09 — Maps and Spatial Data

# .huge[.kjh-lblue[Basic] .kjh-yellow[Maps]]

---

## .huge.right.bottom.squish4[.kjh-yellow[Maps as] .kjh-lblue[polygons]]

---

---

# Packages

```r
library(here)       # manage file paths
library(socviz)     # data and some useful functions
library(tidyverse)  # your friend and mine
library(maps)       # Some basic maps
library(ggforce)    # ggplot extensions
```

---

# Take a look at this data

```r
## This is from the map library
# library(maps)

us_states <- map_data("state")

dim(us_states)
```

```
## [1] 15537     6
```

```r
## Making it a tibble prevents crashes 
## in the slide rendering later on
us_states <- as_tibble(us_states)

us_states
```

```
## # A tibble: 15,537 × 6
##     long   lat group order region  subregion
##    <dbl> <dbl> <dbl> <int> <chr>   <chr>    
##  1 -87.5  30.4     1     1 alabama <NA>     
##  2 -87.5  30.4     1     2 alabama <NA>     
##  3 -87.5  30.4     1     3 alabama <NA>     
##  4 -87.5  30.3     1     4 alabama <NA>     
##  5 -87.6  30.3     1     5 alabama <NA>     
##  6 -87.6  30.3     1     6 alabama <NA>     
##  7 -87.6  30.3     1     7 alabama <NA>     
##  8 -87.6  30.3     1     8 alabama <NA>     
##  9 -87.7  30.3     1     9 alabama <NA>     
## 10 -87.8  30.3     1    10 alabama <NA>     
## # … with 15,527 more rows
```

---

# What is this, at root?

```r
us_states
```

- It's a series of rows defining `x` and `y` coordinatates on a plane.

- If we join those points up as lines while respecting their `group` (i.e. so `ggplot` knows when to "lift the pen", as with the `gapminder` line plot), we will get an outline map of states in the U.S.

---

# Like this, with .kjh-green[`geom_polygon()`]

.pull-left.w35[

```r
us_states |>
  ggplot(mapping = aes(x = long, 
                       y = lat, 
                       group = group)) +
  geom_polygon(fill = "white", 
               color = "black") +
  labs(title = "This looks horrible")
```
]

.pull-right.w65[
<img src="09-slides_files/figure-html/codefig-poly1-1.png" width="576" style="display: block; margin: auto;" />
]

---

# We can represent a .kjh-orange[`fill`], too, like any geom

.pull-left.w35[

```r
us_states |>
  ggplot(mapping = aes(x = long, 
                       y = lat,
*                      fill = region,
                       group = group)) +
  geom_polygon(color = "black") + 
* guides(fill = "none") +
  labs(title = "Still looks horrible", 
       caption = "Set fill = none 
         to stop ggplot from 
         producing a key
         with 50 entries")
```
]

.pull-right.w65[
<img src="09-slides_files/figure-html/codefig-poly2-1.png" width="576" style="display: block; margin: auto;" />
]

---

---

# We need to do two things

### .center.middle.huge[1: Fix the .kjh-lblue[map projection]]

### .center.middle.huge[2: .kjh-orange[Add some data] to fill with.]

---

# For now, we'll do it the direct way

- .middle.large[To make explicit what's happening, and to emphasize how .kjh-pink[_it's all just points and lines made from tables_] we'll first do it at the level of the .kjh-lblue[`ggplot`] grammar with a geom that just draws shapes, .kjh-green[`geom_polygon()`]. After that, we'll introduce a new package, .kjh-lblue[`sf`] and a new geom, .kjh-green[`geom_sf()`] that will handle this for us, and more.]

---

---

```r
*us_states <- as_tibble(map_data("state"))
```
]
 
.panel2-reveal-coord-auto[

]

---
count: false
 
# Fix the projection
.panel1-reveal-coord-auto[

```r
us_states <- as_tibble(map_data("state"))

*us_states
```
]
 
.panel2-reveal-coord-auto[

---
count: false
 
# Fix the projection
.panel1-reveal-coord-auto[

```r
us_states <- as_tibble(map_data("state"))

us_states |>
* ggplot(mapping = aes(x = long,
*                      y = lat,
*                      fill = region,
*                      group = group))
```
]
 
.panel2-reveal-coord-auto[
<img src="09-slides_files/figure-html/reveal-coord_auto_03_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# Fix the projection
.panel1-reveal-coord-auto[

```r
us_states <- as_tibble(map_data("state"))

us_states |>
  ggplot(mapping = aes(x = long,
                       y = lat,
                       fill = region,
                       group = group)) +
* geom_polygon(color = "black")
```
]
 
.panel2-reveal-coord-auto[
<img src="09-slides_files/figure-html/reveal-coord_auto_04_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# Fix the projection
.panel1-reveal-coord-auto[

```r
us_states <- as_tibble(map_data("state"))

us_states |>
  ggplot(mapping = aes(x = long,
                       y = lat,
                       fill = region,
                       group = group)) +
  geom_polygon(color = "black") +
* guides(fill = "none")
```
]
 
.panel2-reveal-coord-auto[
<img src="09-slides_files/figure-html/reveal-coord_auto_05_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# Fix the projection
.panel1-reveal-coord-auto[

```r
us_states <- as_tibble(map_data("state"))

us_states |>
  ggplot(mapping = aes(x = long,
                       y = lat,
                       fill = region,
                       group = group)) +
  geom_polygon(color = "black") +
  guides(fill = "none") +
* coord_map(projection = "albers",
*           lat0 = 39,
*           lat1 = 45)
```
]
 
.panel2-reveal-coord-auto[
<img src="09-slides_files/figure-html/reveal-coord_auto_06_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# Fix the projection
.panel1-reveal-coord-auto[

```r
us_states <- as_tibble(map_data("state"))

us_states |>
  ggplot(mapping = aes(x = long,
                       y = lat,
                       fill = region,
                       group = group)) +
  geom_polygon(color = "black") +
  guides(fill = "none") +
  coord_map(projection = "albers",
            lat0 = 39,
            lat1 = 45)

## A coordinate transformation!
```
]
 
.panel2-reveal-coord-auto[
<img src="09-slides_files/figure-html/reveal-coord_auto_07_output-1.png" width="504" style="display: block; margin: auto;" />
]

---

---

# U.S. Map Projections

---

# U.S. Map Projections

---
layout: false

.right.w90.small[Our U.S. Map again, now transformed]

---

---

# Next, some data

.small[- We can merge our state-level `election` data with the `us_states` table, but we need to do a little work.]

