ct_selection_untitled-A3BCD747.R

gg1 <- gss %>% 
  filter(year <= 2018) %>% 
  mutate(work = fefam) %>% 
  mutate(work = frcode(work == 4 ~ "Strongly Disagree", 
                       work == 3 ~ "Disagree", 
                       work == 2 ~ "Agree", 
                       work == 1 ~ "Strongly Agree")) %>% 
  group_by(year) %>% 
  ct(work, wt= wtssall, show_na = FALSE)

gg2 <- gss %>% 
  filter(year >= 2020) %>% 
  mutate(work = fefam) %>% 
  mutate(work = frcode(work == 4 ~ "Strongly Disagree", 
                       work == 3 ~ "Disagree", 
                       work == 2 ~ "Agree", 
                       work == 1 ~ "Strongly Agree")) %>% 
  group_by(year) %>% 
  ct(work, wt= wtssnrps, show_na = FALSE)

gg <- bind_rows(gg1, gg2)


gg %>% 
  ggplot(., aes(x = year, y = pct, color = work, group = work)) +
  geom_point(stroke = 1, shape = 21, alpha = .45) +
  geom_labelsmooth(aes(label = work),  method = "loess", formula = y ~ x, family = "font", linewidth = 1, text_smoothing = 30, size = 6, linewidth = 1, boxlinewidth = 0.3, hjust = .75) +
  scale_y_continuous(labels = percent, limits = c(0, .50)) + 
  theme_rb() + 
  scale_color_manual(values = c("#28666e", "#5f2680",  "#B5B682", "azure4",  "#7D3C98", "#5f2680", "#033f63", "#28666e", "#e1e1e1", "#B5B682", "#FEDC97",  "#7D3C98", "#5f2680")) +
  theme(strip.text = element_text(size = 25)) +
  labs(x = "", y = "Share Agreeing", title = "It is much better for everyone involved if the man is the achiever outside the home\nand the woman takes care of the home and family", 
        caption = "@ryanburge + @religiondata\nData: General Social Survey, 1977-2022") +
  add_text(x = 1977, y = .038, word = "6%", sz = 7) +
  add_text(x = 1977, y = .21, word = "19%", sz = 7) +
  add_text(x = 1977, y = .257, word = "28%", sz = 7) +
  add_text(x = 1977, y = .49, word = "47%", sz = 7) +
  add_text(x = 2022, y = .46, word = "44%", sz = 7) +
  add_text(x = 2022, y = .36, word = "31%", sz = 7) +
  add_text(x = 2022, y = .155, word = "19%", sz = 7) +
  add_text(x = 2022, y = .075, word = "6%", sz = 7) 
save("gss_women_work.png")

gg1 <- gss %>% 
  mutate(pid7 = partyid + 1) %>% 
  cces_pid3(pid7) %>% 
  filter(year <= 2018) %>% 
  mutate(work = fefam) %>% 
  mutate(work = case_when(work == 1 | work == 2 ~ 1, 
                          work == 3 | work == 4 ~ 0)) %>% 
  group_by(year, pid3) %>% 
  socsci::mean_ci(work, wt= wtssall, ci = .84)

gg2 <- gss %>% 
  filter(year >= 2020) %>% 
  mutate(pid7 = partyid + 1) %>% 
  cces_pid3(pid7) %>% 
  mutate(work = fefam) %>% 
  mutate(work = case_when(work == 1 | work == 2 ~ 1, 
                          work == 3 | work == 4 ~ 0)) %>% 
  group_by(year, pid3) %>% 
  socsci::mean_ci(work, wt= wtssnrps, ci = .84)

gg <- bind_rows(gg1, gg2)


gg %>% 
  filter(year >= 1977) %>% 
  ggplot(., aes(x = year, y = mean, color = pid3, group = pid3)) +
  geom_point(stroke = 1, shape = 21, alpha = .45) +
  geom_labelsmooth(aes(label =pid3),  method = "loess", formula = y ~ x, family = "font", linewidth = 1, text_smoothing = 30, size = 6, linewidth = 1, boxlinewidth = 0.3, hjust = .75) +
  scale_y_continuous(labels = percent, limits = c(0, .70)) + 
  theme_rb() + 
  pid3_color() +
  theme(strip.text = element_text(size = 25)) +
  labs(x = "", y = "Share Agreeing", title = "It is much better for everyone involved if the man is the achiever outside the home\nand the woman takes care of the home and family", 
       caption = "@ryanburge + @religiondata\nData: General Social Survey, 1977-2022") 
save("gss_women_work_pid3.png")




