ct_selection_untitled-5B1FF5C3.R

gg1 <- gss %>% 
  filter(year <= 2018) %>% 
  mutate(porn = pornlaw) %>% 
  mutate(porn = frcode(porn == 1 ~ "Complete Ban", 
                       porn == 2 ~ "Ban for Minors", 
                       porn == 3 ~ "No Ban")) %>% 
  group_by(year) %>% 
  ct(porn, wt = wtssall, show_na = FALSE)

gg2 <- gss %>% 
  filter(year > 2018) %>% 
  mutate(porn = pornlaw) %>% 
  mutate(porn = frcode(porn == 1 ~ "Complete Ban", 
                       porn == 2 ~ "Ban for Minors", 
                       porn == 3 ~ "No Ban")) %>% 
  group_by(year) %>% 
  ct(porn, wt = wtssnrps, show_na = FALSE)

gg <- bind_rows(gg1, gg2)

gg %>% 
  ggplot(., aes(x = year, y = pct, color = porn, group = porn)) +
  geom_point(stroke = .65, shape = 21) +
  geom_labelsmooth(aes(label = porn),  method = "loess", formula = y ~ x, family = "font", linewidth = 1, text_smoothing = 30, size = 7, linewidth = 1, boxlinewidth = 0.3) + 
  theme_rb() +
  y_pct() +
  add_text(x = 1973, y = .52, word = "49%", sz = 6) +
  add_text(x = 1973, y = .44, word = "42%", sz = 6) +
  add_text(x = 1973, y = .13, word = "10%", sz = 6) +
  add_text(x = 2022, y = .30, word = "28%", sz = 6) +
  add_text(x = 2022, y = .70, word = "68%", sz = 6) +
  add_text(x = 2022, y = .08, word = "4%", sz = 6) +
  scale_color_manual(values = c("#28666e", "#5f2680",  "#B5B682", "azure4",  "#7D3C98", "#5f2680", "#033f63", "#28666e", "#e1e1e1", "#B5B682", "#FEDC97",  "#7D3C98", "#5f2680")) +
  labs(x = "", y = "", title = "Which of these statements comes closest to your feelings about pornography laws?", caption = "@ryanburge + @religiondata\nData: General Social Survey, 1973-2022")
save("gss_porn_law.png")


gg1 <- gss %>% 
  mutate(gender = frcode(sex == 1 ~ "Men", 
                         sex == 2 ~ "Women")) %>% 
  filter(year <= 2018) %>% 
  mutate(porn = pornlaw) %>% 
  mutate(porn = frcode(porn == 1 ~ "Complete Ban", 
                       porn == 2 ~ "Ban for Minors", 
                       porn == 3 ~ "No Ban")) %>% 
  group_by(year, gender) %>% 
  ct(porn, wt = wtssall, show_na = FALSE)

gg2 <- gss %>% 
  mutate(gender = frcode(sex == 1 ~ "Men", 
                         sex == 2 ~ "Women")) %>% 
  filter(year > 2018) %>% 
  mutate(porn = pornlaw) %>% 
  mutate(porn = frcode(porn == 1 ~ "Complete Ban", 
                       porn == 2 ~ "Ban for Minors", 
                       porn == 3 ~ "No Ban")) %>% 
  group_by(year, gender) %>% 
  ct(porn, wt = wtssnrps, show_na = FALSE)

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

gg %>% 
  ggplot(., aes(x = year, y = pct, color = gender, group = gender)) +
  geom_point(stroke = .25, shape = 21, alpha = .5) +
  geom_smooth(se= FALSE) +
  facet_wrap(~ porn) +
  scale_y_continuous(labels = percent) +
  scale_color_manual(values = c("#B5B682", "#5f2680")) +
  theme_rb(legend = TRUE) +
  labs(x = "", y = "", title = "Which of these statements comes closest to your feelings about pornography laws?", caption = "@ryanburge + @religiondata\nData: General Social Survey, 1973-2022")
save("porn_law_gender.png")

gg1 <- gss %>% 
  filter(year <= 2018) %>% 
  gss_reltrad6(reltrad) %>% 
  mutate(porn = pornlaw) %>% 
  mutate(porn = frcode(porn == 1 ~ "Complete Ban", 
                       porn == 2 ~ "Ban for Minors", 
                       porn == 3 ~ "No Ban")) %>% 
  group_by(reltrad, year) %>% 
  ct(porn, wt = wtssall, show_na = FALSE)

gg2 <- gss %>% 
  filter(year > 2018) %>% 
  gss_reltrad6(reltrad) %>% 
  mutate(porn = pornlaw) %>% 
  mutate(porn = frcode(porn == 1 ~ "Complete Ban", 
                       porn == 2 ~ "Ban for Minors", 
                       porn == 3 ~ "No Ban")) %>%  
  group_by(reltrad, year) %>% 
  ct(porn, wt = wtssnrps, show_na = FALSE)

gg <- bind_rows(gg1, gg2) %>% filter(reltrad != "NA") %>% na.omit()

