---
title: "Getting started with 'cartographer'"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting started with 'cartographer'}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

Cartographer comes with one example dataset registered (the map of North Carolina
provided by the `{sf}` package where the feature names correspond to counties).
If you install the `{maps}` package, some additional maps are available.

```{r}
library(cartographer)

# list registered datasets
feature_types()

# list feature names in one of the datasets
head(feature_names("sf.nc"))
```

Our example dataset has a column called `county` with the name of the county and
a column `type` that is one of two values. We'd like to draw a map that shows
us where the entries are by type.

```{r}
head(nc_type_example_1)
```

We want to convert our data frame into a spatial data frame, then we'll be able to
use standard `{ggplot2}` tools to draw our map. Cartographer just needs to know
which of its maps we're using (`"sf.nc"`, although it's able to guess that if we
don't specify it), and which column has the feature names (`county`):

```{r, error = TRUE}
add_geometry(nc_type_example_1, county, feature_type = "sf.nc")
```

What happened? The matching of county names was case-insensitive, so that wasn't
the issue; it looks like our data contained a typo. One of the rows contains
`"PAMILCO"` instead of `"PAMLICO"` in the `county` column.

```{r}
library(dplyr, warn.conflicts = FALSE)

# cleaned dataset
nc_type_example_1_fixed <-
  nc_type_example_1 |>
  mutate(county = replace_values(county, "PAMILCO" ~ "PAMLICO"))

add_geometry(nc_type_example_1_fixed, county, feature_type = "sf.nc")
```

Now that we have our spatial geometry added, we can go ahead and plot it, adding
a layer that uses the full `"sf.nc"` dataset to give us the base map:

```{r example-basic, fig.width=8, fig.height=2}
library(ggplot2)

nc_type_example_1_fixed |>
  count(county, type) |>
  add_geometry(county, feature_type = "sf.nc") |>
  ggplot() +
  geom_sf(data = map_sf("sf.nc")) +
  geom_sf(aes(fill = n)) +
  facet_wrap(vars(type))
```

The package `{ggautomap}` uses cartographer to provide some new `{ggplot2}` geoms that
transparently attach the map data.
