ct_selection_untitled-087B163C.R

fun <- function(var, name){
  
  cces20 %>% 
    cces_trad(religpew) %>% 
    mutate(vv = {{var}}) %>% 
    mutate(vv = case_when(vv == 1 ~ 1, 
                          vv == 2 ~ 0)) %>% 
    group_by(trad2) %>% 
    mean_ci(vv, wt = commonweight, ci = .84) %>% 
    mutate(type = name) %>% 
    filter(trad2 != "NA")
  
  
}

vars <- syms(c("CC20_430a_1","CC20_430a_2","CC20_430a_3","CC20_430a_4","CC20_430a_5","CC20_430a_6"))
labs <- c("Attend Political Meeting", "Put Up Political Sign", "Work for Candidate", "Attend Protest/March", "Contact Public Official", "Donate Money to Candidate")


gg <- map2_df(vars, labs, fun)

gg <- gg %>% 
  mutate(fill = frcode(trad2 == "Atheist" ~ 'a', TRUE ~ "b"))

gg %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x= reorder(trad2, mean), y = mean, fill = fill)) +
  geom_col(color = "black") +
  facet_wrap(~ type, ncol = 2) +
  coord_flip() +
  lab_bar(top = FALSE, type = lab, pos = .03, sz = 4) +
  geom_text(aes(y = .03, label = ifelse(fill == "a", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 4, family = "font", color = "white") +
  theme_rb() +
  y_pct() + 
  error_bar() +
  scale_fill_manual(values = c("firebrick3", "azure3")) +
  labs(title = "Have You Engaged in the Following Political Activites in the Previous Month?", caption = "@ryanburge\nData: Cooperative Election Study, 2020")
save("pol_act_2020ces.png", ht = 10)


fun <- function(var, name){
  
  cces22 %>% 
    cces_trad(religpew) %>% 
    mutate(vv = {{var}}) %>% 
    mutate(vv = case_when(vv == 1 ~ 1, 
                          vv == 2 ~ 0)) %>% 
    group_by(trad2) %>% 
    mean_ci(vv, wt = commonweight, ci = .84) %>% 
    mutate(type = name) %>% 
    filter(trad2 != "NA")
  
  
}

vars <- syms(c("CC22_430a_1","CC22_430a_2","CC22_430a_3","CC22_430a_4","CC22_430a_5","CC22_430a_6"))
labs <- c("Attend Political Meeting", "Put Up Political Sign", "Work for Candidate", "Attend Protest/March", "Contact Public Official", "Donate Money to Candidate")


gg <- map2_df(vars, labs, fun)

gg <- gg %>% 
  mutate(fill = frcode(trad2 == "Atheist" ~ 'a', TRUE ~ "b"))

gg %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x= reorder(trad2, mean), y = mean, fill = fill)) +
  geom_col(color = "black") +
  facet_wrap(~ type, ncol = 2) +
  coord_flip() +
  lab_bar(top = FALSE, type = lab, pos = .03, sz = 4) +
  geom_text(aes(y = .03, label = ifelse(fill == "a", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 4, family = "font", color = "white") +
  theme_rb() +
  y_pct() + 
  error_bar() +
  scale_fill_manual(values = c("firebrick3", "azure3")) +
  labs(title = "Have You Engaged in the Following Political Activites in the Previous Month?", caption = "@ryanburge\nData: Cooperative Election Study, 2022")
save("pol_act_2022ces.png", ht = 10)




yyy1 <- cces12 %>% 
  mutate(grp = frcode(evangelical == 1 & white == 1 ~ "White\nEvangelical", 
                      religpew == 9 ~ "Atheist")) %>% 
  mutate(donate = CC417a_4) %>% 
  mutate(donate = case_when(donate == 1 ~ 1, 
                            donate == 2 ~ 0)) %>% 
  group_by(grp) %>% 
  mean_ci(donate, wt = weight_vv, ci = .84) %>% 
  mutate(year = "2012") %>% 
  na.omit()

yyy2 <- cces14 %>% 
  mutate(grp = frcode(evangelical == 1 & white == 1 ~ "White\nEvangelical", 
                      religpew == 9 ~ "Atheist")) %>% 
  mutate(donate = CC417a_4) %>% 
  mutate(donate = case_when(donate == 1 ~ 1, 
                            donate == 2 ~ 0)) %>% 
  group_by(grp) %>% 
  mean_ci(donate, wt = weight, ci = .84) %>% 
  mutate(year = "2014") %>% 
  na.omit()

