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@ryanburge
Created March 23, 2024 16:47
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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)
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