Visualizing spatial data I

---

# Announcements

---

# For today:

- API key: https://docs.stadiamaps.com/tutorials/getting-started-in-r-with-ggmap/#set-up-api-key-authentication

```r
# install ggmap if necessary
# install.packages("ggmap")
usethis::use_course("MACS40700/spatial")
```

---

## Setup

``` r
# load packages
library(leaflet)
library(geofacet)
library(rnaturalearth)
library(maps)
library(ggmap)
library(sf)
library(tidyverse)

# set default theme for ggplot2
ggplot2::theme_set(ggplot2::theme_minimal(base_size = 16))

# set default figure parameters for knitr
knitr::opts_chunk$set(
  fig.width = 8, fig.asp = 0.618,
  fig.retina = 2, dpi = 150
)
```

---

## Geospatial visualizations

* Earliest form of information visualizations
* Geospatial data visualizations
* [Google Maps](https://www.google.com/maps)

---

## Not that Jon Snow

---

## Dr. John Snow

---

## Designing modern maps

* Depict spatial features
* Incorporate additional attributes and information
* Major features
    * Scale
    * Projection
    * Symbols

---

## Scale

* Proportion between distances and sizes on a map and their actual distances and sizes on Earth
* Small-scale map
* Large-scale map

---

## Mapping options:

* maps -- map data
* ggmap -- nice way to render but need API
* leaflet -- more interactive
* plotly -- soon-to-be new friend :) 
* sf -- ways to visualize objects

---
## Large-scale map

<div class="leaflet html-widget html-fill-item" id="htmlwidget-93651e2174a1d344ec45" style="width:80%;height:741.6px;"></div>
<script type="application/json" data-for="htmlwidget-93651e2174a1d344ec45">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addTiles","args":["https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"https://openstreetmap.org/copyright/\">OpenStreetMap<\/a>,  <a href=\"https://opendatacommons.org/licenses/odbl/\">ODbL<\/a>"}]}],"setView":[[41.787,-87.59699999999999],2,[]]},"evals":[],"jsHooks":[]}</script>

---

## Small-scale map

<div class="leaflet html-widget html-fill-item" id="htmlwidget-e31085a05ca4346b7cf9" style="width:80%;height:741.6px;"></div>
<script type="application/json" data-for="htmlwidget-e31085a05ca4346b7cf9">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"setView":[[41.787,-87.59699999999999],16,[]],"calls":[{"method":"addMarkers","args":[41.785958,-87.597086,null,null,null,{"interactive":true,"draggable":false,"keyboard":true,"title":"","alt":"","zIndexOffset":0,"opacity":1,"riseOnHover":false,"riseOffset":250},"You are here",null,null,null,null,{"interactive":false,"permanent":false,"direction":"auto","opacity":1,"offset":[0,0],"textsize":"10px","textOnly":false,"className":"","sticky":true},null]},{"method":"addTiles","args":["https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"https://openstreetmap.org/copyright/\">OpenStreetMap<\/a>,  <a href=\"https://opendatacommons.org/licenses/odbl/\">ODbL<\/a>"}]}],"limits":{"lat":[41.785958,41.785958],"lng":[-87.597086,-87.597086]}},"evals":[],"jsHooks":[]}</script>

---

## Projection

* Process of taking a three-dimensional object and visualizing it on a two-dimensional surface
* No 100% perfect method for this
* Always introduces distortions

* Properties of projection methods
    1. Shape
    1. Area
    1. Angles
    1. Distance
    1. Direction

---

## Conformal projections

---

## Equal-area projections

---

## Mollweide

---

## Gall-Peters (AKA variant of equal area cylindrical)

---

# raster maps

---

## `ggmap`

- Package for drawing maps using `ggplot2` and **raster** map tiles
- Static image files generated by mapping services
- Focus on incorporating data into existing maps
- Severely limits ability to change the appearance of the geographic map
- Don't have to worry about the maps, just the data to go on top
- **NEED API**

---
## `leaflet` (has both R and javascript availabilty)

- similar to `ggmap` but no API needed!

