Visualizing spatial data II

---

# Structuring map data in R

---

## Map data file formats

* Vector files
    * Raster images
    * Numeric data
* Popular formats
    * Shapefile
    * GeoJSON
    
---

## Shapefile

* Encodes points, lines, and polygons
* Collection of files
    * `.shp` - geographic coordinates
    * `.dbf` - data associated with the geographic features
    * `.prj` - projection of the coordinates in the shapefile

```
## -- geo_export_6fd95df5-1136-4829-8f2d-9cb5d1cc2222.dbf
## -- geo_export_6fd95df5-1136-4829-8f2d-9cb5d1cc2222.prj
## -- geo_export_6fd95df5-1136-4829-8f2d-9cb5d1cc2222.shp
## -- geo_export_6fd95df5-1136-4829-8f2d-9cb5d1cc2222.shx
```

---

## GeoJSON

* Uses **J**ava**S**cript **O**bject **N**otation (JSON) file format
    
    ```json
    {
      "type": "Feature",
      "geometry": {
        "type": "Point",
        "coordinates": [125.6, 10.1]
      },
      "properties": {
        "name": "Dinagat Islands"
      }
    }
    ```
* Plain text files

---

## Simple Features for R

---

## What is a feature?

* Thing or an object in the real world
* Sets of features
* Geometry
* Attributes

---

## Simple features

.footnote[
Source: [Simple Features for R](https://r-spatial.github.io/sf/articles/sf1.html#sf-objects-with-simple-features-1)
]

---

## Simple features in R

* Uses basic R data structures
* Data frame with one row per feature
* Lots of list columns

---

## Importing shapefiles

```r
nyc_shape <- st_read(dsn = here("data", "borough-boundaries"))
```

```r
nyc_shape
```

```
## Simple feature collection with 5 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.25559 ymin: 40.49613 xmax: -73.70001 ymax: 40.91553
## Geodetic CRS:  WGS84(DD)
##   boro_code     boro_name shape_area shape_leng                       geometry
## 1         5 Staten Island 1623631283   325924.0 MULTIPOLYGON (((-74.05051 4...
## 2         2         Bronx 1187189499   463277.2 MULTIPOLYGON (((-73.89681 4...
## 3         1     Manhattan  636605816   359103.2 MULTIPOLYGON (((-74.01093 4...
## 4         3      Brooklyn 1934169229   728478.1 MULTIPOLYGON (((-73.86327 4...
## 5         4        Queens 3041397430   888238.6 MULTIPOLYGON (((-73.82645 4...
```

---

## Importing GeoJSON files

```r
nyc_json <- st_read(dsn = here("data", "borough-boundaries.geojson"))
```

```r
nyc_json
```

```
## Simple feature collection with 5 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.25559 ymin: 40.49613 xmax: -73.70001 ymax: 40.91553
## Geodetic CRS:  WGS 84
##   boro_code     boro_name    shape_area    shape_leng                       geometry
## 1         5 Staten Island 1623631283.36 325924.002076 MULTIPOLYGON (((-74.05051 4...
## 2         2         Bronx  1187189499.3 463277.240478 MULTIPOLYGON (((-73.89681 4...
## 3         1     Manhattan 636605816.437 359103.151368 MULTIPOLYGON (((-74.01093 4...
## 4         3      Brooklyn 1934169228.83 728478.125489 MULTIPOLYGON (((-73.86327 4...
## 5         4        Queens 3041397430.33 888238.562635 MULTIPOLYGON (((-73.82645 4...
```

