ryanburge/ct_selection_untitled-A634E44B.R Secret

## ct_selection_untitled-A634E44B.R
library(rio)
library(janitor)

gss <- import("E://data/gss12.dta")

gss <- gss %>%
  filter(rdscmost == 1)


aaa1 <- gss %>%
  mutate(pros = rdscwlth) %>%
  mutate(pros1 = frcode(pros == 1 ~ "Not at All",
                        pros == 2 ~ "To a small extent",
                        pros == 3 ~ "To a moderate extent",
                        pros == 4 ~ "To a considerable extent",
                        pros == 5 ~ "To a great extent")) %>%
  ct(pros1, show_na = FALSE) %>%
  mutate(type = "To Learn About Attaining Wealth or Prosperity")


aaa2 <- gss %>%
  mutate(pros = rdschlth) %>%
  mutate(pros1 = frcode(pros == 1 ~ "Not at All",
                        pros == 2 ~ "To a small extent",
                        pros == 3 ~ "To a moderate extent",
                        pros == 4 ~ "To a considerable extent",
                        pros == 5 ~ "To a great extent")) %>%
  ct(pros1, show_na = FALSE) %>%
  mutate(type = "To Learn About Attaining Health or Healing")

both <- bind_rows(aaa1, aaa2)

both %>%
  mutate(lab = round(pct, 2)) %>%
  ggplot(., aes(x = 1, y = pct, fill = fct_rev(pros1))) +
  geom_col(color = "black") +
  coord_flip() +
  facet_wrap(~ type, ncol =1, strip.position = "top") +
  theme_rb() +
  scale_fill_manual(values = c("#033f63", "#28666e", "#e1e1e1", "#B5B682", "#FEDC97", "#5f2680")) +
  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(pros1 == "To a great extent", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 9, family = "font", color = "white") +
  geom_text(aes(label = ifelse(pros1 == "To a considerable extent", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 9, family = "font", color = "white") +

  # geom_text(aes(label = ifelse(age2 == "36-44", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 4, family = "font", color = "white") +
  theme(plot.title = element_text(size = 16)) +
  theme(strip.text.y.left = element_text(angle = 0, hjust = 1)) +
  theme(strip.text = element_text(size = 20)) +
  labs(x = "", y = "", title = "To what extent did you read the Bible to...", caption = "@ryanburge + @religiondata\nData: General Social Survey, 2012")
save("prosperity_gss_arda.png", wd = 9, ht = 3.5)


aaa1 <- gss %>%
  mutate(rac2 = frcode(race == 1 ~ "White",
                       TRUE ~ "Non-White")) %>%
  mutate(pros = rdscwlth) %>%
  mutate(pros1 = frcode(pros == 1 ~ "Not at All",
                        pros == 2 ~ "To a small extent",
                        pros == 3 ~ "To a moderate extent",
                        pros == 4 ~ "To a considerable extent",
                        pros == 5 ~ "To a great extent")) %>%
  group_by(rac2) %>%
  ct(pros1, show_na = FALSE) %>%
  mutate(type = "To Learn About Attaining Wealth or Prosperity")


aaa2 <- gss %>%
  mutate(rac2 = frcode(race == 1 ~ "White",
                       TRUE ~ "Non-White")) %>%
  mutate(pros = rdschlth) %>%
  mutate(pros1 = frcode(pros == 1 ~ "Not at All",
                        pros == 2 ~ "To a small extent",
                        pros == 3 ~ "To a moderate extent",
                        pros == 4 ~ "To a considerable extent",
                        pros == 5 ~ "To a great extent")) %>%
  group_by(rac2) %>%
  ct(pros1, show_na = FALSE) %>%
  mutate(type = "To Learn About Attaining Health or Healing")

both <- bind_rows(aaa1, aaa2)