---
# Bounding boxes: `leaflet`

``` r
  leaflet() %>%
    fitBounds(-87.936287, 41.679835, -87.447052,42.060835) %>%
    addMarkers(lng=-87.597086, lat=41.785958, popup="You are here") %>%
    setView(lng = -87.597, lat = 41.787, zoom = 8) %>%
    addTiles()  
```

]

<div class="leaflet html-widget html-fill-item" id="htmlwidget-ce66ac108a513ab5ebea" style="width:70%;height:741.6px;"></div>
<script type="application/json" data-for="htmlwidget-ce66ac108a513ab5ebea">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addMarkers","args":[41.785958,-87.597086,null,null,null,{"interactive":true,"draggable":false,"keyboard":true,"title":"","alt":"","zIndexOffset":0,"opacity":1,"riseOnHover":false,"riseOffset":250},"You are here",null,null,null,null,{"interactive":false,"permanent":false,"direction":"auto","opacity":1,"offset":[0,0],"textsize":"10px","textOnly":false,"className":"","sticky":true},null]},{"method":"addTiles","args":["https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"https://openstreetmap.org/copyright/\">OpenStreetMap<\/a>,  <a href=\"https://opendatacommons.org/licenses/odbl/\">ODbL<\/a>"}]}],"limits":{"lat":[41.785958,41.785958],"lng":[-87.597086,-87.597086]},"setView":[[41.787,-87.59699999999999],8,[]]},"evals":[],"jsHooks":[]}</script>

]

---
## Level of detail: zoom = 12 vs 10

.pull-left[ 
<div class="leaflet html-widget html-fill-item" id="htmlwidget-ab2906bafb0a6445939a" style="width:80%;height:741.6px;"></div>
<script type="application/json" data-for="htmlwidget-ab2906bafb0a6445939a">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addTiles","args":["https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"https://openstreetmap.org/copyright/\">OpenStreetMap<\/a>,  <a href=\"https://opendatacommons.org/licenses/odbl/\">ODbL<\/a>"}]},{"method":"addMarkers","args":[41.785958,-87.597086,null,null,null,{"interactive":true,"draggable":false,"keyboard":true,"title":"","alt":"","zIndexOffset":0,"opacity":1,"riseOnHover":false,"riseOffset":250},"You are here",null,null,null,null,{"interactive":false,"permanent":false,"direction":"auto","opacity":1,"offset":[0,0],"textsize":"10px","textOnly":false,"className":"","sticky":true},null]}],"limits":{"lat":[41.785958,41.785958],"lng":[-87.597086,-87.597086]},"setView":[[41.876823,-87.624003],12,[]]},"evals":[],"jsHooks":[]}</script>
]

.pull-right[ 
<div class="leaflet html-widget html-fill-item" id="htmlwidget-b661d87e79301df27f55" style="width:80%;height:741.6px;"></div>
<script type="application/json" data-for="htmlwidget-b661d87e79301df27f55">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addTiles","args":["https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"https://openstreetmap.org/copyright/\">OpenStreetMap<\/a>,  <a href=\"https://opendatacommons.org/licenses/odbl/\">ODbL<\/a>"}]},{"method":"addMarkers","args":[41.785958,-87.597086,null,null,null,{"interactive":true,"draggable":false,"keyboard":true,"title":"","alt":"","zIndexOffset":0,"opacity":1,"riseOnHover":false,"riseOffset":250},"You are here",null,null,null,null,{"interactive":false,"permanent":false,"direction":"auto","opacity":1,"offset":[0,0],"textsize":"10px","textOnly":false,"className":"","sticky":true},null]}],"limits":{"lat":[41.785958,41.785958],"lng":[-87.597086,-87.597086]},"setView":[[41.876823,-87.624003],10,[]]},"evals":[],"jsHooks":[]}</script>
]

---

## Bounding box: `ggmap`

``` r
*chi_bb <- c( left = -87.936287, bottom = 41.679835, right = -87.447052, top = 42.060835)

chicago <- get_stadiamap( bbox = chi_bb, zoom = 11)

ggmap(chicago)
```

]

]

---
## Identifying bounding box
  
  > Use [bboxfinder.com](http://bboxfinder.com/#0.000000,0.000000,0.000000,0.000000) to determine the exact longitude/latitude coordinates for the bounding box you wish to obtain.
    