yyy3 <- cces16 %>% 
  mutate(grp = frcode(evangelical == 1 & white == 1 ~ "White\nEvangelical", 
                      religpew == 9 ~ "Atheist")) %>% 
  mutate(donate = CC16_417a_4) %>% 
  mutate(donate = case_when(donate == 1 ~ 1, 
                            donate == 2 ~ 0)) %>% 
  group_by(grp) %>% 
  mean_ci(donate, wt = commonweight, ci = .84) %>% 
  mutate(year = "2016") %>% 
  na.omit()

yyy4 <- cces18 %>% 
  mutate(grp = frcode(evangelical == 1 & white == 1 ~ "White\nEvangelical", 
                      religpew == 9 ~ "Atheist")) %>% 
  mutate(donate = CC18_417a_6) %>% 
  mutate(donate = case_when(donate == 1 ~ 1, 
                            donate == 2 ~ 0)) %>% 
  group_by(grp) %>% 
  mean_ci(donate, wt = commonweight, ci = .84) %>% 
  mutate(year = "2018") %>% 
  na.omit()


yyy5 <- cces20 %>% 
  mutate(grp = frcode(evangelical == 1 & white == 1 ~ "White\nEvangelical", 
                      religpew == 9 ~ "Atheist")) %>% 
  mutate(donate = CC20_430a_6) %>% 
  mutate(donate = case_when(donate == 1 ~ 1, 
                            donate == 2 ~ 0)) %>% 
  group_by(grp) %>% 
  mean_ci(donate, wt = commonweight, ci = .84) %>% 
  mutate(year = "2020") %>% 
  na.omit()

yyy6 <- cces22 %>% 
  mutate(grp = frcode(evangelical == 1 & white == 1 ~ "White\nEvangelical", 
                      religpew == 9 ~ "Atheist")) %>% 
  mutate(donate = CC22_430a_6) %>% 
  mutate(donate = case_when(donate == 1 ~ 1, 
                            donate == 2 ~ 0)) %>% 
  group_by(grp) %>% 
  mean_ci(donate, wt = commonweight, ci = .84) %>% 
  mutate(year = "2022") %>% 
  na.omit()

graph <- bind_df("yyy")

graph %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = grp, y = mean, fill = grp)) +
  geom_col(color = "black") +
  facet_wrap(~ year) +
  geom_text(aes(y = mean + .05, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 7, family = "font") +
  scale_y_continuous(labels = percent, limits = c(0, .60)) +
  theme_rb() + 
  theme(strip.text = element_text(size = 18)) +
  scale_fill_manual(values = c("#f15a24", "#285e80")) +
  labs(x = "", y = "", title = "During the Past Year, I Donated Money to\na Candidate/Campaign", caption = "@ryanburge\nData: Cooperative Election Study 2012-2022")
save("donate_money22_new.png", wd = 5)


graph <- cces20 %>% 
  mutate(grp = frcode(evangelical == 1 & white == 1 ~ "White Evangelical", 
                      religpew == 9 ~ "Atheist")) %>% 
  filter(CC20_430a_6 != "NA") %>% 
  mutate(donate = CC20_430a_6) %>% 
  mutate(donate = frcode(donate == 1 ~ "Donated", 
                         donate == 2 ~ "Did Not Donate")) %>%
  filter(grp != "NA") %>% 
  group_by(grp) %>% 
  ct(donate, wt = commonweight, show_na = FALSE) %>% 
  mutate(pct = n/51551) %>% 
  mutate(labs = paste(grp, donate, sep = '-')) %>% 
  mutate(lab = round(pct, 3))

graph %>% 
  ggplot(., aes(x = labs, y = lab, fill = labs)) +
  geom_col(color = "black") +
  coord_flip() +
  theme_rb()  +
  scale_fill_manual(values = c("#63C5DA", "#051094","#BC544B", "#60100B")) +
  scale_y_continuous(labels = percent, limits = c(0, .15)) +
  geom_text(aes(y = lab + .01, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 8, family = "font") +
  labs(x = "", y = "", title = "Share of the Full Sample That Donated/Not Donated to Candidate in 2020", caption = "@ryanburge\nData: Cooperative Election Study, 2020")
save("share_donating.png", ht = 3.5)


rfun <- function(var1, var2){
  
  cces20 %>% 
    mutate(vv = {{var1}}) %>% 
    mutate({{var2}} := case_when(vv == 1 ~ 1, 
                          vv == 2 ~ 0)) 
  
