09b — Maps and Spatial Data

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

**Week 09 (Part 2)**

]

---

---

# 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
```

```
## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE
```

```r
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[Simple Features]]

---

---

# .kjh-green[`geom_polygon()`] is limiting

- It's very useful to have the intuition that, when drawing maps, .kjh-orange[we're just working with tables] of `x` and `y` coordinates, and .kjh-orange[shapes represent quantities in our data], in a way that's essentially the same as any other geom. This makes it worth getting comfortable with what  .kjh-green[`geom_polygon()`] and .kjh-green[`coord_map()`] are doing. But the business of having very large map tables and manually specifying projections is inefficient.

- In addition, sometimes our data _really is_ properly spatial, at which point we need a more rigorous and consistent way of specifying those elements. There's a whole world of Geodesic standards and methods devoted to specifying these things for GIS applications. R is not a dedicated GIS, but we can take advantage of these tools.

- .center.large[Enter .kjh-pink[simple features], the .kjh-lblue[`sf`] package, and .kjh-green[`geom_sf()`]]

---

# The Simple Features package

- When we load .kjh-lblue[`sf`] it creates a way to use several standard GIS concepts and tools, such as the .kjh-lblue[GEOS] library for computational geometry, the .kjh-lblue[PROJ] software that transforms spatial coordinates from one reference system to another, as in map projections, and the Simple Features standard for specifying the elements of spatial attributes.

```r
library(sf)
```

```
## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE
```

Let's see the main upshot for us as end-users.

---

# The .kjh-lblue[`nycdogs`] package

.pull-left.w60[

```r
library(nycdogs)
nyc_license
```

```
## # A tibble: 493,072 × 9
##    anima…¹ anima…² anima…³ breed…⁴ borough zip_c…⁵ license_…⁶ license_…⁷ extra…⁸
##    <chr>   <chr>     <dbl> <chr>   <chr>     <int> <date>     <date>       <dbl>
##  1 Paige   F          2014 Pit Bu… Manhat…   10035 2014-09-12 2017-09-12    2016
##  2 Yogi    M          2010 Boxer   Bronx     10465 2014-09-12 2017-10-02    2016
##  3 Ali     M          2014 Basenji Manhat…   10013 2014-09-12 2019-09-12    2016
##  4 Queen   F          2013 Akita … Manhat…   10013 2014-09-12 2017-09-12    2016
##  5 Lola    F          2009 Maltese Manhat…   10028 2014-09-12 2017-10-09    2016
##  6 Ian     M          2006 Unknown Manhat…   10013 2014-09-12 2019-10-30    2016
##  7 Buddy   M          2008 Unknown Manhat…   10025 2014-09-12 2017-10-20    2016
##  8 Chewba… F          2012 Labrad… Manhat…   10013 2014-09-12 2019-10-01    2016
##  9 Heidi-… F          2007 Dachsh… Brookl…   11215 2014-09-13 2017-04-16    2016
## 10 Massimo M          2009 Bull D… Brookl…   11201 2014-09-13 2017-09-17    2016
## # … with 493,062 more rows, and abbreviated variable names ¹animal_name,
## #   ²animal_gender, ³animal_birth_year, ⁴breed_rc, ⁵zip_code,
## #   ⁶license_issued_date, ⁷license_expired_date, ⁸extract_year
```
]

.pull-right.w40[
.center[![:scale 80%](img/09_nycdogs_hex.png)]
]

---

# The .kjh-lblue[`nycdogs`] package

- The metadata tells you this is not a regular tibble.

```r
nyc_zips
```

```
## Simple feature collection with 262 features and 11 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.25576 ymin: 40.49584 xmax: -73.6996 ymax: 40.91517
## Geodetic CRS:  WGS 84
## # A tibble: 262 × 12
##    objec…¹ zip_c…² po_name state borough st_fips cty_f…³ bld_g…⁴ shape…⁵ shape…⁶
##      <int>   <int> <chr>   <chr> <chr>   <chr>   <chr>     <int>   <dbl>   <dbl>
##  1       1   11372 Jackso… NY    Queens  36      081           0  20625.  2.02e7
##  2       2   11004 Glen O… NY    Queens  36      081           0  23003.  2.26e7
##  3       3   11040 New Hy… NY    Queens  36      081           0  15749.  6.27e6
##  4       4   11426 Beller… NY    Queens  36      081           0  35933.  4.94e7
##  5       5   11365 Fresh … NY    Queens  36      081           0  38694.  6.94e7
##  6       6   11373 Elmhur… NY    Queens  36      081           0  33756.  4.27e7
##  7       7   11001 Floral… NY    Queens  36      081           0  13595.  9.16e6
##  8       8   11375 Forest… NY    Queens  36      081           0  36277.  5.56e7
##  9       9   11427 Queens… NY    Queens  36      081           0  31232.  3.96e7
## 10      10   11374 Rego P… NY    Queens  36      081           0  26324.  2.52e7
## # … with 252 more rows, 2 more variables: x_id <chr>, geometry <POLYGON [°]>,
## #   and abbreviated variable names ¹objectid, ²zip_code, ³cty_fips,
## #   ⁴bld_gpostal_code, ⁵shape_leng, ⁶shape_area
```

