ryanburge/ct_selection_untitled-D4676707.R Secret

## ct_selection_untitled-D4676707.R
library(rio)
library(janitor)

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

clergy %>%
  ct(timeserv)

clergy %>%
  ct(hrcong)

good <- clergy %>%
  mutate(good = lifegood) %>%
  mutate(good = frcode(good == 1 ~ "Completely\nDisagree",
                       good == 2 ~ "Moderately\nDisagree",
                       good == 3 ~ "Slightly\nDisagree",
                       good == 4 ~ "Neither",
                       good == 5 ~ "Slightly\nAgree",
                       good == 6 ~ "Moderately\nAgree",
                       good == 7 ~ "Completely\nAgree")) %>%
  ct(good, wt = wt_nsrl_all_attendee, show_na = FALSE)

good %>%
  mutate(lab = round(pct, 2)) %>%
  ggplot(., aes(x = good, y = pct, fill = good)) +
  geom_col(color = "black") +
  theme_rb() +
  y_pct() +
  scale_fill_manual(values = c("#033f63", "#28666e", "#e1e1e1", "#B5B682", "#FEDC97",  "#7D3C98", "#5f2680")) +
  geom_text(aes(y = pct + .025, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 9, family = "font") +
  labs(x = "", y  = "", title = "In most ways my life is close to my ideal - Among Clergy", caption = "@ryanburge + @religiondata\nData: National Survey of Religious Leaders, 2023")
save("life_good_clergy.png", wd = 6)


good1 <- clergy %>%
  mutate(sat = satlife) %>%
  mutate(sat = frcode(sat == 1 ~" Never",
                      sat == 2 ~ "Once or\nTwice",
                      sat == 3 ~ "Weekly",
                      sat == 4 ~ "2-3x\nWeek",
                      sat == 5 ~ "Almost Every\nDay",
                      sat == 6 ~ "Every Day")) %>%
  ct(sat, wt = wt_nsrl_all_attendee, show_na = FALSE) %>%
  mutate(type = "Satisified With Life")

good2 <- clergy %>%
  mutate(sat = happy) %>%
  mutate(sat = frcode(sat == 1 ~" Never",
                      sat == 2 ~ "Once or\nTwice",
                      sat == 3 ~ "Weekly",
                      sat == 4 ~ "2-3x\nWeek",
                      sat == 5 ~ "Almost Every\nDay",
                      sat == 6 ~ "Every Day")) %>%
  ct(sat, wt = wt_nsrl_all_attendee, show_na = FALSE) %>%
  mutate(type = "Happy")

both <- bind_rows(good1, good2)

both %>%
  mutate(lab = round(pct, 2)) %>%
  ggplot(., aes(x = sat, y = pct, fill = type)) +
  geom_col(color = "black", position = "dodge") +
  theme_rb(legend = TRUE) +
  y_pct() +
  scale_fill_manual(values = c("#28666e", "#7D3C98")) +
  geom_text(aes(y = pct + .02, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 7.5, family = "font") +
  labs(x = "", y  = "", title = "During the Past Month, How Often Do You Feel...? - Among Clergy", caption = "@ryanburge + @religiondata\nData: National Survey of Religious Leaders, 2023")
save("happy_bars_clergy.png", wd = 8)

clergy %>%
  ct(timeserv, cum = TRUE)

good <- clergy %>%
  mutate(time = timeserv) %>%
  mutate(time = frcode(time < 10 ~ "< 10",
                       time >= 10 & time < 20 ~ "10-20",
                       time >= 20 & time < 30 ~ "20-30",
                       time >= 40 ~ "40+")) %>%
  mutate(good = lifegood) %>%
  mutate(good = case_when(good == 6 | good == 7 ~ 1,
                          good <= 5 ~ 0)) %>%
  group_by(time) %>%
  mean_ci(good, wt = wt_nsrl_all_attendee, ci = .84) %>%
  na.omit()


good %>%
  mutate(lab = round(mean, 2)) %>%
  ggplot(., aes(x = time, y = mean, fill = time)) +
  geom_col(color = "black") +
  theme_rb() +
  y_pct() +
  scale_fill_manual(values = c("#033f63", "#28666e",  "#7D3C98", "#5f2680")) +
  theme(axis.text.x = element_text(size = 24)) +
  geom_text(aes(y = mean + .055, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 12, family = "font") +
  labs(x = "Number of Years in Ministry", y  = "Share Saying Mostly/Completely Agree", title = "In most ways my life is close to my ideal - Among Clergy", caption = "@ryanburge + @religiondata\nData: National Survey of Religious Leaders, 2023")
save("life_good_clergy_timeserve.png", wd = 6)


