slides-mapping-with-ggplot.qmd

---
title: Mapping with ggplot
---

```{r}
#| echo: false
options(tigris_use_cache = TRUE)
```

### Get Data

```{r}
library(tidyverse)
library(tidycensus)
library(janitor)
```

```{r}
#| code-line-numbers: "3"
load_variables(
  year = 2023,
  dataset = "acs5/subject",
  cache = TRUE
) |>
  filter(str_detect(name, "S1601"))
```

---

```{r}
#| code-line-numbers: "4,5"
get_acs(
  geography = "county",
  variable = "S1601_C01_003",
  summary_var = "S1601_C01_001"
)
```

---

```{r}
#| output: false
speak_language_other_than_english <-
  get_acs(
    geography = "county",
    variable = "S1601_C01_003",
    summary_var = "S1601_C01_001",
    geometry = TRUE
  ) |>
  clean_names() |>
  mutate(pct = estimate / summary_est) |>
  select(name, pct)
```

```{r}
speak_language_other_than_english
```

### Make a Map

```{r}
speak_language_other_than_english |>
  ggplot() +
  geom_sf()
```

### Shift Geometry

```{r}
#| code-line-numbers: "4"
library(tigris)

speak_language_other_than_english |>
  shift_geometry() |>
  ggplot() +
  geom_sf()
```

### Aesthetic Properties

#### Fill

```{r}
speak_language_other_than_english |>
  shift_geometry() |>
  ggplot() +
  geom_sf(aes(fill = pct))
```


#### Adjust Legend

## Your Turn {.your-turn}

- Import a [dataset I've created on the number of refugees from each country in the world](https://raw.githubusercontent.com/rfortherestofus/mapping-with-r-v2/refs/heads/main/data/refugees.geojson)

- Use ggplot to make a map of this data, with countries sending higher numbers of refugees in a different color than countries with lower numbers of refugees

::: {.notes}

```{r}
read_sf("https://raw.githubusercontent.com/rfortherestofus/mapping-with-r-v2/refs/heads/main/data/refugees.geojson") |>
  ggplot() +
  geom_sf(aes(fill = number_of_refugees))
```
:::