---

# The .kjh-lblue[`nycdogs`] package

```r
nyc_zips |> 
  select(objectid:borough)
```

```
## Simple feature collection with 262 features and 5 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.25576 ymin: 40.49584 xmax: -73.6996 ymax: 40.91517
## Geodetic CRS:  WGS 84
## # A tibble: 262 × 6
##    objectid zip_code po_name         state borough                      geometry
##       <int>    <int> <chr>           <chr> <chr>                   <POLYGON [°]>
##  1        1    11372 Jackson Heights NY    Queens  ((-73.86942 40.74916, -73.89…
##  2        2    11004 Glen Oaks       NY    Queens  ((-73.71068 40.75004, -73.70…
##  3        3    11040 New Hyde Park   NY    Queens  ((-73.70098 40.7389, -73.703…
##  4        4    11426 Bellerose       NY    Queens  ((-73.7227 40.75373, -73.722…
##  5        5    11365 Fresh Meadows   NY    Queens  ((-73.81089 40.72717, -73.81…
##  6        6    11373 Elmhurst        NY    Queens  ((-73.88722 40.72753, -73.88…
##  7        7    11001 Floral Park     NY    Queens  ((-73.70098 40.7389, -73.699…
##  8        8    11375 Forest Hills    NY    Queens  ((-73.85625 40.73672, -73.85…
##  9        9    11427 Queens Village  NY    Queens  ((-73.74169 40.73682, -73.73…
## 10       10    11374 Rego Park       NY    Queens  ((-73.86451 40.73407, -73.85…
## # … with 252 more rows
```

- The .kjh-orange[`polygon`] column is a list of lat/lon points that, when joined, draw the outline of the zip code area. This is _much_ more compact than a big table where every row is a single point.

---

# Let's make a summary table

---
count: false
 
# Let's make a summary table
.panel1-reveal-nycfrench-auto[

```r
*nyc_license
```
]
 
.panel2-reveal-nycfrench-auto[

---
count: false
 
# Let's make a summary table
.panel1-reveal-nycfrench-auto[

```r
nyc_license  |>
* filter(extract_year == 2018)
```
]
 
.panel2-reveal-nycfrench-auto[

```
## # A tibble: 117,371 × 9
##    anima…¹ anima…² anima…³ breed…⁴ borough zip_c…⁵ license_…⁶ license_…⁷ extra…⁸
##    <chr>   <chr>     <dbl> <chr>   <chr>     <int> <date>     <date>       <dbl>
##  1 Ali     M          2014 Basenji Manhat…   10013 2014-09-12 2019-09-12    2018
##  2 Ian     M          2006 Unknown Manhat…   10013 2014-09-12 2019-10-30    2018
##  3 Chewba… F          2012 Labrad… Manhat…   10013 2014-09-12 2019-10-01    2018
##  4 Lola    F          2006 Miniat… Manhat…   10022 2014-09-13 2019-10-03    2018
##  5 Lucy    F          2014 Dachsh… Brookl…   11215 2014-09-13 2019-09-13    2018
##  6 June    F          2010 Cavali… Brookl…   11238 2014-09-13 2019-10-28    2018
##  7 Apple   M          2013 Havane… Manhat…   10025 2014-09-13 2019-10-16    2018
##  8 Muneca  F          2013 Beagle  Brookl…   11232 2014-09-13 2019-09-13    2018
##  9 Benson  M          2010 Boxer   Brookl…   11209 2014-09-13 2019-10-29    2018
## 10 Bigs    M          2004 Pit Bu… Brookl…   11208 2014-09-13 2019-10-27    2018
## # … with 117,361 more rows, and abbreviated variable names ¹animal_name,
## #   ²animal_gender, ³animal_birth_year, ⁴breed_rc, ⁵zip_code,
## #   ⁶license_issued_date, ⁷license_expired_date, ⁸extract_year
```
]