---

# Visualizing `sf` objects

---

## Drawing maps with `sf` objects

```r
usa <- st_read(dsn = here("data", "cb_2020_us_state_5m", "cb_2020_us_state_5m.shp"))
```

```r
usa
```

```
## Simple feature collection with 56 features and 9 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -179.1473 ymin: -14.55255 xmax: 179.7785 ymax: 71.35256
## Geodetic CRS:  NAD83
## First 10 features:
##    STATEFP  STATENS    AFFGEOID GEOID STUSPS          NAME LSAD        ALAND      AWATER                       geometry
## 1       55 01779806 0400000US55    55     WI     Wisconsin   00 140292246684 29343721650 MULTIPOLYGON (((-86.9562 45...
## 2       54 01779805 0400000US54    54     WV West Virginia   00  62266296765   489206049 MULTIPOLYGON (((-82.643 38....
## 3       16 01779783 0400000US16    16     ID         Idaho   00 214049923496  2391577745 MULTIPOLYGON (((-117.2427 4...
## 4       27 00662849 0400000US27    27     MN     Minnesota   00 206232157570 18949864226 MULTIPOLYGON (((-97.23921 4...
## 5       19 01779785 0400000US19    19     IA          Iowa   00 144659688848  1085996889 MULTIPOLYGON (((-96.6397 42...
## 6       10 01779781 0400000US10    10     DE      Delaware   00   5046731558  1399179670 MULTIPOLYGON (((-75.5708 39...
## 7       72 01779808 0400000US72    72     PR   Puerto Rico   00   8868948653  4922329963 MULTIPOLYGON (((-65.3375 18...
## 8       29 01779791 0400000US29    29     MO      Missouri   00 178052563675  2487215790 MULTIPOLYGON (((-95.77355 4...
## 9       50 01779802 0400000US50    50     VT       Vermont   00  23873081385  1030243281 MULTIPOLYGON (((-73.43774 4...
## 10      24 01714934 0400000US24    24     MD      Maryland   00  25151895765  6979171386 MULTIPOLYGON (((-76.0494 37...
```
]
---

## USA boundaries

.panelset.sideways[

```r
ggplot(data = usa) +
* geom_sf()
```

<img src="index_files/figure-html/unnamed-chunk-16-1.png" width="80%" style="display: block; margin: auto;" />
]

---

## Plot a subset of a map

.panelset.sideways[

```r
usa_48 <- usa %>%
* filter(NAME %in% state.name) %>%
* filter(NAME != "Alaska", NAME != "Hawaii")

ggplot(data = usa_48) +
  geom_sf()
```

<img src="index_files/figure-html/unnamed-chunk-17-1.png" width="80%" style="display: block; margin: auto;" />
]

---

## Just another `ggplot()`

.panelset.sideways[

```r
ggplot(data = usa_48) +
  geom_sf(
*   fill = "palegreen",
*   color = "black"
  )
```

<img src="index_files/figure-html/unnamed-chunk-18-1.png" width="80%" style="display: block; margin: auto;" />
]

---

---

## `urbnmapr`

.panelset.sideways[

```r
library(urbnmapr)

*states_sf <- get_urbn_map("states", sf = TRUE)

ggplot(data = states_sf) +
  geom_sf()
```

<img src="index_files/figure-html/unnamed-chunk-20-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Identifying points on a map

---

## Points

```r
crimes <- here("data", "nyc-crimes.csv") %>%
  read_csv()
```

```r
crimes_homicide <- filter(.data = crimes, ofns_desc == "MURDER & NON-NEGL. MANSLAUGHTER")
crimes_homicide
```

```
## # A tibble: 269 × 7
##    cmplnt_num    boro_nm   cmplnt_fr_dt        law_cat…¹ ofns_…² latit…³ longi…⁴
##    <chr>         <chr>     <dttm>              <chr>     <chr>     <dbl>   <dbl>
##  1 240954923H1   BROOKLYN  1977-12-20 05:00:00 FELONY    MURDER…    40.7   -74.0
##  2 245958045H1   BROOKLYN  2001-08-13 04:00:00 FELONY    MURDER…    40.7   -73.9
##  3 8101169H6113  MANHATTAN 2005-03-06 05:00:00 FELONY    MURDER…    40.8   -73.9
##  4 8101169H6113  MANHATTAN 2005-03-06 05:00:00 FELONY    MURDER…    40.8   -73.9
##  5 16631466H8909 BROOKLYN  2006-05-24 04:00:00 FELONY    MURDER…    40.7   -73.9
##  6 246056367H1   QUEENS    2015-05-13 04:00:00 FELONY    MURDER…    40.6   -73.7
##  7 243507594H1   MANHATTAN 2020-06-19 04:00:00 FELONY    MURDER…    40.8   -74.0
##  8 243688124H1   BROOKLYN  2021-01-31 05:00:00 FELONY    MURDER…    40.7   -73.9
##  9 240767513H1   BROOKLYN  2021-02-17 05:00:00 FELONY    MURDER…    40.6   -74.0
## 10 240767512H1   BROOKLYN  2021-05-24 04:00:00 FELONY    MURDER…    40.6   -74.0
## # … with 259 more rows, and abbreviated variable names ¹law_cat_cd, ²ofns_desc,
## #   ³latitude, ⁴longitude
```

