02-country_state_maps.Rmd

# Creating Country and State Level Maps

## Introduction

In this first exercise we will use [_rnaturalearth_](https://github.com/ropenscilabs/rnaturalearth), [_simple features_](https://cran.r-project.org/web/packages/sf/vignettes/sf1.html) and
_ggplot2_ to create a map of Brazil and states within Brazil to use as the base of the maps we will create in these exercises.

## Exercises

### Create Country Level Map of Brazil

This is our base layer, Brazil, of the map from (Naturalearth.com)[https://naturalearth.com/] using the base `plot()` to display these data.

Using `ne_states()` will return an `sf` object with state level information. There is also a `ne_countries()` to download country level outlines only as well.

```{r brazil-base}
library(rnaturalearth)

br_sf <- ne_states(geounit = "brazil",
                   returnclass = "sf")

plot(br_sf)
```

#### Plot using `ggplot2`

Using _ggplot2_, we have more control over how the data are displayed.

First plot the whole country using `ggplot()`.

```{r plot_level1, eval=TRUE}
library(ggplot2)

br <- ggplot(br_sf) +
  geom_sf(fill = "white")

br
```

#### Add Labels to the States

The naturalearth data have several columns to work with. One of them is the abbreviation for the states. Using `geom_text()`, we can add this information to the map. 

```{r plot_level2}
br <-
  br +
  geom_text(
    data = br_sf,
    aes(x = longitude,
        y = latitude,
        label = abbrev),
    size = 2.5,
    hjust = 1
  )

br
```

#### Final Touches

Properly label the x and y-axis and set the theme. The _ggplot2_ theme, `theme_bw()` is nice to use with maps, so we will apply that here as well.

```{r plot_level3}
br <- 
  br +
  xlab("Longitude") +
  ylab("Latitude") +
  theme_bw()

br
```

### Create Maps of Individual States

To create a map of only Minas Gerais, subset the _sf_ object, `br` such that it contains only the data for Minas Gerais.

#### Inspect Column Names in `br_sf` Object

To find out what the column names are, we can use, `colnames()`.

```{r colnames}
names(br_sf)
```

We see that a column named "name_pt" exists, with the Portuguese name spelling for each state in this data set.

We can use this to `filter()` the data so that it only represents Minas Gerais and we can use that to make a map.

For more on subsetting _sf_ objects, see: <https://cran.rstudio.com/web/packages/sf/vignettes/sf4.html>.

#### Filter Minas Gerais

```{r subset-mg}
library(dplyr)

mg <- filter(br_sf, name_pt == "Minas Gerais")

mg
```

#### Plot Minas Gerais

```{r ggplot-mg}
ggplot(data = mg) +
  geom_sf() +
  xlab("Longitude") +
  ylab("Latitude") +
  theme_bw()
```

### Highlighting States Within the Country

Using the `br_sf` and `mg` objects that we have created, it is possible to create a map that highlights one state and labels it using the abbreviation, in this case Minas Gerais.

```{r hightlight-mg}
ggplot(data = br_sf) +
  geom_sf(fill = "white") +
  geom_sf(
    data = mg,
    fill = "red") +
  geom_text(
    data = mg,
    aes(x = longitude,
        y = latitude,
        label = abbrev),
    size = 2.5,
    hjust = 1) +
  xlab("Longitude") +
  ylab("Latitude") +
  theme_bw()
```

## Your Turn {.exercise}

Using the examples above, download and create a map of Australia.
Then highlight Bahia.