ct_selection_untitled-5D630A10.R

df <- import("E://data/politics_first.sav") %>% 
  clean_names()

small <- df %>% 
  select(caseid, relig_2, relig_4, weight = weight1_4) %>% 
  mutate(religpew = relig_2) %>% 
  mutate(relig1 = frcode(religpew == 1 ~ "Protestant",
                         religpew == 2 ~ "Catholic",
                         religpew >= 3 & religpew <= 8 ~ "Other\nWorld\nReligions",
                         religpew == 9 ~ "Atheist", 
                         religpew == 10 ~ "Agnostic",
                         religpew == 11 ~ "Nothing in Particular", 
                         religpew == 12 ~ "Something Else")) %>% 
  mutate(religpew = relig_4) %>% 
  mutate(relig2 = frcode(religpew == 1 ~ "Protestant",
                         religpew == 2 ~ "Catholic",
                         religpew >= 3 & religpew <= 8 ~ "Other\nWorld\nReligions",
                         religpew == 9 ~ "Atheist", 
                         religpew == 10 ~ "Agnostic",
                         religpew == 11 ~ "Nothing in Particular", 
                         religpew == 12 ~ "Something Else")) %>% 
  select(caseid, relig1, relig2, weight) %>% 
  as_tibble() %>% 
  filter(relig1 != "NA") %>% 
  filter(relig2 != "NA")


library(reshape2)

test <- small %>% 
  select(-weight) %>% 
  melt(., id = c("caseid")) %>% arrange(caseid) 

test <- test %>% 
  mutate(year = frcode(variable == "relig1" ~ "Summer 2011",
                       variable == "relig2" ~ "Fall 2012"))

library(ggalluvial)

test$value <- factor(test$value, levels = c("Protestant", "Catholic", "Other\nWorld\nReligions", "Atheist", "Agnostic", "Nothing in Particular", "Something Else"))

test$value <- fct_rev(test$value)
test$year  <- fct_rev(test$year)


ggplot(test, aes(x = year, stratum = value, alluvium = caseid, fill = value, label = value)) +
  geom_flow(stat = "alluvium", lode.guidance = "leftright") +
  geom_stratum() +
  theme_rb() + 
  coord_flip() +
  geom_label(fill = "white", stat = "stratum", size = 4, colour = "black", family = "font") +
  scale_fill_manual(values = c("#033f63", "#28666e", "#150023", "#e1e1e1", "#B5B682", "#FFA700", "#5f2680")) +
  scale_color_manual(values = c("#033f63", "#28666e", "#150023", "#e1e1e1", "#B5B682", "#FFA700", "#5f2680")) +
  theme(plot.title = element_text(size = 40, margin = margin(t = 0, r = 0, b = 10, l = 0))) +  # Adjusted the top margin
  labs(x = "Year", y = "Number of Respondents", title = "Religious Shifts Over Time", caption = "@ryanburge + @religiondata\nData: Putting Politics First, Panel Data (2010-2012)") 
save("political_first_alluvial.png", wd = 10) 


small <- df %>% 
  select(caseid, relig_2, relig_4, weight = weight1_4) %>% 
  mutate(religpew = relig_2) %>% 
  mutate(relig1 = frcode(religpew == 1 ~ "Protestant",
                         religpew == 2 ~ "Catholic",
                         religpew >= 3 & religpew <= 8 ~ "Other World Religions",
                         religpew == 9 ~ "Atheist", 
                         religpew == 10 ~ "Agnostic",
                         religpew == 11 ~ "Nothing in Particular", 
                         religpew == 12 ~ "Something Else")) %>% 
  mutate(religpew = relig_4) %>% 
  mutate(relig2 = frcode(religpew == 1 ~ "Protestant",
                         religpew == 2 ~ "Catholic",
                         religpew >= 3 & religpew <= 8 ~ "Other World Religions",
                         religpew == 9 ~ "Atheist", 
                         religpew == 10 ~ "Agnostic",
                         religpew == 11 ~ "Nothing in Particular", 
                         religpew == 12 ~ "Something Else")) %>% 
  select(caseid, relig1, relig2, weight) %>% 
  as_tibble() %>% 
  filter(relig1 != "NA") %>% 
  filter(relig2 != "NA")



small <- small %>% 
  mutate(same = frcode(relig1 == relig2 ~ "Same", 
                       TRUE ~ "Not the Same")) 

graph <- small %>% 
  mutate(switch = case_when(relig1 != relig2 ~ 1, 
                            TRUE ~ 0)) %>% 
  group_by(relig1) %>% 
  mean_ci(switch, wt = weight)

