ct_selection_untitled-2774FB78.R

fun <- function(var, name){
  
  cces20 %>% 
    filter(religpew == 1 | religpew == 2 | religpew == 3 | religpew == 4) %>% 
    mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                        ideo5 == 3 ~ "Moderate", 
                        ideo5 == 4 | ideo5 == 5 ~ "Conservative")) %>% 
    mutate(vv = {{var}}) %>% 
    mutate(vv = case_when(vv == 1 ~ 1, 
                          vv == 2 ~ 0)) %>% 
    group_by(id3) %>% 
    mean_ci(vv, wt = commonweight, ci = .84) %>% 
    mutate(type = name) %>% 
    filter(id3 != "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 %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x= id3, y = mean, fill = id3)) +
  geom_col(color = "black") +
  facet_wrap(~ type, ncol = 3) +
  lab_bar(top = FALSE, type = lab, pos = .04, sz = 8) +
  # 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() +
  pid3_fill() +
  theme(axis.text.x = element_text(size = 10)) +
  theme(plot.title = element_text(size = 15)) +
  labs(title = "Have You Engaged in the Following Political Activites in the Previous Month?", subtitle = "Among Christians", caption = "@ryanburge\nData: Cooperative Election Study, 2020", x = "", y = "")
save("pol_act_2020_xtn_id3.png", wd = 7)


rfun <- function(df, var1, var2){
  df %>% 
    mutate(vv = .data[[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 %>% 
  filter(religpew == 1 | religpew == 2 | religpew == 3 | religpew == 4) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conservative")) %>% 
  cces_race(race) %>% 
  group_by(race, id3) %>% 
  mean_ci(all, wt = commonweight, ci = .84) %>% 
  filter(id3 != "NA")

graph %>% 
  ggplot(., aes(x = fct_rev(race), y = mean, color = id3)) +
  geom_errorbar(aes(ymin=lower, ymax=upper), width=0, linewidth = 1) +
  geom_point(stroke = 1, shape = 21, fill = "white", size = 4) + 
  coord_flip() +
  theme_rb(legend = TRUE) +
  pid3_color() +
  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_race_id3.png", ht = 4)





yyy1 <- cces12 %>% 
  filter(religpew == 1 | religpew == 2 | religpew == 3 | religpew == 4) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conservative")) %>% 
  mutate(meet   = CC417a_1) %>% 
  mutate(sign   = CC417a_2) %>% 
  mutate(donate = CC417a_3) %>% 
  mutate(work   = CC417a_4) %>% 
  mutate(meet   = car::recode(meet, "1=1; 2=0")) %>% 
  mutate(sign   = car::recode(sign, "1=1; 2=0")) %>% 
  mutate(donate = car::recode(donate, "1=1; 2=0")) %>% 
  mutate(work   = car::recode(work, "1=1; 2=0")) %>% 
  mutate(acts = meet + sign + donate + work) %>% 
  group_by(id3) %>% 
  mean_ci(acts, wt = weight_vv, ci = .84) %>% 
  mutate(year = "2012") %>% 
  na.omit()

yyy2 <- cces14 %>% 
  filter(religpew == 1 | religpew == 2 | religpew == 3 | religpew == 4) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conservative")) %>% 
  mutate(meet   = CC417a_1) %>% 
  mutate(sign   = CC417a_2) %>% 
  mutate(donate = CC417a_3) %>% 
  mutate(work   = CC417a_4) %>% 
  mutate(meet   = car::recode(meet, "1=1; 2=0")) %>% 
  mutate(sign   = car::recode(sign, "1=1; 2=0")) %>% 
  mutate(donate = car::recode(donate, "1=1; 2=0")) %>% 
  mutate(work   = car::recode(work, "1=1; 2=0")) %>% 
  mutate(acts = meet + sign + donate + work) %>% 
  group_by(id3) %>% 
  mean_ci(acts, wt = weight, ci = .84) %>% 
  mutate(year = "2014") %>% 
  na.omit()

