ryanburge/ct_selection_untitled-9EE3A6F5.R Secret

## ct_selection_untitled-9EE3A6F5.R
library(readxl)
library(janitor)

cen20 <- read_excel("E://data/rel_cen20.xlsx", sheet = 2)

cen20 <- cen20 %>% clean_names()

mus <- cen20 %>%
  filter(group_name == "Muslim Estimate") %>%
  select(state_name, congregations, adherents, pct_adh = adherents_as_percent_of_total_adherents, pct_pop = adherents_as_percent_of_population)


mus <- mus %>%
  mutate(bins = frcode(pct_pop > .03 ~ "3%+",
                       pct_pop > .01 ~ "1%-3%",
                       pct_pop > 0 ~ "<1%"))

mus$id <- tolower(mus$state_name)


library(fiftystater)

data("fifty_states")

mapp <- left_join(fifty_states, mus, by = c("id")) %>% as_tibble()


centers <- read_csv("D://state_centers_inset.csv") %>% rename(id = full)

centers$id <- tolower(centers$id)

centers <- left_join(mapp, centers, by = c("id")) %>% as_tibble()
centers <- centers %>% distinct(id, .keep_all = TRUE)


fips <- data.frame(
  stringsAsFactors = FALSE,
  id = c("Alabama","Alaska","Arizona",
         "Arkansas","California","Colorado","Connecticut",
         "Delaware","Florida","Georgia","Hawaii","Idaho",
         "Illinois","Indiana","Iowa","Kansas","Kentucky","Louisiana",
         "Maine","Maryland","Massachusetts","Michigan",
         "Minnesota","Mississippi","Missouri","Montana","Nebraska",
         "Nevada","New Hampshire","New Jersey","New Mexico",
         "New York","North Carolina","North Dakota","Ohio",
         "Oklahoma","Oregon","Pennsylvania","Rhode Island",
         "South Carolina","South Dakota","Tennessee","Texas","Utah",
         "Vermont","Virginia","Washington","West Virginia",
         "Wisconsin","Wyoming","American Samoa","Guam",
         "Northern Mariana Islands","Puerto Rico","Virgin Islands"),
  abb = c("AL","AK","AZ","AR","CA",
          "CO","CT","DE","FL","GA","HI","ID","IL","IN","IA",
          "KS","KY","LA","ME","MD","MA","MI","MN","MS",
          "MO","MT","NE","NV","NH","NJ","NM","NY","NC","ND",
          "OH","OK","OR","PA","RI","SC","SD","TN","TX",
          "UT","VT","VA","WA","WV","WI","WY","AS","GU","MP",
          "PR","VI"),
  FIPS = c("01","02","04","05","06",
           "08","09","10","12","13","15","16","17","18","19",
           "20","21","22","23","24","25","26","27","28",
           "29","30","31","32","33","34","35","36","37","38",
           "39","40","41","42","44","45","46","47","48",
           "49","50","51","53","54","55","56","60","66","69",
           "72","78")
)

centers <- left_join(centers, fips)


centers <- centers %>%
  mutate(mean = round(pct_pop, 2)) %>%
  mutate(lab = mean*100) %>%
  mutate(lab = paste0(lab, '%')) %>%
  mutate(lab = str_replace(lab, "0%", "<1%")) %>%
  mutate(label = paste(state, lab, sep = ": "))


library(ggrepel)


mapp %>%
  ggplot(., mapping = aes(x = long, y = lat, group = group, fill = bins)) +
  geom_polygon(color = "black", linewidth = 0.1) +
  coord_map(projection = "albers", lat0 = 41, lat1 = 44) +
  scale_fill_manual(values = c(  "#03045e", "#00b4d8", "#caf0f8")) +
  ggrepel::geom_label_repel(data = centers,
                            aes(x = longitude, y = latitude, label = label, group = group), size = 2.5, fill = "white",
                            seed = 1002, family = "font", force = .001, show.legend = FALSE) +
  theme_rb(legend = TRUE) +
  theme(axis.line = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank(),
        axis.title = element_blank(),
        panel.background = element_blank(),
        panel.border = element_blank(),
        panel.grid = element_blank(),
        panel.spacing = unit(0, "lines"),
        plot.background = element_blank(),
        legend.position = "bottom",
        plot.title = element_text(size = 18)) +
  guides(fill = guide_legend(reverse = TRUE)) +
  labs(title = "Share of the State That is Muslim",  caption = "@ryanburge\nData: 2020 Religion Census")
save("cen20_muslim_state.png")