---

## Points

.panelset.sideways[

```r
ggplot(
  data = crimes_homicide,
  mapping = aes(
    x = longitude,
    y = latitude
  )
) +
* geom_point()
```

<img src="index_files/figure-html/scatter-1.png" width="80%" style="display: block; margin: auto;" />
]

---

## Points

.panelset.sideways[

```r
ggplot(data = nyc_json) +
* geom_sf() +
  geom_point(
    data = crimes_homicide,
    mapping = aes(
      x = longitude,
      y = latitude
    ),
    shape = 1
  )
```

<img src="index_files/figure-html/nyc-crime-1.png" width="80%" style="display: block; margin: auto;" />
]

---

## Converting to `sf` data frame

```r
crimes_homicide_sf <- st_as_sf(x = crimes_homicide, coords = c("longitude", "latitude"))
st_crs(crimes_homicide_sf) <- 4326 # set the coordinate reference system
crimes_homicide_sf
```

```
## Simple feature collection with 269 features and 5 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: -74.08578 ymin: 40.59087 xmax: -73.73331 ymax: 40.90316
## Geodetic CRS:  WGS 84
## # A tibble: 269 × 6
##    cmpln…¹ boro_nm cmplnt_fr_dt        law_c…² ofns_…³             geometry
##  * <chr>   <chr>   <dttm>              <chr>   <chr>            <POINT [°]>
##  1 240954… BROOKL… 1977-12-20 05:00:00 FELONY  MURDER… (-73.97448 40.68079)
##  2 245958… BROOKL… 2001-08-13 04:00:00 FELONY  MURDER… (-73.92013 40.69862)
##  3 810116… MANHAT… 2005-03-06 05:00:00 FELONY  MURDER… (-73.94295 40.81126)
##  4 810116… MANHAT… 2005-03-06 05:00:00 FELONY  MURDER… (-73.94295 40.81126)
##  5 166314… BROOKL… 2006-05-24 04:00:00 FELONY  MURDER…  (-73.91796 40.6703)
##  6 246056… QUEENS  2015-05-13 04:00:00 FELONY  MURDER…  (-73.7491 40.60219)
##  7 243507… MANHAT… 2020-06-19 04:00:00 FELONY  MURDER… (-73.95732 40.81194)
##  8 243688… BROOKL… 2021-01-31 05:00:00 FELONY  MURDER…  (-73.8627 40.67093)
##  9 240767… BROOKL… 2021-02-17 05:00:00 FELONY  MURDER… (-73.97582 40.61046)
## 10 240767… BROOKL… 2021-05-24 04:00:00 FELONY  MURDER… (-73.97582 40.61046)
## # … with 259 more rows, and abbreviated variable names ¹cmplnt_num,
## #   ²law_cat_cd, ³ofns_desc
```

---

## Plotting with two sf data frames

.panelset.sideways[

```r
ggplot() +
  geom_sf(data = nyc_json) +
  geom_sf(
*   data = crimes_homicide_sf,
*   shape = 1
  )
```

<img src="index_files/figure-html/crimes-sf-plot-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Choropleths

---

---

## Fill (choropleths)

```r
fb_state <- read_csv(file = here("data", "foreign-born.csv"))
```

```r
fb_state
```

```
## # A tibble: 52 × 6
##    GEOID NAME                    total   native  foreign pct_foreign
##    <chr> <chr>                   <dbl>    <dbl>    <dbl>       <dbl>
##  1 01    Alabama               4876250  4703303   172947      0.0355
##  2 02    Alaska                 737068   679401    57667      0.0782
##  3 04    Arizona               7050299  6109648   940651      0.133 
##  4 05    Arkansas              2999370  2854323   145047      0.0484
##  5 06    California           39283497 28736287 10547210      0.268 
##  6 08    Colorado              5610349  5063836   546513      0.0974
##  7 10    Delaware               957248   865775    91473      0.0956
##  8 11    District of Columbia   692683   597618    95065      0.137 
##  9 09    Connecticut           3575074  3054123   520951      0.146 
## 10 12    Florida              20901636 16576836  4324800      0.207 
## # … with 42 more rows
```