  
}

cces20 <- cces20 %>% 
  rfun(CC20_430a_1, act1) %>% 
  rfun(CC20_430a_2, act2) %>% 
  rfun(CC20_430a_3, act3) %>% 
  rfun(CC20_430a_4, act4) %>% 
  rfun(CC20_430a_5, act5) %>% 
  rfun(CC20_430a_6, act6) %>% 
  mutate(all = act1 + act2 + act3 + act4 + act5 + act6)
  

graph <- cces20 %>% 
  cces_trad(religpew) %>% 
  group_by(trad2) %>% 
  mean_ci(all, wt = commonweight, ci = .84)

graph %>% 
  ggplot(., aes(x = reorder(trad2, mean), y = mean, color = trad2)) +
  geom_errorbar(aes(ymin=lower, ymax=upper), width=0, position=position_dodge(.9), linewidth = 1) +
  geom_point(stroke = 1, shape = 21, fill = "white", size = 4) + 
  coord_flip() +
  theme_rb() +
  scale_color_manual(values = c(met.brewer("Johnson", 16))) +
  labs(x = "", y = "", title = "Average Number of Political Acts in 2020 - Max is 6", caption = "@ryanburge\nData: Cooperative Election Study, 2020")
save("pol_acts_mean_20.png", ht = 5)

vars <- syms(c("CC20_430a_1","CC20_430a_2","CC20_430a_3","CC20_430a_4","CC20_430a_5","CC20_430a_6"))

cces_educ2 <- function(df, var){
  
  
  df %>% 
    mutate(educ = frcode({{var}} == 1 | {{var}} == 2 ~ "HS or Less",
                         {{var}} == 3 ~ "Some College",
                         {{var}} == 4 ~ "2 Yr.",
                         {{var}} == 5 ~ "4 Yr.",
                         {{var}} == 6 ~ "Post-Grad")) 
  
}

reg <- cces20 %>% 
  filter(faminc_new <= 16) %>% 
  mutate(act1 = car::recode(CC20_430a_1, "1=1; 2=0")) %>% 
  mutate(act2 = car::recode(CC20_430a_2, "1=1; 2=0")) %>% 
  mutate(act3 = car::recode(CC20_430a_3, "1=1; 2=0")) %>% 
  mutate(act4 = car::recode(CC20_430a_4, "1=1; 2=0")) %>% 
  mutate(act5 = car::recode(CC20_430a_5, "1=1; 2=0")) %>% 
  mutate(act6 = car::recode(CC20_430a_6, "1=1; 2=0")) %>% 
  mutate(acts = act1 + act2 + act3 + act4 + act5 + act6) %>% 
  mutate(trad = frcode(evangelical == 1 & race == 1 ~ "White Evangelical",
                       evangelical == 1 & race != 1 ~ "Non-White Evangelical",
                       mainline == 1 ~ "Mainline", 
                       bprot == 1 ~ "Black Prot.",
                       religpew == 2 & race == 1 ~ "White Catholic",
                       religpew == 2 & race != 1 ~ "Non-White Catholic", 
                       religpew == 3 ~ "Mormon",
                       religpew == 4 ~ "Orthodox",
                       religpew == 5 ~ "Jewish",
                       religpew == 6 ~ "Muslim",
                       religpew == 7 ~ "Buddhist",
                       religpew == 8 ~ "Hindu",
                       religpew == 9 ~ "Atheist", 
                       religpew == 10 ~ "Agnostic",
                       religpew == 11 ~ "Nothing in Particular",
                       religpew == 12 ~ "Something Else", 
                       TRUE ~ "Unclassified")) %>% 
  mutate(trad2 = frcode(religpew == 1 & pew_bornagain == 1 ~ "Evangelical Prot.",
                        religpew == 1 & pew_bornagain == 2 ~ "Non-Evangelical Prot.", 
                        religpew == 2 ~ "Catholic",
                        religpew == 5 ~ "Jewish", 
                        religpew == 9 ~ "Atheist")) %>% 
  mutate(male = car::recode(gender, "1=1; else =0")) %>% 
  mutate(white = car::recode(race, "1=1; else =0")) %>% 
  mutate(income = faminc_new) %>% 
  mutate(pid2 = frcode(pid3 == 1 ~ "Democrat",
                       pid3 == 2 ~ "Republican")) %>% 
  mutate(age = 2020- birthyr) %>% 
  cces_educ2(educ) %>% 
  select(acts, trad, trad2, male, white, age, income, pid2, educ, act1, act2, act3, act4, act5, act6)




reg1 <- lm(acts ~ educ*trad2 + male + white + income + age, data = reg)

# gg <- interact_plot(reg1, pred= educ, modx= trad2, modx.labels = c("Protestant", "Catholic", "Atheist", "Agnostic"), int.width = .76, interval = TRUE) 

graph <- cat_plot(reg1, pred = educ, modx = trad2, interval = TRUE, int.width =  .76, errorbar.width = .1, geom = "bar")

graph +
  theme_rb(legend = TRUE) +
  labs(x = "Highest Level of Education", y = "", title = "Number of Political Acts in 2020 Election", subtitle =  "Regression model with controls for: gender, race, income, and age included", 
       caption = "@ryanburge\nData: Cooperative Election Study 2020") 
save("pol_acts_regression.png")