library(readxl)

cen20 <- read_excel("E://data/rel_cen20.xlsx", sheet = 3)

library(janitor)

cen20 <- cen20 %>%
  clean_names()

mi <- cen20 %>%
  filter(group_name == "Muslim Estimate") %>%
  filter(state_name == "Michigan") %>%
  select(fips, county_name, congregations, adherents, pct_adh = adherents_as_percent_of_total_adherents, pct_pop = adherents_as_percent_of_total_population)

mi <- mi %>%
  mutate(bins = frcode(pct_pop > .02 ~ "2%+",
                       pct_pop > .01 ~ "1%-2%",
                       pct_pop > 0   ~ "0%-1%"))


library(urbnmapr)

territories_counties <- get_urbn_map(map = "territories_counties")

territories_counties <-  territories_counties %>%
  rename(fips = county_fips)

mapp <- left_join(territories_counties, mi)


color_palette <- c("25%+" = "#08306B", "10%-25%" = "#2171B5", "5%-10%" = "#6BAED6", "1%-5%" = "#C6DBEF", "NA" = "#BDBDBD")


mapp %>%
  filter(state_name == "Michigan") %>%
  ggplot(aes(long, lat, group = group, fill = bins)) +
  geom_polygon(color = "black", size = 0.05) +
  coord_map(projection = "albers", lat0 = 39, lat1 = 45) +
  labs(title  = "      Muslim Concentration by County", caption = "@ryanburge\nData: 2020 Religious Census", fill = "") +
  theme(legend.title = element_text(), legend.key.width = unit(.5, "in")) +
  theme_rb() +
  scale_fill_manual(values = c("0%-1%" = "#d8bfd8", "1%-2%" = "#9370db", "2%+" = "#4b0082")) +
  urbnthemes::theme_urbn_map() +
  theme(plot.title = element_text(size=20, family= "font", face= "bold")) +
  theme(plot.caption = element_text(size = 14, family="font")) +
  theme(text=element_text(size=18, family="font"))  +
  theme(legend.text = element_text(size = 20))
save("relcen_muslim_michigan.png")


cc20 <- cces20 %>%
  filter(religpew == 6) %>%
  select(immstat, weight = commonweight, inputstate)

cc21 <- cces21 %>%
  filter(religpew == 6) %>%
  select(immstat, weight = commonweight, inputstate)

cc22 <- cces22 %>%
  filter(religpew == 6) %>%
  select(immstat, weight = commonweight, inputstate)

cc23 <- cces23 %>%
  filter(religpew == 6) %>%
  select(immstat, weight = commonweight, inputstate)

all <- bind_rows(cc20, cc21, cc22, cc23)

gg1 <- all %>%
  mutate(imm = frcode(immstat == 1 ~ "Immigrant,\nNaturalized",
                      immstat == 2 ~ "Immigrant,\nNon-Citizen",
                      immstat == 3 ~ "First\nGeneration",
                      immstat == 4 ~ "Second\nGeneration",
                      immstat == 5 ~ "Third\nGeneration\nor More")) %>%
  ct(imm, wt = weight, show_na = FALSE) %>%
  mutate(type = "Muslims")


cc20 <- cces20 %>%
  select(immstat, weight = commonweight, inputstate)

cc21 <- cces21 %>%
  select(immstat, weight = commonweight, inputstate)

cc22 <- cces22 %>%
  select(immstat, weight = commonweight, inputstate)

cc23 <- cces23 %>%
  select(immstat, weight = commonweight, inputstate)

all <- bind_rows(cc20, cc21, cc22, cc23)

