ryanburge/ct_selection_untitled-937FC514.R Secret

## ct_selection_untitled-937FC514.R
graph <- cces %>%
  filter(year == 2008 | year == 2022) %>%
  mutate(single = case_when(marital_status == 5 ~ 1,
                            TRUE ~ 0)) %>%
  group_by(age, year) %>%
  mean_ci(single, wt = weight)

graph %>%
  filter(age <= 75) %>%
  ggplot(., aes(x = age, y = mean, color = factor(year), group = factor(year))) +
  geom_point(stroke = .5, shape = 21, alpha = .45) +
  geom_labelsmooth(aes(label = year),  method = "loess", formula = y ~ x, family = "font",
                   linewidth = 1, text_smoothing = 30, size = 6, linewidth = 1, boxlinewidth = 0.3, hjust = .75) +
  scale_color_calc() +
  theme_rb() +
  y_pct() +
  labs(x = "Age", y = "", title = "Share Reporting That They Have Never Been Married by Age",
       caption = "@ryanburge\nData: Cooperative Election 2008-2022")
save("age_marital_new.png", wd = 8, ht = 6)

graph <- cces %>%
  mutate(gender = frcode(gender == 1 ~ "Men",
                         gender == 2 ~ "Women")) %>%
  filter(year == 2008 | year == 2022) %>%
  mutate(single = case_when(marital_status == 5 ~ 1,
                            TRUE ~ 0)) %>%
  group_by(age, year, gender) %>%
  mean_ci(single, wt = weight) %>% filter(gender != "NA")

graph %>%
  filter(age <= 75) %>%
  ggplot(., aes(x = age, y = mean, color = gender, group = gender)) +
  geom_point(stroke = .5, shape = 21, alpha = .45, show.legend = FALSE) +
  geom_smooth(se = FALSE) +
  scale_color_calc() +
  facet_wrap(~ year) +
  theme_rb(legend = TRUE) +
  y_pct() +
  theme(strip.text = element_text(size = 20))+
  labs(x = "Age", y = "", title = "Share Reporting That They Have Never Been Married by Age",
       caption = "@ryanburge\nData: Cooperative Election 2008-2022")
save("age_marital_gender.png", wd = 8, ht = 6)


graph <- cces %>%
  mutate(id3 = frcode(ideo5 == 1 | ideo5 == 2 ~ "Liberal",
                      ideo5 == 3 ~ "Moderate",
                      ideo5 == 4 | ideo5 == 5 ~ "Conservative")) %>%
  mutate(gender = frcode(gender == 1 ~ "Men",
                         gender == 2 ~ "Women")) %>%
  filter(year >= 2020) %>%
  mutate(single = case_when(marital_status == 5 ~ 1,
                            TRUE ~ 0)) %>%
  group_by(age, id3, gender) %>%
  mean_ci(single, wt = weight) %>% filter(gender != "NA") %>% filter(id3 != "NA")

graph %>%
  filter(age <= 75) %>%
  ggplot(., aes(x = age, y = mean, color = gender, group = gender)) +
  geom_point(stroke = .5, shape = 21, alpha = .45, show.legend = FALSE) +
  geom_smooth(se = FALSE) +
  scale_color_calc() +
  facet_wrap(~ id3) +
  theme_rb(legend = TRUE) +
  y_pct() +
  theme(strip.text = element_text(size = 20))+
  labs(x = "Age", y = "", title = "Share Reporting That They Have Never Been Married by Age",
       caption = "@ryanburge\nData: Cooperative Election 2020-2022")
save("age_marital_gender_id3.png", wd = 8, ht = 6)


graph <- cces %>%
  mutate(rel = frcode(religion == 1 | religion == 2 | religion == 3 | religion == 4 ~ "Christian",
                      religion == 5 | religion == 6 | religion == 7 | religion == 8 ~ "Other Faith",
                      religion == 9 | religion == 10 | religion == 11 ~ "Nones")) %>%
  mutate(gender = frcode(gender == 1 ~ "Men",
                         gender == 2 ~ "Women")) %>%
  filter(year >= 2020) %>%
  mutate(single = case_when(marital_status == 5 ~ 1,
                            TRUE ~ 0)) %>%
  group_by(age, rel, gender) %>%
  mean_ci(single, wt = weight) %>% filter(gender != "NA") %>% filter(rel != "NA")

