Chapter 5 Crosstabs

Two cases:

5.1 Case 1: with survey weights

nhanes %>% 
  select(Gender, Education, wt=WTINT2YR) %>% 
  # There's a clever way to do this, but it's easier to write this 3 times.
  filter(!is.na(Gender), !is.na(wt), !is.na(Education)) %>% 
  group_by(Gender, Education) %>% 
  summarise(wt = sum(wt)) %>% 
  group_by(Gender) %>% 
  mutate(wt = scales::percent(wt/sum(wt))) %>% 
  pivot_wider(id_cols = c(Gender, Education), names_from=Gender, values_from=wt) %>% 
  knitr::kable()

## `summarise()` has grouped output by 'Gender'. You can override using the `.groups` argument.

Education	female	male
8th Grade	5.9%	6.24%
9 - 11th Grade	11.4%	12.14%
High School	20.4%	22.62%
Some College	32.8%	29.73%
College Grad	29.4%	29.28%

5.2 Case 2: Without survey weights

Using the janitor package.

nhanes %>% 
  filter(!is.na(Education), !is.na(Gender)) %>% 
  tabyl(Education, Gender) %>% 
  adorn_percentages("col") %>% 
  adorn_pct_formatting(digits = 2) %>% 
  knitr::kable()

Education	female	male
8th Grade	11.01%	11.47%
9 - 11th Grade	14.77%	15.65%
High School	20.83%	23.37%
Some College	31.00%	26.72%
College Grad	22.39%	22.80%