gg2 <- all %>%
  mutate(imm = frcode(immstat == 1 ~ "Immigrant,\nNaturalized",
                      immstat == 2 ~ "Immigrant,\nNon-Citizen",
                      immstat == 3 ~ "First\nGeneration",
                      immstat == 4 ~ "Second\nGeneration",
                      immstat == 5 ~ "Third\nGeneration\nor More")) %>%
  ct(imm, wt = weight, show_na = FALSE) %>%
  mutate(type = "Entire\nSample")

all <- bind_rows(gg1, gg2)

all %>%
  mutate(lab = round(pct, 2)) %>%
  ggplot(., aes(x = type, y = pct, fill = type)) +
  geom_col(color = "black") +
  facet_wrap(~ imm) +
  theme_rb() +
  scale_fill_calc() +
  y_pct() +
  geom_text(aes(y = pct + .045, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 8, family = "font") +
  labs(x = "", y = "", title = "Immigration Status of Muslims vs All Americans", caption = '@ryanburge\nData: Cooperative Election Study, 2020-2023')
save("muslim_immigration.png", ht = 8, wd = 5)


## There are 64 Muslims from Michigan in the sample
## Total sample is 171200
## A total of 1518 Muslims

## Total votes cast in Michigan: 5,453,892
## Turnout was 70.5% of all eligible population in Michigan in 2020.  https://www.michigan.gov/sos/-/media/Project/Websites/sos/Election-Results-and-Statistics/General-Voter-Reg-Turnout-Stats.pdf
## Total number of Muslims in Michigan in 241,828

gg <- cces %>%
  filter(year >= 2020) %>%
  mutate(trad = frcode(religion == 1 ~ "Protestant",
                       religion == 2 ~ "Catholic",
                       religion == 3 ~ "Mormon",
                       religion == 4 ~ "Orthodox",
                       religion == 5 ~ "Jewish",
                       religion == 6 ~ "Muslim",
                       religion == 7 ~ "Buddhist",
                       religion == 8 ~ "Hindu",
                       religion == 9 ~ "Atheist",
                       religion == 10 ~ "Agnostic",
                       religion == 11 ~ "Nothing in Particular")) %>%
  mutate(kids = case_when(havekids == 1 ~ 1,
                          havekids == 2 ~ 0)) %>%
  group_by(trad) %>%
  mean_ci(kids, wt = weight, ci = .84) %>% filter(trad != 'NA')

gg %>%
  mutate(lab = round(mean, 2)) %>%
  ggplot(., aes(x = reorder(trad, mean), y = mean, fill = trad)) +
  geom_col(color = "black") +
  coord_flip() +
  theme_rb() +
  y_pct() +
  error_bar()+
  scale_fill_manual(values = c(moma.colors("Warhol", 11))) +
  lab_bar(top = FALSE, type = lab, pos = .03, sz = 9) +
  labs(x = "", y = "", title = "Share of Each Tradition That Are Currently Parents to Minor Children", caption = "@ryanburge\nData: Cooperative Election Study, 2020-2023")
save("parents_trads_23.png")


gg <- cces %>%
  filter(year >= 2020) %>%
  mutate(trad = frcode(religion == 1 ~ "Protestant",
                       religion == 2 ~ "Catholic",
                       religion == 3 ~ "Mormon",
                       religion == 4 ~ "Orthodox",
                       religion == 5 ~ "Jewish",
                       religion == 6 ~ "Muslim",
                       religion == 7 ~ "Buddhist",
                       religion == 8 ~ "Hindu",
                       religion == 9 ~ "Atheist",
                       religion == 10 ~ "Agnostic",
                       religion == 11 ~ "Nothing in Particular")) %>%
  mutate(kids = case_when(havekids == 1 ~ 1,
                          havekids == 2 ~ 0)) %>%
  filter(havekids == 1) %>%
  # group_by(trad) %>%
  mean_ci(numkids, wt = weight, ci = .84) %>% filter(trad != 'NA')

gg %>%
  mutate(lab = round(mean, 1)) %>%
  ggplot(., aes(x = reorder(trad, mean), y = mean, fill = trad)) +
  geom_col(color = "black") +
  coord_flip() +
  theme_rb() +
  y_pct() +
  scale_fill_manual(values = c(moma.colors("Warhol", 11))) +
  geom_text(aes(y = mean + .14, label = lab), position = position_dodge(width = .9), size = 8, family = "font") +
  labs(x = "", y = "", title = "Among Parents, Average Number of Children", caption = "@ryanburge\nData: Cooperative Election Study, 2020-2023")
save("parents_trads_23_numkids.png")