both %>%
  mutate(lab = round(pct, 2)) %>%
  ggplot(., aes(x = fct_rev(rac2), y = pct, fill = fct_rev(pros1))) +
  geom_col(color = "black") +
  coord_flip() +
  facet_wrap(~ type, ncol =1, strip.position = "top") +
  theme_rb() +
  scale_fill_manual(values = c("#033f63", "#28666e", "#e1e1e1", "#B5B682", "#FEDC97", "#5f2680")) +
  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(pct >.05 & pros1 == "To a great extent", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 9, family = "font", color = "white") +
  geom_text(aes(label = ifelse(pct >.05 & pros1 == "To a considerable extent", 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.y.left = element_text(angle = 0, hjust = 1)) +
  theme(strip.text = element_text(size = 20)) +
  labs(x = "", y = "", title = "To what extent did you read the Bible to...", caption = "@ryanburge + @religiondata\nData: General Social Survey, 2012")
save("prosperity_gss_arda_race.png", wd = 9, ht = 4.5)


aaa1 <- gss %>%
  mutate(gender = frcode(sex == 1 ~ "Men",
                         sex == 2 ~ "Women")) %>%
  mutate(pros = rdscwlth) %>%
  mutate(pros1 = frcode(pros == 1 ~ "Not at All",
                        pros == 2 ~ "To a small extent",
                        pros == 3 ~ "To a moderate extent",
                        pros == 4 ~ "To a considerable extent",
                        pros == 5 ~ "To a great extent")) %>%
  group_by(gender) %>%
  ct(pros1, show_na = FALSE) %>%
  mutate(type = "To Learn About Attaining Wealth or Prosperity")


aaa2 <- gss %>%
  mutate(gender = frcode(sex == 1 ~ "Men",
                         sex == 2 ~ "Women")) %>%
  mutate(pros = rdschlth) %>%
  mutate(pros1 = frcode(pros == 1 ~ "Not at All",
                        pros == 2 ~ "To a small extent",
                        pros == 3 ~ "To a moderate extent",
                        pros == 4 ~ "To a considerable extent",
                        pros == 5 ~ "To a great extent")) %>%
  group_by(gender) %>%
  ct(pros1, show_na = FALSE) %>%
  mutate(type = "To Learn About Attaining Health or Healing")


both <- bind_rows(aaa1, aaa2)

both %>%
  mutate(lab = round(pct, 2)) %>%
  ggplot(., aes(x = fct_rev(gender), y = pct, fill = fct_rev(pros1))) +
  geom_col(color = "black") +
  coord_flip() +
  facet_wrap(~ type, ncol =1, strip.position = "top") +
  theme_rb() +
  scale_fill_manual(values = c("#033f63", "#28666e", "#e1e1e1", "#B5B682", "#FEDC97", "#5f2680")) +
  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(pct >.05 & pros1 == "To a great extent", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 9, family = "font", color = "white") +
  # geom_text(aes(label = ifelse(age2 == "36-44", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 4, family = "font", color = "white") +
  theme(plot.title = element_text(size = 16)) +
  theme(strip.text.y.left = element_text(angle = 0, hjust = 1)) +
  theme(strip.text = element_text(size = 20)) +
  labs(x = "", y = "", title = "To what extent did you read the Bible to...", caption = "@ryanburge + @religiondata\nData: General Social Survey, 2012")
save("prosperity_gss_arda_gender.png", wd = 9, ht = 4.5)


aaa1 <- gss %>%
  mutate(pros = rdscwlth) %>%
  mutate(pros = case_when(pros == 2 | pros == 3 | pros == 4 | pros == 5 ~ 1,
                          pros == 1 ~ 0)) %>%
  mutate(income = INCOME06) %>%
  mutate(inc = frcode(income <= 8 ~ "<$10K",
                       income >= 9 & income <= 12 ~ "$10K-$20K",
                       income >= 13 & income <= 15 ~ "$20K-$30K",
                       income == 16 | income == 17 ~ "$30K-$40K",
                       income == 18 ~ "$40K-$50K",
                       income == 19 ~ "$50K-$60K",
                       income == 20 ~ "$60K-$75K",
                      income == 21 ~ "$75K-$90K",
                      income == 22 ~ "$90K-$110K",
                      income == 23 | income == 24 ~ "$110K-$150K",
                      income == 25 ~ "$150K+")) %>%
  group_by(inc) %>%
  mean_ci(pros, ci = .84) %>%
  mutate(type = "Wealth/Prosperity") %>%
  na.omit()


