ct_selection_untitled-AB3B5DC2.R

gg <- cces %>% 
  mutate(nip = case_when(religion == 11 ~ 1, 
                         TRUE ~ 0)) %>% 
  group_by(year) %>% 
  mean_ci(nip, wt = weight, ci = .84)

gg %>% 
  filter(year >= 2008) %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = factor(year), y = mean, fill = mean)) +
  geom_col(color = "black") +
  theme_rb() +
  y_pct() + 
  scale_fill_gradient2(low = "#c6fff9", mid = "#00a4a4", high = "#003034", midpoint = .20) +
  geom_text(aes(y = mean + .008, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 7, family = "font") +
  labs(x = "", y = "", title = "Share of Americans Who Describe Their Present Religion as Nothing in Particular", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022")
save("nip_ces22.png")

gg <- cces %>% 
  filter(year == 2022) %>% 
  mutate(religpew = religion) %>% 
  mutate(trad = frcode(religpew == 1 ~ "Protestant",
                       religpew == 2 ~ "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"))  %>%
  mutate(ind = case_when(pid7 == 4 | pid7 == 8 ~ 1, 
                         TRUE ~ 0)) %>% 
  mutate(other = case_when(ideo5 == 6 ~ 1, 
                           TRUE ~ 0)) %>% 
  mutate(both = ind + other) %>% 
  mutate(both = case_when(both == 2 ~ 1, TRUE ~ 0)) %>% 
  group_by(trad) %>% 
  mean_ci(both, wt = weight, ci = .84) %>% 
  filter(trad != "NA") 


gg %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = reorder(trad, mean), y = mean, fill = trad)) +
  geom_col(color = "black") +
  coord_flip() +
  y_pct() + 
  theme_rb() + 
  scale_fill_manual(values = c(moma.colors("Warhol", 12))) +
  geom_text(aes(y = mean + .0075, label = ifelse(mean > .09, paste0(lab*100, '%'),"")), position = position_dodge(width = .9), size = 7, family = "font") +
  geom_text(aes(y = mean + .005, label = ifelse(mean <= .087, paste0(lab*100, '%'),"")), position = position_dodge(width = .9), size = 7, family = "font") +
  labs(x = "", y = "", title = "Share Describing Their Partisanship as Independent/Other and Ideology as Not Sure", caption = "@ryanburge\nData: Cooperative Election Study, 2022")
save("id_pid_other.png")


gg1 <- cces %>% 
  mutate(ind = case_when(pid7 == 4 | pid7 == 8 ~ 1, 
                         TRUE ~ 0)) %>% 
  mutate(other = case_when(ideo5 == 6 ~ 1, 
                           TRUE ~ 0)) %>% 
  mutate(both = ind + other) %>% 
  mutate(both = case_when(both == 2 ~ 1, TRUE ~ 0)) %>% 
  group_by(year) %>% 
  mean_ci(both, wt = weight, ci = .84) %>% 
  mutate(grp = "Entire Sample")


gg2 <- cces %>% 
  filter(religion == 11) %>% 
  mutate(ind = case_when(pid7 == 4 | pid7 == 8 ~ 1, 
                         TRUE ~ 0)) %>% 
  mutate(other = case_when(ideo5 == 6 ~ 1, 
                           TRUE ~ 0)) %>% 
  mutate(both = ind + other) %>% 
  mutate(both = case_when(both == 2 ~ 1, TRUE ~ 0)) %>% 
  group_by(year) %>% 
  mean_ci(both, wt = weight, ci = .84) %>% 
  mutate(grp = "Nothing in Particular")

all <- bind_rows(gg1, gg2) %>% filter(year >= 2012)

all %>% 
  ggplot(., aes(x = factor(year), y = mean, color = grp, group = grp)) +
  geom_point(stroke = 1, shape = 21, fill = "white") +
  geom_labelsmooth(aes(label = grp),  method = "loess", formula = y ~ x, family = "font", linewidth = 1, text_smoothing = 30, size = 7, linewidth = 1, boxlinewidth = 0.3) +
  theme_rb() + 
  scale_color_calc() +
  scale_y_continuous(labels = percent, limits = c(0, .18)) +
  labs(x = "Year", y = "", title = "Share Describing Their Partisanship as Independent/Other and Ideology as Not Sure", caption = "@ryanburge\nData: Cooperative Election Study, 2012-2022")
save("dones_nip_all_trendlines.png")
  

gg1 <- cces %>% 
  filter(year == 2008 | year == 2022) %>% 
  mutate(religpew = religion) %>% 
  mutate(trad = frcode(religpew == 1 ~ "Protestant",
                       religpew == 2 ~ "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"))  %>%
  mutate(college = case_when(educ == 5 | educ == 6 ~1, TRUE ~ 0)) %>% 
  group_by(trad, year) %>% 
  mean_ci(college, wt = weight, ci = .84) %>% 
  na.omit()