gg <- cces %>%
  filter(year >= 2020) %>%
  mutate(trad = frcode(religion == 1 ~ "Protestant",
                       religion == 2 ~ "Catholic",
                       religion == 3 ~ "Mormon",
                       religion == 4 ~ "Orthodox",
                       religion == 5 ~ "Jewish",
                       religion == 6 ~ "Muslim",
                       religion == 7 ~ "Buddhist",
                       religion == 8 ~ "Hindu",
                       religion == 9 ~ "Atheist",
                       religion == 10 ~ "Agnostic",
                       religion == 11 ~ "Nothing in Particular")) %>%
  mutate(age2 = frcode(age >= 18 & age <= 35 ~ "18-35",
                       age >= 36 & age <= 44 ~ "36-44",
                       age >= 45 & age <= 54 ~ "45-54",
                       age >= 55 & age <= 64 ~ "55-64",
                       age >= 65 ~ "65+")) %>%
  group_by(trad) %>%
  ct(age2, wt = weight, show_na = FALSE) %>% filter(trad != 'NA')


lvl <- gg %>%
  filter(age2 == "18-35") %>%
  select(trad, sort = pct)


gg <- left_join(gg, lvl)

gg$trad <- fct_reorder(gg$trad, gg$sort, .desc = TRUE)


gg %>%
  mutate(lab = round(pct, 2)) %>%
  ggplot(., aes(x = 1, y = pct, fill = fct_rev(age2))) +
  geom_col(color = "black") +
  coord_flip() +
  facet_wrap(~ trad, ncol =1, strip.position = "left") +
  scale_fill_manual(values = c(moma.colors("Connors", 5))) +
  theme_rb() +
  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 = 7, family = "font", color = "black") +
  labs(x = "", y = "", title = "Age Distribution of Adults in Religious Traditions", caption = "@ryanburge\nData: Cooperative Election Study, 2020-2023")
save("age2_trad2_distribution.png", wd = 9, ht = 6)

table <- mi %>%
  select(county = county_name, congregations, adherents, pct_pop) %>%
  mutate(pct_pop = pct_pop*1)

table <- table %>%
  arrange(desc(pct_pop))

library(gt)

table %>%
  gt() %>%
  tab_header(
    title = "List of Counties with a Mosque in Michigan",
    subtitle = "") %>%
  opt_table_font(
    font = c(
      "Abel Regular", "Segoe UI",
      default_fonts()[-c(1:3)]
    )) %>%
  tab_options(
    table.font.size = px(40L)
  ) %>%
  cols_label(county = "County",
             congregations = "Congregations",
             adherents = "Adherents",
             pct_pop = "% Muslim") %>%
  fmt_percent(
    columns = vars(pct_pop),
    decimals = 2
  ) %>%
  fmt_number(
    columns = vars(adherents),
    use_seps = TRUE,
    decimals = 0
  ) %>%
  gtsave("E://graphs23/michigan_muslims.png")


county <- cen20 %>%
  filter(group_name == "Muslim Estimate") %>%
  select(fips, county_name, state_name, congregations, adherents, pct_adh = adherents_as_percent_of_total_adherents, pct_pop = adherents_as_percent_of_total_population)


gg <- cces %>%
  filter(year >= 2020) %>%
  mutate(trad = frcode(religion == 1 ~ "Protestant",
                       religion == 2 ~ "Catholic",
                       religion == 3 ~ "Mormon",
                       religion == 4 ~ "Orthodox",
                       religion == 5 ~ "Jewish",
                       religion == 6 ~ "Muslim",
                       religion == 7 ~ "Buddhist",
                       religion == 8 ~ "Hindu",
                       religion == 9 ~ "Atheist",
                       religion == 10 ~ "Agnostic",
                       religion == 11 ~ "Nothing in Particular")) %>%
  group_by(trad) %>%
  mean_ci(age, wt = weight) %>% filter(trad != 'NA')
	library(readxl)
	library(janitor)