.pull-left.w45[

```r
us_states
```

]

.pull-right.w45[

```r
election
```

```
## # A tibble: 51 × 22
##    state        st     fips total…¹ vote_…² winner party pct_m…³ r_poi…⁴ d_poi…⁵
##    <chr>        <chr> <dbl>   <dbl>   <dbl> <chr>  <chr>   <dbl>   <dbl>   <dbl>
##  1 Alabama      AL        1  2.12e6  588708 Trump  Repu…  0.277    27.7   -27.7 
##  2 Alaska       AK        2  3.19e5   46933 Trump  Repu…  0.147    14.7   -14.7 
##  3 Arizona      AZ        4  2.60e6   91234 Trump  Repu…  0.035     3.5    -3.5 
##  4 Arkansas     AR        5  1.13e6  304378 Trump  Repu…  0.269    26.9   -26.9 
##  5 California   CA        6  1.42e7 4269978 Clint… Demo…  0.300   -30.0    30.0 
##  6 Colorado     CO        8  2.78e6  136386 Clint… Demo…  0.0491   -4.91    4.91
##  7 Connecticut  CT        9  1.64e6  224357 Clint… Demo…  0.136   -13.6    13.6 
##  8 Delaware     DE       10  4.44e5   50476 Clint… Demo…  0.114   -11.4    11.4 
##  9 District of… DC       11  3.11e5  270107 Clint… Demo…  0.868   -86.8    86.8 
## 10 Florida      FL       12  9.50e6  112911 Trump  Repu…  0.0119    1.19   -1.19
## # … with 41 more rows, 12 more variables: pct_clinton <dbl>, pct_trump <dbl>,
## #   pct_johnson <dbl>, pct_other <dbl>, clinton_vote <dbl>, trump_vote <dbl>,
## #   johnson_vote <dbl>, other_vote <dbl>, ev_dem <dbl>, ev_rep <dbl>,
## #   ev_oth <dbl>, census <chr>, and abbreviated variable names ¹total_vote,
## #   ²vote_margin, ³pct_margin, ⁴r_points, ⁵d_points
```
]

.center[To merge, or .kjh-pink[_join_] these tables, they need to have a column in common to act as a key.]

---

# Recode to make a key

```r
election <- election |>
  mutate(region = tolower(state)) |> 
  relocate(region)

election
```

```
## # A tibble: 51 × 23
##    region state st     fips total…¹ vote_…² winner party pct_m…³ r_poi…⁴ d_poi…⁵
##    <chr>  <chr> <chr> <dbl>   <dbl>   <dbl> <chr>  <chr>   <dbl>   <dbl>   <dbl>
##  1 alaba… Alab… AL        1  2.12e6  588708 Trump  Repu…  0.277    27.7   -27.7 
##  2 alaska Alas… AK        2  3.19e5   46933 Trump  Repu…  0.147    14.7   -14.7 
##  3 arizo… Ariz… AZ        4  2.60e6   91234 Trump  Repu…  0.035     3.5    -3.5 
##  4 arkan… Arka… AR        5  1.13e6  304378 Trump  Repu…  0.269    26.9   -26.9 
##  5 calif… Cali… CA        6  1.42e7 4269978 Clint… Demo…  0.300   -30.0    30.0 
##  6 color… Colo… CO        8  2.78e6  136386 Clint… Demo…  0.0491   -4.91    4.91
##  7 conne… Conn… CT        9  1.64e6  224357 Clint… Demo…  0.136   -13.6    13.6 
##  8 delaw… Dela… DE       10  4.44e5   50476 Clint… Demo…  0.114   -11.4    11.4 
##  9 distr… Dist… DC       11  3.11e5  270107 Clint… Demo…  0.868   -86.8    86.8 
## 10 flori… Flor… FL       12  9.50e6  112911 Trump  Repu…  0.0119    1.19   -1.19
## # … with 41 more rows, 12 more variables: pct_clinton <dbl>, pct_trump <dbl>,
## #   pct_johnson <dbl>, pct_other <dbl>, clinton_vote <dbl>, trump_vote <dbl>,
## #   johnson_vote <dbl>, other_vote <dbl>, ev_dem <dbl>, ev_rep <dbl>,
## #   ev_oth <dbl>, census <chr>, and abbreviated variable names ¹total_vote,
## #   ²vote_margin, ³pct_margin, ⁴r_points, ⁵d_points
```

---

# Now we can join them

.pull-left.w45[

```r
us_states
```

]

.pull-right.w45[

```r
election
```

---

# This is a .kjh-pink[_left join_]

```r
us_states_elec <- left_join(us_states, election, by = "region")

us_states_elec
```

```
## # A tibble: 15,537 × 28
##     long   lat group order region  subregion state   st     fips total…¹ vote_…²
##    <dbl> <dbl> <dbl> <int> <chr>   <chr>     <chr>   <chr> <dbl>   <dbl>   <dbl>
##  1 -87.5  30.4     1     1 alabama <NA>      Alabama AL        1 2123372  588708
##  2 -87.5  30.4     1     2 alabama <NA>      Alabama AL        1 2123372  588708
##  3 -87.5  30.4     1     3 alabama <NA>      Alabama AL        1 2123372  588708
##  4 -87.5  30.3     1     4 alabama <NA>      Alabama AL        1 2123372  588708
##  5 -87.6  30.3     1     5 alabama <NA>      Alabama AL        1 2123372  588708
##  6 -87.6  30.3     1     6 alabama <NA>      Alabama AL        1 2123372  588708
##  7 -87.6  30.3     1     7 alabama <NA>      Alabama AL        1 2123372  588708
##  8 -87.6  30.3     1     8 alabama <NA>      Alabama AL        1 2123372  588708
##  9 -87.7  30.3     1     9 alabama <NA>      Alabama AL        1 2123372  588708
## 10 -87.8  30.3     1    10 alabama <NA>      Alabama AL        1 2123372  588708
## # … with 15,527 more rows, 17 more variables: winner <chr>, party <chr>,
## #   pct_margin <dbl>, r_points <dbl>, d_points <dbl>, pct_clinton <dbl>,
## #   pct_trump <dbl>, pct_johnson <dbl>, pct_other <dbl>, clinton_vote <dbl>,
## #   trump_vote <dbl>, johnson_vote <dbl>, other_vote <dbl>, ev_dem <dbl>,
## #   ev_rep <dbl>, ev_oth <dbl>, census <chr>, and abbreviated variable names
## #   ¹total_vote, ²vote_margin
```

.center[Now our `us_states_elec` table has both the line-drawing information and (very redundantly) the election data merged in, with rows repeated as necessary.]

