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{apyramid} can also be used to visualize pre-aggregated data. This example is the US census data from 2018:
us_labels <- labs( x = "Age group", y = "Thousands of people", title = "US Cenus Data 2018", caption = "source: https://census.gov/data/tables/2018/demo/age-and-sex/2018-age-sex-composition.html" ) data(us_2018) us_2018 p <- age_pyramid(us_2018, age_group = age, split_by = gender, count = count) p + us_labels
You can also use another factor to split the data:
data(us_ins_2018) # stratified by gender and health insurance status data(us_gen_2018) # stratified by gender and generational status p_ins <- age_pyramid(us_ins_2018, age_group = age, split_by = gender, stack_by = insured, count = count) p_gen <- age_pyramid(us_gen_2018, age_group = age, split_by = gender, stack_by = generation, count = count) p_ins + us_labels p_gen + us_labels
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