graph %>%
  filter(age <= 75) %>%
  ggplot(., aes(x = age, y = mean, color = gender, group = gender)) +
  geom_point(stroke = .5, shape = 21, alpha = .45, show.legend = FALSE) +
  geom_smooth(se = FALSE) +
  scale_color_calc() +
  facet_wrap(~ rel) +
  theme_rb(legend = TRUE) +
  y_pct() +
  theme(strip.text = element_text(size = 20))+
  labs(x = "Age", y = "", title = "Share Reporting That They Have Never Been Married by Age",
       caption = "@ryanburge\nData: Cooperative Election 2020-2022")
save("age_marital_gender_rel.png", wd = 8, ht = 6)


graph <- cces %>%
  filter(year >= 2020) %>%
  mutate(gender = frcode(gender == 1 ~ "Men",
                         gender == 2 ~ "Women")) %>%
  mutate(single = case_when(havekids == 1 ~ 1,
                            havekids == 2 ~ 0)) %>%
  group_by(age, gender) %>%
  mean_ci(single, wt = weight)

graph %>%
  filter(age <= 75) %>%
  filter(gender != "NA") %>%
  ggplot(., aes(x = age, y = mean, color = gender, group = gender)) +
  geom_point(stroke = .5, shape = 21, alpha = .45) +
  geom_labelsmooth(aes(label = gender),  method = "loess", formula = y ~ x, family = "font",
                   linewidth = 1, text_smoothing = 30, size = 6, linewidth = 1, boxlinewidth = 0.3, hjust = .15) +
  scale_color_calc() +
  theme_rb() +
  y_pct() +
  labs(x = "Age", y = "", title = "Share Reporting That They Are a Parent/Guardian of a Child Under the Age of 18",
       caption = "@ryanburge\nData: Cooperative Election 2020-2022")
save("age_havekids_new.png", wd = 8, ht = 6)

graph <- cces %>%
  mutate(birthyr = year - age) %>%
  mutate(cohorts = frcode( birthyr >= 1955 & birthyr <= 1959 ~ "1955-1959",
                           birthyr >= 1960 & birthyr <= 1964 ~ "1960-1964",
                           birthyr >= 1965 & birthyr <= 1969 ~ "1965-1969",
                           birthyr >= 1970 & birthyr <= 1974 ~ "1970-1974",
                           birthyr >= 1975 & birthyr <= 1979 ~ "1975-1979",
                           birthyr >= 1980 & birthyr <= 1984 ~ "1980-1984",
                           birthyr >= 1985 & birthyr <= 1989 ~ "1985-1989",
                           birthyr >= 1990 & birthyr <= 1994 ~ "1990-1994",
                           birthyr >= 1995 & birthyr <= 2000 ~ "1995-2000")) %>%
  mutate(nokids = case_when(havekids == 2 ~ 1,
                            havekids == 1 ~ 0)) %>%
  mutate(single = case_when(marital_status == 5 ~ 1,
                            TRUE ~ 0)) %>%
  mutate(both = nokids + single) %>%
  mutate(both = case_when(both == 2 ~ 1,
                          TRUE ~ 0)) %>%
  mutate(gender = frcode(gender == 1 ~ "Men",
                         gender == 2 ~ "Women")) %>%
  group_by(year, gender, cohorts) %>%
  mean_ci(both, wt = weight) %>%
  filter(mean > 0) %>%
  filter(cohorts != "NA") %>%
  filter(gender != "NA")


graph %>%
  ggplot(., aes(x = year, y = mean, color = gender, group = gender)) +
  geom_point(stroke = .5, shape = 21, alpha = .5) +
  geom_smooth(se = FALSE) +
  theme_rb(legend = TRUE) +
  facet_wrap(~ cohorts) +
  scale_color_calc() +
  y_pct() +
  labs(x = "Year", y = "", title = "Share Who Have Never Been Married and\nAre Not Parents to Young Children", caption = "@ryanburge\nData: Cooperative Election 2008-2022")
save("cohorts_nokid_never_married.png", ht = 8, wd = 6)