graph %>% 
  mutate(lab = round(mean, 2)) %>% 
  ggplot(., aes(x = reorder(relig1, mean), y = mean, fill = relig1)) +
  geom_col(color = "black")  +
  coord_flip() +
  theme_rb() + 
  error_bar() +
  scale_fill_manual(values = c("#5f2680", "#FFA700", "#B5B682", "#e1e1e1", "#150023", "#28666e", "#033f63")) +
  lab_bar(top = FALSE, type = lab, pos = .025, sz = 7) +
  geom_text(aes(y = .025, label = ifelse(relig1 == "Agnostic", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 7, family = "font", color = "white") +   
  geom_text(aes(y = .025, label = ifelse(relig1 == "Protestant", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 7, family = "font", color = "white") + 
  geom_text(aes(y = .025, label = ifelse(relig1 == "Something Else", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 7, family = "font", color = "white") +   
  geom_text(aes(y = .025, label = ifelse(relig1 == "Nothing in Particular", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 7, family = "font", color = "white") +   
  y_pct() + 
  theme(plot.title = element_text(size = 15)) +
  labs(x = "", y = "", title = "Share Who Left Their Religious Tradition Between Summer of 2011 and Fall of 2012", caption = "@ryanburge + @religiondata\nData: Putting Politics First, Panel Data (2010-2012)")
save("politics_first_switching.png", ht = 4)

switch <- small %>% 
  filter(same == "Not the Same")

graph <- switch %>% 
  mutate(relig1 = frcode(relig1 == "Protestant" | relig1 == "Catholic" ~ "Prot./Cath.", 
                         relig1 == "Other World Religions" ~ "Other World Religions", 
                         relig1 == "Atheist" | relig1 == "Agnostic" | relig1 == "Nothing in Particular" ~ "No Religion", 
                         relig1 == "Something Else" ~ "Something Else")) %>% 
  mutate(relig2 = frcode(relig2 == "Protestant" | relig2 == "Catholic" ~ "Prot./Cath.", 
                         relig2 == "Other World Religions" ~ "Other World Religions", 
                         relig2 == "Atheist" | relig2 == "Agnostic" | relig2 == "Nothing in Particular" ~ "No Religion", 
                         relig2 == "Something Else" ~ "Something Else")) %>% 
  group_by(relig1) %>% 
  ct(relig2, wt = weight) %>% 
  filter(relig1 != "Other World Religions")


graph %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x = 1, y = pct, fill = fct_rev(relig2))) +
  geom_col(color = "black") + 
  coord_flip() +
  facet_wrap(~ relig1, ncol =1, strip.position = "top") +
  theme_rb() +
  scale_fill_manual(values = c("#5f2680", "#FFA700", "#B5B682", "#28666e")) +
  theme(legend.position = "bottom") +
  scale_y_continuous(labels = percent) +
  theme(strip.text.y.left = element_text(angle=0)) +
  guides(fill = guide_legend(reverse=T, nrow = 1)) +
  theme(axis.title.y=element_blank(), axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
  theme(panel.grid.minor.y=element_blank(), panel.grid.major.y=element_blank()) +
  geom_text(aes(label = ifelse(pct >.05, paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 9, family = "font", color = "black") +
  geom_text(aes(label = ifelse(relig2 == "Something Else", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 9, family = "font", color = "white") +
  geom_text(aes(label = ifelse(relig2 == "Prot./Cath.", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 9, family = "font", color = "white") +
  theme(plot.title = element_text(size = 16)) +
  theme(strip.text.x.top = element_text(size = 20)) +
  theme(strip.text.y.left = element_text(angle = 0, hjust = 1)) +
  labs(x = "", y = "", title = "Among Switchers, Where Do They End Up?", caption = "@ryanburge + @religiondata\nData: Putting Politics First, Panel Data (2010-2012)") 
save("switchers_politics_first.png", wd = 9, ht = 4.5)


regg <- df %>% 
  select(caseid, relig_2, relig_4, weight = weight1_4, educ = ppeduc_2, income = ppincimp_2, gender = ppgender_2, race = ppethm_2, age = i_age, attend = relattend_2, pid3 = partyid1_2) %>% 
  mutate(white = case_when(race == 1 ~ 1, 
                           TRUE ~ 0)) %>% 
  mutate(male = case_when(gender == 1 ~ 1, 
                          TRUE ~ 0)) %>% 
  mutate(rep = case_when(pid3 == 1 ~ 1, 
                         TRUE ~ 0)) %>% 
  mutate(attend = 10 - attend) %>% 
  mutate(religpew = relig_2) %>% 
  mutate(relig1 = frcode(religpew == 1 ~ "Protestant",
                         religpew == 2 ~ "Catholic",
                         religpew >= 3 & religpew <= 8 ~ "Other World Religions",
                         religpew == 9 ~ "Atheist", 
                         religpew == 10 ~ "Agnostic",
                         religpew == 11 ~ "Nothing in Particular", 
                         religpew == 12 ~ "Something Else")) %>% 
  mutate(religpew = relig_4) %>% 
  mutate(relig2 = frcode(religpew == 1 ~ "Protestant",
                         religpew == 2 ~ "Catholic",
                         religpew >= 3 & religpew <= 8 ~ "Other World Religions",
                         religpew == 9 ~ "Atheist", 
                         religpew == 10 ~ "Agnostic",
                         religpew == 11 ~ "Nothing in Particular", 
                         religpew == 12 ~ "Something Else")) %>% 
  as_tibble() %>% 
  filter(relig1 != "NA") %>% 
  filter(relig2 != "NA") %>% 
  mutate(switch = case_when(relig1 != relig2 ~ 1, 
                            TRUE ~ 0)) %>% 
  select(-relig_2, -relig_4, -religpew, -relig1, -relig2) 