aaa2 <- gss %>%
  mutate(pros = rdschlth) %>%
  mutate(pros = case_when(pros == 2 | pros == 3 | pros == 4 | pros == 5 ~ 1,
                          pros == 1 ~ 0)) %>%
  mutate(income = INCOME06) %>%
  mutate(inc = frcode(income <= 8 ~ "<$10K",
                      income >= 9 & income <= 12 ~ "$10K-$20K",
                      income >= 13 & income <= 15 ~ "$20K-$30K",
                      income == 16 | income == 17 ~ "$30K-$40K",
                      income == 18 ~ "$40K-$50K",
                      income == 19 ~ "$50K-$60K",
                      income == 20 ~ "$60K-$75K",
                      income == 21 ~ "$75K-$90K",
                      income == 22 ~ "$90K-$110K",
                      income == 23 | income == 24 ~ "$110K-$150K",
                      income == 25 ~ "$150K+")) %>%
  group_by(inc) %>%
  mean_ci(pros, ci = .84) %>%
  mutate(type = "Health/Healing") %>%
  na.omit()

both <- bind_rows(aaa1, aaa2)

both %>%
  mutate(lab = round(mean, 2)) %>%
  ggplot(., aes(x = inc, y = mean, fill = type)) +
  geom_col(position = "dodge", color = "black") +
  coord_flip() +
  scale_fill_manual(values = c("#033f63", "#FEDC97")) +
  error_bar() +
  y_pct() +
  lab_bar(top = FALSE, type = lab, pos = .04, sz = 8) +
  geom_text(aes(y = .04, label = ifelse(type == "Health/Healing", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 8, family = "font", color = "white") +
  theme_rb(legend = TRUE) +
  labs(x = "", y = "Share Agreeing At Least to a Small Extent", title = "To what extent did you read the Bible to Learn About...", caption = "@ryanburge + @religiondata\nData: General Social Survey, 2012")
save("pros_gospel_arda_income.png", ht = 10)


gg <- gss %>%
  mutate(pros1 = rdschlth) %>%
  mutate(pros2 = rdscwlth) %>%
  mutate(prosper = pros1 + pros2) %>%
  mutate(prosper = prosper - 2) %>%
  mutate(income = INCOME06) %>%
  mutate(rac2 = frcode(race == 1 ~ "White",
                       TRUE ~ "Non-White")) %>%
  mutate(inc = frcode(income <= 8 ~ "<$10K",
                      income >= 9 & income <= 12 ~ "$10K-$20K",
                      income >= 13 & income <= 15 ~ "$20K-$30K",
                      income == 16 | income == 17 ~ "$30K-$40K",
                      income == 18 ~ "$40K-$50K",
                      income == 19 ~ "$50K-$60K",
                      income == 20 ~ "$60K-$75K",
                      income == 21 ~ "$75K-$90K",
                      income == 22 ~ "$90K-$110K",
                      income == 23 | income == 24 ~ "$110K-$150K",
                      income == 25 ~ "$150K+")) %>%
  group_by(inc, rac2) %>%
  mean_ci(prosper, ci = .84) %>%
  filter(inc != "NA")