---

---

# Now we can start drawing choropleths

.pull-left.w45[

```r
us_states_elec |>
  ggplot(mapping = aes(x = long, 
                       y = lat,
*                      fill = party,
                       group = group)) + 
  geom_polygon(color = "gray90", 
               size = 0.1) +
  coord_map(projection = "albers", 
            lat0 = 39, lat1 = 45) +
  guides(fill = "none")
```
]

.pull-right.w55[
<img src="09-slides_files/figure-html/codefig-choroparty-1.png" width="460" style="display: block; margin: auto;" />

]

---

# Let's turn off the gridlines

This is a .kjh-pink[_theme function_].

```r
theme_map <- function(base_size=9, base_family="") {
    require(grid)
    theme_bw(base_size=base_size, base_family=base_family) %+replace%
        theme(axis.line=element_blank(),
              axis.text=element_blank(),
              axis.ticks=element_blank(),
              axis.title=element_blank(),
              panel.background=element_blank(),
              panel.border=element_blank(),
              panel.grid=element_blank(),
              panel.spacing=unit(0, "lines"),
              plot.background=element_blank(),
              legend.justification = c(0,0),
              legend.position = c(0,0)
              )
}
```

---

# Add the theme function at the end

.pull-left.w45[

```r
us_states_elec |> 
  ggplot(mapping = aes(x = long, 
                       y = lat,
*                      fill = party,
                       group = group)) + 
  geom_polygon(color = "gray90", 
               size = 0.1) +
  coord_map(projection = "albers", 
            lat0 = 39, lat1 = 45) +
  theme_map()
```
]

.pull-right.w55[
<img src="09-slides_files/figure-html/codefig-choropartytheme-1.png" width="460" style="display: block; margin: auto;" />

]

---

# Fix the Party Colors

.pull-left.w45[

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

us_states_elec |> 
  ggplot(mapping = aes(x = long, 
                       y = lat,
*                      fill = party,
                       group = group)) + 
  geom_polygon(color = "gray90", 
               size = 0.1) +
  scale_fill_manual(values = party_colors) + 
  coord_map(projection = "albers", 
            lat0 = 39, lat1 = 45) +
  theme_map()
```
]

.pull-right.w55[
<img src="09-slides_files/figure-html/codefig-choropartycolors-1.png" width="460" style="display: block; margin: auto;" />
]

---

# On maps, continuous measures are _gradients_

.pull-left.w45[

```r
us_states_elec |> 
  ggplot(mapping = aes(x = long, 
                       y = lat,
*                      fill = pct_trump,
                       group = group)) + 
  geom_polygon(color = "gray90", 
               size = 0.1) +
  coord_map(projection = "albers", 
            lat0 = 39, lat1 = 45) +
  labs(title = "Trump vote", 
       fill = "Percent") +  
  theme_map()
```
]

.pull-right.w55[
<img src="09-slides_files/figure-html/codefig-gradient1-1.png" width="460" style="display: block; margin: auto;" />
]

---

# Fix the gradient scale with its .kjh-orange[scale function]

.pull-left.w45[

```r
us_states_elec |> 
  ggplot(mapping = aes(x = long, 
                       y = lat,
                       fill = pct_trump,
                       group = group)) + 
  geom_polygon(color = "gray90", 
               size = 0.1) +
* scale_fill_gradient(low = "white",
*                     high = "#CB454A") +
        labs(title = "Trump vote") +
  coord_map(projection = "albers", 
            lat0 = 39, lat1 = 45) +
  labs(title = "Trump vote", 
       fill = "Percent") +  
  theme_map()
```
]

.pull-right.w55[
<img src="09-slides_files/figure-html/codefig-gradient2-1.png" width="460" style="display: block; margin: auto;" />

]

---

# Some gradients are .kjh-yellow[_diverging_]

.pull-left.w45[

```r
us_states_elec |> 
  ggplot(mapping = aes(x = long, 
                       y = lat,
*                      fill = d_points,
                       group = group)) + 
  geom_polygon(color = "gray90", 
               size = 0.1) +
* scale_fill_gradient2() +
  coord_map(projection = "albers", 
            lat0 = 39, lat1 = 45) +
  labs(title = "Winning Margins", 
       fill = "Percent") +  
  theme_map()
```
]

.pull-right.w55[
<img src="09-slides_files/figure-html/codefig-diverging1-1.png" width="460" style="display: block; margin: auto;" />

]

---

# Purple America Map

.pull-left.w45[

```r
us_states_elec |> 
  ggplot(mapping = aes(x = long, 
                       y = lat,
*                      fill = d_points,
                       group = group)) + 
  geom_polygon(color = "gray90", 
               size = 0.1) +
* scale_fill_gradient2(low = "red",
*               mid = scales::muted("purple"),
*               high = "blue",
*               breaks = c(-25, 0, 25,
*                       50, 75)) +
  coord_map(projection = "albers", 
            lat0 = 39, lat1 = 45) +
  labs(title = "Winning Margins", 
       fill = "Percent") +  
  theme_map()
```
]

.pull-right.w55[
<img src="09-slides_files/figure-html/codefig-purpleamerica-1.png" width="460" style="display: block; margin: auto;" />
]

---
layout: false

.right.w90.small[Take a closer look at this, though.]

---
layout: false

.right.w90.small[Washington, DC]

---

---

# Purple America Map, without DC

.pull-left.w45[

```r
us_states_elec |> 
* filter(region %nin% "district of columbia") |>
  ggplot(mapping = aes(x = long, 
                       y = lat,
                       fill = d_points,
                       group = group)) + 
  geom_polygon(color = "gray90", 
               size = 0.1) +
  scale_fill_gradient2(low = "red",
                mid = scales::muted("purple"),
                high = "blue") +
  coord_map(projection = "albers", 
            lat0 = 39, lat1 = 45) +
  labs(title = "Winning Margins", 
       fill = "Percent") +  
  theme_map()
```
]

.pull-right.w55[
<img src="09-slides_files/figure-html/codefig-purpleamerica2-1.png" width="460" style="display: block; margin: auto;" />
]

---
layout: false

.right.w90.small[More balanced.]

---

## .huge.right.bottom.squish4[.kjh-yellow[America's]<br />.kjh-lblue[Ur-Choropleths]]

---

---

# County-level choropleths

- Conceptually identical to state ones. The tables are just bigger, because there are way more lines to draw.

```r
county_map <- as_tibble(county_map)
county_map
```

```
## # A tibble: 191,382 × 7
##        long       lat order hole  piece group            id   
##       <dbl>     <dbl> <int> <lgl> <fct> <fct>            <chr>
##  1 1225889. -1275020.     1 FALSE 1     0500000US01001.1 01001
##  2 1235324. -1274008.     2 FALSE 1     0500000US01001.1 01001
##  3 1244873. -1272331.     3 FALSE 1     0500000US01001.1 01001
##  4 1244129. -1267515.     4 FALSE 1     0500000US01001.1 01001
##  5 1272010. -1262889.     5 FALSE 1     0500000US01001.1 01001
##  6 1276797. -1295514.     6 FALSE 1     0500000US01001.1 01001
##  7 1273832. -1297124.     7 FALSE 1     0500000US01001.1 01001
##  8 1272727. -1296631.     8 FALSE 1     0500000US01001.1 01001
##  9 1272513. -1299771.     9 FALSE 1     0500000US01001.1 01001
## 10 1269950. -1302038.    10 FALSE 1     0500000US01001.1 01001
## # … with 191,372 more rows
```

- 191,000 or so rows
- `id` here is the county FIPS code.

---

# County-level choropleths