gg1 <- gss %>% 
  mutate(pid7 = partyid + 1) %>% 
  cces_pid3(pid7) %>% 
  mutate(gender = frcode(sex == 1 ~ "Men", 
                         sex == 2 ~ "Women")) %>% 
  filter(year <= 2018) %>% 
  mutate(work = fefam) %>% 
  mutate(work = case_when(work == 1 | work == 2 ~ 1, 
                          work == 3 | work == 4 ~ 0)) %>% 
  group_by(year, pid3, gender) %>% 
  socsci::mean_ci(work, wt= wtssall, ci = .84)

gg2 <- gss %>% 
  filter(year >= 2020) %>% 
  mutate(pid7 = partyid + 1) %>% 
  cces_pid3(pid7) %>% 
  mutate(gender = frcode(sex == 1 ~ "Men", 
                         sex == 2 ~ "Women")) %>% 
  mutate(work = fefam) %>% 
  mutate(work = case_when(work == 1 | work == 2 ~ 1, 
                          work == 3 | work == 4 ~ 0)) %>% 
  group_by(year, pid3, gender) %>% 
  socsci::mean_ci(work, wt= wtssnrps, ci = .84)

gg <- bind_rows(gg1, gg2) %>% filter(gender != "NA")

gg %>% 
  filter(year >= 1977) %>% 
  filter(pid3 == "Democrat" | pid3 == "Republican") %>% 
  ggplot(., aes(x = year, y = mean, color = pid3, group = pid3)) +
  geom_point(stroke = 1, shape = 21, alpha = .45) +
  geom_labelsmooth(aes(label =pid3),  method = "loess", formula = y ~ x, family = "font", linewidth = 1, text_smoothing = 30, size = 6, linewidth = 1, boxlinewidth = 0.3, hjust = .75) +
  scale_y_continuous(labels = percent, limits = c(0, .72)) + 
  theme_rb() + 
  scale_color_manual(values = c("dodgerblue3", "firebrick3")) +
  facet_wrap(~ gender) +
  theme(strip.text = element_text(size = 25)) +
  labs(x = "", y = "Share Agreeing", title = "It is much better for everyone involved if the man is the achiever outside the home\nand the woman takes care of the home and family", 
       caption = "@ryanburge + @religiondata\nData: General Social Survey, 1977-2022") 
save("gss_women_work_pid3_gender.png")



all <- gss %>% 
  filter(year >= 2020) %>% 
  gss_reltrad6(reltrad) %>% 
  mutate(work = fefam) %>% 
  mutate(work = case_when(work == 1 | work == 2 ~ 1, 
                          work == 3 | work == 4 ~ 0)) %>% 
  group_by(reltrad) %>% 
  socsci::mean_ci(work, wt= wtssnrps, ci = .84) %>% filter(reltrad != "NA")

# all <- gss %>% 
#   filter(year >= 2020) %>% 
#   mutate(work = fefam) %>% 
#   mutate(work = case_when(work == 1 | work == 2 ~ 1, 
#                           work == 3 | work == 4 ~ 0)) %>% 
#   mean_ci(work, wt= wtssnrps, ci = .84) 

all %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = reorder(reltrad, mean), y = mean, fill = reltrad)) +
  geom_col(color = "black") +
  coord_flip() +
  y_pct() +
  theme_rb() +
  error_bar() +
  lab_bar(top = FALSE, type = lab, pos = .03, sz = 12) +
  geom_text(aes(y = .03, label = ifelse(reltrad == "Black Prot.", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 12, family = "font", color = "white")   +
  geom_text(aes(y = .03, label = ifelse(reltrad == "No Religion", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 12, family = "font", color = "white")   +
  geom_text(aes(y = .03, label = ifelse(reltrad == "Mainline", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 12, family = "font", color = "white")   +
  scale_fill_manual(values = c("#e1e1e1","#28666e", "#5f2680",  "#B5B682", "#FEDC97",  "#7D3C98", "#5f2680", "#033f63", "#28666e", "#e1e1e1", "#B5B682", "#FEDC97",  "#7D3C98", "#5f2680")) +
  labs(x = "", y = "Share Agreeing", title = "It is much better for everyone involved if the man is the achiever outside the home\nand the woman takes care of the home and family", 
       caption = "@ryanburge + @religiondata\nData: General Social Survey, 2021-2022") 
save("reltrad_women_work.png")