gg %>%
  mutate(lab = round(mean, 1)) %>%
  ggplot(., aes(x = inc, y = mean, fill = rac2, group = rac2)) +
  geom_col(color = "black", position = "dodge") +
  error_bar() +
  scale_fill_manual(values = c("#033f63", "#FEDC97")) +
  theme_rb(legend = TRUE) +
  geom_text(aes(y = .5, label = lab), position = position_dodge(width = .9), size = 6, family = "font") +
  geom_text(aes(y = .5, label = ifelse(rac2 == "White", lab, "")), position = position_dodge(width = .9), size = 6, family = "font", color = "white") +
  labs(x = "Household Income", y = "Prosperity Gospel Index",
       subtitle = "Scale Runs from 0 to 8",
       title = "Belief in the Prosperity Gospel by Race and Income",
       caption = "@ryanburge + @religiondata\nData: General Social Survey, 2012")
save("pros_gospel_arda.png")


regg <- gss %>%
  mutate(pros1 = rdschlth) %>%
  mutate(pros2 = rdscwlth) %>%
  mutate(prosper = pros1 + pros2) %>%
  mutate(prosper = prosper - 2) %>%
  mutate(white = case_when(race == 1 ~ 1,
                           TRUE ~ 0)) %>%
  mutate(male = case_when(sex == 1 ~ 1,
                          sex == 2 ~ 0)) %>%
  mutate(dem = case_when(partyid <= 2 ~ 1,
                         partyid <= 6 ~ 0)) %>%
  select(prosper, white, male, educ, income = INCOME06, attend, dem, age)


out <- lm(prosper ~ white + male + educ + income + attend + dem + age, data = regg)

library(jtools)


coef_names <- c("White" = "white",
                "Income" = "income",
                "Education" = "educ",
                "Age" = "age",
                "Male" = "male",
                "Democrat" = "dem",
                "Church Attendance" = "attend")

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

gg +
  theme_rb() +
  add_text(x = -1.5, y = 3.5, word = "Less Belief in PG", sz = 9) +
  labs(x = "", y = "", title = "What Factors Lead to More Adherence to the Prosperity Gospel?", caption = "@ryanburge + @religiondata\nData: General Social Survey, 2012")
save("reg_prosperity_arda.png", ht = 4)
	library(rio)
	library(janitor)

	gss <- import("E://data/gss12.dta")

	gss <- gss %>%
	filter(rdscmost == 1)


	aaa1 <- gss %>%
	mutate(pros = rdscwlth) %>%
	mutate(pros1 = frcode(pros == 1 ~ "Not at All",
	pros == 2 ~ "To a small extent",
	pros == 3 ~ "To a moderate extent",
	pros == 4 ~ "To a considerable extent",
	pros == 5 ~ "To a great extent")) %>%
	ct(pros1, show_na = FALSE) %>%
	mutate(type = "To Learn About Attaining Wealth or Prosperity")


	aaa2 <- gss %>%
	mutate(pros = rdschlth) %>%
	mutate(pros1 = frcode(pros == 1 ~ "Not at All",
	pros == 2 ~ "To a small extent",
	pros == 3 ~ "To a moderate extent",
	pros == 4 ~ "To a considerable extent",
	pros == 5 ~ "To a great extent")) %>%
	ct(pros1, show_na = FALSE) %>%
	mutate(type = "To Learn About Attaining Health or Healing")

	both <- bind_rows(aaa1, aaa2)

	both %>%
	mutate(lab = round(pct, 2)) %>%
	ggplot(., aes(x = 1, y = pct, fill = fct_rev(pros1))) +
	geom_col(color = "black") +
	coord_flip() +
	facet_wrap(~ type, ncol =1, strip.position = "top") +
	theme_rb() +
	scale_fill_manual(values = c("#033f63", "#28666e", "#e1e1e1", "#B5B682", "#FEDC97", "#5f2680")) +
	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(pros1 == "To a great extent", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 9, family = "font", color = "white") +
	geom_text(aes(label = ifelse(pros1 == "To a considerable extent", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 9, family = "font", color = "white") +

