---
title: "Lab 13.1 - Gerrymandering in Michigan?"
author: "Author Goes Here"
date: "Semester Goes Here"
output:
  html_document:
    code_download: true
---

```{r include = FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r echo = FALSE, message = FALSE}
library(tidyverse)
library(mdsr)
```

The goal of this exercise is assess whether gerrymandering is an issue in Michigan.

## Exploratory Data Analysis Assignment

🚧
Work through the text's analysis of gerrymandering in North Carolina, but use data for Michigan instead. Go through the same steps, giving abbreviated but complete explanations on all data and visualizations. Finally, give your conclusion on whether Michigan also has gerrymandering, supporting your conclusion with accurate data and compelling visualizations.

## Data Wrangling Notes

GIS data tends to be voluminous. In this lab, you'll discover that the district shapes data files used in the text consume around 80 MB (plus the original zip file). Note that you need to download these files to do your work, either to your local or your cloud RStudio environment, but that you do not want to include them in your lab assignment submission.

The text includes code to download and unzip the district shapefiles needes for this assignment, but that code doesn't reliably work (for us, anyway), so, as a work-around, we suggest that you manually download and unzip the specified file: `http://cdmaps.polisci.ucla.edu/shp/districts113.zip`. Be sure that the district shapes sub-directory is stored directly in your data sub-directory, i.e., in `data/districtShapes`.

When the data is fully downloaded and unzipped, you can use this code chunk to load `dsn_districts` directly from your local copy.

```
dsn_districts <- fs::path(fs::path_wd(), "data", "districtShapes")

st_layers(dsn_districts)
```
