = element_text(size = 20)) +
  labs(x = "", y = "", title = "Place Yourself, Democrats, and Republicans in Ideological Space", caption = "@ryanburge\nData: Cooperative ELection Study, 2012-2022") 
save("your_self_dems_republicans_bprot.png", ht = 9, wd = 7)