	cen20 <- read_excel("E://data/rel_cen20.xlsx", sheet = 2)

	cen20 <- cen20 %>% clean_names()

	mus <- cen20 %>%
	filter(group_name == "Muslim Estimate") %>%
	select(state_name, congregations, adherents, pct_adh = adherents_as_percent_of_total_adherents, pct_pop = adherents_as_percent_of_population)


	mus <- mus %>%
	mutate(bins = frcode(pct_pop > .03 ~ "3%+",
	pct_pop > .01 ~ "1%-3%",
	pct_pop > 0 ~ "<1%"))

	mus$id <- tolower(mus$state_name)


	library(fiftystater)

	data("fifty_states")

	mapp <- left_join(fifty_states, mus, by = c("id")) %>% as_tibble()



	centers <- read_csv("D://state_centers_inset.csv") %>% rename(id = full)

	centers$id <- tolower(centers$id)

	centers <- left_join(mapp, centers, by = c("id")) %>% as_tibble()
	centers <- centers %>% distinct(id, .keep_all = TRUE)


	fips <- data.frame(
	stringsAsFactors = FALSE,
	id = c("Alabama","Alaska","Arizona",
	"Arkansas","California","Colorado","Connecticut",
	"Delaware","Florida","Georgia","Hawaii","Idaho",
	"Illinois","Indiana","Iowa","Kansas","Kentucky","Louisiana",
	"Maine","Maryland","Massachusetts","Michigan",
	"Minnesota","Mississippi","Missouri","Montana","Nebraska",
	"Nevada","New Hampshire","New Jersey","New Mexico",
	"New York","North Carolina","North Dakota","Ohio",
	"Oklahoma","Oregon","Pennsylvania","Rhode Island",
	"South Carolina","South Dakota","Tennessee","Texas","Utah",
	"Vermont","Virginia","Washington","West Virginia",
	"Wisconsin","Wyoming","American Samoa","Guam",
	"Northern Mariana Islands","Puerto Rico","Virgin Islands"),
	abb = c("AL","AK","AZ","AR","CA",
	"CO","CT","DE","FL","GA","HI","ID","IL","IN","IA",
	"KS","KY","LA","ME","MD","MA","MI","MN","MS",
	"MO","MT","NE","NV","NH","NJ","NM","NY","NC","ND",
	"OH","OK","OR","PA","RI","SC","SD","TN","TX",
	"UT","VT","VA","WA","WV","WI","WY","AS","GU","MP",
	"PR","VI"),
	FIPS = c("01","02","04","05","06",
	"08","09","10","12","13","15","16","17","18","19",
	"20","21","22","23","24","25","26","27","28",
	"29","30","31","32","33","34","35","36","37","38",
	"39","40","41","42","44","45","46","47","48",
	"49","50","51","53","54","55","56","60","66","69",
	"72","78")
	)

	centers <- left_join(centers, fips)


	centers <- centers %>%
	mutate(mean = round(pct_pop, 2)) %>%
	mutate(lab = mean*100) %>%
	mutate(lab = paste0(lab, '%')) %>%
	mutate(lab = str_replace(lab, "0%", "<1%")) %>%
	mutate(label = paste(state, lab, sep = ": "))



	library(ggrepel)


	mapp %>%
	ggplot(., mapping = aes(x = long, y = lat, group = group, fill = bins)) +
	geom_polygon(color = "black", linewidth = 0.1) +
	coord_map(projection = "albers", lat0 = 41, lat1 = 44) +
	scale_fill_manual(values = c( "#03045e", "#00b4d8", "#caf0f8")) +
	ggrepel::geom_label_repel(data = centers,
	aes(x = longitude, y = latitude, label = label, group = group), size = 2.5, fill = "white",
	seed = 1002, family = "font", force = .001, show.legend = FALSE) +
	theme_rb(legend = TRUE) +
	theme(axis.line = element_blank(),
	axis.text = element_blank(),
	axis.ticks = element_blank(),
	axis.title = element_blank(),
	panel.background = element_blank(),
	panel.border = element_blank(),
	panel.grid = element_blank(),
	panel.spacing = unit(0, "lines"),
	plot.background = element_blank(),
	legend.position = "bottom",
	plot.title = element_text(size = 18)) +
	guides(fill = guide_legend(reverse = TRUE)) +
	labs(title = "Share of the State That is Muslim", caption = "@ryanburge\nData: 2020 Religion Census")
	save("cen20_muslim_state.png")


