09a — Maps and Spatial Data

# .kjh-yellow[Maps and]<br /> .kjh-lblue[Spatial Data]

**Week 09 (Part 1)**

]

---

---

# Load our libraries

.SMALL[

```r
library(here)       # manage file paths
library(socviz)     # data and some useful functions
library(tidyverse)  # your friend and mine
library(tidycensus) # Tidily interact with the US Census
library(maps)       # Some basic maps
```

```
## 
## Attaching package: 'maps'
```

```
## The following object is masked from 'package:purrr':
## 
##     map
```

```r
library(sf)         # Make maps in ggplot
library(tigris)     # Talk to the Census's TIGER data
```

```
## To enable caching of data, set `options(tigris_use_cache = TRUE)`
## in your R script or .Rprofile.
```

```r
library(ggforce)    # Useful enhancements to ggplot
```
]

---

# .huge[.kjh-lblue[Choropleths]]

---
layout: false

.right.w90.small[State-level; vote share; diverging; binned into four categories.]

---
layout: false

.right.w90.small[County level; winner only]

---
layout: false

.right.w90.small[County level vote share; diverging; binned into six categories]

---
layout: false

.right.w90.small[County level vote share; diverging continuous; purple midpoint]

---
layout: false

.right.w90.small[County level vote share; purple midpoint; county area deformed in proportion to population. By Mark Newman]

---
layout: false

.right.w90.small[Electoral college cartogram]

---

---
layout: false
class: main-title main-title-inv middle

# .middle.squish4.medium[.kjh-lblue[Problems showing] .kjh-orange[Non-Spatial Data]<br /> .kjh-lblue[in choropleth maps]]

---

.right.w90.big[Pretty, Big, and Pretty Empty]

---

.right.w90.big[Pretty, Big, and Pretty Empty]

---

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# Aside: What the hell's that?

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# Zoom and Enhance

.center.large[Suprisingly, not a coding error on my part.]

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# It's the .kjh-pink[Transcontinental Railroad]

.center.medium[Making its way through the .kjh-lblue[Great Basin], America's largest .kjh-orange[endorheic watershed]. The checkerboard is a deliberate assignation of property rights along the borders of the railway line.]
---

# Still with us, too

.center.medium[Not identical, as Interstate 80 was able to go through some parts the railroad had to go around. OK, now back to scheduled programming.]

---

---

## .huge.right.bottom.squish4[.kjh-yellow[U.S. State-Level]<br />.kjh-lblue[Election Data]]

---

# Set up the data

.pull-left.w60[

```r
## Hex color codes for Democratic Blue and Republican Red
party_colors <- c("#2E74C0", "#CB454A")
```

| _Decimal_ | _Binary_ | _Hexadecimal_ |
|---------|--------:|-------------:|
| 0       | 0      | 0           |
| 1       | 1      | 1           |
| 2       | 10     | 2           |
| 3       | 11     | 3           |
| 4       | 100    | 4           |
| 5       | 101    | 5           |
| 6       | 110    | 6           |
| 7       | 111    | 7           |
| 8       | 1000   | 8           |
| 9       | 1001   | 9           |
| 10      | 1010   | A           |
| 11      | 1011   | B           |
| 12      | 1100   | C           |
| 13      | 1101   | D           |
| 14      | 1110   | E           |
| 15      | 1111   | F           |

]
]

.pull-right.w40[

## `#`.kjh-red[`2E`].kjh-green[`116`].kjh-blue[`192`]

]

???

Hexadecimal numbers are "natural" to computers, because computers store and handle binary digits, and four binary digits make one hexadecimal digit. So one hexadecimal digit can have 16 different values (0 to 15 in decimal), and two hexadecimal digits together (called a "byte") can make 16×16=256 different levels of color. Because each of the three colors can have values from 0 to 255 (256 possible values), there are: 256 × 256 × 256 = 16,777,216 possible color combinations

---

# Set up the data

```r
election |> 
  select(state, total_vote, r_points, pct_trump, party, census)
```

```
## # A tibble: 51 × 6
##    state                total_vote r_points pct_trump party      census   
##    <chr>                     <dbl>    <dbl>     <dbl> <chr>      <chr>    
##  1 Alabama                 2123372    27.7      62.1  Republican South    
##  2 Alaska                   318608    14.7      51.3  Republican West     
##  3 Arizona                 2604657     3.5      48.1  Republican West     
##  4 Arkansas                1130635    26.9      60.6  Republican South    
##  5 California             14237893   -30.0      31.5  Democratic West     
##  6 Colorado                2780247    -4.91     43.2  Democratic West     
##  7 Connecticut             1644920   -13.6      40.9  Democratic Northeast
##  8 Delaware                 443814   -11.4      41.7  Democratic South    
##  9 District of Columbia     311268   -86.8       4.09 Democratic South    
## 10 Florida                 9502747     1.19     48.6  Republican South    
## # … with 41 more rows
```