---

## Types of map tiles: Leaflet

<div class="leaflet html-widget html-fill-item" id="htmlwidget-0c3976fa137aa7486b98" style="width:60%;height:741.6px;"></div>
<script type="application/json" data-for="htmlwidget-0c3976fa137aa7486b98">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addTiles","args":["https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"https://openstreetmap.org/copyright/\">OpenStreetMap<\/a>,  <a href=\"https://opendatacommons.org/licenses/odbl/\">ODbL<\/a>"}]}],"setView":[[41.876823,-87.624003],10,[]]},"evals":[],"jsHooks":[]}</script>

---
## B&W

``` r
m %>% addProviderTiles(providers$CartoDB.Positron)
```

<div class="leaflet html-widget html-fill-item" id="htmlwidget-d5dc1ed8f261d27e1e3c" style="width:60%;height:741.6px;"></div>
<script type="application/json" data-for="htmlwidget-d5dc1ed8f261d27e1e3c">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addTiles","args":["https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"https://openstreetmap.org/copyright/\">OpenStreetMap<\/a>,  <a href=\"https://opendatacommons.org/licenses/odbl/\">ODbL<\/a>"}]},{"method":"addProviderTiles","args":["CartoDB.Positron",null,null,{"errorTileUrl":"","noWrap":false,"detectRetina":false}]}],"setView":[[41.876823,-87.624003],10,[]]},"evals":[],"jsHooks":[]}</script>

---

## Nat Geo

``` r
m %>% addProviderTiles(providers$Esri.NatGeoWorldMap)
```

<div class="leaflet html-widget html-fill-item" id="htmlwidget-83110bec486b6b7e274d" style="width:60%;height:741.6px;"></div>
<script type="application/json" data-for="htmlwidget-83110bec486b6b7e274d">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addTiles","args":["https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"https://openstreetmap.org/copyright/\">OpenStreetMap<\/a>,  <a href=\"https://opendatacommons.org/licenses/odbl/\">ODbL<\/a>"}]},{"method":"addProviderTiles","args":["Esri.NatGeoWorldMap",null,null,{"errorTileUrl":"","noWrap":false,"detectRetina":false}]}],"setView":[[41.876823,-87.624003],10,[]]},"evals":[],"jsHooks":[]}</script>

``` r
leaflet() %>% setView(lng = -87.624003, lat = 41.876823, zoom = 8)  %>% addTiles()%>% 
  addWMSTiles(
    "http://mesonet.agron.iastate.edu/cgi-bin/wms/nexrad/n0r.cgi",
    layers = "nexrad-n0r-900913",
    options = WMSTileOptions(format = "image/png", transparent = TRUE),
    attribution = "Weather data © 2012 IEM Nexrad"
  )
```

<div class="leaflet html-widget html-fill-item" id="htmlwidget-aaeea2d81a87aa0ccc01" style="width:80%;height:741.6px;"></div>
<script type="application/json" data-for="htmlwidget-aaeea2d81a87aa0ccc01">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"setView":[[41.876823,-87.624003],8,[]],"calls":[{"method":"addTiles","args":["https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"https://openstreetmap.org/copyright/\">OpenStreetMap<\/a>,  <a href=\"https://opendatacommons.org/licenses/odbl/\">ODbL<\/a>"}]},{"method":"addWMSTiles","args":["http://mesonet.agron.iastate.edu/cgi-bin/wms/nexrad/n0r.cgi",null,null,{"styles":"","format":"image/png","transparent":true,"version":"1.1.1","attribution":"Weather data © 2012 IEM Nexrad","layers":"nexrad-n0r-900913"}]}]},"evals":[],"jsHooks":[]}</script>

---
## Types of map tiles: `ggmap`

---
class: inverse, middle
                         
 <img src="https://media.giphy.com/media/oOK9AZGnf9b0c/giphy.gif" width="80%" style="display: block; margin: auto;" />
                         