gg2 <- cces %>% 
  filter(year == 2008 | year == 2022) %>% 
  mutate(college = case_when(educ == 5 | educ == 6 ~1, TRUE ~ 0)) %>% 
  group_by(year) %>% 
  mean_ci(college, wt = weight, ci = .84) %>% 
  mutate(trad = "Entire Sample")

gg <- bind_rows(gg1, gg2)

gg$diff <- c("+5", "+5", "+11", "+11", "+8", "+8", "-7", "-7", "+9", "+9", "+13", "+13", "+2", "+2", "0", "0", "+18", "+18", "+11", "+11", "+7", "+7", "+10", "+10")

gg <- gg %>% 
  filter(trad == "Protestant" | trad == "Catholic" | trad == "Entire Sample" | trad == "Atheist" | trad == "Agnostic" | trad == "Nothing in Particular")

lvl <- gg %>% 
  group_by(trad) %>% 
  summarise(sum = sum(n))


gg <- left_join(gg, lvl)

gg$trad <- fct_reorder(gg$trad, gg$sum, .desc = TRUE)

library(ggpubr)


gg %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = year, y = mean, fill = factor(year))) +
  geom_col(color = "black") +
  facet_wrap(~ trad, nrow = 2) +
  geom_bracket(data = gg, aes(label = diff), xmin = 2008, xmax = 2022, y.position = .57, label = "test", family = "font", label.size = 8, size = 1) +
  theme_rb() + 
  scale_x_continuous(labels = c("2008", "2022"), breaks = c(2008, 2022)) +
  scale_y_continuous(labels = percent, limits = c(0, .60), breaks = c(0, .25, .50, .75)) +
  scale_fill_manual(values = c("#ed3049", "#26b5f8")) +
  theme(panel.grid.major.x = element_blank()) +
  theme(strip.text = element_text(size = 14)) +
  geom_text(aes(y = mean + .025, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 8, family = "font") +
  labs(x = "", y = "", title = "Share with a Bachelor's Degree in 2008 vs 2022", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022")
save("educ_change_2008_2022_nn.png", wd = 6, ht = 10)



yyy1 <- cces16 %>% 
  mutate(trad = frcode(religpew == 1 ~ "Protestant",
                       religpew == 2 ~ "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"))  %>%
  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(trad) %>% 
  mutate(none = case_when(acts == 0 ~ 1, 
                          acts >= 1 ~ 0)) %>% 
  socsci::mean_ci(none, wt = commonweight, ci = .84) %>% 
  mutate(year = "2016") %>% 
  na.omit()


yyy2 <- cces18 %>% 
  mutate(trad = frcode(religpew == 1 ~ "Protestant",
                       religpew == 2 ~ "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"))  %>%
  mutate(meet   = CC18_417a_1) %>% 
  mutate(sign   = CC18_417a_2) %>% 
  mutate(work   = CC18_417a_3) %>% 
  mutate(protest = CC18_417a_4) %>% 
  mutate(contact = CC18_417a_5) %>% 
  mutate(donate = CC18_417a_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(contact   = car::recode(contact, "1=1; 2=0")) %>% 
  mutate(protest   = car::recode(protest, "1=1; 2=0")) %>% 
  mutate(acts = meet + sign + donate + work + contact + protest) %>% 
  mutate(none = case_when(acts == 0 ~ 1, 
                          acts >= 1 ~ 0)) %>% 
  group_by(trad) %>% 
  socsci::mean_ci(none, wt = commonweight, ci = .84) %>% 
  mutate(year = "2018") %>% 
  na.omit()


yyy3 <- cces20 %>% 
  mutate(trad = frcode(religpew == 1 ~ "Protestant",
                       religpew == 2 ~ "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"))  %>%
  mutate(meet   = CC20_430a_1) %>% 
  mutate(sign   = CC20_430a_2) %>% 
  mutate(work   = CC20_430a_3) %>% 
  mutate(protest = CC20_430a_4) %>% 
  mutate(contact = CC20_430a_5) %>% 
  mutate(donate = CC20_430a_6) %>% 
  mutate(meet   = car::recode(meet, "1=1; 2=0")) %>% 
  mutate(sign   = car::recode(sign, "1=1; 2=0")) %>% 
  mutate(work   = car::recode(work, "1=1; 2=0")) %>% 
  mutate(protest   = car::recode(protest, "1=1; 2=0")) %>% 
  mutate(contact   = car::recode(contact, "1=1; 2=0")) %>% 
  mutate(donate = car::recode(donate, "1=1; 2=0")) %>% 
  mutate(acts = meet + sign + donate + work + protest + contact) %>% 
  mutate(none = case_when(acts == 0 ~ 1, 
                          acts >= 1 ~ 0)) %>% 
  group_by(trad) %>% 
  socsci::mean_ci(none, wt = commonweight, ci = .84) %>% 
  mutate(year = "2020") %>% 
  na.omit()