regg <- cces %>%
  filter(year >= 2020) %>%
  filter(age >= 35 & age <= 50) %>%
  mutate(nokids = case_when(havekids == 2 ~ 1,
                            havekids == 1 ~ 0)) %>%
  mutate(single = case_when(marital_status == 5 ~ 1,
                            TRUE ~ 0)) %>%
  mutate(both = nokids + single) %>%
  mutate(both = case_when(both == 2 ~ 1,
                          TRUE ~ 0)) %>%
  mutate(gender = frcode(gender == 1 ~ "Men",
                         gender == 2 ~ "Women")) %>%
  mutate(white = case_when(race == 1 ~ 1, TRUE ~ 0)) %>%
  mutate(none = case_when(religion == 9 | religion == 10 | religion == 11 ~ 1,
                          TRUE ~ 0)) %>%
  mutate(liberal = case_when(ideo5 == 1 | ideo5 == 2 ~ 1,
                             ideo5 <= 5 ~ 0)) %>%
   select(educ, age, gender, both, white, income, none, liberal)

women <- regg %>% filter(gender == "Women")
men  <- regg %>% filter(gender == "Men")

ww <- glm(both ~ educ + income + none + liberal + white, family = "binomial", data = women)
mm <- glm(both ~ educ + income + none + liberal + white, family = "binomial", data = men)


coef_names <- c("Age" = "age",
                "Male" = "male",
                "White" = "white",
                "Income" = "income",
                "Liberal" = "liberal",
                "Attendance" = "att",
                "Education" = "educ",
                "No Religion" = "none")

library(jtools)

gg1 <- plot_summs(ww, mm, robust = "HC3", scale = TRUE, coefs = coef_names, model.names = c("Women", "Men"))

gg1 +
  theme_rb() +
  scale_fill_calc() +
  scale_color_calc() +
  theme(legend.position = c(.75, .75)) +
  guides(shape = FALSE) +
  guides(color = guide_legend(reverse = TRUE)) +
  add_text(x = .25, y = 5.25, word = "More Likely to Be Never Married\nand Never a Parent", sz = 6) +
  labs(x = "", y = "", title = "What Factors Predict Someone to be Never Married and Never a Parent?", subtitle = "Sample Restricted to 35-50 Year Olds", caption = "@ryanburge\nData: Cooperative Election Study, 2020-2022")
save("reg_predict_no_marry_no_kids.png")


regg <- cces %>%
  filter(income <= 16) %>%
  filter(year >= 2020) %>%
  filter(age >= 35 & age <= 50) %>%
  mutate(nokids = case_when(havekids == 2 ~ 1,
                            havekids == 1 ~ 0)) %>%
  mutate(single = case_when(marital_status == 5 ~ 1,
                            TRUE ~ 0)) %>%
  mutate(both = nokids + single) %>%
  mutate(both = case_when(both == 2 ~ 1,
                          TRUE ~ 0)) %>%
  mutate(gender = frcode(gender == 1 ~ "Men",
                         gender == 2 ~ "Women")) %>%
  mutate(white = case_when(race == 1 ~ 1, TRUE ~ 0)) %>%
  mutate(none = case_when(religion == 9 | religion == 10 | religion == 11 ~ 1,
                          TRUE ~ 0)) %>%
  mutate(liberal = case_when(ideo5 == 1 | ideo5 == 2 ~ 1,
                             ideo5 <= 5 ~ 0)) %>%
  mutate(ed2 = frcode(educ == 1 | educ == 2 ~ "HS or Less",
                      educ == 5 | educ == 6 ~ "College Degree")) %>%
  select(ed2, age, gender, both, white, income, none, liberal)


regg <- glm(both ~ ed2*gender*income + white + none + liberal, family = "binomial", data = regg)

library(interactions)

int <- interact_plot(regg, pred= income, modx = gender, mod2 = ed2, int.width = .76, interval = TRUE, modx.labels = c("Men", "Women"), mod2.labels = c("HS or Less", "College Grad"))

int +
  theme_rb(legend = TRUE) +
  scale_fill_calc() +
  scale_color_calc() +
  scale_x_continuous(breaks = c(4, 8, 12, 16), labels = c("$30K-$40K", "$70K-$80K", "$150K-$200K", "$500K+")) +
  scale_y_continuous(labels = percent) +
  theme(strip.text = element_text(size = 20)) +
  labs(x = "Household Income", y = "", title = "Predicted Share Who Are Never Married + Not Currently a Parent", subtitle = "Sample Restricted to 35-50 Year Olds", caption = "@ryanburge\nData: Cooperative Election Study, 2020-2022")
save("interact_income_educ_married_kids.png")
	graph <- cces %>%
	filter(year == 2008 \| year == 2022) %>%
	mutate(single = case_when(marital_status == 5 ~ 1,
	TRUE ~ 0)) %>%
	group_by(age, year) %>%
	mean_ci(single, wt = weight)