	library(readxl)

	cen20 <- read_excel("E://data/rel_cen20.xlsx", sheet = 3)

	library(janitor)

	cen20 <- cen20 %>%
	clean_names()

	mi <- cen20 %>%
	filter(group_name == "Muslim Estimate") %>%
	filter(state_name == "Michigan") %>%
	select(fips, county_name, congregations, adherents, pct_adh = adherents_as_percent_of_total_adherents, pct_pop = adherents_as_percent_of_total_population)

	mi <- mi %>%
	mutate(bins = frcode(pct_pop > .02 ~ "2%+",
	pct_pop > .01 ~ "1%-2%",
	pct_pop > 0 ~ "0%-1%"))


	library(urbnmapr)

	territories_counties <- get_urbn_map(map = "territories_counties")

	territories_counties <- territories_counties %>%
	rename(fips = county_fips)

	mapp <- left_join(territories_counties, mi)



	color_palette <- c("25%+" = "#08306B", "10%-25%" = "#2171B5", "5%-10%" = "#6BAED6", "1%-5%" = "#C6DBEF", "NA" = "#BDBDBD")


	mapp %>%
	filter(state_name == "Michigan") %>%
	ggplot(aes(long, lat, group = group, fill = bins)) +
	geom_polygon(color = "black", size = 0.05) +
	coord_map(projection = "albers", lat0 = 39, lat1 = 45) +
	labs(title = " Muslim Concentration by County", caption = "@ryanburge\nData: 2020 Religious Census", fill = "") +
	theme(legend.title = element_text(), legend.key.width = unit(.5, "in")) +
	theme_rb() +
	scale_fill_manual(values = c("0%-1%" = "#d8bfd8", "1%-2%" = "#9370db", "2%+" = "#4b0082")) +
	urbnthemes::theme_urbn_map() +
	theme(plot.title = element_text(size=20, family= "font", face= "bold")) +
	theme(plot.caption = element_text(size = 14, family="font")) +
	theme(text=element_text(size=18, family="font")) +
	theme(legend.text = element_text(size = 20))
	save("relcen_muslim_michigan.png")


	cc20 <- cces20 %>%
	filter(religpew == 6) %>%
	select(immstat, weight = commonweight, inputstate)

	cc21 <- cces21 %>%
	filter(religpew == 6) %>%
	select(immstat, weight = commonweight, inputstate)

	cc22 <- cces22 %>%
	filter(religpew == 6) %>%
	select(immstat, weight = commonweight, inputstate)

	cc23 <- cces23 %>%
	filter(religpew == 6) %>%
	select(immstat, weight = commonweight, inputstate)

	all <- bind_rows(cc20, cc21, cc22, cc23)

	gg1 <- all %>%
	mutate(imm = frcode(immstat == 1 ~ "Immigrant,\nNaturalized",
	immstat == 2 ~ "Immigrant,\nNon-Citizen",
	immstat == 3 ~ "First\nGeneration",
	immstat == 4 ~ "Second\nGeneration",
	immstat == 5 ~ "Third\nGeneration\nor More")) %>%
	ct(imm, wt = weight, show_na = FALSE) %>%
	mutate(type = "Muslims")


	cc20 <- cces20 %>%
	select(immstat, weight = commonweight, inputstate)

	cc21 <- cces21 %>%
	select(immstat, weight = commonweight, inputstate)

	cc22 <- cces22 %>%
	select(immstat, weight = commonweight, inputstate)

	cc23 <- cces23 %>%
	select(immstat, weight = commonweight, inputstate)

	all <- bind_rows(cc20, cc21, cc22, cc23)