```r
county_data <- as_tibble(county_data)
county_data
```

```
## # A tibble: 3,195 × 32
##    id    name  state censu…¹ pop_d…² pop_d…³ pop_d…⁴ pct_b…⁵    pop female white
##    <chr> <chr> <fct> <fct>   <fct>   <fct>   <fct>   <fct>    <int>  <dbl> <dbl>
##  1 0     <NA>  <NA>  <NA>    [   50… [ 45, … [ 82, … [10.0,… 3.19e8   50.8  77.7
##  2 01000 1     AL    South   [   50… [ 45, … [ 82, … [25.0,… 4.85e6   51.5  69.8
##  3 01001 Auta… AL    South   [   50… [ 45, … [ 82, … [15.0,… 5.54e4   51.5  78.1
##  4 01003 Bald… AL    South   [  100… [118,7… [ 82, … [ 5.0,… 2.00e5   51.2  87.3
##  5 01005 Barb… AL    South   [   10… [ 17, … [ 25, … [25.0,… 2.69e4   46.5  50.2
##  6 01007 Bibb… AL    South   [   10… [ 17, … [ 25, … [15.0,… 2.25e4   46    76.3
##  7 01009 Blou… AL    South   [   50… [ 45, … [ 82, … [ 0.0,… 5.77e4   50.6  96  
##  8 01011 Bull… AL    South   [   10… [ 17, … [  9, … [50.0,… 1.08e4   45.2  27.2
##  9 01013 Butl… AL    South   [   10… [ 17, … [ 25, … [25.0,… 2.03e4   53.4  54.3
## 10 01015 Calh… AL    South   [  100… [118,7… [ 82, … [15.0,… 1.16e5   51.7  76  
## # … with 3,185 more rows, 21 more variables: black <dbl>, travel_time <dbl>,
## #   land_area <dbl>, hh_income <int>, su_gun4 <fct>, su_gun6 <fct>, fips <dbl>,
## #   votes_dem_2016 <int>, votes_gop_2016 <int>, total_votes_2016 <int>,
## #   per_dem_2016 <dbl>, per_gop_2016 <dbl>, diff_2016 <int>,
## #   per_dem_2012 <dbl>, per_gop_2012 <dbl>, diff_2012 <int>, winner <chr>,
## #   partywinner16 <chr>, winner12 <chr>, partywinner12 <chr>, flipped <chr>,
## #   and abbreviated variable names ¹census_region, ²pop_dens, ³pop_dens4, …
```

---

# County-level choropleths

- 3,195 entities, including states (FIPS `id` ends in four zeros) 
- And the US as a whole (FIPS `id` of `0`)

- Sample a few rows, with specific columns:

```r
county_data  |> 
    select(id, name, state, pop_dens, pct_black) |>
    sample_n(10)
```

```
## # A tibble: 10 × 5
##    id    name              state pop_dens      pct_black  
##    <chr> <chr>             <fct> <fct>         <fct>      
##  1 21071 Floyd County      KY    [   50,  100) [ 0.0, 2.0)
##  2 12105 Polk County       FL    [  100,  500) [15.0,25.0)
##  3 17013 Calhoun County    IL    [   10,   50) [ 0.0, 2.0)
##  4 24000 21                MD    [  500, 1000) [25.0,50.0)
##  5 36101 Steuben County    NY    [   50,  100) [ 0.0, 2.0)
##  6 35017 Grant County      NM    [    0,   10) [ 0.0, 2.0)
##  7 48437 Swisher County    TX    [    0,   10) [ 5.0,10.0)
##  8 51660 Harrisonburg city VA    [ 1000, 5000) [ 5.0,10.0)
##  9 55019 Clark County      WI    [   10,   50) [ 0.0, 2.0)
## 10 24001 Allegany County   MD    [  100,  500) [ 5.0,10.0)
```

---

---

# Joined table

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

county_full
```

```
## # A tibble: 191,382 × 38
##        long       lat order hole  piece group  id    name  state censu…¹ pop_d…²
##       <dbl>     <dbl> <int> <lgl> <fct> <fct>  <chr> <chr> <fct> <fct>   <fct>  
##  1 1225889. -1275020.     1 FALSE 1     05000… 01001 Auta… AL    South   [   50…
##  2 1235324. -1274008.     2 FALSE 1     05000… 01001 Auta… AL    South   [   50…
##  3 1244873. -1272331.     3 FALSE 1     05000… 01001 Auta… AL    South   [   50…
##  4 1244129. -1267515.     4 FALSE 1     05000… 01001 Auta… AL    South   [   50…
##  5 1272010. -1262889.     5 FALSE 1     05000… 01001 Auta… AL    South   [   50…
##  6 1276797. -1295514.     6 FALSE 1     05000… 01001 Auta… AL    South   [   50…
##  7 1273832. -1297124.     7 FALSE 1     05000… 01001 Auta… AL    South   [   50…
##  8 1272727. -1296631.     8 FALSE 1     05000… 01001 Auta… AL    South   [   50…
##  9 1272513. -1299771.     9 FALSE 1     05000… 01001 Auta… AL    South   [   50…
## 10 1269950. -1302038.    10 FALSE 1     05000… 01001 Auta… AL    South   [   50…
## # … with 191,372 more rows, 27 more variables: pop_dens4 <fct>,
## #   pop_dens6 <fct>, pct_black <fct>, pop <int>, female <dbl>, white <dbl>,
## #   black <dbl>, travel_time <dbl>, land_area <dbl>, hh_income <int>,
## #   su_gun4 <fct>, su_gun6 <fct>, fips <dbl>, votes_dem_2016 <int>,
## #   votes_gop_2016 <int>, total_votes_2016 <int>, per_dem_2016 <dbl>,
## #   per_gop_2016 <dbl>, diff_2016 <int>, per_dem_2012 <dbl>,
## #   per_gop_2012 <dbl>, diff_2012 <int>, winner <chr>, partywinner16 <chr>, …
```

---
count: false
 
# County Population Density
.panel1-reveal-countypop-auto[

```r
*county_full <- as_tibble(left_join(county_map, county_data, by = "id"))
```
]
 
.panel2-reveal-countypop-auto[

]

---
count: false
 
# County Population Density
.panel1-reveal-countypop-auto[

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

*county_full
```
]
 
.panel2-reveal-countypop-auto[

---
count: false
 
# County Population Density
.panel1-reveal-countypop-auto[

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

county_full |>
* ggplot(mapping = aes(x = long, y = lat,
*                         fill = pop_dens,
*                         group = group))
```
]
 
.panel2-reveal-countypop-auto[
<img src="09-slides_files/figure-html/reveal-countypop_auto_03_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# County Population Density
.panel1-reveal-countypop-auto[

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

county_full |>
  ggplot(mapping = aes(x = long, y = lat,
                          fill = pop_dens,
                          group = group)) +
* geom_polygon(color = "gray70",
*              size = 0.1)
```
]
 
.panel2-reveal-countypop-auto[
<img src="09-slides_files/figure-html/reveal-countypop_auto_04_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# County Population Density
.panel1-reveal-countypop-auto[

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

county_full |>
  ggplot(mapping = aes(x = long, y = lat,
                          fill = pop_dens,
                          group = group)) +
  geom_polygon(color = "gray70",
               size = 0.1) +
* coord_fixed()
```
]
 