                         
---

# Example: using `ggmap`

---
## Import crime data
 
* City of Chicago open data portal
* [Crime data from 2017](https://data.cityofchicago.org/Public-Safety/Crimes-2017/d62x-nvdr)

``` r
crimes <- read_csv("data/chicago-crimes.csv")
glimpse(crimes)
```

```
## Rows: 268,862
## Columns: 22
## $ ID                     <dbl> 11227287, 11227583, 11227293, 11227634, 1122750…
## $ `Case Number`          <chr> "JB147188", "JB147595", "JB147230", "JB147599",…
## $ Date                   <chr> "10/08/2017 03:00:00 AM", "03/28/2017 02:00:00 …
## $ Block                  <chr> "092XX S RACINE AVE", "026XX W 79TH ST", "060XX…
## $ IUCR                   <chr> "0281", "0620", "0810", "0281", "1754", "0810",…
## $ `Primary Type`         <chr> "CRIM SEXUAL ASSAULT", "BURGLARY", "THEFT", "CR…
## $ Description            <chr> "NON-AGGRAVATED", "UNLAWFUL ENTRY", "OVER $500"…
## $ `Location Description` <chr> "RESIDENCE", "OTHER", "RESIDENCE", "HOTEL/MOTEL…
## $ Arrest                 <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
## $ Domestic               <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
## $ Beat                   <chr> "2222", "0835", "0313", "0122", "1033", "1432",…
## $ District               <chr> "022", "008", "003", "001", "010", "014", "001"…
## $ Ward                   <dbl> 21, 18, 20, 42, 12, 32, 2, 8, 39, 40, 8, 44, 47…
## $ `Community Area`       <dbl> 73, 70, 42, 32, 30, 22, 32, 44, 13, 1, 46, 6, 4…
## $ `FBI Code`             <chr> "02", "05", "06", "02", "02", "06", "11", "14",…
## $ `X Coordinate`         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ `Y Coordinate`         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Year                   <dbl> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017,…
## $ `Updated On`           <chr> "02/11/2018 03:57:41 PM", "02/11/2018 03:57:41 …
## $ Latitude               <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Longitude              <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Location               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
```

---

## Plot high-level map of crime

``` r
*ggmap(chicago)
```

]

]
]

---

## Using `geom_point()`

``` r
ggmap(chicago) +
* geom_point(
*   data = crimes,
*   mapping = aes(
*     x = Longitude,
*     y = Latitude
*   )
* )
```

]

]
]

---

## Using `geom_point()`

``` r
ggmap(chicago) +
  geom_point(
    data = crimes,
    mapping = aes(
      x = Longitude,
      y = Latitude
    ),
*   size = .25,
*   alpha = .01
  )
```

]

]
]

---

## Using `geom_density_2d()`

``` r
ggmap(chicago) +
* geom_density_2d(
    data = crimes,
    mapping = aes(
      x = Longitude,
      y = Latitude
    )
  )
```

]

]
]

---

## Using `stat_density_2d()`

``` r
ggmap(chicago) +
* stat_density_2d(
    data = crimes,
    mapping = aes(
      x = Longitude,
      y = Latitude,
*     fill = after_stat(level)
    ),
*   geom = "polygon"
  )
```

]

]
]

https://ggplot2.tidyverse.org/reference/geom_density_2d.html 
---

## Using `stat_density_2d()`

``` r
ggmap(chicago) +
  stat_density_2d(
    data = crimes,
    mapping = aes(
      x = Longitude,
      y = Latitude,
      fill = after_stat(level)
    ),
*   alpha = .2,
*   bins = 25,
    geom = "polygon"
  )
```

]

]
]

---

## Using `stat_density_2d()` but make it ~**FANCY**~

``` r
ggmap(chicago) +
  stat_density_2d(
    data = crimes,
    mapping = aes(
      x = Longitude,
      y = Latitude,
      fill = after_stat(level)
    ),
    alpha = .2, 
    bins = 25, 
    geom = "polygon"
  ) + scale_fill_viridis_c(option = "inferno")
```

]

]
]

---

## Looking for variation

``` r
ggmap(chicago) +
  stat_density_2d(
    data = crimes %>%
      filter(`Primary Type` %in% c(
        "BURGLARY", "MOTOR VEHICLE THEFT",
        "NARCOTICS", "ROBBERY"
      )),
    mapping = aes(
      x = Longitude,
      y = Latitude,
      fill = after_stat(level)
    ),
    alpha = .4,
    bins = 10,
    geom = "polygon"
  ) +
* facet_wrap(facets = vars(`Primary Type`), labeller = label_wrap_gen(10))
```

]

]
]

---

# API keys: what are they and how to store?

---
## API Keys
APIs: application programming interface. They **provide the opportunity for users to respectfully access data in a structured format**.