	# geom_text(aes(label = ifelse(age2 == "36-44", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 4, family = "font", color = "white") +
	theme(plot.title = element_text(size = 16)) +
	theme(strip.text.y.left = element_text(angle = 0, hjust = 1)) +
	theme(strip.text = element_text(size = 20)) +
	labs(x = "", y = "", title = "To what extent did you read the Bible to...", caption = "@ryanburge + @religiondata\nData: General Social Survey, 2012")
	save("prosperity_gss_arda.png", wd = 9, ht = 3.5)



	aaa1 <- gss %>%
	mutate(rac2 = frcode(race == 1 ~ "White",
	TRUE ~ "Non-White")) %>%
	mutate(pros = rdscwlth) %>%
	mutate(pros1 = frcode(pros == 1 ~ "Not at All",
	pros == 2 ~ "To a small extent",
	pros == 3 ~ "To a moderate extent",
	pros == 4 ~ "To a considerable extent",
	pros == 5 ~ "To a great extent")) %>%
	group_by(rac2) %>%
	ct(pros1, show_na = FALSE) %>%
	mutate(type = "To Learn About Attaining Wealth or Prosperity")


	aaa2 <- gss %>%
	mutate(rac2 = frcode(race == 1 ~ "White",
	TRUE ~ "Non-White")) %>%
	mutate(pros = rdschlth) %>%
	mutate(pros1 = frcode(pros == 1 ~ "Not at All",
	pros == 2 ~ "To a small extent",
	pros == 3 ~ "To a moderate extent",
	pros == 4 ~ "To a considerable extent",
	pros == 5 ~ "To a great extent")) %>%
	group_by(rac2) %>%
	ct(pros1, show_na = FALSE) %>%
	mutate(type = "To Learn About Attaining Health or Healing")

	both <- bind_rows(aaa1, aaa2)

	both %>%
	mutate(lab = round(pct, 2)) %>%
	ggplot(., aes(x = fct_rev(rac2), y = pct, fill = fct_rev(pros1))) +
	geom_col(color = "black") +
	coord_flip() +
	facet_wrap(~ type, ncol =1, strip.position = "top") +
	theme_rb() +
	scale_fill_manual(values = c("#033f63", "#28666e", "#e1e1e1", "#B5B682", "#FEDC97", "#5f2680")) +
	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(pct >.05 & pros1 == "To a great extent", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 9, family = "font", color = "white") +
	geom_text(aes(label = ifelse(pct >.05 & pros1 == "To a considerable extent", 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.y.left = element_text(angle = 0, hjust = 1)) +
	theme(strip.text = element_text(size = 20)) +
	labs(x = "", y = "", title = "To what extent did you read the Bible to...", caption = "@ryanburge + @religiondata\nData: General Social Survey, 2012")
	save("prosperity_gss_arda_race.png", wd = 9, ht = 4.5)





	aaa1 <- gss %>%
	mutate(gender = frcode(sex == 1 ~ "Men",
	sex == 2 ~ "Women")) %>%
	mutate(pros = rdscwlth) %>%
	mutate(pros1 = frcode(pros == 1 ~ "Not at All",
	pros == 2 ~ "To a small extent",
	pros == 3 ~ "To a moderate extent",
	pros == 4 ~ "To a considerable extent",
	pros == 5 ~ "To a great extent")) %>%
	group_by(gender) %>%
	ct(pros1, show_na = FALSE) %>%
	mutate(type = "To Learn About Attaining Wealth or Prosperity")


	aaa2 <- gss %>%
	mutate(gender = frcode(sex == 1 ~ "Men",
	sex == 2 ~ "Women")) %>%
	mutate(pros = rdschlth) %>%
	mutate(pros1 = frcode(pros == 1 ~ "Not at All",
	pros == 2 ~ "To a small extent",
	pros == 3 ~ "To a moderate extent",
	pros == 4 ~ "To a considerable extent",
	pros == 5 ~ "To a great extent")) %>%
	group_by(gender) %>%
	ct(pros1, show_na = FALSE) %>%
	mutate(type = "To Learn About Attaining Health or Healing")