	gg2 <- all %>%
	mutate(imm = frcode(immstat == 1 ~ "Immigrant,\nNaturalized",
	immstat == 2 ~ "Immigrant,\nNon-Citizen",
	immstat == 3 ~ "First\nGeneration",
	immstat == 4 ~ "Second\nGeneration",
	immstat == 5 ~ "Third\nGeneration\nor More")) %>%
	ct(imm, wt = weight, show_na = FALSE) %>%
	mutate(type = "Entire\nSample")

	all <- bind_rows(gg1, gg2)

	all %>%
	mutate(lab = round(pct, 2)) %>%
	ggplot(., aes(x = type, y = pct, fill = type)) +
	geom_col(color = "black") +
	facet_wrap(~ imm) +
	theme_rb() +
	scale_fill_calc() +
	y_pct() +
	geom_text(aes(y = pct + .045, label = paste0(lab*100, '%')), position = position_dodge(width = .9), size = 8, family = "font") +
	labs(x = "", y = "", title = "Immigration Status of Muslims vs All Americans", caption = '@ryanburge\nData: Cooperative Election Study, 2020-2023')
	save("muslim_immigration.png", ht = 8, wd = 5)


	## There are 64 Muslims from Michigan in the sample
	## Total sample is 171200
	## A total of 1518 Muslims

	## Total votes cast in Michigan: 5,453,892
	## Turnout was 70.5% of all eligible population in Michigan in 2020. https://www.michigan.gov/sos/-/media/Project/Websites/sos/Election-Results-and-Statistics/General-Voter-Reg-Turnout-Stats.pdf
	## Total number of Muslims in Michigan in 241,828

	gg <- cces %>%
	filter(year >= 2020) %>%
	mutate(trad = frcode(religion == 1 ~ "Protestant",
	religion == 2 ~ "Catholic",
	religion == 3 ~ "Mormon",
	religion == 4 ~ "Orthodox",
	religion == 5 ~ "Jewish",
	religion == 6 ~ "Muslim",
	religion == 7 ~ "Buddhist",
	religion == 8 ~ "Hindu",
	religion == 9 ~ "Atheist",
	religion == 10 ~ "Agnostic",
	religion == 11 ~ "Nothing in Particular")) %>%
	mutate(kids = case_when(havekids == 1 ~ 1,
	havekids == 2 ~ 0)) %>%
	group_by(trad) %>%
	mean_ci(kids, wt = weight, ci = .84) %>% filter(trad != 'NA')

	gg %>%
	mutate(lab = round(mean, 2)) %>%
	ggplot(., aes(x = reorder(trad, mean), y = mean, fill = trad)) +
	geom_col(color = "black") +
	coord_flip() +
	theme_rb() +
	y_pct() +
	error_bar()+
	scale_fill_manual(values = c(moma.colors("Warhol", 11))) +
	lab_bar(top = FALSE, type = lab, pos = .03, sz = 9) +
	labs(x = "", y = "", title = "Share of Each Tradition That Are Currently Parents to Minor Children", caption = "@ryanburge\nData: Cooperative Election Study, 2020-2023")
	save("parents_trads_23.png")



	gg <- cces %>%
	filter(year >= 2020) %>%
	mutate(trad = frcode(religion == 1 ~ "Protestant",
	religion == 2 ~ "Catholic",
	religion == 3 ~ "Mormon",
	religion == 4 ~ "Orthodox",
	religion == 5 ~ "Jewish",
	religion == 6 ~ "Muslim",
	religion == 7 ~ "Buddhist",
	religion == 8 ~ "Hindu",
	religion == 9 ~ "Atheist",
	religion == 10 ~ "Agnostic",
	religion == 11 ~ "Nothing in Particular")) %>%
	mutate(kids = case_when(havekids == 1 ~ 1,
	havekids == 2 ~ 0)) %>%
	filter(havekids == 1) %>%
	# group_by(trad) %>%
	mean_ci(numkids, wt = weight, ci = .84) %>% filter(trad != 'NA')

	gg %>%
	mutate(lab = round(mean, 1)) %>%
	ggplot(., aes(x = reorder(trad, mean), y = mean, fill = trad)) +
	geom_col(color = "black") +
	coord_flip() +
	theme_rb() +
	y_pct() +
	scale_fill_manual(values = c(moma.colors("Warhol", 11))) +
	geom_text(aes(y = mean + .14, label = lab), position = position_dodge(width = .9), size = 8, family = "font") +
	labs(x = "", y = "", title = "Among Parents, Average Number of Children", caption = "@ryanburge\nData: Cooperative Election Study, 2020-2023")
	save("parents_trads_23_numkids.png")



