healthatlas

Unofficial R Metopio Health Atlas Wrapper

https://github.com/ryanzomorrodi/healthatlas

Repository

Unofficial R Metopio Health Atlas Wrapper

README.Rmd

--- 
output: github_document
---



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

# healthatlas



[![healthatlas status badge](https://ryanzomorrodi.r-universe.dev/badges/healthatlas)](https://ryanzomorrodi.r-universe.dev/healthatlas) [![R-CMD-check](https://github.com/ryanzomorrodi/healthatlas/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/ryanzomorrodi/healthatlas/actions/workflows/R-CMD-check.yaml)
[![Codecov test coverage](https://codecov.io/gh/ryanzomorrodi/healthatlas/branch/main/graph/badge.svg)](https://app.codecov.io/gh/ryanzomorrodi/healthatlas?branch=main)


Explore and Import *Metopio* Powered Health Atlases.

[Metopio](https://metopio.com/) helps many public health departments build curated data platforms. These data platforms are a convenient way for community members and researchers to explore and download public health data. With the same spirit in mind, this package aims to make interfacing with these data portals within R more **painless** and **reproducible**.

**Note:** This is an unofficial R wrapper for Metopio Health Atlases. This package is in no way affiliated with the Metopio or any of the public health departments Metopio serves.

Examples of Metopio health atlases include:

-   Chicago Health Atlas:   
-   Cook County Health Atlas:   
-   University of Illinois Cancer Center Data Hub:   
-   Idaho Oregon Community Health Atlas:   
-   Northern Kentucky Atlas: 
-   Allen County Health Atlas: 
-   Kane County Health Atlas: 
-   Contra Costa Health Atlas: 

## Installation

You can install healthatlas from CRAN.
``` r
install.packages("healthatlas")
```

Or, you can install the development version of healthatlas from [R-universe](https://ryanzomorrodi.r-universe.dev/healthatlas) with:

```{r eval = FALSE}
install.packages("healthatlas", repos = c("https://ryanzomorrodi.r-universe.dev", "https://cloud.r-project.org"))
```

## Usage

```{r}
library(healthatlas)
```

Set your health atlas. For this example, we are going to use the Chicago Health Atlas, and can do so, by providing the Chicago Health Atlas URL to `ha_set()`.

```{r}
ha_set("chicagohealthatlas.org")
```

We can list all the topics (aka indicators) present within Chicago Health Atlas using `ha_topics()`. The most important column here is the `topic_key` which can be used to identify the topic within subsequent functions.

```{r}
ha_topics(progress = FALSE)
```

Then, we can explore what populations, time periods, and geographic scales that data is available for using `ha_coverage()`. Again, the most important columns here are the key columns which can be used to specify the data desired.

```{r}
ha_coverage("EDA", progress = FALSE)
```

Now, we can import our data using `ha_data()` specifying the keys we identified above.

```{r}
data <- ha_data(
  topic_key = "EDA",
  population_key = "",
  period_key = "2018-2022",
  layer_key = "neighborhood"
)
data
```

Let's create a map. But first, we will need to download the Community Areas geographic layer. We can do that with `ha_layer()`.

```{r}
layer <- ha_layer(
  layer_key = "neighborhood"
)
layer
```

Now we can make our map!

```{r message=FALSE}
library(dplyr)
library(ggplot2)

map_data <- layer |>
  left_join(data, "geoid") 

ggplot(map_data) +
  geom_sf(aes(fill = value), alpha = 0.7) +
  scale_fill_distiller(palette = "GnBu", direction = 1) +
  labs(
    title = "9th Grade Education Rate",
    fill = ""
  ) +
  theme_minimal()
```

Owner

GitHub Events

Packages