---

## Join the data

```r
usa_fb <- left_join(x = usa_48, y = fb_state, by = c("STATEFP" = "GEOID", "NAME"))
usa_fb
```

```
## Simple feature collection with 48 features and 13 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -124.7332 ymin: 24.51496 xmax: -66.9499 ymax: 49.38436
## Geodetic CRS:  NAD83
## First 10 features:
##    STATEFP  STATENS    AFFGEOID GEOID STUSPS          NAME LSAD        ALAND
## 1       55 01779806 0400000US55    55     WI     Wisconsin   00 140292246684
## 2       54 01779805 0400000US54    54     WV West Virginia   00  62266296765
## 3       16 01779783 0400000US16    16     ID         Idaho   00 214049923496
## 4       27 00662849 0400000US27    27     MN     Minnesota   00 206232157570
## 5       19 01779785 0400000US19    19     IA          Iowa   00 144659688848
## 6       10 01779781 0400000US10    10     DE      Delaware   00   5046731558
## 7       29 01779791 0400000US29    29     MO      Missouri   00 178052563675
## 8       50 01779802 0400000US50    50     VT       Vermont   00  23873081385
## 9       24 01714934 0400000US24    24     MD      Maryland   00  25151895765
## 10      33 01779794 0400000US33    33     NH New Hampshire   00  23190113978
##         AWATER   total  native foreign pct_foreign
## 1  29343721650 5790716 5500994  289722  0.05003215
## 2    489206049 1817305 1787134   30171  0.01660206
## 3   2391577745 1717750 1615307  102443  0.05963790
## 4  18949864226 5563378 5090529  472849  0.08499315
## 5   1085996889 3139508 2973069  166439  0.05301436
## 6   1399179670  957248  865775   91473  0.09555831
## 7   2487215790 6104910 5849191  255719  0.04188743
## 8   1030243281  624313  594985   29328  0.04697644
## 9   6979171386 6018848 5105961  912887  0.15167138
## 10  1025973001 1348124 1265430   82694  0.06134005
##                          geometry
## 1  MULTIPOLYGON (((-86.9562 45...
## 2  MULTIPOLYGON (((-82.643 38....
## 3  MULTIPOLYGON (((-117.2427 4...
## 4  MULTIPOLYGON (((-97.23921 4...
## 5  MULTIPOLYGON (((-96.6397 42...
## 6  MULTIPOLYGON (((-75.5708 39...
## 7  MULTIPOLYGON (((-95.77355 4...
## 8  MULTIPOLYGON (((-73.43774 4...
## 9  MULTIPOLYGON (((-76.0494 37...
## 10 MULTIPOLYGON (((-72.55725 4...
```

---

## Draw the map

.panelset.sideways[

```r
ggplot(data = usa_fb) +
* geom_sf(mapping = aes(fill = pct_foreign))
```

<img src="index_files/figure-html/geom-map-state-1.png" width="80%" style="display: block; margin: auto;" />
]

---

# Use better colors

---

## `colorspace`

Scale name: `scale_<aesthetic>_<datatype>_<colorscale>()`

- `<aesthetic>`: name of the aesthetic (`fill`, `color`, `colour`)
- `<datatype>`: type of variable plotted (`discrete`, `continuous`, `binned`)
- `<colorscale>`: type of the color scale (`qualitative`, `sequential`, `diverging`)

---

---

---

---

## Use better colors

.panelset.sideways[

```r
*library(colorspace)

ggplot(data = usa_fb) +
  geom_sf(mapping = aes(
    fill = pct_foreign
  )) +
* scale_fill_continuous_sequential(palette = "mako")
```

<img src="index_files/figure-html/geom-map-state-pal-1.png" width="100%" style="display: block; margin: auto;" />
]