---
count: false
 
# Let's make a summary table
.panel1-reveal-nycfrench-auto[

```r
nyc_license  |>
  filter(extract_year == 2018) |>
* group_by(breed_rc, zip_code)
```
]
 
.panel2-reveal-nycfrench-auto[

```
## # A tibble: 117,371 × 9
## # Groups:   breed_rc, zip_code [18,945]
##    anima…¹ anima…² anima…³ breed…⁴ borough zip_c…⁵ license_…⁶ license_…⁷ extra…⁸
##    <chr>   <chr>     <dbl> <chr>   <chr>     <int> <date>     <date>       <dbl>
##  1 Ali     M          2014 Basenji Manhat…   10013 2014-09-12 2019-09-12    2018
##  2 Ian     M          2006 Unknown Manhat…   10013 2014-09-12 2019-10-30    2018
##  3 Chewba… F          2012 Labrad… Manhat…   10013 2014-09-12 2019-10-01    2018
##  4 Lola    F          2006 Miniat… Manhat…   10022 2014-09-13 2019-10-03    2018
##  5 Lucy    F          2014 Dachsh… Brookl…   11215 2014-09-13 2019-09-13    2018
##  6 June    F          2010 Cavali… Brookl…   11238 2014-09-13 2019-10-28    2018
##  7 Apple   M          2013 Havane… Manhat…   10025 2014-09-13 2019-10-16    2018
##  8 Muneca  F          2013 Beagle  Brookl…   11232 2014-09-13 2019-09-13    2018
##  9 Benson  M          2010 Boxer   Brookl…   11209 2014-09-13 2019-10-29    2018
## 10 Bigs    M          2004 Pit Bu… Brookl…   11208 2014-09-13 2019-10-27    2018
## # … with 117,361 more rows, and abbreviated variable names ¹animal_name,
## #   ²animal_gender, ³animal_birth_year, ⁴breed_rc, ⁵zip_code,
## #   ⁶license_issued_date, ⁷license_expired_date, ⁸extract_year
```
]

---
count: false
 
# Let's make a summary table
.panel1-reveal-nycfrench-auto[

```r
nyc_license  |>
  filter(extract_year == 2018) |>
  group_by(breed_rc, zip_code) |>
* tally()
```
]
 
.panel2-reveal-nycfrench-auto[

```
## # A tibble: 18,945 × 3
## # Groups:   breed_rc [311]
##    breed_rc      zip_code     n
##    <chr>            <int> <int>
##  1 Affenpinscher    10005     1
##  2 Affenpinscher    10011     1
##  3 Affenpinscher    10013     1
##  4 Affenpinscher    10014     1
##  5 Affenpinscher    10016     1
##  6 Affenpinscher    10017     1
##  7 Affenpinscher    10018     1
##  8 Affenpinscher    10019     1
##  9 Affenpinscher    10021     1
## 10 Affenpinscher    10023     1
## # … with 18,935 more rows
```
]

---
count: false
 
# Let's make a summary table
.panel1-reveal-nycfrench-auto[

```r
nyc_license  |>
  filter(extract_year == 2018) |>
  group_by(breed_rc, zip_code) |>
  tally() |>
* mutate(freq = n / sum(n))
```
]
 
.panel2-reveal-nycfrench-auto[

```
## # A tibble: 18,945 × 4
## # Groups:   breed_rc [311]
##    breed_rc      zip_code     n   freq
##    <chr>            <int> <int>  <dbl>
##  1 Affenpinscher    10005     1 0.0303
##  2 Affenpinscher    10011     1 0.0303
##  3 Affenpinscher    10013     1 0.0303
##  4 Affenpinscher    10014     1 0.0303
##  5 Affenpinscher    10016     1 0.0303
##  6 Affenpinscher    10017     1 0.0303
##  7 Affenpinscher    10018     1 0.0303
##  8 Affenpinscher    10019     1 0.0303
##  9 Affenpinscher    10021     1 0.0303
## 10 Affenpinscher    10023     1 0.0303
## # … with 18,935 more rows
```
]