clergy %>%
  ct(hrcong, cum = TRUE)


good <- clergy %>%
  mutate(time = hrcong) %>%
  mutate(time = frcode(time < 20 ~ "< 20",
                       time >= 20 & time < 40 ~ "20-40",
                       time >= 40 & time < 50 ~ "40-50",
                       time >= 50 ~ "50+")) %>%
  mutate(good = lifegood) %>%
  mutate(good = case_when(good == 6 | good == 7 ~ 1,
                          good <= 5 ~ 0)) %>%
  group_by(time) %>%
  mean_ci(good, wt = wt_nsrl_all_attendee, ci = .84) %>%
  na.omit()


good %>%
  mutate(lab = round(mean, 2)) %>%
  ggplot(., aes(x = time, y = mean, fill = time)) +
  geom_col(color = "black") +
  theme_rb() +
  y_pct() +
  scale_fill_manual(values = c("#28666e", "#B5B682", "#FEDC97",  "#7D3C98")) +
  theme(axis.text.x = element_text(size = 24)) +
  geom_text(aes(y = mean + .055, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 12, family = "font") +
  labs(x = "Number of Hours Worked per Week", y  = "Share Saying Mostly/Completely Agree", title = "In most ways my life is close to my ideal - Among Clergy", caption = "@ryanburge + @religiondata\nData: National Survey of Religious Leaders, 2023")
save("life_good_clergy_hours_served.png", wd = 6)


gg <- clergy %>%
  mutate(relig = i_religion) %>%
  mutate(relig = frcode(relig == 1 ~ "Catholic",
                        relig == 2 ~ "White Evangelical",
                        relig == 3 ~ "Black Protestant",
                        relig == 4 ~ "White Liberal/Moderate",
                        relig == 5 ~ "Non-Christian")) %>%
  mutate(good = lifegood) %>%
  mutate(good = case_when(good == 6 | good == 7 ~ 1,
                          good <= 5 ~ 0)) %>%
  group_by(relig) %>%
  mean_ci(good, wt = wt_nsrl_all_attendee, ci = .84) %>%
  na.omit()

gg %>%
  mutate(lab = round(mean, 2)) %>%
  ggplot(., aes(x = reorder(relig, mean), y = mean, fill = relig)) +
  geom_col(color = "black") +
  coord_flip() +
  theme_rb() +
  error_bar() +
  y_pct() +
  scale_fill_manual(values = c("#033f63", "#28666e", "#B5B682", "#FEDC97",  "#7D3C98", "#5f2680")) +
  lab_bar(top = FALSE, type = lab, pos = .04, sz = 8) +
  geom_text(aes(y = .04, label = ifelse(relig == "Catholic", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 8, family = "font", color = "white") +
  geom_text(aes(y = .04, label = ifelse(relig == "White Evangelical", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 8, family = "font", color = "white") +
  geom_text(aes(y = .04, label = ifelse(relig == "Non-Christian", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 8, family = "font", color = "white") +
  labs(x = "", y  = "Share Saying Mostly/Completely Agree", title = "In most ways my life is close to my ideal - Among Clergy", caption = "@ryanburge + @religiondata\nData: National Survey of Religious Leaders, 2023")
save("relig_life_good.png", ht = 4)


reg <- clergy %>%
  mutate(good = lifegood) %>%
  mutate(good = case_when(good == 6 | good == 7 ~ 1,
                          good <= 5 ~ 0)) %>%
  mutate(white = case_when(i_race == 1 ~ 1,
                           TRUE ~ 0)) %>%
  mutate(male = case_when(i_gender == 1 ~ 1,
                          i_gender == 2 ~ 0)) %>%
  mutate(cons = case_when(i_politics == 3 ~ 1,
                          i_politics == 2 | i_politics == 1 ~ 0)) %>%
  mutate(educ = i_educ) %>%
  mutate(size = cong_size) %>%
  mutate(income = hhincome) %>%
  mutate(age = 8 - yearborn) %>%
  select(good, hrcong, timeserv, white, male, cons, educ, size, income, age)

regg <- glm(good ~ hrcong + timeserv + white + male + cons + educ + size + income + age, family = "binomial", data = reg)


library(jtools)

coef_names <- c("Age" = "age",
                "Male" = "male",
                "White" = "white",
                "Income" = "income",
                "Conservative" = "cons",
                "Hours Worked" = "hrcong",
                "Years in Ministry" = "timeserv",
                "Education" = "educ",
                "Size of Cong." = "size")

gg1 <- plot_summs(regg, 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 Lead Life Satisfaction for Clergy?", caption = "@ryanburge + @religiondata\nData: National Survey of Religious Leaders, 2023")
save("reg_predict_clergy_happy.png", ht  = 4)
	library(rio)
	library(janitor)