---

# Spatial aggregation

---

## Spatial aggregation

```r
st_join(x = nyc_json, y = crimes_homicide_sf)
```

```
## Simple feature collection with 269 features and 9 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.25559 ymin: 40.49613 xmax: -73.70001 ymax: 40.91553
## Geodetic CRS:  WGS 84
## First 10 features:
##     boro_code     boro_name    shape_area    shape_leng  cmplnt_num
## 1           5 Staten Island 1623631283.36 325924.002076 239347883H1
## 1.1         5 Staten Island 1623631283.36 325924.002076 244818447H1
## 1.2         5 Staten Island 1623631283.36 325924.002076 246985876H1
## 2           2         Bronx  1187189499.3 463277.240478 242029394H1
## 2.1         2         Bronx  1187189499.3 463277.240478 242029394H1
## 2.2         2         Bronx  1187189499.3 463277.240478 243573864H1
## 2.3         2         Bronx  1187189499.3 463277.240478 244256173H1
## 2.4         2         Bronx  1187189499.3 463277.240478 240776535H1
## 2.5         2         Bronx  1187189499.3 463277.240478 240776535H1
## 2.6         2         Bronx  1187189499.3 463277.240478 240776535H1
##           boro_nm        cmplnt_fr_dt law_cat_cd
## 1   STATEN ISLAND 2022-01-18 05:00:00     FELONY
## 1.1 STATEN ISLAND 2022-05-09 04:00:00     FELONY
## 1.2 STATEN ISLAND 2022-06-21 04:00:00     FELONY
## 2           BRONX 2021-06-01 04:00:00     FELONY
## 2.1         BRONX 2021-06-01 04:00:00     FELONY
## 2.2         BRONX 2021-10-24 04:00:00     FELONY
## 2.3         BRONX 2021-12-17 05:00:00     FELONY
## 2.4         BRONX 2022-01-01 05:00:00     FELONY
## 2.5         BRONX 2022-01-01 05:00:00     FELONY
## 2.6         BRONX 2022-01-01 05:00:00     FELONY
##                           ofns_desc                       geometry
## 1   MURDER & NON-NEGL. MANSLAUGHTER MULTIPOLYGON (((-74.05051 4...
## 1.1 MURDER & NON-NEGL. MANSLAUGHTER MULTIPOLYGON (((-74.05051 4...
## 1.2 MURDER & NON-NEGL. MANSLAUGHTER MULTIPOLYGON (((-74.05051 4...
## 2   MURDER & NON-NEGL. MANSLAUGHTER MULTIPOLYGON (((-73.89681 4...
## 2.1 MURDER & NON-NEGL. MANSLAUGHTER MULTIPOLYGON (((-73.89681 4...
## 2.2 MURDER & NON-NEGL. MANSLAUGHTER MULTIPOLYGON (((-73.89681 4...
## 2.3 MURDER & NON-NEGL. MANSLAUGHTER MULTIPOLYGON (((-73.89681 4...
## 2.4 MURDER & NON-NEGL. MANSLAUGHTER MULTIPOLYGON (((-73.89681 4...
## 2.5 MURDER & NON-NEGL. MANSLAUGHTER MULTIPOLYGON (((-73.89681 4...
## 2.6 MURDER & NON-NEGL. MANSLAUGHTER MULTIPOLYGON (((-73.89681 4...
```

---

```r
*nyc_json
```
]
 
.panel2-crimes-agg-map-auto[

```
## Simple feature collection with 5 features and 4 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.25559 ymin: 40.49613 xmax: -73.70001 ymax: 40.91553
## Geodetic CRS:  WGS 84
##   boro_code     boro_name    shape_area    shape_leng
## 1         5 Staten Island 1623631283.36 325924.002076
## 2         2         Bronx  1187189499.3 463277.240478
## 3         1     Manhattan 636605816.437 359103.151368
## 4         3      Brooklyn 1934169228.83 728478.125489
## 5         4        Queens 3041397430.33 888238.562635
##                         geometry
## 1 MULTIPOLYGON (((-74.05051 4...
## 2 MULTIPOLYGON (((-73.89681 4...
## 3 MULTIPOLYGON (((-74.01093 4...
## 4 MULTIPOLYGON (((-73.86327 4...
## 5 MULTIPOLYGON (((-73.82645 4...
```
]

---
count: false

```r
nyc_json %>%
* st_join(y = crimes_homicide_sf)
```
]
 
