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https://github.com/UrbanInstitute/urbnmapr

State and county maps with Alaska and Hawaii
https://github.com/UrbanInstitute/urbnmapr

Last synced: 3 months ago
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State and county maps with Alaska and Hawaii

Host: GitHub
URL: https://github.com/UrbanInstitute/urbnmapr
Owner: UrbanInstitute
Created: 2018-02-15T20:01:53.000Z (almost 7 years ago)
Default Branch: master
Last Pushed: 2022-04-04T16:30:35.000Z (almost 3 years ago)
Last Synced: 2024-08-03T21:03:27.670Z (6 months ago)
Language: R
Homepage: https://urbaninstitute.github.io/urbnmapr/
Size: 51.5 MB
Stars: 142
Watchers: 19
Forks: 25
Open Issues: 12
Metadata Files:
- Readme: README.Rmd
- Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

awesome-r-dataviz - urbnmapr - State and county maps with Alaska and Hawaii. (Spatial Visualization / Miscellaneous)

README

        ---

output: github_document

---

# urbnmapr 

[![Travis-CI Build Status](https://travis-ci.org/UrbanInstitute/urbnmapr.svg?branch=master)](https://travis-ci.org/UrbanInstitute/urbnmapr)

The `urbnmapr` package provides state and county shapefiles that are compatible to map with `ggplot2`.

Shapefiles include Alaska and Hawaii, transformed to be displayed as insets within the continental United States. There are options to include the territories for both the state and county maps.

This package is heavily inspired by and derived in part from the [fiftystater package](https://cran.r-project.org/package=fiftystater) by William Murphy. In contrast, `urbnmapr`:

  - Uses shapefiles from the US Census Bureau

  - Converts the shapefile data to `sf` format

  - Adds various identifiers for merging

  - Includes a county-level shapefile and options to add territories

## Stay up-to-date

Sign up [here](https://app.smartsheet.com/b/form/9029a0a8254e4044a52cdebaebe343bf) to stay up-to-date with R package releases and R resources from the Urban Institute. We will not share your information and we will not email more than once per month.

## Installation

You can install the latest version of `urbnmapr` from GitHub:

```{r installation, message=FALSE, warning=FALSE, eval=FALSE}

# install.packages("devtools")

devtools::install_github("UrbanInstitute/urbnmapr")

```

## Usage

### Quick maps

The `get_urbn_map()` function can be used to call shapefiles. Using the `sf = TRUE` option will load `sf` objects. These can be used with `geom_sf()` to create base maps of the continental United States, with Alaska and Hawaii displayed as insets:

```{r sf-state, message=FALSE, warning=FALSE}

library(tidyverse)

library(urbnmapr)

states_sf <- get_urbn_map("states", sf = TRUE)

states_sf %>% 

  ggplot(aes()) +

  geom_sf(fill = "grey", color = "#ffffff")

```

```{r sf-county, message=FALSE, warning=FALSE}

library(tidyverse)

library(urbnmapr)

counties_sf <- get_urbn_map("counties", sf = TRUE)

counties_sf %>% 

  ggplot(aes()) +

  geom_sf(fill = "grey", color = "#ffffff")

```

The default projection is US National Atlas Equal Area.

### More maps

Maps with US territories can also be called with `get_urbn_map()`. 

```{r terr}

territories_counties <- get_urbn_map(map = "territories_counties", sf = TRUE)

ggplot() +

  geom_sf(territories_counties,

          mapping = aes(),

          fill = "grey", color = "#ffffff")

```

### Labels

Labels for states maps can be accessed with `get_urbn_labels()`. Only state labels are custom, so they can be displayed next to smaller states. Use the function to call the appropriate labels and then label the map with `geom_sf_text()`.

```{r quick-labels}

states_sf <- get_urbn_map(map = "states", sf = TRUE)

states_sf %>%

  ggplot() +

  geom_sf(aes(), 

          fill = "grey", color = "#ffffff", size = 0.25) +

  geom_sf_text(data = get_urbn_labels(map = "states", sf = TRUE), 

                aes(label = state_abbv), 

            size = 3)

```

## Merging Data

The states and counties spatial data include various identifiers to simplify merging data. The states `states` tibble contains `state_fips`, `state_abbv`, and `state_name`. The `counties` tibble contains `county_fips`, `state_abbv`, `state_fips`,  `county_name`, and `state_name`. 

Continuous data can be mapping on a color scale.

```{r us-choropleth}

spatial_data <- left_join(get_urbn_map(map = "states", sf = TRUE),

                          statedata,

                          by = "state_name")

ggplot() +

  geom_sf(spatial_data,

          mapping = aes(fill = horate),

          color = "#ffffff", size = 0.25) +

  labs(fill = "Homeownership rate")

  

```

Categorical data can be mapped on a discrete color scale.

```{r county}

counties_sf <- get_urbn_map(map = "counties", sf = TRUE)

county_groups <- countydata %>% 

  mutate(cat_var = paste0("Group ",

                          sample(1:4, nrow(countydata), replace = TRUE)))

household_data <- left_join(counties_sf, county_groups, by = "county_fips")

household_data %>%

  ggplot() +

  geom_sf(mapping = aes(fill = cat_var),

          color = NA, size = 0.05) +

  labs(fill = "Categorical variable")

```

## Styles

`library(urbnmapr)` works well with [`library(urbnthemes)](https://github.com/UrbanInstitute/urbnthemes)

```{r load-theme, message=FALSE}

library(urbnthemes)

set_urbn_defaults(style = "map")

```

```{r theme-state}

states_sf <- get_urbn_map(map = "states", sf = TRUE)

states_sf %>% 

  left_join(statedata, by = "state_name") %>% 

  ggplot() +

  geom_sf(mapping = aes(fill = horate),

          color = "#ffffff", size = 0.25) +

  scale_fill_gradientn(labels = scales::percent) +

  labs(fill = "Homeownership rate") +

  coord_sf(datum = NA)

```

Map smaller geographies with `filter()`. You may need to reproject the data based on which areas you are mapping.

```{r theme-counties}

household_data %>%

  filter(state_name == "California") %>%

  ggplot() +

    geom_sf(mapping = aes(fill = medhhincome),

                 color = "#ffffff", size = 0.05) +

    coord_sf(datum = NA) +

    scale_fill_gradientn(labels = scales::dollar) +

  labs(fill = "Median household income")

```

A discrete color scale can also be used for categorical data.

```{r state-discrete}

state_categorical <- left_join(get_urbn_map(map = "states", sf = TRUE),

                               statedata,

                               by = "state_name") %>% 

  mutate(cat_var = paste0("Group ",

                          sample(1:4, nrow(statedata), replace = TRUE)))

ggplot() +

  geom_sf(state_categorical, mapping = aes(fill = cat_var),

          color = "#ffffff") +

  scale_fill_discrete() +

  coord_sf(datum = NA) +

  labs(fill = "Categorical variable")

```

## License

Code released under the GNU General Public License v3.0.

## Code of conduct

Please note that this project is released with a [Contributor Code of Conduct](CODE_OF_CONDUCT.md). By participating in this project you agree to abide by its terms.