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

	clergy %>%
	ct(timeserv)

	clergy %>%
	ct(hrcong)

	good <- clergy %>%
	mutate(good = lifegood) %>%
	mutate(good = frcode(good == 1 ~ "Completely\nDisagree",
	good == 2 ~ "Moderately\nDisagree",
	good == 3 ~ "Slightly\nDisagree",
	good == 4 ~ "Neither",
	good == 5 ~ "Slightly\nAgree",
	good == 6 ~ "Moderately\nAgree",
	good == 7 ~ "Completely\nAgree")) %>%
	ct(good, wt = wt_nsrl_all_attendee, show_na = FALSE)

	good %>%
	mutate(lab = round(pct, 2)) %>%
	ggplot(., aes(x = good, y = pct, fill = good)) +
	geom_col(color = "black") +
	theme_rb() +
	y_pct() +
	scale_fill_manual(values = c("#033f63", "#28666e", "#e1e1e1", "#B5B682", "#FEDC97", "#7D3C98", "#5f2680")) +
	geom_text(aes(y = pct + .025, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 9, family = "font") +
	labs(x = "", y = "", title = "In most ways my life is close to my ideal - Among Clergy", caption = "@ryanburge + @religiondata\nData: National Survey of Religious Leaders, 2023")
	save("life_good_clergy.png", wd = 6)


	good1 <- clergy %>%
	mutate(sat = satlife) %>%
	mutate(sat = frcode(sat == 1 ~" Never",
	sat == 2 ~ "Once or\nTwice",
	sat == 3 ~ "Weekly",
	sat == 4 ~ "2-3x\nWeek",
	sat == 5 ~ "Almost Every\nDay",
	sat == 6 ~ "Every Day")) %>%
	ct(sat, wt = wt_nsrl_all_attendee, show_na = FALSE) %>%
	mutate(type = "Satisified With Life")

	good2 <- clergy %>%
	mutate(sat = happy) %>%
	mutate(sat = frcode(sat == 1 ~" Never",
	sat == 2 ~ "Once or\nTwice",
	sat == 3 ~ "Weekly",
	sat == 4 ~ "2-3x\nWeek",
	sat == 5 ~ "Almost Every\nDay",
	sat == 6 ~ "Every Day")) %>%
	ct(sat, wt = wt_nsrl_all_attendee, show_na = FALSE) %>%
	mutate(type = "Happy")

	both <- bind_rows(good1, good2)

	both %>%
	mutate(lab = round(pct, 2)) %>%
	ggplot(., aes(x = sat, y = pct, fill = type)) +
	geom_col(color = "black", position = "dodge") +
	theme_rb(legend = TRUE) +
	y_pct() +
	scale_fill_manual(values = c("#28666e", "#7D3C98")) +
	geom_text(aes(y = pct + .02, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 7.5, family = "font") +
	labs(x = "", y = "", title = "During the Past Month, How Often Do You Feel...? - Among Clergy", caption = "@ryanburge + @religiondata\nData: National Survey of Religious Leaders, 2023")
	save("happy_bars_clergy.png", wd = 8)

	clergy %>%
	ct(timeserv, cum = TRUE)

	good <- clergy %>%
	mutate(time = timeserv) %>%
	mutate(time = frcode(time < 10 ~ "< 10",
	time >= 10 & time < 20 ~ "10-20",
	time >= 20 & time < 30 ~ "20-30",
	time >= 40 ~ "40+")) %>%
	mutate(good = lifegood) %>%
	mutate(good = case_when(good == 6 \| good == 7 ~ 1,
	good <= 5 ~ 0)) %>%
	group_by(time) %>%
	mean_ci(good, wt = wt_nsrl_all_attendee, ci = .84) %>%
	na.omit()


	good %>%
	mutate(lab = round(mean, 2)) %>%
	ggplot(., aes(x = time, y = mean, fill = time)) +
	geom_col(color = "black") +
	theme_rb() +
	y_pct() +
	scale_fill_manual(values = c("#033f63", "#28666e", "#7D3C98", "#5f2680")) +
	theme(axis.text.x = element_text(size = 24)) +
	geom_text(aes(y = mean + .055, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 12, family = "font") +
	labs(x = "Number of Years in Ministry", y = "Share Saying Mostly/Completely Agree", title = "In most ways my life is close to my ideal - Among Clergy", caption = "@ryanburge + @religiondata\nData: National Survey of Religious Leaders, 2023")
	save("life_good_clergy_timeserve.png", wd = 6)


	clergy %>%
	ct(hrcong, cum = TRUE)


