---
title: "Introduction to MazamaSpatialPlots"
author: "Rachel Carroll, Mazama Science"
date: "2022-11-07"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to MazamaSpatialPlots}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  message = FALSE,
  fig.width = 7,
  fig.height = 7
)
```

## Background

Maps colored by state and county (aka "chloropleth maps") are among the most common
data graphics and are used to convey everything from vote totals to economic and 
health statistics. Well designed maps involve a careful choice of map projections
and colors for both the state/county fill and boundary lines. Unfortunately,
creating beautiful maps in R is still somewhat involved.

The **MazamaSpatialPlots** package provides plotting functionality to make it as
easy as possible to produce beautiful US state and county level maps. It builds
on the excellent **tmap** package and harnesses datasets from the 
**MazamaSpatialUtils** package. High-level plotting functions make it easy for 
users to create beautiful chloropleth maps. The underlying code uses **ggplot2**
so users familiar with **ggplot2** can easily enhance the returned plot objects 
to create highly customized plots.

## Installation

This package is designed to be used with [R](https://cran.r-project.org) 
(>= 3.5) and [RStudio](https://posit.co) so make sure you have those 
installed first.

Users will want to install the **devtools** package to have access to the latest 
development version of the package from GitHub.

The following packages should be installed by typing the following at the 
RStudio console:

```
# Note that vignettes require knitr and rmarkdown
install.packages('knitr')
install.packages('rmarkdown')
install.packages('MazamaSpatialUtils')
devtools::install_github('MazamaScience/MazamaSpatialPlots')
```

**MazamaSpatialPlots** requires spatial polygon data to plot 
the shapes of states and counties. These spatial datasets are provided by 
**MazamaSpatialUtils** and can be installed by running the following in the 
RStudio console:

```
library(MazamaSpatialUtils)
dir.create('~/Data/Spatial', recursive = TRUE)
setSpatialDataDir('~/Data/Spatial')
installSpatialData("USCensusStates_02")
installSpatialData("USCensusCounties_02")
```

## Features

Currently, **MazamaSpatialPlots** contains high level functions for creating 
choropleth maps for US counties and states:

* `countyMap()` - plot a choropleth map for county level data. Uses the 
`USCensusCounties_02` dataset for spatial polygons.
* `stateMap()` - plot a choropleth map for state level data. Uses the 
`USCensusStates_02` dataset for spatial polygons.

Additionally, **MazamaSpatialPlots** provides two example datasets formatted for
use with the package's functions:

* `example_US_stateObesity` - state level obesity rate
* `example_US_countyCovid` - county level COVID19 cases and deaths

## Input Data 

The _SpatialPolygonsDataFrame_ used to create a state or county map is provided
by the **MazamaSpatialUtils** package once that is installed.

### State-level data

User provided data must be provided as simple dataframes. For the `stateMap()` function, the 
`data` input dataframe must have a `stateCode` column containing the 2-character US
state code. Any other column of data can be used as the `parameter` by which states will
be colored.

The following functions from **MazamaSpatialUtils** can help in creating the
`stateCode` column if it does not already exist:

* `MazamaSpatialUtils::US_stateNameToCode()`
* `MazamaSpatialUtils::US_stateFIPSToCode()`

### County-level data

For county maps, the input `data` must contain a `countyFIPS` column uniquely
identifying each county. The `MazamaSpatialUtils::US_countyCodes` dataset
can be used to determine the `countyFIPS` codes.

## Example Maps

### Example state map

Here we create a state map using the package `example_US_stateObesity` dataset
showing the obesity rate for each state in the United States. A single call to
the `stateMap()` function is all that is required. Customization is performed
entirely through function arguments.

```{r stateMap, warning = FALSE, message = FALSE }
library(MazamaSpatialPlots)

# Look at input data
head(example_US_stateObesity)

# Create the map
stateMap(
  data = example_US_stateObesity,
  parameter = 'obesityRate',
  palette = 'BuPu',
  breaks = seq(20, 38, 3),
  stateBorderColor = 'white',
  title = "Obesity Rate in U.S. States"
)

```

### Example county map

For the `countyMap()` example, we use COVID19 case data from June 01, 2020.

```{r countyMap, warning = FALSE, message = FALSE }
# Look at the input data
head(example_US_countyCovid)

# Create the map
countyMap(
  data = example_US_countyCovid,
  parameter = "cases",
  breaks = c(0,100,200,500,1000,2000,5000,10000,20000,50000,1e6),
  countyBorderColor = "white",
  title = "COVID19 Cases in U.S. States"
)
```
