22 Mormon Hispanics

22_mormon_hispanics.r

library(tidyverse)
library(binom)
library(haven)
library(car)
library(ggtext)
library(sjlabelled)

# Load the dataset
data <- readRDS("./data/CES/cumulative_2006-2024.rds")

state_to_region <- c(
    # Northeast Region
    "CT" = "Northeast", "ME" = "Northeast", "MA" = "Northeast", "NH" = "Northeast",
    "RI" = "Northeast", "VT" = "Northeast", "NJ" = "Northeast", "NY" = "Northeast",
    "PA" = "Northeast",

    # Midwest Region
    "IL" = "Midwest", "IN" = "Midwest", "MI" = "Midwest", "OH" = "Midwest",
    "WI" = "Midwest", "IA" = "Midwest", "KS" = "Midwest", "MN" = "Midwest",
    "MO" = "Midwest", "NE" = "Midwest", "ND" = "Midwest", "SD" = "Midwest",

    # South Region
    "DE" = "South", "FL" = "South", "GA" = "South", "MD" = "South",
    "NC" = "South", "SC" = "South", "VA" = "South", "WV" = "South",
    "AL" = "South", "KY" = "South", "MS" = "South", "TN" = "South",
    "AR" = "South", "LA" = "South", "OK" = "South", "TX" = "South",
    "DC" = "South", # DC is typically grouped with the South/South Atlantic Division

    # West Region (Highly relevant for your Mormon Metrics work!)
    "AZ" = "West", "CO" = "West", "ID" = "West", "MT" = "West",
    "NV" = "West", "NM" = "West", "UT" = "West", "WY" = "West",
    "AK" = "West", "CA" = "West", "HI" = "West", "OR" = "West",
    "WA" = "West"
)

data$region <- as.character(state_to_region[data$st])

data$relig_church_rc <- car::recode(as.numeric(data$relig_church),"1:2 = 'Weekly or More'; 3 = 'Monthly'; 4:5 = 'A few times a year/Seldom'; 6 = 'Never'")
data$relig_church_rc <- factor(data$relig_church_rc, 
    levels = c("Weekly or More", "Monthly", "A few times a year/Seldom", "Never"))

data$relig_church_rc2 <- car::recode(as.numeric(data$relig_church),"1:2 = 'Weekly or More'; 3:4 = 'Monthly/A few times a year'; 5:6 = 'Seldom/Never'")
data$relig_church_rc2 <- factor(data$relig_church_rc2, 
    levels = c("Weekly or More", "Monthly/A few times a year", "Seldom/Never"))

data$year_4 <- car::recode(as.numeric(data$year), "2008:2012 = '2008-12'; 2013:2016 = '2013-16'; 2017:2020 = '2017-20'; 2021:2024 = '2021-24'")
data$year_4 <- factor(data$year_4, 
    levels = c("2008-12", "2013-16", "2017-20", "2021-24"))

data$year_2 <- car::recode(as.numeric(data$year), "2007:2008 = '2007-08'; 2009:2010 = '2009-10'; 2011:2012 = '2011-12'; 2013:2014 = '2013-14'; 2015:2016 = '2015-16'; 2017:2018 = '2017-18'; 2019:2020 = '2019-20'; 2021:2022 = '2021-22'; 2023:2024 = '2023-24'")
data$year_2 <- factor(data$year_2, 
    levels = c("2007-08", "2009-10", "2011-12", "2013-14", "2015-16", "2017-18", "2019-20", "2021-22", "2023-24"))