	graph %>%
	filter(age <= 75) %>%
	ggplot(., aes(x = age, y = mean, color = factor(year), group = factor(year))) +
	geom_point(stroke = .5, shape = 21, alpha = .45) +
	geom_labelsmooth(aes(label = year), method = "loess", formula = y ~ x, family = "font",
	linewidth = 1, text_smoothing = 30, size = 6, linewidth = 1, boxlinewidth = 0.3, hjust = .75) +
	scale_color_calc() +
	theme_rb() +
	y_pct() +
	labs(x = "Age", y = "", title = "Share Reporting That They Have Never Been Married by Age",
	caption = "@ryanburge\nData: Cooperative Election 2008-2022")
	save("age_marital_new.png", wd = 8, ht = 6)

	graph <- cces %>%
	mutate(gender = frcode(gender == 1 ~ "Men",
	gender == 2 ~ "Women")) %>%
	filter(year == 2008 \| year == 2022) %>%
	mutate(single = case_when(marital_status == 5 ~ 1,
	TRUE ~ 0)) %>%
	group_by(age, year, gender) %>%
	mean_ci(single, wt = weight) %>% filter(gender != "NA")

	graph %>%
	filter(age <= 75) %>%
	ggplot(., aes(x = age, y = mean, color = gender, group = gender)) +
	geom_point(stroke = .5, shape = 21, alpha = .45, show.legend = FALSE) +
	geom_smooth(se = FALSE) +
	scale_color_calc() +
	facet_wrap(~ year) +
	theme_rb(legend = TRUE) +
	y_pct() +
	theme(strip.text = element_text(size = 20))+
	labs(x = "Age", y = "", title = "Share Reporting That They Have Never Been Married by Age",
	caption = "@ryanburge\nData: Cooperative Election 2008-2022")
	save("age_marital_gender.png", wd = 8, ht = 6)



	graph <- cces %>%
	mutate(id3 = frcode(ideo5 == 1 \| ideo5 == 2 ~ "Liberal",
	ideo5 == 3 ~ "Moderate",
	ideo5 == 4 \| ideo5 == 5 ~ "Conservative")) %>%
	mutate(gender = frcode(gender == 1 ~ "Men",
	gender == 2 ~ "Women")) %>%
	filter(year >= 2020) %>%
	mutate(single = case_when(marital_status == 5 ~ 1,
	TRUE ~ 0)) %>%
	group_by(age, id3, gender) %>%
	mean_ci(single, wt = weight) %>% filter(gender != "NA") %>% filter(id3 != "NA")

	graph %>%
	filter(age <= 75) %>%
	ggplot(., aes(x = age, y = mean, color = gender, group = gender)) +
	geom_point(stroke = .5, shape = 21, alpha = .45, show.legend = FALSE) +
	geom_smooth(se = FALSE) +
	scale_color_calc() +
	facet_wrap(~ id3) +
	theme_rb(legend = TRUE) +
	y_pct() +
	theme(strip.text = element_text(size = 20))+
	labs(x = "Age", y = "", title = "Share Reporting That They Have Never Been Married by Age",
	caption = "@ryanburge\nData: Cooperative Election 2020-2022")
	save("age_marital_gender_id3.png", wd = 8, ht = 6)




	graph <- cces %>%
	mutate(rel = frcode(religion == 1 \| religion == 2 \| religion == 3 \| religion == 4 ~ "Christian",
	religion == 5 \| religion == 6 \| religion == 7 \| religion == 8 ~ "Other Faith",
	religion == 9 \| religion == 10 \| religion == 11 ~ "Nones")) %>%
	mutate(gender = frcode(gender == 1 ~ "Men",
	gender == 2 ~ "Women")) %>%
	filter(year >= 2020) %>%
	mutate(single = case_when(marital_status == 5 ~ 1,
	TRUE ~ 0)) %>%
	group_by(age, rel, gender) %>%
	mean_ci(single, wt = weight) %>% filter(gender != "NA") %>% filter(rel != "NA")