all <- gss %>% 
  filter(year >= 2020) %>% 
  gss_reltrad6(reltrad) %>% 
  mutate(gender = frcode(sex == 1 ~ "Men", 
                         sex == 2 ~ "Women")) %>%
  mutate(work = fefam) %>% 
  mutate(work = case_when(work == 1 | work == 2 ~ 1, 
                          work == 3 | work == 4 ~ 0)) %>% 
  group_by(reltrad, gender) %>% 
  socsci::mean_ci(work, wt= wtssnrps, ci = .84) %>% 
  filter(reltrad != "NA") %>% 
  filter(gender != "NA")

men <- all %>%
  filter(gender == "Men") %>% 
  select(reltrad, men = mean)
  
women <- all %>%
  filter(gender == "Women") %>% 
  select(reltrad, women = mean)

lab <- left_join(men, women) %>% 
  mutate(diff = men - women) %>% 
  select(reltrad, diff) %>% 
  mutate(diff = round(diff, 2)) %>% 
  mutate(diff = diff*100) %>% 
  mutate(diff = paste0(diff, "%"))

ttt <- left_join(all, lab)


library(ggpubr)

ttt %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = gender, y = mean, fill = factor(gender))) +
  geom_col(color = "black") + 
  geom_bracket(data = ttt, aes(label = diff), xmin = 1, xmax = 2, y.position = .475, label = "test", family = "font", label.size = 7, size = 1) +
  facet_wrap(~ reltrad) +
  theme_rb() +
  scale_fill_manual(values = c("#28666e", "#5f2680")) +
  scale_y_continuous(labels = percent, limits = c(0, .5)) +
  geom_text(aes(y = .025, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 8, family = "font", color = 'white') +   
  theme(plot.title = element_text(size = 14)) +
  labs(x = "", y = "Share Agreeing", title = "It is much better for everyone involved if the man is the achiever\noutside the home and the woman takes care of the home and family", 
       caption = "@ryanburge + @religiondata\nData: General Social Survey, 2021-2022") 
save("gender_reltrad_compare.png", ht = 10, wd = 6)  


regg <- gss %>% 
  filter(year >= 2020) %>% 
  mutate(work = fefam) %>% 
  mutate(work = case_when(work == 1 | work == 2 ~ 1, 
                          work == 3 | work == 4 ~ 0)) %>% 
  mutate(white = case_when(race == 1 ~ 1, 
                           TRUE ~ 0)) %>% 
  mutate(male = case_when(sex == 1 ~ 1, 
                          sex == 2 ~ 0)) %>% 
  mutate(rep = case_when(partyid == 4 | partyid == 5 | partyid == 6 ~ 1, 
                         partyid <= 3 ~ 0)) %>% 
  gss_reltrad6(reltrad) %>% 
  select(work, white, male, educ, income, attend, rep, age, reltrad)

regg$reltrad <- relevel(factor(regg$reltrad), ref = "Catholic")



out <- glm(work ~ white + male + educ + income + attend + rep + age + reltrad, family = "binomial", data = regg)

library(jtools)



coef_names <- c("White" = "white", 
                "Income" = "income", 
                "Education" = "educ", 
                "Age" = "age", 
                "Male" = "male", 
                "Republican" = "rep", 
                "Church Attendance" = "attend", 
                "Evangelical" = "reltradEvangelical", 
                "Mainline" = "reltradMainline", 
                "Black Prot." = "reltradBlack Prot.", 
                "Other Faith" = "reltradOther Faith", 
                "No Religion" = "reltradNo Religion")

gg <- plot_summs(out, scale = TRUE, robust = "HC3", coefs = coef_names, colors = "#5f2680")


gg + 
  theme_rb() +
  # add_text(x = -.5, y = 3.5, word = "Less Agreement", sz = 9) +
  add_text(x = .85, y = 11.5, word = "More Agreement", sz = 9) +
  labs(x = "", y = "", title = "It is much better for everyone involved if the man is the achiever\noutside the home and the woman takes care of the home and family", 
       caption = "@ryanburge + @religiondata + @religiondata\nData: General Social Survey, 2021-2022")
save("reg_women_home.png", ht = 6)