.panel2-reveal-countypop-auto[
<img src="09-slides_files/figure-html/reveal-countypop_auto_05_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# County Population Density
.panel1-reveal-countypop-auto[

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

county_full |>
  ggplot(mapping = aes(x = long, y = lat,
                          fill = pop_dens,
                          group = group)) +
  geom_polygon(color = "gray70",
               size = 0.1) +
  coord_fixed() +
* scale_fill_brewer(palette="Blues",
*                   labels = c("0-10", "10-50", "50-100",
*                    "100-500", "500-1,000",
*                     "1,000-5,000", ">5,000"))
```
]
 
.panel2-reveal-countypop-auto[
<img src="09-slides_files/figure-html/reveal-countypop_auto_06_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# County Population Density
.panel1-reveal-countypop-auto[

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

county_full |>
  ggplot(mapping = aes(x = long, y = lat,
                          fill = pop_dens,
                          group = group)) +
  geom_polygon(color = "gray70",
               size = 0.1) +
  coord_fixed() +
  scale_fill_brewer(palette="Blues",
                    labels = c("0-10", "10-50", "50-100",
                     "100-500", "500-1,000",
                      "1,000-5,000", ">5,000")) +
* labs(fill = "Population per\nsquare mile")
```
]
 
.panel2-reveal-countypop-auto[
<img src="09-slides_files/figure-html/reveal-countypop_auto_07_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# County Population Density
.panel1-reveal-countypop-auto[

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

county_full |>
  ggplot(mapping = aes(x = long, y = lat,
                          fill = pop_dens,
                          group = group)) +
  geom_polygon(color = "gray70",
               size = 0.1) +
  coord_fixed() +
  scale_fill_brewer(palette="Blues",
                    labels = c("0-10", "10-50", "50-100",
                     "100-500", "500-1,000",
                      "1,000-5,000", ">5,000")) +
  labs(fill = "Population per\nsquare mile") +
* kjhslides::kjh_theme_map()
```
]
 
.panel2-reveal-countypop-auto[
<img src="09-slides_files/figure-html/reveal-countypop_auto_08_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# County Population Density
.panel1-reveal-countypop-auto[

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

county_full |>
  ggplot(mapping = aes(x = long, y = lat,
                          fill = pop_dens,
                          group = group)) +
  geom_polygon(color = "gray70",
               size = 0.1) +
  coord_fixed() +
  scale_fill_brewer(palette="Blues",
                    labels = c("0-10", "10-50", "50-100",
                     "100-500", "500-1,000",
                      "1,000-5,000", ">5,000")) +
  labs(fill = "Population per\nsquare mile") +
  kjhslides::kjh_theme_map() +
* guides(fill = guide_legend(nrow = 1))
```
]
 
.panel2-reveal-countypop-auto[
<img src="09-slides_files/figure-html/reveal-countypop_auto_09_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# County Population Density
.panel1-reveal-countypop-auto[

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

county_full |>
  ggplot(mapping = aes(x = long, y = lat,
                          fill = pop_dens,
                          group = group)) +
  geom_polygon(color = "gray70",
               size = 0.1) +
  coord_fixed() +
  scale_fill_brewer(palette="Blues",
                    labels = c("0-10", "10-50", "50-100",
                     "100-500", "500-1,000",
                      "1,000-5,000", ">5,000")) +
  labs(fill = "Population per\nsquare mile") +
  kjhslides::kjh_theme_map() +
  guides(fill = guide_legend(nrow = 1)) +
* theme(legend.position = "bottom")
```
]
 
.panel2-reveal-countypop-auto[
<img src="09-slides_files/figure-html/reveal-countypop_auto_10_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
layout: false

.right.w90.small[Population Density by County, binned]

---

---
count: false
 
# Same again for Percent Black
.panel1-reveal-choroblack-auto[

```r
*county_full <- as_tibble(left_join(county_map, county_data, by = "id"))
```
]
 
.panel2-reveal-choroblack-auto[

]

---
count: false
 
# Same again for Percent Black
.panel1-reveal-choroblack-auto[

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

*county_full
```
]
 
.panel2-reveal-choroblack-auto[

---
count: false
 
# Same again for Percent Black
.panel1-reveal-choroblack-auto[

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

county_full |>
* ggplot(mapping = aes(x = long, y = lat,
*                      fill = pct_black,
*                      group = group))
```
]
 
.panel2-reveal-choroblack-auto[
<img src="09-slides_files/figure-html/reveal-choroblack_auto_03_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# Same again for Percent Black
.panel1-reveal-choroblack-auto[

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

county_full |>
  ggplot(mapping = aes(x = long, y = lat,
                       fill = pct_black,
                       group = group)) +
* geom_polygon(color = "gray70",
*              size = 0.1)
```
]
 
.panel2-reveal-choroblack-auto[
<img src="09-slides_files/figure-html/reveal-choroblack_auto_04_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# Same again for Percent Black
.panel1-reveal-choroblack-auto[

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

county_full |>
  ggplot(mapping = aes(x = long, y = lat,
                       fill = pct_black,
                       group = group)) +
  geom_polygon(color = "gray70",
               size = 0.1) +
* coord_fixed()
```
]
 
.panel2-reveal-choroblack-auto[
<img src="09-slides_files/figure-html/reveal-choroblack_auto_05_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# Same again for Percent Black
.panel1-reveal-choroblack-auto[

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

county_full |>
  ggplot(mapping = aes(x = long, y = lat,
                       fill = pct_black,
                       group = group)) +
  geom_polygon(color = "gray70",
               size = 0.1) +
  coord_fixed() +
* scale_fill_brewer(palette="Greens",
*                   labels = c("0-2%", "2-5%", "5-10%",
*                              "10-15%", "15-25%",
*                              "25-50%", ">50%"))
```
]
 
.panel2-reveal-choroblack-auto[
<img src="09-slides_files/figure-html/reveal-choroblack_auto_06_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# Same again for Percent Black
.panel1-reveal-choroblack-auto[

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

county_full |>
  ggplot(mapping = aes(x = long, y = lat,
                       fill = pct_black,
                       group = group)) +
  geom_polygon(color = "gray70",
               size = 0.1) +
  coord_fixed() +
  scale_fill_brewer(palette="Greens",
                    labels = c("0-2%", "2-5%", "5-10%",
                               "10-15%", "15-25%",
                               "25-50%", ">50%")) +
* labs(fill = "US Population, percent Black")
```
]
 
.panel2-reveal-choroblack-auto[
<img src="09-slides_files/figure-html/reveal-choroblack_auto_07_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# Same again for Percent Black
.panel1-reveal-choroblack-auto[

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

county_full |>
  ggplot(mapping = aes(x = long, y = lat,
                       fill = pct_black,
                       group = group)) +
  geom_polygon(color = "gray70",
               size = 0.1) +
  coord_fixed() +
  scale_fill_brewer(palette="Greens",
                    labels = c("0-2%", "2-5%", "5-10%",
                               "10-15%", "15-25%",
                               "25-50%", ">50%")) +
  labs(fill = "US Population, percent Black") +
* kjhslides::kjh_theme_map()
```
]
 