yyy4 <- cces22 %>% 
  mutate(trad = frcode(religpew == 1 ~ "Protestant",
                       religpew == 2 ~ "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"))  %>%
  mutate(meet   = CC22_430a_1) %>% 
  mutate(sign   = CC22_430a_2) %>% 
  mutate(work   = CC22_430a_3) %>% 
  mutate(protest = CC22_430a_4) %>% 
  mutate(contact = CC22_430a_5) %>% 
  mutate(donate = CC22_430a_6) %>% 
  mutate(meet   = car::recode(meet, "1=1; 2=0")) %>% 
  mutate(sign   = car::recode(sign, "1=1; 2=0")) %>% 
  mutate(work   = car::recode(work, "1=1; 2=0")) %>% 
  mutate(protest   = car::recode(protest, "1=1; 2=0")) %>% 
  mutate(contact   = car::recode(contact, "1=1; 2=0")) %>% 
  mutate(donate = car::recode(donate, "1=1; 2=0")) %>% 
  mutate(acts = meet + sign + donate + work + protest + contact) %>% 
  mutate(none = case_when(acts == 0 ~ 1, 
                          acts >= 1 ~ 0)) %>% 
  group_by(trad) %>% 
  socsci::mean_ci(none, wt = commonweight, ci = .84) %>% 
  mutate(year = "2022") %>% 
  na.omit()


graph <- bind_rows(yyy1, yyy2, yyy3, yyy4)


graph %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = reorder(trad, mean), y = mean, fill = trad)) +
  geom_col(color = "black") +
  facet_wrap(~ year) +
  coord_flip() +
  theme_rb() + 
  error_bar() + 
  lab_bar(top = FALSE, type = lab, pos = .055, sz = 5) +
  geom_text(aes(y = .055, label = ifelse(trad == "Nothing in Particular" | trad == "Protestant" | trad == "Orthodox" | trad == "Muslim" | trad == "Catholic", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 5, family = "font", color = "white") +   
  scale_fill_manual(values = c(moma.colors("Warhol", 12))) +
  y_pct() + 
  theme(strip.text = element_text(size = 20)) +
  labs(x = "", y = "", title = "Share Engaging in Zero Political Acts in the Prior Year", caption = "@ryanburge\nData: Cooperative Election Study, 2016-2022")
save("no_polacts.png", ht = 8)


graph1 <- cces %>% 
  filter(year >= 2020) %>% 
  filter(religion == 11) %>% 
  mutate(single = case_when(marital_status == 5 ~ 1, 
                            TRUE ~ 0)) %>% 
  group_by(age) %>% 
  socsci::mean_ci(single, wt = weight) %>% 
  mutate(type = "Nothing in Particular")


graph2 <- cces %>% 
  filter(year >= 2020) %>% 
  mutate(single = case_when(marital_status == 5 ~ 1, 
                            TRUE ~ 0)) %>% 
  group_by(age) %>% 
  socsci::mean_ci(single, wt = weight) %>% 
  mutate(type = "Entire Sample")

both <- bind_rows(graph1, graph2)


both %>% 
  filter(age <= 75) %>% 
  ggplot(., aes(x = age, y = mean, color = type, group = type)) +
  geom_point(stroke = .5, shape = 21, alpha = .45) +
  geom_labelsmooth(aes(label = type),  method = "loess", formula = y ~ x, family = "font", 
                   linewidth = 1, text_smoothing = 30, size = 6, linewidth = 1, boxlinewidth = 0.3, hjust = .75) +
  scale_color_calc() +
  theme_rb() +
  y_pct() +
  labs(x = "Age", y = "", title = "Share Reporting That They Have Never Been Married by Age",
       caption = "@ryanburge\nData: Cooperative Election 2020-2022") 
save("age_marital_nip.png", wd = 8, ht = 6)

regg <- cces %>% 
  filter(year >= 2020) %>% 
  filter(age >= 35 & age <= 45) %>% 
  mutate(nokids = case_when(havekids == 2 ~ 1, 
                            havekids == 1 ~ 0)) %>% 
  mutate(single = case_when(marital_status == 5 ~ 1, 
                            TRUE ~ 0)) %>% 
  mutate(both = nokids + single) %>% 
  mutate(both = case_when(both == 2 ~ 1, 
                          TRUE ~ 0)) %>% 
  mutate(religpew = religion) %>% 
  mutate(trad = frcode(religpew == 1 ~ "Protestant",
                       religpew == 2 ~ "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"))  %>%
  mutate(male = case_when(gender == 1 ~ 1, 
                          gender == 2 ~ 0)) %>% 
  mutate(white = case_when(race == 1 ~ 1, 
                           TRUE ~ 0)) %>% 
  mutate(lib = case_when(ideo5 == 1 | ideo5 == 2 ~ 1, 
                         ideo5 <= 5 ~ 0)) %>% 
  select(age, income, educ, male, white, lib, single, nokids, both, trad)

one <- glm(nokids ~ trad + male + white + age + income + educ + lib, family = "binomial", data = regg)