---
count: false
 
# Let's make a summary table
.panel1-reveal-nycfrench-auto[

```
## # A tibble: 161 × 4
## # Groups:   breed_rc [1]
##    breed_rc       zip_code     n    freq
##    <chr>             <int> <int>   <dbl>
##  1 French Bulldog    10001    27 0.0167 
##  2 French Bulldog    10002    20 0.0123 
##  3 French Bulldog    10003    36 0.0222 
##  4 French Bulldog    10004     9 0.00555
##  5 French Bulldog    10005    15 0.00925
##  6 French Bulldog    10006     8 0.00494
##  7 French Bulldog    10007    17 0.0105 
##  8 French Bulldog    10009    51 0.0315 
##  9 French Bulldog    10010    31 0.0191 
## 10 French Bulldog    10011    88 0.0543 
## # … with 151 more rows
```
]

---
count: false
 
# Let's make a summary table
.panel1-reveal-nycfrench-auto[

]

---
count: false
 
# Let's make a summary table
.panel1-reveal-nycfrench-auto[

]

---

# Now we have two tables again

.pull-left.w70[

```r
nyc_zips |> select(objectid:st_fips)
```

```
## Simple feature collection with 262 features and 6 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.25576 ymin: 40.49584 xmax: -73.6996 ymax: 40.91517
## Geodetic CRS:  WGS 84
## # A tibble: 262 × 7
##    objectid zip_code po_name     state borough st_fips                  geometry
##       <int>    <int> <chr>       <chr> <chr>   <chr>               <POLYGON [°]>
##  1        1    11372 Jackson He… NY    Queens  36      ((-73.86942 40.74916, -7…
##  2        2    11004 Glen Oaks   NY    Queens  36      ((-73.71068 40.75004, -7…
##  3        3    11040 New Hyde P… NY    Queens  36      ((-73.70098 40.7389, -73…
##  4        4    11426 Bellerose   NY    Queens  36      ((-73.7227 40.75373, -73…
##  5        5    11365 Fresh Mead… NY    Queens  36      ((-73.81089 40.72717, -7…
##  6        6    11373 Elmhurst    NY    Queens  36      ((-73.88722 40.72753, -7…
##  7        7    11001 Floral Park NY    Queens  36      ((-73.70098 40.7389, -73…
##  8        8    11375 Forest Hil… NY    Queens  36      ((-73.85625 40.73672, -7…
##  9        9    11427 Queens Vil… NY    Queens  36      ((-73.74169 40.73682, -7…
## 10       10    11374 Rego Park   NY    Queens  36      ((-73.86451 40.73407, -7…
## # … with 252 more rows
```

]

.pull-right.w30[

```r
nyc_fb |> select(breed_rc:n)
```

```
## # A tibble: 161 × 3
## # Groups:   breed_rc [1]
##    breed_rc       zip_code     n
##    <chr>             <int> <int>
##  1 French Bulldog    10001    27
##  2 French Bulldog    10002    20
##  3 French Bulldog    10003    36
##  4 French Bulldog    10004     9
##  5 French Bulldog    10005    15
##  6 French Bulldog    10006     8
##  7 French Bulldog    10007    17
##  8 French Bulldog    10009    51
##  9 French Bulldog    10010    31
## 10 French Bulldog    10011    88
## # … with 151 more rows
```

]

- Join them:

```r
fb_map <- left_join(nyc_zips, nyc_fb, by = "zip_code")
```

---

# Ready to map

```r
fb_map |> select(zip_code, po_name, borough, breed_rc:freq, geometry)
```

```
## Simple feature collection with 262 features and 6 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.25576 ymin: 40.49584 xmax: -73.6996 ymax: 40.91517
## Geodetic CRS:  WGS 84
## # A tibble: 262 × 7
##    zip_code po_name     borough breed…¹     n     freq                  geometry
##       <int> <chr>       <chr>   <chr>   <int>    <dbl>             <POLYGON [°]>
##  1    11372 Jackson He… Queens  French…    13  8.02e-3 ((-73.86942 40.74916, -7…
##  2    11004 Glen Oaks   Queens  French…     1  6.17e-4 ((-73.71068 40.75004, -7…
##  3    11040 New Hyde P… Queens  <NA>       NA NA       ((-73.70098 40.7389, -73…
##  4    11426 Bellerose   Queens  French…     1  6.17e-4 ((-73.7227 40.75373, -73…
##  5    11365 Fresh Mead… Queens  French…     7  4.32e-3 ((-73.81089 40.72717, -7…
##  6    11373 Elmhurst    Queens  French…    14  8.64e-3 ((-73.88722 40.72753, -7…
##  7    11001 Floral Park Queens  <NA>       NA NA       ((-73.70098 40.7389, -73…
##  8    11375 Forest Hil… Queens  French…     8  4.94e-3 ((-73.85625 40.73672, -7…
##  9    11427 Queens Vil… Queens  French…     2  1.23e-3 ((-73.74169 40.73682, -7…
## 10    11374 Rego Park   Queens  French…     6  3.70e-3 ((-73.86451 40.73407, -7…
## # … with 252 more rows, and abbreviated variable name ¹breed_rc
```

