ryanburge/ct_selection_untitled-7AA1B3F0.R Secret

## ct_selection_untitled-7AA1B3F0.R

cces_educ <- function(df, var){


  df %>%
    mutate(educ = frcode({{var}} == 1 ~ "No HS",
                         {{var}} == 2 ~ "HS\nGrad",
                         {{var}} == 3 ~ "Some\nColl.",
                         {{var}} == 4 ~ "2 Yr.",
                         {{var}} == 5 ~ "4 Yr.",
                         {{var}} == 6 ~ "Post-\nGrad"))

}


graph <- cces %>%
  filter(year >= 2008) %>%
  cces_educ(educ) %>%
  mutate(none = case_when(religion == 9 | religion == 10 | religion == 11 ~ 1,
                          TRUE ~ 0)) %>%
  group_by(educ, year) %>%
  mean_ci(none, wt = weight, ci = .84)


graph %>%
  mutate(lab = round(mean, 2)) %>%
  ggplot(., aes(x = educ, y = mean, group = factor(year), fill = factor(year))) +
  geom_col(color = "black") +
  facet_wrap(~ year) +
  theme_rb() +
  geom_smooth(se = FALSE, method = lm, color = "black", linetype = "twodash", linewidth = .5) +
  scale_fill_manual(values = c(met.brewer("Cross", 15))) +
  lab_bar(top = FALSE, type = lab, pos = .053, sz = 4) +
  geom_text(aes(y = .053, label = ifelse(year >= 2020, paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 4, family = "font", color = "white") +
  y_pct() +
  error_bar() +
  labs(x = "Highest Level of Education", y = "", title = "Share Who Identify as Atheist, Agnostic, or Nothing in Particular", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022")
save("educ_nones_ces22.png", ht = 9)


gg <- ns %>%
  mutate(educ = frcode(education <= 3 ~ "Some HS",
                       education == 4 ~ "HS Grad",
                       education == 5 ~ "Trade\nSchool",
                       education == 6 ~ "Some\nColl.",
                       education == 7 ~ "2 Yr.",
                       education == 8 ~ "4 Yr.",
                       education == 9 ~ "Some\nGrad.",
                       education == 10 ~ "Masters",
                       education == 11 ~ "Doctorate")) %>%
  mutate(none = case_when(religion == 10 | religion == 11 | religion == 12 ~ 1, TRUE ~ 0)) %>%
  group_by(educ) %>%
  mean_ci(none, wt = weight, ci = .84)

gg %>%
  mutate(lab = round(mean, 2)) %>%
  ggplot(., aes(x = educ, y = mean, fill = educ)) +
  geom_col(color = 'black') +
  theme_rb() +
  y_pct() +
  lab_bar(top = FALSE, type = lab, pos= .015, sz = 10) +
  geom_text(aes(y = .015, label = ifelse(educ == "Doctorate" | educ == "Masters", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 10, family = "font", color = "white") +
  error_bar() +
  scale_fill_manual(values=met.brewer("Cross", 9)) +
  theme(axis.text = element_text(size = 17)) +
  labs(x = "Education Level", y = "", title = "Share Who Are Nones by Educational Level", caption = "@ryanburge\nData: Nationscape 2019-2021")
save("ns_ed_nones23.png")


cces_educ <- function(df, var){


  df %>%
    mutate(educ = frcode({{var}} == 1 ~ "No HS",
                         {{var}} == 2 ~ "HS\nGrad",
                         {{var}} == 3 ~ "Some\nColl.",
                         {{var}} == 4 ~ "2 Yr.",
                         {{var}} == 5 ~ "4 Yr.",
                         {{var}} == 6 ~ "Post-\nGrad"))

