---
title: "Typical Usage"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Typical Usage}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Discover indicators

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

Let's set our 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")
```

If at anytime, we need to check which health atlas we are using, we can using `ha_get()`.
```{r}
ha_get()
```

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}
topics <- ha_topics(progress = FALSE)
topics
```

Often, you may have an topic area that you are interested in exploring. You can explore these topic areas using `ha_subcategories()`.
```{r}
subcategories <- ha_subcategories()
subcategories
```

You can use a `subcategory_key` to subset the list of topics too.
```{r}
ha_topics("diet-exercise")
```

Once we have a topic or topics in mind, 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}
coverage <- ha_coverage("HCSFVAP", progress = FALSE)
coverage
```

## Import tabular data

Now, we can import our data using `ha_data()` specifying the keys we identified above.
```{r}
ease_of_access <- ha_data(
  topic_key = "HCSFVAP",
  population_key = "",
  period_key = "2022-2023",
  layer_key = "neighborhood"
)
ease_of_access
```

## Import spatial data

We can check what layers we can import with `ha_layers()`.
```{r}
layers <- ha_layers()
layers
```

Since we just downloaded our data at the Community Area level, let's import the Community Area geographic layer with `ha_layer()`.
```{r}
community_areas <- ha_layer("neighborhood")
```

Let's map our data!
```{r}
library(dplyr)
library(ggplot2)

map_data <- community_areas |>
  left_join(ease_of_access, "geoid") 

plot <- ggplot(map_data) +
  geom_sf(aes(fill = value), alpha = 0.7) +
  scale_fill_distiller(palette = "GnBu", direction = 1) +
  labs(
    title = "Easy Access to Fruits and Vegetables within Chicago",
    fill = "Percent of adults who reported\nthat it is very easy for them to\nget fresh fruits and vegetables."
  ) +
  theme_minimal()
plot
```

Our map looks pretty good, but perhaps there is a point layer that may provide more insight into the spatial variation of the ease of access to fruits and vegetables. We can use `ha_point_layers()` to list all the point layers available in the Chicago Health Atlas.
```{r}
point_layers <- ha_point_layers()
point_layers
```

Grocery store locations may be an important aspect of the ease of access to fruits and vegetables. We can import this layer by providing the `point_layer_uuid` to `ha_point_layer()`.
```{r}
grocery_stores <- ha_point_layer("7d9caf3c-75e6-4382-8c97-069696a3efbf")
```

Now that we have imported our grocery stores, let's layer them on top of our map.
```{r}
plot +
  geom_sf(data = grocery_stores, size = 0.5)
```

As expected, it seems that the areas with more grocery stores have a higher percent of adults who report that it is very easy to get fresh fruits and vegetables. 

This is a typical use case for the `healthatlas`, in which we explored every function that `healthatlas` has to offer. Now it's time for you to explore.