---

# A NYC map theme

- Just moving the legend, really.

```r
theme_nymap <- 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(0.05, 0.58), 
              legend.direction = "horizontal"
        )
}
```

---
count: false
 
# First cut at a map
.panel1-reveal-bulldogmap-auto[

```r
# This is just for slide purposes
*load("fb_map.Rdata")
```
]
 
.panel2-reveal-bulldogmap-auto[

]

---
count: false
 
# First cut at a map
.panel1-reveal-bulldogmap-auto[

```r
# This is just for slide purposes
load("fb_map.Rdata")
#
*fb_map
```
]
 
.panel2-reveal-bulldogmap-auto[

```
## Simple feature collection with 262 features and 14 fields
## Geometry type: POLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.25576 ymin: 40.49584 xmax: -73.6996 ymax: 40.91517
## Geodetic CRS:  WGS 84
## # A tibble: 262 × 15
##    objec…¹ zip_c…² po_name state borough st_fips cty_f…³ bld_g…⁴ shape…⁵ shape…⁶
##      <int>   <int> <chr>   <chr> <chr>   <chr>   <chr>     <int>   <dbl>   <dbl>
##  1       1   11372 Jackso… NY    Queens  36      081           0  20625.  2.02e7
##  2       2   11004 Glen O… NY    Queens  36      081           0  23003.  2.26e7
##  3       3   11040 New Hy… NY    Queens  36      081           0  15749.  6.27e6
##  4       4   11426 Beller… NY    Queens  36      081           0  35933.  4.94e7
##  5       5   11365 Fresh … NY    Queens  36      081           0  38694.  6.94e7
##  6       6   11373 Elmhur… NY    Queens  36      081           0  33756.  4.27e7
##  7       7   11001 Floral… NY    Queens  36      081           0  13595.  9.16e6
##  8       8   11375 Forest… NY    Queens  36      081           0  36277.  5.56e7
##  9       9   11427 Queens… NY    Queens  36      081           0  31232.  3.96e7
## 10      10   11374 Rego P… NY    Queens  36      081           0  26324.  2.52e7
## # … with 252 more rows, 5 more variables: x_id <chr>, geometry <POLYGON [°]>,
## #   breed_rc <chr>, n <int>, freq <dbl>, and abbreviated variable names
## #   ¹objectid, ²zip_code, ³cty_fips, ⁴bld_gpostal_code, ⁵shape_leng,
## #   ⁶shape_area
```
]

---
count: false
 
# First cut at a map
.panel1-reveal-bulldogmap-auto[

```r
# This is just for slide purposes
load("fb_map.Rdata")
#
fb_map |>
*   ggplot(mapping = aes(fill = freq))
```
]
 
.panel2-reveal-bulldogmap-auto[
<img src="09b-slides_files/figure-html/reveal-bulldogmap_auto_03_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# First cut at a map
.panel1-reveal-bulldogmap-auto[

```r
# This is just for slide purposes
load("fb_map.Rdata")
#
fb_map |>
    ggplot(mapping = aes(fill = freq)) +
*   geom_sf(color = "gray30", size = 0.1)   #<<
```
]
 
.panel2-reveal-bulldogmap-auto[
<img src="09b-slides_files/figure-html/reveal-bulldogmap_auto_04_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# First cut at a map
.panel1-reveal-bulldogmap-auto[

```r
# This is just for slide purposes
load("fb_map.Rdata")
#
fb_map |>
    ggplot(mapping = aes(fill = freq)) +
*   geom_sf(color = "gray30", size = 0.1) +
*   scale_fill_viridis_c(option = "A")
```
]
 
.panel2-reveal-bulldogmap-auto[
<img src="09b-slides_files/figure-html/reveal-bulldogmap_auto_05_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# First cut at a map
.panel1-reveal-bulldogmap-auto[

```r
# This is just for slide purposes
load("fb_map.Rdata")
#
fb_map |>
    ggplot(mapping = aes(fill = freq)) +
*   geom_sf(color = "gray30", size = 0.1) +
    scale_fill_viridis_c(option = "A") +
*   labs(fill = "Percent of All French Bulldogs")
```
]
 