You can think of like when you order through an app -- you can check off the items you want and then you receive them in a tidy package. This is the same idea here -- you can choose from a menu of data options (e.g. variables) and then you receive them in a structured format (maybe JSON, maybe for mapping, etc.).

---
## How to store API keys:
* In document: e.g. register_stadiamaps(apikey)
* In RProfile
* In R envion

For more: https://cran.r-project.org/web/packages/httr/vignettes/secrets.html

The file looks something like

`VAR1 = value1`

`VAR2 = value2`

And you can access the values in R using `Sys.getenv():`

This would then be used, something like: `apikey <- Sys.getenv("VAR1")`

---

## Exercise using `ggmap`: needs API key

- City of Chicago data portal
- 311 service requests
- API key: https://docs.stadiamaps.com/tutorials/getting-started-in-r-with-ggmap/#set-up-api-key-authentication

```r
# install ggmap if necessary
# install.packages("ggmap")
usethis::use_course("MACS40700/spatial")
```

<countdown-timer class="countdown" id="timer_43687f40" minutes="15" seconds="0" update-every="1" tabindex="0" style="right:0;bottom:0;"></countdown-timer>

---

# Geofaceting

---

---

## Daily US vaccine data by state

``` r
us_state_vaccinations <- read_csv(here::here("data/us_state_vaccinations.csv"))
```

``` r
glimpse(us_state_vaccinations)
```

```
## Rows: 30,692
## Columns: 16
## $ date                                <date> 2021-01-12, 2021-01-13, 2021-01-1…
## $ location                            <chr> "Alabama", "Alabama", "Alabama", "…
## $ total_vaccinations                  <dbl> 78134, 84040, 92300, 100567, NA, N…
## $ total_distributed                   <dbl> 377025, 378975, 435350, 444650, NA…
## $ people_vaccinated                   <dbl> 70861, 74792, 80480, 86956, NA, NA…
## $ people_fully_vaccinated_per_hundred <dbl> 0.15, 0.19, NA, 0.28, NA, NA, NA, …
## $ total_vaccinations_per_hundred      <dbl> 1.59, 1.71, 1.88, 2.05, NA, NA, NA…
## $ people_fully_vaccinated             <dbl> 7270, 9245, NA, 13488, NA, NA, NA,…
## $ people_vaccinated_per_hundred       <dbl> 1.45, 1.53, 1.64, 1.77, NA, NA, NA…
## $ distributed_per_hundred             <dbl> 7.69, 7.73, 8.88, 9.07, NA, NA, NA…
## $ daily_vaccinations_raw              <dbl> NA, 5906, 8260, 8267, NA, NA, NA, …
## $ daily_vaccinations                  <dbl> NA, 5906, 7083, 7478, 7498, 7509, …
## $ daily_vaccinations_per_million      <dbl> NA, 1205, 1445, 1525, 1529, 1531, …
## $ share_doses_used                    <dbl> 0.207, 0.222, 0.212, 0.226, NA, NA…
## $ total_boosters                      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ total_boosters_per_hundred          <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA…
```

]

---

## Facet by location

.panelset.sideways[
.panel[.panel-name[Code]

``` r
ggplot(
  us_state_vaccinations,
  aes(x = date, y = people_fully_vaccinated_per_hundred)
) +
  geom_area() +
* facet_wrap(facets = vars(location))
```

]

]
]

---

## Data cleaning

``` r
us_state_vaccinations <- us_state_vaccinations %>%
  mutate(location = if_else(location == "New York State", "New York", location)) %>%
  filter(location %in% c(state.name, "District of Columbia"))
```