	both <- bind_rows(aaa1, aaa2)

	both %>%
	mutate(lab = round(pct, 2)) %>%
	ggplot(., aes(x = fct_rev(gender), y = pct, fill = fct_rev(pros1))) +
	geom_col(color = "black") +
	coord_flip() +
	facet_wrap(~ type, ncol =1, strip.position = "top") +
	theme_rb() +
	scale_fill_manual(values = c("#033f63", "#28666e", "#e1e1e1", "#B5B682", "#FEDC97", "#5f2680")) +
	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(pct >.05 & pros1 == "To a great extent", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 9, family = "font", color = "white") +
	# geom_text(aes(label = ifelse(age2 == "36-44", paste0(lab*100, '%'), '')), position = position_stack(vjust = 0.5), size = 4, family = "font", color = "white") +
	theme(plot.title = element_text(size = 16)) +
	theme(strip.text.y.left = element_text(angle = 0, hjust = 1)) +
	theme(strip.text = element_text(size = 20)) +
	labs(x = "", y = "", title = "To what extent did you read the Bible to...", caption = "@ryanburge + @religiondata\nData: General Social Survey, 2012")
	save("prosperity_gss_arda_gender.png", wd = 9, ht = 4.5)




	aaa1 <- gss %>%
	mutate(pros = rdscwlth) %>%
	mutate(pros = case_when(pros == 2 \| pros == 3 \| pros == 4 \| pros == 5 ~ 1,
	pros == 1 ~ 0)) %>%
	mutate(income = INCOME06) %>%
	mutate(inc = frcode(income <= 8 ~ "<$10K",
	income >= 9 & income <= 12 ~ "$10K-$20K",
	income >= 13 & income <= 15 ~ "$20K-$30K",
	income == 16 \| income == 17 ~ "$30K-$40K",
	income == 18 ~ "$40K-$50K",
	income == 19 ~ "$50K-$60K",
	income == 20 ~ "$60K-$75K",
	income == 21 ~ "$75K-$90K",
	income == 22 ~ "$90K-$110K",
	income == 23 \| income == 24 ~ "$110K-$150K",
	income == 25 ~ "$150K+")) %>%
	group_by(inc) %>%
	mean_ci(pros, ci = .84) %>%
	mutate(type = "Wealth/Prosperity") %>%
	na.omit()


	aaa2 <- gss %>%
	mutate(pros = rdschlth) %>%
	mutate(pros = case_when(pros == 2 \| pros == 3 \| pros == 4 \| pros == 5 ~ 1,
	pros == 1 ~ 0)) %>%
	mutate(income = INCOME06) %>%
	mutate(inc = frcode(income <= 8 ~ "<$10K",
	income >= 9 & income <= 12 ~ "$10K-$20K",
	income >= 13 & income <= 15 ~ "$20K-$30K",
	income == 16 \| income == 17 ~ "$30K-$40K",
	income == 18 ~ "$40K-$50K",
	income == 19 ~ "$50K-$60K",
	income == 20 ~ "$60K-$75K",
	income == 21 ~ "$75K-$90K",
	income == 22 ~ "$90K-$110K",
	income == 23 \| income == 24 ~ "$110K-$150K",
	income == 25 ~ "$150K+")) %>%
	group_by(inc) %>%
	mean_ci(pros, ci = .84) %>%
	mutate(type = "Health/Healing") %>%
	na.omit()

	both <- bind_rows(aaa1, aaa2)