	graph %>%
	filter(age <= 75) %>%
	ggplot(., aes(x = age, y = mean, color = gender, group = gender)) +
	geom_point(stroke = .5, shape = 21, alpha = .45, show.legend = FALSE) +
	geom_smooth(se = FALSE) +
	scale_color_calc() +
	facet_wrap(~ rel) +
	theme_rb(legend = TRUE) +
	y_pct() +
	theme(strip.text = element_text(size = 20))+
	labs(x = "Age", y = "", title = "Share Reporting That They Have Never Been Married by Age",
	caption = "@ryanburge\nData: Cooperative Election 2020-2022")
	save("age_marital_gender_rel.png", wd = 8, ht = 6)



	graph <- cces %>%
	filter(year >= 2020) %>%
	mutate(gender = frcode(gender == 1 ~ "Men",
	gender == 2 ~ "Women")) %>%
	mutate(single = case_when(havekids == 1 ~ 1,
	havekids == 2 ~ 0)) %>%
	group_by(age, gender) %>%
	mean_ci(single, wt = weight)

	graph %>%
	filter(age <= 75) %>%
	filter(gender != "NA") %>%
	ggplot(., aes(x = age, y = mean, color = gender, group = gender)) +
	geom_point(stroke = .5, shape = 21, alpha = .45) +
	geom_labelsmooth(aes(label = gender), method = "loess", formula = y ~ x, family = "font",
	linewidth = 1, text_smoothing = 30, size = 6, linewidth = 1, boxlinewidth = 0.3, hjust = .15) +
	scale_color_calc() +
	theme_rb() +
	y_pct() +
	labs(x = "Age", y = "", title = "Share Reporting That They Are a Parent/Guardian of a Child Under the Age of 18",
	caption = "@ryanburge\nData: Cooperative Election 2020-2022")
	save("age_havekids_new.png", wd = 8, ht = 6)

	graph <- cces %>%
	mutate(birthyr = year - age) %>%
	mutate(cohorts = frcode( birthyr >= 1955 & birthyr <= 1959 ~ "1955-1959",
	birthyr >= 1960 & birthyr <= 1964 ~ "1960-1964",
	birthyr >= 1965 & birthyr <= 1969 ~ "1965-1969",
	birthyr >= 1970 & birthyr <= 1974 ~ "1970-1974",
	birthyr >= 1975 & birthyr <= 1979 ~ "1975-1979",
	birthyr >= 1980 & birthyr <= 1984 ~ "1980-1984",
	birthyr >= 1985 & birthyr <= 1989 ~ "1985-1989",
	birthyr >= 1990 & birthyr <= 1994 ~ "1990-1994",
	birthyr >= 1995 & birthyr <= 2000 ~ "1995-2000")) %>%
	mutate(nokids = case_when(havekids == 2 ~ 1,
	havekids == 1 ~ 0)) %>%
	mutate(single = case_when(marital_status == 5 ~ 1,
	TRUE ~ 0)) %>%
	mutate(both = nokids + single) %>%
	mutate(both = case_when(both == 2 ~ 1,
	TRUE ~ 0)) %>%
	mutate(gender = frcode(gender == 1 ~ "Men",
	gender == 2 ~ "Women")) %>%
	group_by(year, gender, cohorts) %>%
	mean_ci(both, wt = weight) %>%
	filter(mean > 0) %>%
	filter(cohorts != "NA") %>%
	filter(gender != "NA")


	graph %>%
	ggplot(., aes(x = year, y = mean, color = gender, group = gender)) +
	geom_point(stroke = .5, shape = 21, alpha = .5) +
	geom_smooth(se = FALSE) +
	theme_rb(legend = TRUE) +
	facet_wrap(~ cohorts) +
	scale_color_calc() +
	y_pct() +
	labs(x = "Year", y = "", title = "Share Who Have Never Been Married and\nAre Not Parents to Young Children", caption = "@ryanburge\nData: Cooperative Election 2008-2022")
	save("cohorts_nokid_never_married.png", ht = 8, wd = 6)