.panel2-crimes-agg-map-auto[

---
count: false

```r
nyc_json %>%
  st_join(y = crimes_homicide_sf) %>%
* group_by(boro_name)
```
]
 
.panel2-crimes-agg-map-auto[

```
## Simple feature collection with 269 features and 9 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.25559 ymin: 40.49613 xmax: -73.70001 ymax: 40.91553
## Geodetic CRS:  WGS 84
## # A tibble: 269 × 10
## # Groups:   boro_name [5]
##    boro_code boro_…¹ shape…² shape…³ cmpln…⁴ boro_nm cmplnt_fr_dt        law_c…⁵
##    <chr>     <chr>   <chr>   <chr>   <chr>   <chr>   <dttm>              <chr>  
##  1 5         Staten… 162363… 325924… 239347… STATEN… 2022-01-18 05:00:00 FELONY 
##  2 5         Staten… 162363… 325924… 244818… STATEN… 2022-05-09 04:00:00 FELONY 
##  3 5         Staten… 162363… 325924… 246985… STATEN… 2022-06-21 04:00:00 FELONY 
##  4 2         Bronx   118718… 463277… 242029… BRONX   2021-06-01 04:00:00 FELONY 
##  5 2         Bronx   118718… 463277… 242029… BRONX   2021-06-01 04:00:00 FELONY 
##  6 2         Bronx   118718… 463277… 243573… BRONX   2021-10-24 04:00:00 FELONY 
##  7 2         Bronx   118718… 463277… 244256… BRONX   2021-12-17 05:00:00 FELONY 
##  8 2         Bronx   118718… 463277… 240776… BRONX   2022-01-01 05:00:00 FELONY 
##  9 2         Bronx   118718… 463277… 240776… BRONX   2022-01-01 05:00:00 FELONY 
## 10 2         Bronx   118718… 463277… 240776… BRONX   2022-01-01 05:00:00 FELONY 
## # … with 259 more rows, 2 more variables: ofns_desc <chr>,
## #   geometry <MULTIPOLYGON [°]>, and abbreviated variable names ¹boro_name,
## #   ²shape_area, ³shape_leng, ⁴cmplnt_num, ⁵law_cat_cd
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
```
]

---
count: false

```r
nyc_json %>%
  st_join(y = crimes_homicide_sf) %>%
  group_by(boro_name) %>%
* count()
```
]
 
.panel2-crimes-agg-map-auto[

```
## Simple feature collection with 5 features and 2 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -74.25559 ymin: 40.49613 xmax: -73.70001 ymax: 40.91553
## Geodetic CRS:  WGS 84
## # A tibble: 5 × 3
##   boro_name         n                                                   geometry
## * <chr>         <int>                                         <MULTIPOLYGON [°]>
## 1 Bronx           101 (((-73.89679 40.79633, -73.89713 40.7968, -73.89788 40.79…
## 2 Brooklyn         75 (((-73.86318 40.58406, -73.86283 40.58442, -73.8625 40.58…
## 3 Manhattan        46 (((-74.0086 40.68591, -74.00851 40.68596, -74.00843 40.68…
## 4 Queens           44 (((-73.82646 40.59059, -73.82647 40.59065, -73.82648 40.5…
## 5 Staten Island     3 (((-74.05054 40.56644, -74.05062 40.56651, -74.05067 40.5…
```
]

---
count: false

```r
nyc_json %>%
  st_join(y = crimes_homicide_sf) %>%
  group_by(boro_name) %>%
  count() %>%
* ggplot()
```
]
 
.panel2-crimes-agg-map-auto[
<img src="index_files/figure-html/crimes-agg-map_auto_05_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---
count: false

```r
nyc_json %>%
  st_join(y = crimes_homicide_sf) %>%
  group_by(boro_name) %>%
  count() %>%
  ggplot() +
* geom_sf(mapping = aes(fill = n))
```
]
 
.panel2-crimes-agg-map-auto[
<img src="index_files/figure-html/crimes-agg-map_auto_06_output-1.png" width="80%" style="display: block; margin: auto;" />
]

---

## Which is better for comparisons?

---

## Exercise using household income

---

# Bin continuous data to discrete intervals

---

## Bin data to discrete intervals

* Continuous vs. discrete variables for color
* Collapse to a discrete variable

---

## `cut_interval()`