.panel2-reveal-choroblack-auto[
<img src="09-slides_files/figure-html/reveal-choroblack_auto_08_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# Same again for Percent Black
.panel1-reveal-choroblack-auto[

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

county_full |>
  ggplot(mapping = aes(x = long, y = lat,
                       fill = pct_black,
                       group = group)) +
  geom_polygon(color = "gray70",
               size = 0.1) +
  coord_fixed() +
  scale_fill_brewer(palette="Greens",
                    labels = c("0-2%", "2-5%", "5-10%",
                               "10-15%", "15-25%",
                               "25-50%", ">50%")) +
  labs(fill = "US Population, percent Black") +
  kjhslides::kjh_theme_map() +
* guides(fill = guide_legend(nrow = 1))
```
]
 
.panel2-reveal-choroblack-auto[
<img src="09-slides_files/figure-html/reveal-choroblack_auto_09_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# Same again for Percent Black
.panel1-reveal-choroblack-auto[

```r
county_full <- as_tibble(left_join(county_map, county_data, by = "id"))

county_full |>
  ggplot(mapping = aes(x = long, y = lat,
                       fill = pct_black,
                       group = group)) +
  geom_polygon(color = "gray70",
               size = 0.1) +
  coord_fixed() +
  scale_fill_brewer(palette="Greens",
                    labels = c("0-2%", "2-5%", "5-10%",
                               "10-15%", "15-25%",
                               "25-50%", ">50%")) +
  labs(fill = "US Population, percent Black") +
  kjhslides::kjh_theme_map() +
  guides(fill = guide_legend(nrow = 1)) +
* theme(legend.position = "bottom")
```
]
 
.panel2-reveal-choroblack-auto[
<img src="09-slides_files/figure-html/reveal-choroblack_auto_10_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
layout: false

.right.w90.small[Percent Black, by County, binned]

---

## .huge.right.bottom.squish4[.kjh-yellow[Big counties, small populations,] .kjh-lblue[rare events]]

---

---

# Example: Reverse coding

.pull-left.w45[

```r
orange_pal <- RColorBrewer::brewer.pal(n = 6, 
                                       name = "Oranges")
orange_pal
```

```
## [1] "#FEEDDE" "#FDD0A2" "#FDAE6B" "#FD8D3C" "#E6550D" "#A63603"
```

<img src="09-slides_files/figure-html/07-make-maps-35-1.png" width="360" style="display: block; margin: auto;" />
]

.pull-right.w45[

```r
# Just reverse it
orange_rev <- rev(orange_pal)
orange_rev
```

```
## [1] "#A63603" "#E6550D" "#FD8D3C" "#FDAE6B" "#FDD0A2" "#FEEDDE"
```

<img src="09-slides_files/figure-html/07-make-maps-37-1.png" width="360" style="display: block; margin: auto;" />
]

---

# Build a plot

```r
p_g1 <- county_full |> 
  ggplot(mapping = aes(x = long, y = lat,
*                      fill = su_gun6,
                       group = group)) + 
  geom_polygon(color = "gray70", 
               size = 0.1) + 
  coord_fixed() + 
* scale_fill_manual(values = orange_pal) +
  labs(title = "Gun-Related Suicides, 1999-2015",
       fill = "Rate per 100,000 pop.") + 
  theme_map() + 
  guides(fill = guide_legend(nrow = 1)) + 
  theme(legend.position = "bottom")
```

---
layout: false
class: middle

.right.w90.small[Regular palette]

---

---

# And another

```r
p_g2 <- county_full |> 
  ggplot(mapping = aes(x = long, y = lat,
*                      fill = pop_dens6,
                       group = group)) + 
  geom_polygon(color = "gray70", 
               size = 0.1) + 
  coord_fixed() + 
* scale_fill_manual(values = orange_rev) +
  labs(title = "Reverse-coded Population Density",
       fill = "Persons per square mile") + 
  theme_map() + 
  guides(fill = guide_legend(nrow = 1)) + 
  theme(legend.position = "bottom")
```

---
layout: false
class: middle

.right.w90.small[Reverse-coded density]

---

.pull-left[
<img src="09-slides_files/figure-html/07-make-maps-42-1.png" width="576" style="display: block; margin: auto;" />

]

.pull-right[
<img src="09-slides_files/figure-html/07-make-maps-43-1.png" width="576" style="display: block; margin: auto;" />

]

.right.w90.small[Comparing the plots]

---

## .huge.right.bottom.squish4[.kjh-yellow[Small multiples] .kjh-lblue[for maps]]

---

---

# Opiate-related Mortality, 1999-2014

```r
opiates
```

```
## # A tibble: 800 × 11
##     year state    fips deaths popul…¹ crude adjus…² adjus…³ region abbr  divis…⁴
##    <int> <chr>   <int>  <int>   <int> <dbl>   <dbl>   <dbl> <ord>  <chr> <chr>  
##  1  1999 Alabama     1     37  4.43e6   0.8     0.8     0.1 South  AL    East S…
##  2  1999 Alaska      2     27  6.25e5   4.3     4       0.8 West   AK    Pacific
##  3  1999 Arizona     4    229  5.02e6   4.6     4.7     0.3 West   AZ    Mounta…
##  4  1999 Arkans…     5     28  2.65e6   1.1     1.1     0.2 South  AR    West S…
##  5  1999 Califo…     6   1474  3.35e7   4.4     4.5     0.1 West   CA    Pacific
##  6  1999 Colora…     8    164  4.23e6   3.9     3.7     0.3 West   CO    Mounta…
##  7  1999 Connec…     9    151  3.39e6   4.5     4.4     0.4 North… CT    New En…
##  8  1999 Delawa…    10     32  7.75e5   4.1     4.1     0.7 South  DE    South …
##  9  1999 Distri…    11     28  5.70e5   4.9     4.9     0.9 South  DC    <NA>   
## 10  1999 Florida    12    402  1.58e7   2.6     2.6     0.1 South  FL    South …
## # … with 790 more rows, and abbreviated variable names ¹population, ²adjusted,
## #   ³adjusted_se, ⁴division_name
```

```r
opiates$state <- tolower(opiates$state)
us_states$state <- us_states$region
opiates_map <- left_join(us_states, opiates, by = "state")
```

---

```r
p_out <- opiates_map |> 
  ggplot(mapping = aes(x = long, y = lat,
                       group = group,
                       fill = cut_interval(adjusted, n = 5))) + 
  geom_polygon(color = "lightblue", size = 0.2) +
  coord_map(projection = "albers", lat0 = 39, lat1 = 45) +
  scale_fill_brewer(type = "seq", palette = "Oranges") +
  kjhslides::kjh_theme_map() + 
  facet_wrap(~ year, ncol = 4) +
  guides(fill = guide_legend(nrow = 1)) + 
  theme(legend.position = "bottom",
        strip.background = element_blank()) +
  labs(fill = "Death rate per 100,000 population",
       title = "Opiate-Related Deaths by State, 1999-2014")
```

---
layout: false
class: middle

.right.w90.small[Faceting works just as it would for any other kind of plot.]

---

## .huge.right.bottom.squish4[.kjh-yellow[Is your data<br/ >] .kjh-lblue[really spatial?]]

---

---
count: false
 
# The two leading states in each region in 2014
.panel1-reveal-maxstates-auto[