.panel2-reveal-bulldogmap-auto[
<img src="09b-slides_files/figure-html/reveal-bulldogmap_auto_06_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# First cut at a map
.panel1-reveal-bulldogmap-auto[

```r
# This is just for slide purposes
load("fb_map.Rdata")
#
fb_map |>
    ggplot(mapping = aes(fill = freq)) +
*   geom_sf(color = "gray30", size = 0.1) +
    scale_fill_viridis_c(option = "A") +
    labs(fill = "Percent of All French Bulldogs") +
    annotate(geom = "text",
             x = -74.145 + 0.029,
             y = 40.82-0.012,
           label = "New York City's French Bulldogs",
           size = 6) +
*   annotate(geom = "text",
*            x = -74.1468 + 0.029,
*            y = 40.8075-0.012,
*          label = "By Zip Code. Based on Licensing Data",
*          size = 5)
```
]
 
.panel2-reveal-bulldogmap-auto[
<img src="09b-slides_files/figure-html/reveal-bulldogmap_auto_08_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# First cut at a map
.panel1-reveal-bulldogmap-auto[

```r
# This is just for slide purposes
load("fb_map.Rdata")
#
fb_map |>
    ggplot(mapping = aes(fill = freq)) +
*   geom_sf(color = "gray30", size = 0.1) +
    scale_fill_viridis_c(option = "A") +
    labs(fill = "Percent of All French Bulldogs") +
    annotate(geom = "text",
             x = -74.145 + 0.029,
             y = 40.82-0.012,
           label = "New York City's French Bulldogs",
           size = 6) +
    annotate(geom = "text",
             x = -74.1468 + 0.029,
             y = 40.8075-0.012,
           label = "By Zip Code. Based on Licensing Data",
           size = 5) +
*   kjhslides::kjh_theme_nymap()
```
]
 
.panel2-reveal-bulldogmap-auto[
<img src="09b-slides_files/figure-html/reveal-bulldogmap_auto_09_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# First cut at a map
.panel1-reveal-bulldogmap-auto[

```r
# This is just for slide purposes
load("fb_map.Rdata")
#
fb_map |>
    ggplot(mapping = aes(fill = freq)) +
*   geom_sf(color = "gray30", size = 0.1) +
    scale_fill_viridis_c(option = "A") +
    labs(fill = "Percent of All French Bulldogs") +
    annotate(geom = "text",
             x = -74.145 + 0.029,
             y = 40.82-0.012,
           label = "New York City's French Bulldogs",
           size = 6) +
    annotate(geom = "text",
             x = -74.1468 + 0.029,
             y = 40.8075-0.012,
           label = "By Zip Code. Based on Licensing Data",
           size = 5) +
    kjhslides::kjh_theme_nymap() +
*  guides(fill =
*          guide_legend(title.position = "top",
*               label.position = "bottom",
*               keywidth = 1,
*               nrow = 1))
```
]
 
.panel2-reveal-bulldogmap-auto[
<img src="09b-slides_files/figure-html/reveal-bulldogmap_auto_10_output-1.png" width="504" style="display: block; margin: auto;" />
]

---
count: false
 
# First cut at a map
.panel1-reveal-bulldogmap-auto[

```r
# This is just for slide purposes
load("fb_map.Rdata")
#
fb_map |>
    ggplot(mapping = aes(fill = freq)) +
*   geom_sf(color = "gray30", size = 0.1) +
    scale_fill_viridis_c(option = "A") +
    labs(fill = "Percent of All French Bulldogs") +
    annotate(geom = "text",
             x = -74.145 + 0.029,
             y = 40.82-0.012,
           label = "New York City's French Bulldogs",
           size = 6) +
    annotate(geom = "text",
             x = -74.1468 + 0.029,
             y = 40.8075-0.012,
           label = "By Zip Code. Based on Licensing Data",
           size = 5) +
    kjhslides::kjh_theme_nymap() +
   guides(fill =
           guide_legend(title.position = "top",
                label.position = "bottom",
                keywidth = 1,
                nrow = 1))
```
]
 
.panel2-reveal-bulldogmap-auto[
<img src="09b-slides_files/figure-html/reveal-bulldogmap_auto_11_output-1.png" width="504" style="display: block; margin: auto;" />
]

---

```r
# This is just for slide purposes
*load("nyc_fb.Rdata")
```
]
 
.panel2-reveal-bulldogmap2-auto[

]