---

## Geofacet by state

Using `geofacet::facet_geo()`:

.panelset.sideways[
.panel[.panel-name[Code]

``` r
ggplot(us_state_vaccinations, 
       aes(x = date, y = people_fully_vaccinated_per_hundred)) +
  geom_area() +
* facet_geo(facets = vars(location)) +
  labs(
    x = NULL, y = NULL,
    title = "Covid-19 vaccination rate in the US",
    subtitle = "Daily number of people fully vaccinated, per hundred",
    caption = "Source: Our World in Data"
  )
```

]

]
]

---

## Geofacet by state, with improvements

.panelset.sideways[
.panel[.panel-name[Plot]
<img src="index_files/figure-html/unnamed-chunk-19-1.png" width="95%" style="display: block; margin: auto;" />
]
.panel[.panel-name[Code]
.small[

``` r
ggplot(us_state_vaccinations, aes(x = date, y = people_fully_vaccinated_per_hundred, group = location)) +
  geom_area() +
  facet_geo(facets = vars(location)) +
* scale_y_continuous(
*   limits = c(0, 100),
*   breaks = c(0, 50, 100),
*   minor_breaks = c(25, 75)
*   ) +
* scale_x_date(breaks = c(ymd("2021-01-01", "2021-07-01", "2022-01-01")), date_labels = "%b-%y") +
  labs(
    x = NULL, y = NULL,
    title = "Covid-19 vaccination rate in the US",
    subtitle = "Daily number of people fully vaccinated, per hundred",
    caption = "Source: Our World in Data"
  ) +
  theme(
*   strip.text.x = element_text(size = 7),
*   axis.text = element_text(size = 8),
    plot.title.position = "plot"
  )
```
]
]
]

---

## Bring in 2020 Presidential election results

``` r
election_2020 <- read_csv(here::here("data/us-election-2020.csv"))
```

``` r
election_2020
```

```
## # A tibble: 51 × 5
##    state                electoal_votes biden trump win       
##    <chr>                         <dbl> <dbl> <dbl> <chr>     
##  1 Alabama                           9     0     9 Republican
##  2 Alaska                            3     0     3 Republican
##  3 Arizona                          11    11     0 Democrat  
##  4 Arkansas                          6     0     6 Republican
##  5 California                       55    55     0 Democrat  
##  6 Colorado                          9     9     0 Democrat  
##  7 Connecticut                       7     7     0 Democrat  
##  8 Delaware                          3     3     0 Democrat  
##  9 District of Columbia              3     3     0 Democrat  
## 10 Florida                          29     0    29 Republican
## # ℹ 41 more rows
```

---

## Geofacet by state, color by presidential election result

``` r
us_state_vaccinations %>%
  left_join(election_2020, by = c("location" = "state")) %>%
  ggplot(aes(x = date, y = people_fully_vaccinated_per_hundred)) +
* geom_area(aes(fill = win)) +
  facet_geo(facets = vars(location)) +
* scale_y_continuous(limits = c(0, 100), breaks = c(0, 50, 100), minor_breaks = c(25, 75)) +
  scale_x_date(breaks = c(ymd("2021-01-01", "2021-07-01", "2022-01-01")), date_labels = "%b") +
* scale_fill_manual(values = c("#2D69A1", "#BD3028")) +
  labs(
    x = NULL, y = NULL,
    title = "Covid-19 vaccination rate in the US",
    subtitle = "Daily number of people fully vaccinated, per hundred",
    caption = "Source: Our World in Data",
    fill = "2020 Presidential\nElection"
  ) +
  theme(
    strip.text.x = element_text(size = 7),
    axis.text = element_text(size = 8),
    plot.title.position = "plot",
*   legend.position = c(0.93, 0.15),
*   legend.text = element_text(size = 9),
*   legend.title = element_text(size = 11),
*   legend.background = element_rect(color = "gray", size = 0.5)
  )
```

]

]
]
]

---
# Leaflet vs ggmap

* ggmap connects to google open street data
* leaflet has interactive factor

---

# Recap

* Lots we can do spatially
* Maps can require a LOT and options vary / update all the time