gg %>% 
  ggplot(., aes(x = year, y = pct, color = porn, group = porn)) +
  geom_point(stroke = .25, shape = 21) +
  geom_smooth(se = FALSE) +
  facet_wrap(~ reltrad) +
  scale_color_manual(values = c("#28666e", "#5f2680",  "#B5B682", "azure4",  "#7D3C98", "#5f2680", "#033f63", "#28666e", "#e1e1e1", "#B5B682", "#FEDC97",  "#7D3C98", "#5f2680")) +
  theme_rb(legend = TRUE) +
  y_pct() + 
  theme(strip.text = element_text(size = 20)) +
  labs(x = "", y = "", title = "Which of these statements comes closest to your feelings about pornography laws?", caption = "@ryanburge + @religiondata\nData: General Social Survey, 1973-2022")
save("porn_law_reltrad.png", ht = 9)


gg1 <- gss %>% 
  filter(year <= 2018) %>% 
  mutate(birthyr = year - age) %>% 
  mutate(cohorts = frcode(birthyr >= 1940 & birthyr <= 1944 ~ "1940-1944",
                          birthyr >= 1945 & birthyr <= 1949 ~ "1945-1949",
                          birthyr >= 1950 & birthyr <= 1954 ~ "1950-1954",
                          birthyr >= 1955 & birthyr <= 1959 ~ "1955-1959",
                          birthyr >= 1960 & birthyr <= 1964 ~ "1960-1964",
                          birthyr >= 1965 & birthyr <= 1969 ~ "1965-1969",
                          birthyr >= 1970 & birthyr <= 1974 ~ "1970-1974",
                          birthyr >= 1975 & birthyr <= 1979 ~ "1975-1979",
                          birthyr >= 1980 & birthyr <= 1984 ~ "1980-1984",
                          birthyr >= 1985 & birthyr <= 1989 ~ "1985-1989",
                          birthyr >= 1990 & birthyr <= 1994 ~ "1990-1994",
                          birthyr >= 1995 & birthyr <= 2000 ~ "1995-2000")) %>% 
  mutate(porn = pornlaw) %>% 
  mutate(porn = frcode(porn == 1 ~ "Complete Ban", 
                       porn == 2 ~ "Ban for Minors", 
                       porn == 3 ~ "No Ban")) %>%  
  group_by(cohorts, year) %>% 
  ct(porn, wt = wtssall, show_na = FALSE)

gg2 <- gss %>% 
  filter(year > 2018) %>% 
  mutate(birthyr = year - age) %>% 
  mutate(cohorts = frcode(birthyr >= 1940 & birthyr <= 1944 ~ "1940-1944",
                          birthyr >= 1945 & birthyr <= 1949 ~ "1945-1949",
                          birthyr >= 1950 & birthyr <= 1954 ~ "1950-1954",
                          birthyr >= 1955 & birthyr <= 1959 ~ "1955-1959",
                          birthyr >= 1960 & birthyr <= 1964 ~ "1960-1964",
                          birthyr >= 1965 & birthyr <= 1969 ~ "1965-1969",
                          birthyr >= 1970 & birthyr <= 1974 ~ "1970-1974",
                          birthyr >= 1975 & birthyr <= 1979 ~ "1975-1979",
                          birthyr >= 1980 & birthyr <= 1984 ~ "1980-1984",
                          birthyr >= 1985 & birthyr <= 1989 ~ "1985-1989",
                          birthyr >= 1990 & birthyr <= 1994 ~ "1990-1994",
                          birthyr >= 1995 & birthyr <= 2000 ~ "1995-2000")) %>% 
  mutate(porn = pornlaw) %>% 
  mutate(porn = frcode(porn == 1 ~ "Complete Ban", 
                       porn == 2 ~ "Ban for Minors", 
                       porn == 3 ~ "No Ban")) %>%  
  group_by(cohorts, year) %>% 
  ct(porn, wt = wtssnrps, show_na = FALSE)

all <- bind_rows(gg1, gg2) %>% 
  filter(cohorts != "NA") 

all <- all[!(all$cohorts == "1995-2000" & all$year == 2014), ]


all %>% 
  ggplot(., aes(x = year, y = pct, color = porn, group = porn)) +
  geom_point(stroke = .15, shape = 21, alpha = 1) + 
  geom_smooth(se = FALSE) +
  facet_wrap(~ cohorts) +
  scale_color_manual(values = c("#28666e", "#5f2680",  "#B5B682", "azure4",  "#7D3C98", "#5f2680", "#033f63", "#28666e", "#e1e1e1", "#B5B682", "#FEDC97",  "#7D3C98", "#5f2680")) +
  theme_rb(legend = TRUE) +
  y_pct() + 
  labs(x = "", y = "", title = "Which of these statements comes closest to your feelings about pornography laws?", caption = "@ryanburge + @religiondata\nData: General Social Survey, 1973-2022")
save("porn_law_birth_cohort.png", ht = 12)






regg <- gss %>% 
  filter(year >= 2020) %>% 
  mutate(porn = pornlaw) %>% 
  mutate(porn = case_when(porn == 1 ~ 1, 
                          porn == 2 | porn == 3 ~ 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(porn, white, male, educ, income, attend, rep, age, reltrad)

regg$reltrad <- relevel(factor(regg$reltrad), ref = "No Religion")



out <- glm(porn ~ 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", 
                "Catholic" = "reltradCatholic")

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 = .5, y = 11.5, word = "Favor a Ban", sz = 9) +
  labs(x = "", y = "", title = "Factors That Predict a Total Ban on Pornography", 
       caption = "@ryanburge + @religiondata + @religiondata\nData: General Social Survey, 2021-2022")
save("reg_porn_ban.png", ht = 5)