	both %>%
	mutate(lab = round(mean, 2)) %>%
	ggplot(., aes(x = inc, y = mean, fill = type)) +
	geom_col(position = "dodge", color = "black") +
	coord_flip() +
	scale_fill_manual(values = c("#033f63", "#FEDC97")) +
	error_bar() +
	y_pct() +
	lab_bar(top = FALSE, type = lab, pos = .04, sz = 8) +
	geom_text(aes(y = .04, label = ifelse(type == "Health/Healing", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 8, family = "font", color = "white") +
	theme_rb(legend = TRUE) +
	labs(x = "", y = "Share Agreeing At Least to a Small Extent", title = "To what extent did you read the Bible to Learn About...", caption = "@ryanburge + @religiondata\nData: General Social Survey, 2012")
	save("pros_gospel_arda_income.png", ht = 10)



	gg <- gss %>%
	mutate(pros1 = rdschlth) %>%
	mutate(pros2 = rdscwlth) %>%
	mutate(prosper = pros1 + pros2) %>%
	mutate(prosper = prosper - 2) %>%
	mutate(income = INCOME06) %>%
	mutate(rac2 = frcode(race == 1 ~ "White",
	TRUE ~ "Non-White")) %>%
	mutate(inc = frcode(income <= 8 ~ "<$10K",
	income >= 9 & income <= 12 ~ "$10K-$20K",
	income >= 13 & income <= 15 ~ "$20K-$30K",
	income == 16 \| income == 17 ~ "$30K-$40K",
	income == 18 ~ "$40K-$50K",
	income == 19 ~ "$50K-$60K",
	income == 20 ~ "$60K-$75K",
	income == 21 ~ "$75K-$90K",
	income == 22 ~ "$90K-$110K",
	income == 23 \| income == 24 ~ "$110K-$150K",
	income == 25 ~ "$150K+")) %>%
	group_by(inc, rac2) %>%
	mean_ci(prosper, ci = .84) %>%
	filter(inc != "NA")

	gg %>%
	mutate(lab = round(mean, 1)) %>%
	ggplot(., aes(x = inc, y = mean, fill = rac2, group = rac2)) +
	geom_col(color = "black", position = "dodge") +
	error_bar() +
	scale_fill_manual(values = c("#033f63", "#FEDC97")) +
	theme_rb(legend = TRUE) +
	geom_text(aes(y = .5, label = lab), position = position_dodge(width = .9), size = 6, family = "font") +
	geom_text(aes(y = .5, label = ifelse(rac2 == "White", lab, "")), position = position_dodge(width = .9), size = 6, family = "font", color = "white") +
	labs(x = "Household Income", y = "Prosperity Gospel Index",
	subtitle = "Scale Runs from 0 to 8",
	title = "Belief in the Prosperity Gospel by Race and Income",
	caption = "@ryanburge + @religiondata\nData: General Social Survey, 2012")
	save("pros_gospel_arda.png")



	regg <- gss %>%
	mutate(pros1 = rdschlth) %>%
	mutate(pros2 = rdscwlth) %>%
	mutate(prosper = pros1 + pros2) %>%
	mutate(prosper = prosper - 2) %>%
	mutate(white = case_when(race == 1 ~ 1,
	TRUE ~ 0)) %>%
	mutate(male = case_when(sex == 1 ~ 1,
	sex == 2 ~ 0)) %>%
	mutate(dem = case_when(partyid <= 2 ~ 1,
	partyid <= 6 ~ 0)) %>%
	select(prosper, white, male, educ, income = INCOME06, attend, dem, age)


	out <- lm(prosper ~ white + male + educ + income + attend + dem + age, data = regg)

	library(jtools)


	coef_names <- c("White" = "white",
	"Income" = "income",
	"Education" = "educ",
	"Age" = "age",
	"Male" = "male",
	"Democrat" = "dem",
	"Church Attendance" = "attend")

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

	gg +
	theme_rb() +
	add_text(x = -1.5, y = 3.5, word = "Less Belief in PG", sz = 9) +
	labs(x = "", y = "", title = "What Factors Lead to More Adherence to the Prosperity Gospel?", caption = "@ryanburge + @religiondata\nData: General Social Survey, 2012")
	save("reg_prosperity_arda.png", ht = 4)