}


graph <- cces %>%
  filter(year >= 2008) %>%
  cces_educ(educ) %>%
  mutate(never = case_when(pew_attendance == 1 | pew_attendance == 2 ~ 1,
                          TRUE ~ 0)) %>%
  group_by(educ, year) %>%
  mean_ci(never, wt = weight, ci = .84)


graph %>%
  mutate(lab = round(mean, 2)) %>%
  ggplot(., aes(x = educ, y = mean, group = factor(year), fill = factor(year))) +
  geom_col(color = "black") +
  facet_wrap(~ year) +
  theme_rb() +
  geom_smooth(se = FALSE, method = lm, color = "black", linetype = "twodash", linewidth = .5) +
  scale_fill_manual(values = c(met.brewer("Homer1", 15))) +
  lab_bar(top = FALSE, type = lab, pos = .053, sz = 4) +
  geom_text(aes(y = .053, label = ifelse(year <= 2010, paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 4, family = "font", color = "white") +
  y_pct() +
  error_bar() +
  labs(x = "Highest Level of Education", y = "", title = "Share Who Attend Religious Services At Least Once a Week", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022")
save("educ_nevers_ces22.png", ht = 9)


cces_inc <- function(df, var){

  df %>%
    mutate(inc = frcode({{var}} == 1 ~ "<$10k",
                        {{var}} == 2 ~ "$10k-\n$20k",
                        {{var}} == 3 ~ "$20k-\n$30k",
                        {{var}} == 4 ~ "$30k-\n$40k",
                        {{var}} == 5 ~ "$40k-\n$50k",
                        {{var}} == 6 ~ "$50k-\n$60k",
                        {{var}} == 7 ~ "$60k-\n$70k",
                        {{var}} == 8 ~ "$70k-\n$80k",
                        {{var}} == 9 ~ "$80k-\n$100k",
                        {{var}} == 10 ~ "$100k-\n$120k",
                        {{var}} == 11 ~ "$120k-\n$150k",
                        {{var}} == 12 ~ "$150k-\n$200k",
                        {{var}} == 13 | {{var}} == 14 | {{var}} == 15 ~ "$200k+"))

}

graph <- cces %>%
  cces_inc(income) %>%
  filter(year == 2008 | year == 2012 | year == 2016 | year == 2020 | year == 2022) %>%
  filter(inc != "NA") %>%
  mutate(ed2 = frcode(educ == 1 | educ == 2 ~ "HS or Less",
                      educ == 5 | educ == 6 ~ "College Degree")) %>%
  filter(ed2 != "NA") %>%
  mutate(never = case_when(pew_attendance == 1 | pew_attendance == 2 ~ 1,
                           TRUE ~ 0)) %>%
  group_by(inc, ed2, year) %>%
  mean_ci(never, wt = weight, ci = .84) %>% filter(n > 100)

every_nth = function(n) {
  return(function(x) {x[c(TRUE, rep(FALSE, n - 1))]})
}


graph %>%
  ggplot(., aes(x = inc, y = mean, color = ed2, group = ed2)) +
  geom_point(stroke = .5, shape = 21, alpha = .45, show.legend = FALSE) +
  geom_smooth(se = FALSE) +
  facet_wrap(~ year) +
  y_pct() +
  theme_rb() +
  scale_color_calc() +
  scale_x_discrete(breaks = every_nth(n = 3)) +
  guides(color = guide_legend(reverse = TRUE)) +
  theme(legend.position = c(.85, .15)) +
  labs(x= "Household Income", y = "", title = "Share Attending Weekly by Education and Income Level", caption = "@ryanburge\nData: Cooperative Election Study 2008-2022")
save("inc_educ_wk_att.png")

graph <- cces %>%
  filter(year >= 2020) %>%
  mutate(mar = frcode(marital_status == 1 ~ "Married",
                      marital_status == 2 | marital_status == 3 ~ "Separated/Divorced",
                      marital_status == 5 ~ "Never Married")) %>%
  group_by(age, mar) %>%
  mutate(never = case_when(pew_attendance == 1 | pew_attendance == 2 ~ 1,
                           TRUE ~ 0)) %>%
  mean_ci(never, wt = weight) %>%
  filter(mar != 'NA')

