---
title: "getting_started"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{getting_started}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
# Getting started with countyhealthR 
```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r eval = FALSE}
library(countyhealthR)
```

# --- 1. Explore available measures ---
# See all County Health Rankings & Roadmaps measures for a given release year

```{r eval = FALSE}
list_chrr_measures(release_year = 2024)  # useful for discovering measure IDs or names
```

# --- 2. Pull county-level data ---
# Examine ALL health measures for a specified location 
# Example: county-level data for Dane County, Wisconsin
```{r eval = FALSE}
county_data <- get_chrr_county_data(
  state = "WI", 
  county = "Dane", 
  release_year = 2025
)
head(county_data)
```


# --- 3. Pull measure-level data ---
# Examine one health measure across a specified geographic unit
# Example: "Uninsured adults" across all US counties
```{r eval = FALSE}
measure_data <- get_chrr_measure_data(
  geography = "county", 
  measure = "uninsured adults",
  release_year = 2022
)
head(measure_data)
```



# --- 4. Get metadata for a measure ---
# See details about a measure including description, precision, and years represented
```{r eval = FALSE}
meta_data <- get_chrr_measure_metadata(
  measure = "Uninsured adults", 
  release_year = 2024
)
head(meta_data)

```
# Note: 
All functions are build to interact with Zenodo and therefore require internet access. As a result, examples are wrapped in `\donttest{}` so that CRAN checks do not depend on external network access.