	regg <- cces %>%
	filter(year >= 2020) %>%
	filter(age >= 35 & age <= 50) %>%
	mutate(nokids = case_when(havekids == 2 ~ 1,
	havekids == 1 ~ 0)) %>%
	mutate(single = case_when(marital_status == 5 ~ 1,
	TRUE ~ 0)) %>%
	mutate(both = nokids + single) %>%
	mutate(both = case_when(both == 2 ~ 1,
	TRUE ~ 0)) %>%
	mutate(gender = frcode(gender == 1 ~ "Men",
	gender == 2 ~ "Women")) %>%
	mutate(white = case_when(race == 1 ~ 1, TRUE ~ 0)) %>%
	mutate(none = case_when(religion == 9 \| religion == 10 \| religion == 11 ~ 1,
	TRUE ~ 0)) %>%
	mutate(liberal = case_when(ideo5 == 1 \| ideo5 == 2 ~ 1,
	ideo5 <= 5 ~ 0)) %>%
	select(educ, age, gender, both, white, income, none, liberal)

	women <- regg %>% filter(gender == "Women")
	men <- regg %>% filter(gender == "Men")

	ww <- glm(both ~ educ + income + none + liberal + white, family = "binomial", data = women)
	mm <- glm(both ~ educ + income + none + liberal + white, family = "binomial", data = men)



	coef_names <- c("Age" = "age",
	"Male" = "male",
	"White" = "white",
	"Income" = "income",
	"Liberal" = "liberal",
	"Attendance" = "att",
	"Education" = "educ",
	"No Religion" = "none")

	library(jtools)

	gg1 <- plot_summs(ww, mm, robust = "HC3", scale = TRUE, coefs = coef_names, model.names = c("Women", "Men"))

	gg1 +
	theme_rb() +
	scale_fill_calc() +
	scale_color_calc() +
	theme(legend.position = c(.75, .75)) +
	guides(shape = FALSE) +
	guides(color = guide_legend(reverse = TRUE)) +
	add_text(x = .25, y = 5.25, word = "More Likely to Be Never Married\nand Never a Parent", sz = 6) +
	labs(x = "", y = "", title = "What Factors Predict Someone to be Never Married and Never a Parent?", subtitle = "Sample Restricted to 35-50 Year Olds", caption = "@ryanburge\nData: Cooperative Election Study, 2020-2022")
	save("reg_predict_no_marry_no_kids.png")



	regg <- cces %>%
	filter(income <= 16) %>%
	filter(year >= 2020) %>%
	filter(age >= 35 & age <= 50) %>%
	mutate(nokids = case_when(havekids == 2 ~ 1,
	havekids == 1 ~ 0)) %>%
	mutate(single = case_when(marital_status == 5 ~ 1,
	TRUE ~ 0)) %>%
	mutate(both = nokids + single) %>%
	mutate(both = case_when(both == 2 ~ 1,
	TRUE ~ 0)) %>%
	mutate(gender = frcode(gender == 1 ~ "Men",
	gender == 2 ~ "Women")) %>%
	mutate(white = case_when(race == 1 ~ 1, TRUE ~ 0)) %>%
	mutate(none = case_when(religion == 9 \| religion == 10 \| religion == 11 ~ 1,
	TRUE ~ 0)) %>%
	mutate(liberal = case_when(ideo5 == 1 \| ideo5 == 2 ~ 1,
	ideo5 <= 5 ~ 0)) %>%
	mutate(ed2 = frcode(educ == 1 \| educ == 2 ~ "HS or Less",
	educ == 5 \| educ == 6 ~ "College Degree")) %>%
	select(ed2, age, gender, both, white, income, none, liberal)


	regg <- glm(both ~ ed2genderincome + white + none + liberal, family = "binomial", data = regg)

	library(interactions)

	int <- interact_plot(regg, pred= income, modx = gender, mod2 = ed2, int.width = .76, interval = TRUE, modx.labels = c("Men", "Women"), mod2.labels = c("HS or Less", "College Grad"))

	int +
	theme_rb(legend = TRUE) +
	scale_fill_calc() +
	scale_color_calc() +
	scale_x_continuous(breaks = c(4, 8, 12, 16), labels = c("$30K-$40K", "$70K-$80K", "$150K-$200K", "$500K+")) +
	scale_y_continuous(labels = percent) +
	theme(strip.text = element_text(size = 20)) +
	labs(x = "Household Income", y = "", title = "Predicted Share Who Are Never Married + Not Currently a Parent", subtitle = "Sample Restricted to 35-50 Year Olds", caption = "@ryanburge\nData: Cooperative Election Study, 2020-2022")
	save("interact_income_educ_married_kids.png")