graph %>%
  filter(mean <= .50) %>%
  filter(age <= 70) %>%
  ggplot(., aes(x = age, y= mean, color = mar, group = mar)) +
  geom_point(stroke = .5, shape = 21, alpha = .45, show.legend = FALSE) +
  geom_smooth(se = FALSE) +
  scale_color_calc() +
  y_pct() +
  theme_rb(legend = TRUE) +
  labs(x = "Age", y = "", title = "Share Attending Religious Services Weekly Based on Marital Status", caption = "@ryanburge\nData: Cooperative Election Study 2020-2022")
save("mar_status_wk.png")


graph <- cces %>%
  # filter(age >= 30 & age <= 50) %>%
  filter(year >= 2020) %>%
  mutate(mar = frcode(marital_status == 1 ~ "Married",
                      marital_status == 2 | marital_status == 3 | marital_status == 5 ~ "Not Married")) %>%
  mutate(kids = frcode(havekids == 1 ~ "Parent",
                        TRUE ~ "Not a Parent")) %>%
  mutate(never = case_when(pew_attendance == 1 | pew_attendance == 2 ~ 1,
                           TRUE ~ 0)) %>%
  group_by(mar, kids, age) %>%
  mean_ci(never, wt = weight) %>% filter(mar != "NA")

graph <- graph %>%
  mutate(comb = paste(mar, kids, sep = "+"))

graph %>%
  filter(age <= 50) %>%
  ggplot(., aes(x = age, y= mean, color = comb, group = comb)) +
  geom_point(stroke = .5, shape = 21, alpha = .45, show.legend = FALSE) +
  geom_smooth(se = FALSE) +
  scale_color_calc() +
  y_pct() +
  theme_rb(legend = TRUE) +
  labs(x = "Age", y = "", title = "Share Attending Religious Services Weekly Based on Marital and Parental Status", caption = "@ryanburge\nData: Cooperative Election Study 2020-2022")
save("mar_status_parents_wk.png")

	cces_educ <- function(df, var){


	df %>%
	mutate(educ = frcode({{var}} == 1 ~ "No HS",
	{{var}} == 2 ~ "HS\nGrad",
	{{var}} == 3 ~ "Some\nColl.",
	{{var}} == 4 ~ "2 Yr.",
	{{var}} == 5 ~ "4 Yr.",
	{{var}} == 6 ~ "Post-\nGrad"))

	}


	graph <- cces %>%
	filter(year >= 2008) %>%
	cces_educ(educ) %>%
	mutate(none = case_when(religion == 9 \| religion == 10 \| religion == 11 ~ 1,
	TRUE ~ 0)) %>%
	group_by(educ, year) %>%
	mean_ci(none, wt = weight, ci = .84)


	graph %>%
	mutate(lab = round(mean, 2)) %>%
	ggplot(., aes(x = educ, y = mean, group = factor(year), fill = factor(year))) +
	geom_col(color = "black") +
	facet_wrap(~ year) +
	theme_rb() +
	geom_smooth(se = FALSE, method = lm, color = "black", linetype = "twodash", linewidth = .5) +
	scale_fill_manual(values = c(met.brewer("Cross", 15))) +
	lab_bar(top = FALSE, type = lab, pos = .053, sz = 4) +
	geom_text(aes(y = .053, label = ifelse(year >= 2020, paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 4, family = "font", color = "white") +
	y_pct() +
	error_bar() +
	labs(x = "Highest Level of Education", y = "", title = "Share Who Identify as Atheist, Agnostic, or Nothing in Particular", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022")
	save("educ_nones_ces22.png", ht = 9)




	gg <- ns %>%
	mutate(educ = frcode(education <= 3 ~ "Some HS",
	education == 4 ~ "HS Grad",
	education == 5 ~ "Trade\nSchool",
	education == 6 ~ "Some\nColl.",
	education == 7 ~ "2 Yr.",
	education == 8 ~ "4 Yr.",
	education == 9 ~ "Some\nGrad.",
	education == 10 ~ "Masters",
	education == 11 ~ "Doctorate")) %>%
	mutate(none = case_when(religion == 10 \| religion == 11 \| religion == 12 ~ 1, TRUE ~ 0)) %>%
	group_by(educ) %>%
	mean_ci(none, wt = weight, ci = .84)

