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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 0.00188These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.