The goal of tidyrates is to compute adjusted rates and other epidemiological indicators in a tidy way, wrapping functions from the epitools package.
You can install the development version of tidyrates from GitHub with:
library(tidyrates)
head(fleiss_data)
#> key age_group name value
#> 1 k1 Under 20 population 230061
#> 2 k1 Under 20 events 107
#> 3 k1 20-24 population 329449
#> 4 k1 20-24 events 141
#> 5 k1 25-29 population 114920
#> 6 k1 25-29 events 60
standard_pop <- tibble::tibble(
age_group = c("Under 20", "20-24", "25-29", "30-34", "35-39", "40 and over"),
population = c(63986.6, 186263.6, 157302.2, 97647.0, 47572.6, 12262.6)
)
rate_adj_direct(fleiss_data, .std = standard_pop, .keys = "key")
#> # A tibble: 5 × 5
#> key crude.rate adj.rate lci uci
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 k1 0.000563 0.000923 0.000804 0.00106
#> 2 k2 0.000676 0.000912 0.000824 0.00101
#> 3 k3 0.000833 0.000851 0.000772 0.000942
#> 4 k4 0.00115 0.000927 0.000800 0.00115
#> 5 k5plus 0.00167 0.000755 0.000677 0.00188