	gg %>%
	mutate(lab = round(mean, 2)) %>%
	ggplot(., aes(x = educ, y = mean, fill = educ)) +
	geom_col(color = 'black') +
	theme_rb() +
	y_pct() +
	lab_bar(top = FALSE, type = lab, pos= .015, sz = 10) +
	geom_text(aes(y = .015, label = ifelse(educ == "Doctorate" \| educ == "Masters", paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 10, family = "font", color = "white") +
	error_bar() +
	scale_fill_manual(values=met.brewer("Cross", 9)) +
	theme(axis.text = element_text(size = 17)) +
	labs(x = "Education Level", y = "", title = "Share Who Are Nones by Educational Level", caption = "@ryanburge\nData: Nationscape 2019-2021")
	save("ns_ed_nones23.png")


	cces_educ <- function(df, var){


	df %>%
	mutate(educ = frcode({{var}} == 1 ~ "No HS",
	{{var}} == 2 ~ "HS\nGrad",
	{{var}} == 3 ~ "Some\nColl.",
	{{var}} == 4 ~ "2 Yr.",
	{{var}} == 5 ~ "4 Yr.",
	{{var}} == 6 ~ "Post-\nGrad"))

	}


	graph <- cces %>%
	filter(year >= 2008) %>%
	cces_educ(educ) %>%
	mutate(never = case_when(pew_attendance == 1 \| pew_attendance == 2 ~ 1,
	TRUE ~ 0)) %>%
	group_by(educ, year) %>%
	mean_ci(never, wt = weight, ci = .84)


	graph %>%
	mutate(lab = round(mean, 2)) %>%
	ggplot(., aes(x = educ, y = mean, group = factor(year), fill = factor(year))) +
	geom_col(color = "black") +
	facet_wrap(~ year) +
	theme_rb() +
	geom_smooth(se = FALSE, method = lm, color = "black", linetype = "twodash", linewidth = .5) +
	scale_fill_manual(values = c(met.brewer("Homer1", 15))) +
	lab_bar(top = FALSE, type = lab, pos = .053, sz = 4) +
	geom_text(aes(y = .053, label = ifelse(year <= 2010, paste0(lab*100, '%'), "")), position = position_dodge(width = .9), size = 4, family = "font", color = "white") +
	y_pct() +
	error_bar() +
	labs(x = "Highest Level of Education", y = "", title = "Share Who Attend Religious Services At Least Once a Week", caption = "@ryanburge\nData: Cooperative Election Study, 2008-2022")
	save("educ_nevers_ces22.png", ht = 9)


	cces_inc <- function(df, var){

	df %>%
	mutate(inc = frcode({{var}} == 1 ~ "<$10k",
	{{var}} == 2 ~ "$10k-\n$20k",
	{{var}} == 3 ~ "$20k-\n$30k",
	{{var}} == 4 ~ "$30k-\n$40k",
	{{var}} == 5 ~ "$40k-\n$50k",
	{{var}} == 6 ~ "$50k-\n$60k",
	{{var}} == 7 ~ "$60k-\n$70k",
	{{var}} == 8 ~ "$70k-\n$80k",
	{{var}} == 9 ~ "$80k-\n$100k",
	{{var}} == 10 ~ "$100k-\n$120k",
	{{var}} == 11 ~ "$120k-\n$150k",
	{{var}} == 12 ~ "$150k-\n$200k",
	{{var}} == 13 \| {{var}} == 14 \| {{var}} == 15 ~ "$200k+"))