```r
## Put this in an object called `st_top`
*opiates
```
]
 
.panel2-reveal-maxstates-auto[

---
count: false
 
# The two leading states in each region in 2014
.panel1-reveal-maxstates-auto[

```r
## Put this in an object called `st_top`
opiates |>
* filter(year == max(year),
*        abbr != "DC")
```
]
 
.panel2-reveal-maxstates-auto[

```
## # A tibble: 50 × 11
##     year state    fips deaths popul…¹ crude adjus…² adjus…³ region abbr  divis…⁴
##    <int> <chr>   <int>  <int>   <int> <dbl>   <dbl>   <dbl> <ord>  <chr> <chr>  
##  1  2014 Alabama     1    270  4.85e6   5.6     5.6     0.3 South  AL    East S…
##  2  2014 Alaska      2     76  7.37e5  10.3    10.6     1.2 West   AK    Pacific
##  3  2014 Arizona     4    589  6.73e6   8.7     8.8     0.4 West   AZ    Mounta…
##  4  2014 Arkans…     5    173  2.97e6   5.8     6.3     0.5 South  AR    West S…
##  5  2014 Califo…     6   2024  3.88e7   5.2     5       0.1 West   CA    Pacific
##  6  2014 Colora…     8    517  5.36e6   9.7     9.4     0.4 West   CO    Mounta…
##  7  2014 Connec…     9    525  3.60e6  14.6    15.2     0.7 North… CT    New En…
##  8  2014 Delawa…    10    124  9.36e5  13.3    13.9     1.3 South  DE    South …
##  9  2014 Florida    12   1399  1.99e7   7       7.2     0.2 South  FL    South …
## 10  2014 Georgia    13    710  1.01e7   7       7       0.3 South  GA    South …
## # … with 40 more rows, and abbreviated variable names ¹population, ²adjusted,
## #   ³adjusted_se, ⁴division_name
```
]

---
count: false
 
# The two leading states in each region in 2014
.panel1-reveal-maxstates-auto[

```r
## Put this in an object called `st_top`
opiates |>
  filter(year == max(year),
         abbr != "DC") |>
* group_by(region)
```
]
 
.panel2-reveal-maxstates-auto[

```
## # A tibble: 50 × 11
## # Groups:   region [4]
##     year state    fips deaths popul…¹ crude adjus…² adjus…³ region abbr  divis…⁴
##    <int> <chr>   <int>  <int>   <int> <dbl>   <dbl>   <dbl> <ord>  <chr> <chr>  
##  1  2014 Alabama     1    270  4.85e6   5.6     5.6     0.3 South  AL    East S…
##  2  2014 Alaska      2     76  7.37e5  10.3    10.6     1.2 West   AK    Pacific
##  3  2014 Arizona     4    589  6.73e6   8.7     8.8     0.4 West   AZ    Mounta…
##  4  2014 Arkans…     5    173  2.97e6   5.8     6.3     0.5 South  AR    West S…
##  5  2014 Califo…     6   2024  3.88e7   5.2     5       0.1 West   CA    Pacific
##  6  2014 Colora…     8    517  5.36e6   9.7     9.4     0.4 West   CO    Mounta…
##  7  2014 Connec…     9    525  3.60e6  14.6    15.2     0.7 North… CT    New En…
##  8  2014 Delawa…    10    124  9.36e5  13.3    13.9     1.3 South  DE    South …
##  9  2014 Florida    12   1399  1.99e7   7       7.2     0.2 South  FL    South …
## 10  2014 Georgia    13    710  1.01e7   7       7       0.3 South  GA    South …
## # … with 40 more rows, and abbreviated variable names ¹population, ²adjusted,
## #   ³adjusted_se, ⁴division_name
```
]

---
count: false
 
# The two leading states in each region in 2014
.panel1-reveal-maxstates-auto[

```r
## Put this in an object called `st_top`
opiates |>
  filter(year == max(year),
         abbr != "DC") |>
  group_by(region) |>
* slice_max(order_by = adjusted,
*           n = 2)
```
]
 
.panel2-reveal-maxstates-auto[

```
## # A tibble: 8 × 11
## # Groups:   region [4]
##    year state     fips deaths popul…¹ crude adjus…² adjus…³ region abbr  divis…⁴
##   <int> <chr>    <int>  <int>   <int> <dbl>   <dbl>   <dbl> <ord>  <chr> <chr>  
## 1  2014 New Ham…    33    297  1.33e6  22.4    23.4     1.4 North… NH    New En…
## 2  2014 Rhode I…    44    205  1.06e6  19.4    19.8     1.4 North… RI    New En…
## 3  2014 Ohio        39   2106  1.16e7  18.2    19.1     0.4 Midwe… OH    East N…
## 4  2014 Missouri    29    696  6.06e6  11.5    12       0.5 Midwe… MO    West N…
## 5  2014 New Mex…    35    402  2.09e6  19.3    20.2     1   West   NM    Mounta…
## 6  2014 Utah        49    455  2.94e6  15.5    16.8     0.8 West   UT    Mounta…
## 7  2014 West Vi…    54    554  1.85e6  29.9    31.6     1.4 South  WV    South …
## 8  2014 Kentucky    21    729  4.41e6  16.5    16.8     0.6 South  KY    East S…
## # … with abbreviated variable names ¹population, ²adjusted, ³adjusted_se,
## #   ⁴division_name
```
]

---
count: false
 
# The two leading states in each region in 2014
.panel1-reveal-maxstates-auto[

```r
## Put this in an object called `st_top`
opiates |>
  filter(year == max(year),
         abbr != "DC") |>
  group_by(region) |>
  slice_max(order_by = adjusted,
            n = 2)
```
]
 
.panel2-reveal-maxstates-auto[

---
count: false
 
# Opiates Time Series plot
.panel1-reveal-opiateline-auto[

```r
*st_top <- opiates |>  filter(year == max(year), abbr != "DC")
```
]
 
.panel2-reveal-opiateline-auto[

]

---
count: false
 
# Opiates Time Series plot
.panel1-reveal-opiateline-auto[

```r
st_top <- opiates |>  filter(year == max(year), abbr != "DC") |>
* group_by(region)
```
]
 
.panel2-reveal-opiateline-auto[

]

---
count: false
 
# Opiates Time Series plot
.panel1-reveal-opiateline-auto[

```r
st_top <- opiates |>  filter(year == max(year), abbr != "DC") |>
  group_by(region) |>
* slice_max(order_by = adjusted, n = 2)
```
]
 
.panel2-reveal-opiateline-auto[

]

---
count: false
 
# Opiates Time Series plot
.panel1-reveal-opiateline-auto[