yyy3 <- cces16 %>% 
  filter(religpew == 1 | religpew == 2 | religpew == 3 | religpew == 4) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conservative")) %>% 
  mutate(meet   = CC16_417a_1) %>% 
  mutate(sign   = CC16_417a_2) %>% 
  mutate(work   = CC16_417a_3) %>% 
  mutate(donate = CC16_417a_4) %>% 
  mutate(meet   = car::recode(meet, "1=1; 2=0")) %>% 
  mutate(sign   = car::recode(sign, "1=1; 2=0")) %>% 
  mutate(donate = car::recode(donate, "1=1; 2=0")) %>% 
  mutate(work   = car::recode(work, "1=1; 2=0")) %>% 
  mutate(acts = meet + sign + donate + work) %>% 
  group_by(id3) %>% 
  mean_ci(acts, wt = commonweight, ci = .84) %>% 
  mutate(year = "2016") %>% 
  na.omit()

yyy4 <- cces18 %>% 
  filter(religpew == 1 | religpew == 2 | religpew == 3 | religpew == 4) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conservative")) %>% 
  mutate(meet   = CC18_417a_1) %>% 
  mutate(sign   = CC18_417a_2) %>% 
  mutate(work   = CC18_417a_3) %>% 
  mutate(donate = CC18_417a_4) %>% 
  mutate(meet   = car::recode(meet, "1=1; 2=0")) %>% 
  mutate(sign   = car::recode(sign, "1=1; 2=0")) %>% 
  mutate(donate = car::recode(donate, "1=1; 2=0")) %>% 
  mutate(work   = car::recode(work, "1=1; 2=0")) %>% 
  mutate(acts = meet + sign + donate + work) %>% 
  group_by(id3) %>% 
  mean_ci(acts, wt = commonweight, ci = .84) %>% 
  mutate(year = "2018") %>% 
  na.omit()


yyy5 <- cces20 %>% 
  filter(religpew == 1 | religpew == 2 | religpew == 3 | religpew == 4) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conservative")) %>% 
  mutate(meet   = CC20_430a_1) %>% 
  mutate(sign   = CC20_430a_2) %>% 
  mutate(work   = CC20_430a_3) %>% 
  mutate(donate = CC20_430a_6) %>% 
  mutate(meet   = car::recode(meet, "1=1; 2=0")) %>% 
  mutate(sign   = car::recode(sign, "1=1; 2=0")) %>% 
  mutate(donate = car::recode(donate, "1=1; 2=0")) %>% 
  mutate(work   = car::recode(work, "1=1; 2=0")) %>% 
  mutate(acts = meet + sign + donate + work) %>% 
  group_by(id3) %>% 
  mean_ci(acts, wt = commonweight, ci = .84) %>% 
  mutate(year = "2020") %>% 
  na.omit()

yyy6 <- cces22 %>% 
  filter(religpew == 1 | religpew == 2 | religpew == 3 | religpew == 4) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conservative")) %>% 
  mutate(meet   = CC22_430a_1) %>% 
  mutate(sign   = CC22_430a_2) %>% 
  mutate(work   = CC22_430a_3) %>% 
  mutate(donate = CC22_430a_6) %>% 
  mutate(meet   = car::recode(meet, "1=1; 2=0")) %>% 
  mutate(sign   = car::recode(sign, "1=1; 2=0")) %>% 
  mutate(donate = car::recode(donate, "1=1; 2=0")) %>% 
  mutate(work   = car::recode(work, "1=1; 2=0")) %>% 
  mutate(acts = meet + sign + donate + work) %>% 
  group_by(id3) %>% 
  mean_ci(acts, wt = commonweight, ci = .84) %>% 
  mutate(year = "2022") %>% 
  na.omit()

graph <- bind_df("yyy")



graph %>% 
  ggplot(., aes(x = fct_rev(year), y = mean, color = id3)) +
  geom_errorbar(aes(ymin=lower, ymax=upper), width=0, linewidth = 1) +
  geom_point(stroke = 1, shape = 21, fill = "white", size = 4) + 
  coord_flip() +
  theme_rb(legend = TRUE) +
  pid3_color() +
  labs(x = "", y = "", title = "Average Number of Political Acts Among Christians Over Time - Max is 4", 
       subtitle = "Among Christians",
       caption = "@ryanburge\nData: Cooperative Election Study, 2012-2022")
save("pol_acts_mean_id3_over_time.png", ht = 4)