	gg <- cces %>%
	filter(year >= 2020) %>%
	mutate(trad = frcode(religion == 1 ~ "Protestant",
	religion == 2 ~ "Catholic",
	religion == 3 ~ "Mormon",
	religion == 4 ~ "Orthodox",
	religion == 5 ~ "Jewish",
	religion == 6 ~ "Muslim",
	religion == 7 ~ "Buddhist",
	religion == 8 ~ "Hindu",
	religion == 9 ~ "Atheist",
	religion == 10 ~ "Agnostic",
	religion == 11 ~ "Nothing in Particular")) %>%
	mutate(age2 = frcode(age >= 18 & age <= 35 ~ "18-35",
	age >= 36 & age <= 44 ~ "36-44",
	age >= 45 & age <= 54 ~ "45-54",
	age >= 55 & age <= 64 ~ "55-64",
	age >= 65 ~ "65+")) %>%
	group_by(trad) %>%
	ct(age2, wt = weight, show_na = FALSE) %>% filter(trad != 'NA')


	lvl <- gg %>%
	filter(age2 == "18-35") %>%
	select(trad, sort = pct)


	gg <- left_join(gg, lvl)

	gg$trad <- fct_reorder(gg$trad, gg$sort, .desc = TRUE)


	gg %>%
	mutate(lab = round(pct, 2)) %>%
	ggplot(., aes(x = 1, y = pct, fill = fct_rev(age2))) +
	geom_col(color = "black") +
	coord_flip() +
	facet_wrap(~ trad, ncol =1, strip.position = "left") +
	scale_fill_manual(values = c(moma.colors("Connors", 5))) +
	theme_rb() +
	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 = 7, family = "font", color = "black") +
	labs(x = "", y = "", title = "Age Distribution of Adults in Religious Traditions", caption = "@ryanburge\nData: Cooperative Election Study, 2020-2023")
	save("age2_trad2_distribution.png", wd = 9, ht = 6)

	table <- mi %>%
	select(county = county_name, congregations, adherents, pct_pop) %>%
	mutate(pct_pop = pct_pop*1)

	table <- table %>%
	arrange(desc(pct_pop))

	library(gt)

	table %>%
	gt() %>%
	tab_header(
	title = "List of Counties with a Mosque in Michigan",
	subtitle = "") %>%
	opt_table_font(
	font = c(
	"Abel Regular", "Segoe UI",
	default_fonts()[-c(1:3)]
	)) %>%
	tab_options(
	table.font.size = px(40L)
	) %>%
	cols_label(county = "County",
	congregations = "Congregations",
	adherents = "Adherents",
	pct_pop = "% Muslim") %>%
	fmt_percent(
	columns = vars(pct_pop),
	decimals = 2
	) %>%
	fmt_number(
	columns = vars(adherents),
	use_seps = TRUE,
	decimals = 0
	) %>%
	gtsave("E://graphs23/michigan_muslims.png")




	county <- cen20 %>%
	filter(group_name == "Muslim Estimate") %>%
	select(fips, county_name, state_name, congregations, adherents, pct_adh = adherents_as_percent_of_total_adherents, pct_pop = adherents_as_percent_of_total_population)




	gg <- cces %>%
	filter(year >= 2020) %>%
	mutate(trad = frcode(religion == 1 ~ "Protestant",
	religion == 2 ~ "Catholic",
	religion == 3 ~ "Mormon",
	religion == 4 ~ "Orthodox",
	religion == 5 ~ "Jewish",
	religion == 6 ~ "Muslim",
	religion == 7 ~ "Buddhist",
	religion == 8 ~ "Hindu",
	religion == 9 ~ "Atheist",
	religion == 10 ~ "Agnostic",
	religion == 11 ~ "Nothing in Particular")) %>%
	group_by(trad) %>%
	mean_ci(age, wt = weight) %>% filter(trad != 'NA')