README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# tidyrates

<!-- badges: start -->
[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[![CRAN status](https://www.r-pkg.org/badges/version/tidyrates)](https://CRAN.R-project.org/package=tidyrates)
[![R-CMD-check](https://github.com/rfsaldanha/tidyrates/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/rfsaldanha/tidyrates/actions/workflows/R-CMD-check.yaml)
<!-- badges: end -->

The goal of tidyrates is to compute adjusted rates and other epidemiological indicators in a tidy way, wrapping functions from the `epitools` package.

## Installation

You can install the release version of tidyrates from CRAN:

```{r eval=FALSE}
install.packages("tidyrates")
```

Or the development version from [GitHub](https://github.com/rfsaldanha/tidyrates):

```{r eval=FALSE}
# install.packages("remotes")
remotes::install_github("rfsaldanha/tidyrates")
```

## Example

```{r example}
library(tidyrates)

head(fleiss_data)

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")
```