	good <- clergy %>%
	mutate(time = hrcong) %>%
	mutate(time = frcode(time < 20 ~ "< 20",
	time >= 20 & time < 40 ~ "20-40",
	time >= 40 & time < 50 ~ "40-50",
	time >= 50 ~ "50+")) %>%
	mutate(good = lifegood) %>%
	mutate(good = case_when(good == 6 \| good == 7 ~ 1,
	good <= 5 ~ 0)) %>%
	group_by(time) %>%
	mean_ci(good, wt = wt_nsrl_all_attendee, ci = .84) %>%
	na.omit()


	good %>%
	mutate(lab = round(mean, 2)) %>%
	ggplot(., aes(x = time, y = mean, fill = time)) +
	geom_col(color = "black") +
	theme_rb() +
	y_pct() +
	scale_fill_manual(values = c("#28666e", "#B5B682", "#FEDC97", "#7D3C98")) +
	theme(axis.text.x = element_text(size = 24)) +
	geom_text(aes(y = mean + .055, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 12, family = "font") +
	labs(x = "Number of Hours Worked per Week", y = "Share Saying Mostly/Completely Agree", title = "In most ways my life is close to my ideal - Among Clergy", caption = "@ryanburge + @religiondata\nData: National Survey of Religious Leaders, 2023")
	save("life_good_clergy_hours_served.png", wd = 6)



	gg <- clergy %>%
	mutate(relig = i_religion) %>%
	mutate(relig = frcode(relig == 1 ~ "Catholic",
	relig == 2 ~ "White Evangelical",
	relig == 3 ~ "Black Protestant",
	relig == 4 ~ "White Liberal/Moderate",
	relig == 5 ~ "Non-Christian")) %>%
	mutate(good = lifegood) %>%
	mutate(good = case_when(good == 6 \| good == 7 ~ 1,
	good <= 5 ~ 0)) %>%
	group_by(relig) %>%
	mean_ci(good, wt = wt_nsrl_all_attendee, ci = .84) %>%
	na.omit()

	gg %>%
	mutate(lab = round(mean, 2)) %>%
	ggplot(., aes(x = reorder(relig, mean), y = mean, fill = relig)) +
	geom_col(color = "black") +
	coord_flip() +
	theme_rb() +
	error_bar() +
	y_pct() +
	scale_fill_manual(values = c("#033f63", "#28666e", "#B5B682", "#FEDC97", "#7D3C98", "#5f2680")) +
	lab_bar(top = FALSE, type = lab, pos = .04, sz = 8) +
	geom_text(aes(y = .04, label = ifelse(relig == "Catholic", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 8, family = "font", color = "white") +
	geom_text(aes(y = .04, label = ifelse(relig == "White Evangelical", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 8, family = "font", color = "white") +
	geom_text(aes(y = .04, label = ifelse(relig == "Non-Christian", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 8, family = "font", color = "white") +
	labs(x = "", y = "Share Saying Mostly/Completely Agree", title = "In most ways my life is close to my ideal - Among Clergy", caption = "@ryanburge + @religiondata\nData: National Survey of Religious Leaders, 2023")
	save("relig_life_good.png", ht = 4)



	reg <- clergy %>%
	mutate(good = lifegood) %>%
	mutate(good = case_when(good == 6 \| good == 7 ~ 1,
	good <= 5 ~ 0)) %>%
	mutate(white = case_when(i_race == 1 ~ 1,
	TRUE ~ 0)) %>%
	mutate(male = case_when(i_gender == 1 ~ 1,
	i_gender == 2 ~ 0)) %>%
	mutate(cons = case_when(i_politics == 3 ~ 1,
	i_politics == 2 \| i_politics == 1 ~ 0)) %>%
	mutate(educ = i_educ) %>%
	mutate(size = cong_size) %>%
	mutate(income = hhincome) %>%
	mutate(age = 8 - yearborn) %>%
	select(good, hrcong, timeserv, white, male, cons, educ, size, income, age)

	regg <- glm(good ~ hrcong + timeserv + white + male + cons + educ + size + income + age, family = "binomial", data = reg)


	library(jtools)

	coef_names <- c("Age" = "age",
	"Male" = "male",
	"White" = "white",
	"Income" = "income",
	"Conservative" = "cons",
	"Hours Worked" = "hrcong",
	"Years in Ministry" = "timeserv",
	"Education" = "educ",
	"Size of Cong." = "size")

	gg1 <- plot_summs(regg, 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 Lead Life Satisfaction for Clergy?", caption = "@ryanburge + @religiondata\nData: National Survey of Religious Leaders, 2023")
	save("reg_predict_clergy_happy.png", ht = 4)