yyy1 <- cces12 %>% 
  filter(religpew == 1 | religpew == 2 | religpew == 3 | religpew == 4) %>% 
  mutate(race = frcode(race == 1 ~ "White", TRUE ~ "Non-White")) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conservative")) %>% 
  mutate(meet   = CC417a_1) %>% 
  mutate(sign   = CC417a_2) %>% 
  mutate(donate = CC417a_3) %>% 
  mutate(work   = CC417a_4) %>% 
  mutate(meet   = car::recode(meet, "1=1; 2=0")) %>% 
  mutate(sign   = car::recode(sign, "1=1; 2=0")) %>% 
  mutate(donate = car::recode(donate, "1=1; 2=0")) %>% 
  mutate(work   = car::recode(work, "1=1; 2=0")) %>% 
  mutate(acts = meet + sign + donate + work) %>% 
  group_by(id3, race) %>% 
  mean_ci(acts, wt = weight_vv, ci = .84) %>% 
  mutate(year = "2012") %>% 
  na.omit()

yyy2 <- cces14 %>% 
  filter(religpew == 1 | religpew == 2 | religpew == 3 | religpew == 4) %>% 
  mutate(race = frcode(race == 1 ~ "White", TRUE ~ "Non-White")) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conservative")) %>% 
  mutate(meet   = CC417a_1) %>% 
  mutate(sign   = CC417a_2) %>% 
  mutate(donate = CC417a_3) %>% 
  mutate(work   = CC417a_4) %>% 
  mutate(meet   = car::recode(meet, "1=1; 2=0")) %>% 
  mutate(sign   = car::recode(sign, "1=1; 2=0")) %>% 
  mutate(donate = car::recode(donate, "1=1; 2=0")) %>% 
  mutate(work   = car::recode(work, "1=1; 2=0")) %>% 
  mutate(acts = meet + sign + donate + work) %>% 
  group_by(id3, race) %>% 
  mean_ci(acts, wt = weight, ci = .84) %>% 
  mutate(year = "2014") %>% 
  na.omit()

yyy3 <- cces16 %>% 
  filter(religpew == 1 | religpew == 2 | religpew == 3 | religpew == 4) %>% 
  mutate(race = frcode(race == 1 ~ "White", TRUE ~ "Non-White")) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conservative")) %>% 
  mutate(meet   = CC16_417a_1) %>% 
  mutate(sign   = CC16_417a_2) %>% 
  mutate(work   = CC16_417a_3) %>% 
  mutate(donate = CC16_417a_4) %>% 
  mutate(meet   = car::recode(meet, "1=1; 2=0")) %>% 
  mutate(sign   = car::recode(sign, "1=1; 2=0")) %>% 
  mutate(donate = car::recode(donate, "1=1; 2=0")) %>% 
  mutate(work   = car::recode(work, "1=1; 2=0")) %>% 
  mutate(acts = meet + sign + donate + work) %>% 
  group_by(id3, race) %>% 
  mean_ci(acts, wt = commonweight, ci = .84) %>% 
  mutate(year = "2016") %>% 
  na.omit()

yyy4 <- cces18 %>% 
  filter(religpew == 1 | religpew == 2 | religpew == 3 | religpew == 4) %>% 
  mutate(race = frcode(race == 1 ~ "White", TRUE ~ "Non-White")) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conservative")) %>% 
  mutate(meet   = CC18_417a_1) %>% 
  mutate(sign   = CC18_417a_2) %>% 
  mutate(work   = CC18_417a_3) %>% 
  mutate(donate = CC18_417a_4) %>% 
  mutate(meet   = car::recode(meet, "1=1; 2=0")) %>% 
  mutate(sign   = car::recode(sign, "1=1; 2=0")) %>% 
  mutate(donate = car::recode(donate, "1=1; 2=0")) %>% 
  mutate(work   = car::recode(work, "1=1; 2=0")) %>% 
  mutate(acts = meet + sign + donate + work) %>% 
  group_by(id3, race) %>% 
  mean_ci(acts, wt = commonweight, ci = .84) %>% 
  mutate(year = "2018") %>% 
  na.omit()


yyy5 <- cces20 %>% 
  filter(religpew == 1 | religpew == 2 | religpew == 3 | religpew == 4) %>% 
  mutate(race = frcode(race == 1 ~ "White", TRUE ~ "Non-White")) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conservative")) %>% 
  mutate(meet   = CC20_430a_1) %>% 
  mutate(sign   = CC20_430a_2) %>% 
  mutate(work   = CC20_430a_3) %>% 
  mutate(donate = CC20_430a_6) %>% 
  mutate(meet   = car::recode(meet, "1=1; 2=0")) %>% 
  mutate(sign   = car::recode(sign, "1=1; 2=0")) %>% 
  mutate(donate = car::recode(donate, "1=1; 2=0")) %>% 
  mutate(work   = car::recode(work, "1=1; 2=0")) %>% 
  mutate(acts = meet + sign + donate + work) %>% 
  group_by(id3, race) %>% 
  mean_ci(acts, wt = commonweight, ci = .84) %>% 
  mutate(year = "2020") %>% 
  na.omit()