# prayer was not in the cumulative file, so I need to merge it in
# I went through each year and added the prayer variable manually.
data <- data |> 
    mutate(id = paste0(year, "X", case_id)) |> 
    left_join(read.csv("./data/CES/prayer.csv"), by = "id") |> 
    mutate(year = year.x)

data <- data |> 
    mutate(id = paste0(year, "X", case_id)) |> 
    left_join(read.csv("./data/CES/hisp_origin.csv"), by = "id") |> 
    mutate(year = year.x)

data$Utah <- ifelse(data$state == "Utah", "Utah Resident", "Non-Utah Resident")
data$jello_belt <- ifelse(data$state %in% c("Utah", "Idaho", "Arizona"), 1, 0)

mormon_data <- data |> filter(data$religion == 3)

new_nms <- c(
    'all_hisp_origin_1',
    'all_hisp_origin_2',
    'all_hisp_origin_3',
    'all_hisp_origin_4',
    'all_hisp_origin_5',
    'all_hisp_origin_6',
    'all_hisp_origin_7',
    'all_hisp_origin_8',
    'all_hisp_origin_9',
    'all_hisp_origin_10',
    'all_hisp_origin_11',
    'all_hisp_origin_12'
)

do.call(rbind,lapply(new_nms,function(x){
        data |> 
            count(!!as.symbol(x), wt = weight) |> 
            drop_na() |> 
            mutate(
                nm = x,
                prop = n / sum(n),
                total_n = sum(n),
                lower = binom.confint(x = n, n = total_n, method = "asymptotic")$lower,
                upper = binom.confint(x = n, n = total_n, method = "asymptotic")$upper
            ) |> 
            filter(!!as.symbol(x) == 1) |> 
            select(nm, n, total_n, prop, lower, upper)
}))

do.call(rbind,lapply(new_nms,function(x){
        mormon_data |> 
            count(!!as.symbol(x), wt = weight) |> 
            drop_na() |> 
            mutate(
                nm = x,
                prop = n / sum(n),
                total_n = sum(n),
                lower = binom.confint(x = n, n = total_n, method = "asymptotic")$lower,
                upper = binom.confint(x = n, n = total_n, method = "asymptotic")$upper
            ) |> 
            filter(!!as.symbol(x) == 1) |> 
            select(nm, n, total_n, prop, lower, upper)
}))

data |> 
    filter(race == 3) |> 
    count(region, wt = weight) |> 
    mutate(
        prop = n / sum(n),
        total_n = sum(n),
        lower = binom.confint(x = n, n = total_n, method = "asymptotic")$lower,
        upper = binom.confint(x = n, n = total_n, method = "asymptotic")$upper
    )

data |> 
    filter(religion == 3) |> 
    filter(race == 3) |> 
    count(region, wt = weight) |> 
    mutate(
        prop = n / sum(n),
        total_n = sum(n),
        lower = binom.confint(x = n, n = total_n, method = "asymptotic")$lower,
        upper = binom.confint(x = n, n = total_n, method = "asymptotic")$upper
    )

data |> 
    filter(race == 3) |> 
    count(Utah, wt = weight) |> 
    mutate(
        prop = n / sum(n),
        total_n = sum(n),
        lower = binom.confint(x = n, n = total_n, method = "asymptotic")$lower,
        upper = binom.confint(x = n, n = total_n, method = "asymptotic")$upper
    )

data |> 
    filter(religion == 3) |> 
    filter(race == 3) |> 
    count(Utah, wt = weight) |> 
    mutate(
        prop = n / sum(n),
        total_n = sum(n),
        lower = binom.confint(x = n, n = total_n, method = "asymptotic")$lower,
        upper = binom.confint(x = n, n = total_n, method = "asymptotic")$upper
    )

data |> 
    filter(race == 3) |> 
    count(jello_belt, wt = weight) |> 
    mutate(
        prop = n / sum(n),
        total_n = sum(n),
        lower = binom.confint(x = n, n = total_n, method = "asymptotic")$lower,
        upper = binom.confint(x = n, n = total_n, method = "asymptotic")$upper
    )

data |> 
    filter(religion == 3) |> 
    filter(race == 3) |> 
    count(jello_belt, wt = weight) |> 
    mutate(
        prop = n / sum(n),
        total_n = sum(n),
        lower = binom.confint(x = n, n = total_n, method = "asymptotic")$lower,
        upper = binom.confint(x = n, n = total_n, method = "asymptotic")$upper
    )