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

library(jtools)



coef_names <- c("Age" = "age", 
                "Male" = "male", 
                "White" = "white", 
                "Income" = "income", 
                "Republican" = "rep", 
                "Attendance" = "attend", 
                "Education" = "educ", 
                "No Religion" = "none")

gg1 <- plot_summs(out, robust = "HC3", scale = TRUE, coefs = coef_names)


gg1 + 
  theme_rb() +
  add_text(x = -.55, y = 6.5, word = "Less Likely to Switch", sz = 8) +
  labs(x = "", y = "", title = "What Factors Predict Someone Switching Religions?", caption = "@ryanburge + @religiondata\nData: Putting Politics First, Panel Data (2010-2012)")
save("reg_predict_switching.png")



graph <- df %>% 
  select(caseid, relig_2, relig_4, weight = weight1_4, educ = ppeduc_2) %>% 
  mutate(religpew = relig_2) %>% 
  mutate(relig1 = frcode(religpew == 1 ~ "Protestant",
                         religpew == 2 ~ "Catholic",
                         religpew >= 3 & religpew <= 8 ~ "Other World Religions",
                         religpew == 9 ~ "Atheist", 
                         religpew == 10 ~ "Agnostic",
                         religpew == 11 ~ "Nothing in Particular", 
                         religpew == 12 ~ "Something Else")) %>% 
  mutate(religpew = relig_4) %>% 
  mutate(relig2 = frcode(religpew == 1 ~ "Protestant",
                         religpew == 2 ~ "Catholic",
                         religpew >= 3 & religpew <= 8 ~ "Other World Religions",
                         religpew == 9 ~ "Atheist", 
                         religpew == 10 ~ "Agnostic",
                         religpew == 11 ~ "Nothing in Particular", 
                         religpew == 12 ~ "Something Else")) %>% 
  select(caseid, relig1, relig2, weight, educ) %>% 
  as_tibble() %>% 
  filter(relig1 != "NA") %>% 
  filter(relig2 != "NA") %>% 
  mutate(switch = case_when(relig1 != relig2 ~ 1, 
                            TRUE ~ 0)) %>% 
  mutate(educ = frcode(educ <= 9 ~ "HS or Less", 
                       educ == 10 | educ == 11 ~ "Some College", 
                       educ == 12 ~ "4 Year", 
                       educ == 13 | educ == 14 ~ "Graduate School")) %>% 
  group_by(educ) %>% 
  mean_ci(switch, wt = weight, ci = .84)



heat <- df %>% 
  select(caseid, attend1 = relattend_2, attend2 = relattend_4,  weight = weight1_4) %>% 
  mutate(attend1 = frcode(attend1 == 9 ~ "Never", 
                          attend1 == 8 | attend1 == 7 | attend1 == 6 ~ "Yearly", 
                          attend1 == 5 | attend1 == 4 ~ "Monthly", 
                          attend1 == 3 | attend1 == 2 | attend1 == 1 ~ "Weekly")) %>% 
  mutate(attend2 = frcode(attend2 == 9 ~ "Never", 
                          attend2 == 8 | attend2 == 7 | attend2 == 6 ~ "Yearly", 
                          attend2 == 5 | attend2 == 4 ~ "Monthly", 
                          attend2 == 3 | attend2 == 2 | attend2 == 1 ~ "Weekly")) %>% 
  group_by(attend1) %>% 
  ct(attend2, wt = weight, show_na = FALSE) %>% 
  filter(attend1 != "NA")

heat <- heat %>% 
  mutate(bins = frcode(pct < .10 ~ "a", 
                       pct > .10 & pct <= .31 ~ "b", 
                       pct > .32 ~ "c"))



heat %>% 
  mutate(lab = round(pct, 2)) %>% 
  ggplot(., aes(x= attend2, y = attend1)) +
  geom_tile(aes(fill = bins), color = "black") +
  scale_fill_manual(values = c("#ADDAB6", "#688081", "#013749")) +
  theme_rb() + 
  # theme(plot.subtitle = element_text(size = 24)) +
  geom_text(aes(x= attend2, y = attend1, label = paste0(lab*100, '%')), size = 9, family = "font") +
  geom_text(aes(x= attend2, y = attend1, label = ifelse(bins == "c", paste0(lab*100, '%'), "")), size = 9, family = "font", color = "white") +
  theme(strip.text = element_text(size = 20)) +
  labs(x= "Attendance in 2012", y = "Attendance in 2011", title = "How Does Religious Attendance Change?",
       caption = "@ryanburge + @religiondata\nData: Putting Politics First, Panel Data (2010-2012)") 
save('politics_first_att_heat.png', wd = 6, ht = 5)