yyy6 <- cces22 %>% 
  filter(religpew == 1 | religpew == 2 | religpew == 3 | religpew == 4) %>% 
  mutate(race = frcode(race == 1 ~ "White", TRUE ~ "Non-White")) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conservative")) %>% 
  mutate(meet   = CC22_430a_1) %>% 
  mutate(sign   = CC22_430a_2) %>% 
  mutate(work   = CC22_430a_3) %>% 
  mutate(donate = CC22_430a_6) %>% 
  mutate(meet   = car::recode(meet, "1=1; 2=0")) %>% 
  mutate(sign   = car::recode(sign, "1=1; 2=0")) %>% 
  mutate(donate = car::recode(donate, "1=1; 2=0")) %>% 
  mutate(work   = car::recode(work, "1=1; 2=0")) %>% 
  mutate(acts = meet + sign + donate + work) %>% 
  group_by(id3, race) %>% 
  mean_ci(acts, wt = commonweight, ci = .84) %>% 
  mutate(year = "2022") %>% 
  na.omit()

graph <- bind_df("yyy")



graph %>% 
  ggplot(., aes(x = fct_rev(year), y = mean, color = id3)) +
  geom_errorbar(aes(ymin=lower, ymax=upper), width=0, linewidth = 1) +
  geom_point(stroke = 1, shape = 21, fill = "white", size = 4) + 
  coord_flip() +
  facet_wrap(~ race, ncol = 1) +
  theme_rb(legend = TRUE) +
  pid3_color() +
  theme(strip.text = element_text(size = 20)) +
  labs(x = "", y = "", title = "Average Number of Political Acts Among Christians Over Time - Max is 4", 
       subtitle = "Among Christians", 
       caption = "@ryanburge\nData: Cooperative Election Study, 2012-2022")
save("pol_acts_mean_id3_over_time_race2.png", ht = 6)



reg <- cces20 %>% 
  filter(religpew == 1 | religpew == 2 | religpew == 3 | religpew == 4) %>% 
  mutate(race = frcode(race == 1 ~ "White", TRUE ~ "Non-White")) %>% 
  filter(faminc_new <= 16) %>% 
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal", 
                      ideo5 == 3 ~ "Moderate", 
                      ideo5 == 4 | ideo5 == 5 ~ "Conservative")) %>% 
  mutate(att = car::recode(pew_churatd, "6=1; 5=2; 4=3; 3=4; 2=5; 1=6; else = NA")) %>% 
  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(male = car::recode(gender, "1=1; else =0")) %>% 
  mutate(white = car::recode(race, "1=1; else =0")) %>% 
  mutate(income = faminc_new) %>% 
  cces_pid3(pid7) %>% 
  mutate(age = 2020- birthyr) %>% 
  select(acts, male, race, age, income, id3, educ, act1, act2, act3, act4, act5, act6, att)

library(interactions)

reg1 <- lm(acts ~ id3*att*race + male + income + age + educ, data = reg)

graph <- interact_plot(reg1, pred = att, modx = id3, mod2 = race, interval = TRUE, int.width =  .76, mod2.labels = c("White", "Non-White"))

graph +
  theme_rb(legend = TRUE) +
  pid3_color() +
  pid3_fill() + 
  theme(plot.subtitle = element_text(size = 10)) +
  theme(strip.text = element_text(size = 20)) +
  scale_x_continuous(breaks = c(1,2,3,4,5,6), labels = c("Never", "Seldom", "Yearly", "Monthly", "Weekly", "Weekly+")) +
  labs(x = "Religious Attendance", y = "", title = "Number of Political Acts in 2020 Election Among Christians", subtitle =  "Regression model with controls for: gender, income, education, and age included", 
       caption = "@ryanburge\nData: Cooperative Election Study 2020") 
save("pol_acts_regression_att_race_xtn.png", wd=8)