```r
st_top <- opiates |>  filter(year == max(year), abbr != "DC") |>
  group_by(region) |>
  slice_max(order_by = adjusted, n = 2)

*opiates
```
]
 
.panel2-reveal-opiateline-auto[

---
count: false
 
# Opiates Time Series plot
.panel1-reveal-opiateline-auto[

```r
st_top <- opiates |>  filter(year == max(year), abbr != "DC") |>
  group_by(region) |>
  slice_max(order_by = adjusted, n = 2)

opiates |>
* ggplot(aes(x = year,
*            y = adjusted))
```
]
 
.panel2-reveal-opiateline-auto[
<img src="09-slides_files/figure-html/reveal-opiateline_auto_05_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# Opiates Time Series plot
.panel1-reveal-opiateline-auto[

```r
st_top <- opiates |>  filter(year == max(year), abbr != "DC") |>
  group_by(region) |>
  slice_max(order_by = adjusted, n = 2)

opiates |>
  ggplot(aes(x = year,
             y = adjusted)) +
* geom_line(aes(group = state),
*           color = "gray50")
```
]
 
.panel2-reveal-opiateline-auto[
<img src="09-slides_files/figure-html/reveal-opiateline_auto_06_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# Opiates Time Series plot
.panel1-reveal-opiateline-auto[

```r
st_top <- opiates |>  filter(year == max(year), abbr != "DC") |>
  group_by(region) |>
  slice_max(order_by = adjusted, n = 2)

opiates |>
  ggplot(aes(x = year,
             y = adjusted)) +
  geom_line(aes(group = state),
            color = "gray50") +
* geom_smooth(aes(group = region),
*             se = FALSE)
```
]
 
.panel2-reveal-opiateline-auto[
<img src="09-slides_files/figure-html/reveal-opiateline_auto_07_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# Opiates Time Series plot
.panel1-reveal-opiateline-auto[

```r
st_top <- opiates |>  filter(year == max(year), abbr != "DC") |>
  group_by(region) |>
  slice_max(order_by = adjusted, n = 2)

opiates |>
  ggplot(aes(x = year,
             y = adjusted)) +
  geom_line(aes(group = state),
            color = "gray50") +
  geom_smooth(aes(group = region),
              se = FALSE) +
* ggrepel::geom_text_repel(
*   data = st_top,
*   mapping = aes(x = year,
*                 y = adjusted,
*                 label = abbr),
*   size = 3,
*   segment.color = NA,
*   nudge_x = 0.5)
```
]
 
.panel2-reveal-opiateline-auto[
<img src="09-slides_files/figure-html/reveal-opiateline_auto_08_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# Opiates Time Series plot
.panel1-reveal-opiateline-auto[

```r
st_top <- opiates |>  filter(year == max(year), abbr != "DC") |>
  group_by(region) |>
  slice_max(order_by = adjusted, n = 2)

opiates |>
  ggplot(aes(x = year,
             y = adjusted)) +
  geom_line(aes(group = state),
            color = "gray50") +
  geom_smooth(aes(group = region),
              se = FALSE) +
  ggrepel::geom_text_repel(
    data = st_top,
    mapping = aes(x = year,
                  y = adjusted,
                  label = abbr),
    size = 3,
    segment.color = NA,
    nudge_x = 0.5) +
* coord_cartesian(c(min(opiates$year),
*                   max(opiates$year) + 1))
```
]
 
.panel2-reveal-opiateline-auto[
<img src="09-slides_files/figure-html/reveal-opiateline_auto_09_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# Opiates Time Series plot
.panel1-reveal-opiateline-auto[

```r
st_top <- opiates |>  filter(year == max(year), abbr != "DC") |>
  group_by(region) |>
  slice_max(order_by = adjusted, n = 2)

opiates |>
  ggplot(aes(x = year,
             y = adjusted)) +
  geom_line(aes(group = state),
            color = "gray50") +
  geom_smooth(aes(group = region),
              se = FALSE) +
  ggrepel::geom_text_repel(
    data = st_top,
    mapping = aes(x = year,
                  y = adjusted,
                  label = abbr),
    size = 3,
    segment.color = NA,
    nudge_x = 0.5) +
  coord_cartesian(c(min(opiates$year),
                    max(opiates$year) + 1)) +
* labs(x = NULL,
*      y = "Rate per 100,000 population",
*      title = "State-Level Opiate Death
*         Rates by Region, 1999-2014")
```
]
 
.panel2-reveal-opiateline-auto[
<img src="09-slides_files/figure-html/reveal-opiateline_auto_10_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# Opiates Time Series plot
.panel1-reveal-opiateline-auto[

```r
st_top <- opiates |>  filter(year == max(year), abbr != "DC") |>
  group_by(region) |>
  slice_max(order_by = adjusted, n = 2)

opiates |>
  ggplot(aes(x = year,
             y = adjusted)) +
  geom_line(aes(group = state),
            color = "gray50") +
  geom_smooth(aes(group = region),
              se = FALSE) +
  ggrepel::geom_text_repel(
    data = st_top,
    mapping = aes(x = year,
                  y = adjusted,
                  label = abbr),
    size = 3,
    segment.color = NA,
    nudge_x = 0.5) +
  coord_cartesian(c(min(opiates$year),
                    max(opiates$year) + 1)) +
  labs(x = NULL,
       y = "Rate per 100,000 population",
       title = "State-Level Opiate Death
          Rates by Region, 1999-2014") +
* facet_wrap(~ reorder(region, adjusted,
*                      na.rm = TRUE),
*            nrow  = 1)
```
]
 
.panel2-reveal-opiateline-auto[
<img src="09-slides_files/figure-html/reveal-opiateline_auto_11_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# Opiates Time Series plot
.panel1-reveal-opiateline-auto[

```r
st_top <- opiates |>  filter(year == max(year), abbr != "DC") |>
  group_by(region) |>
  slice_max(order_by = adjusted, n = 2)

opiates |>
  ggplot(aes(x = year,
             y = adjusted)) +
  geom_line(aes(group = state),
            color = "gray50") +
  geom_smooth(aes(group = region),
              se = FALSE) +
  ggrepel::geom_text_repel(
    data = st_top,
    mapping = aes(x = year,
                  y = adjusted,
                  label = abbr),
    size = 3,
    segment.color = NA,
    nudge_x = 0.5) +
  coord_cartesian(c(min(opiates$year),
                    max(opiates$year) + 1)) +
  labs(x = NULL,
       y = "Rate per 100,000 population",
       title = "State-Level Opiate Death
          Rates by Region, 1999-2014") +
  facet_wrap(~ reorder(region, adjusted,
                       na.rm = TRUE),
             nrow  = 1)
```
]
 
.panel2-reveal-opiateline-auto[
<img src="09-slides_files/figure-html/reveal-opiateline_auto_12_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
layout: false

.right.w90.small[Regional trends in opiate-related mortality.]