	}

	graph <- cces %>%
	cces_inc(income) %>%
	filter(year == 2008 \| year == 2012 \| year == 2016 \| year == 2020 \| year == 2022) %>%
	filter(inc != "NA") %>%
	mutate(ed2 = frcode(educ == 1 \| educ == 2 ~ "HS or Less",
	educ == 5 \| educ == 6 ~ "College Degree")) %>%
	filter(ed2 != "NA") %>%
	mutate(never = case_when(pew_attendance == 1 \| pew_attendance == 2 ~ 1,
	TRUE ~ 0)) %>%
	group_by(inc, ed2, year) %>%
	mean_ci(never, wt = weight, ci = .84) %>% filter(n > 100)

	every_nth = function(n) {
	return(function(x) {x[c(TRUE, rep(FALSE, n - 1))]})
	}


	graph %>%
	ggplot(., aes(x = inc, y = mean, color = ed2, group = ed2)) +
	geom_point(stroke = .5, shape = 21, alpha = .45, show.legend = FALSE) +
	geom_smooth(se = FALSE) +
	facet_wrap(~ year) +
	y_pct() +
	theme_rb() +
	scale_color_calc() +
	scale_x_discrete(breaks = every_nth(n = 3)) +
	guides(color = guide_legend(reverse = TRUE)) +
	theme(legend.position = c(.85, .15)) +
	labs(x= "Household Income", y = "", title = "Share Attending Weekly by Education and Income Level", caption = "@ryanburge\nData: Cooperative Election Study 2008-2022")
	save("inc_educ_wk_att.png")

	graph <- cces %>%
	filter(year >= 2020) %>%
	mutate(mar = frcode(marital_status == 1 ~ "Married",
	marital_status == 2 \| marital_status == 3 ~ "Separated/Divorced",
	marital_status == 5 ~ "Never Married")) %>%
	group_by(age, mar) %>%
	mutate(never = case_when(pew_attendance == 1 \| pew_attendance == 2 ~ 1,
	TRUE ~ 0)) %>%
	mean_ci(never, wt = weight) %>%
	filter(mar != 'NA')

	graph %>%
	filter(mean <= .50) %>%
	filter(age <= 70) %>%
	ggplot(., aes(x = age, y= mean, color = mar, group = mar)) +
	geom_point(stroke = .5, shape = 21, alpha = .45, show.legend = FALSE) +
	geom_smooth(se = FALSE) +
	scale_color_calc() +
	y_pct() +
	theme_rb(legend = TRUE) +
	labs(x = "Age", y = "", title = "Share Attending Religious Services Weekly Based on Marital Status", caption = "@ryanburge\nData: Cooperative Election Study 2020-2022")
	save("mar_status_wk.png")


	graph <- cces %>%
	# filter(age >= 30 & age <= 50) %>%
	filter(year >= 2020) %>%
	mutate(mar = frcode(marital_status == 1 ~ "Married",
	marital_status == 2 \| marital_status == 3 \| marital_status == 5 ~ "Not Married")) %>%
	mutate(kids = frcode(havekids == 1 ~ "Parent",
	TRUE ~ "Not a Parent")) %>%
	mutate(never = case_when(pew_attendance == 1 \| pew_attendance == 2 ~ 1,
	TRUE ~ 0)) %>%
	group_by(mar, kids, age) %>%
	mean_ci(never, wt = weight) %>% filter(mar != "NA")

	graph <- graph %>%
	mutate(comb = paste(mar, kids, sep = "+"))

	graph %>%
	filter(age <= 50) %>%
	ggplot(., aes(x = age, y= mean, color = comb, group = comb)) +
	geom_point(stroke = .5, shape = 21, alpha = .45, show.legend = FALSE) +
	geom_smooth(se = FALSE) +
	scale_color_calc() +
	y_pct() +
	theme_rb(legend = TRUE) +
	labs(x = "Age", y = "", title = "Share Attending Religious Services Weekly Based on Marital and Parental Status", caption = "@ryanburge\nData: Cooperative Election Study 2020-2022")
	save("mar_status_parents_wk.png")
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