Spring 2026 Slides

class: inverse, bottom
background-image: url(https://images.unsplash.com/photo-1588591795084-1770cb3be374?q=80&w=2070&auto=format&fit=crop&ixlib=rb-4.1.0&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D)
background-size: cover

## .large[ASA COWY: General deterministic semantic positional encoding]
## .small[Reprising and Reflecting on R Medicine talk :-)]
#### .tiny[Dr. Evangeline Reynolds | 2026-05-08 | DU, Image credit: Ross Sneddon, Upsplash]

???

Title slide

---
class: inverse

# R medicine title: 'ggregions for building intuitive coding interfaces for anatomical visualizations'

---
class: inverse

# Motivations for ggregions

> ### I am told there are people who do not care for maps, and I find it hard to believe. - Robert Louis Stevenson

> ### 'My teacher, Mr. Jayson, says a map is a picture of someplace from above.  It's like flying over that spot in an airplane.' - 'Lisa' in Loreen Leedy's in 'Mappying Penny's World'

---

# But ...

> ### I hate when I work with spatial data that so much of my mindshare goes to thinking about 'how do I wrangle this map and what on earth is going on' versus the actual meaning behind what I'm trying to map. - Emily Riederer

---
class: inverse

## There is great support for mapping in R and in the data visualization library {ggplot2} which supports many R packages.

## But, producing a simple map, like a choropleth, can feel much harder than producing other plots, like a scatterplot for example in ggplot2.

---

``` r
*tribble(~var1, ~var2, ~var3,
*           1,     4,   500,
*           2,     3,   302)
```
]
 
.panel2-classic_v_semantic-auto[

```
# A tibble: 2 × 3
   var1  var2  var3
  <dbl> <dbl> <dbl>
1     1     4   500
2     2     3   302
```
]

---
count: false

``` r
tribble(~var1, ~var2, ~var3,
            1,     4,   500,
            2,     3,   302) |>
* ggplot()
```
]
 
.panel2-classic_v_semantic-auto[
![](asa-cowy-ggregions_files/figure-html/classic_v_semantic_auto_02_output-1.png)
]

---
count: false

``` r
tribble(~var1, ~var2, ~var3,
            1,     4,   500,
            2,     3,   302) |>
  ggplot() +
* aes(x = var1,
*     y = var2)
```
]
 
.panel2-classic_v_semantic-auto[
![](asa-cowy-ggregions_files/figure-html/classic_v_semantic_auto_03_output-1.png)
]

---
count: false

``` r
tribble(~var1, ~var2, ~var3,
            1,     4,   500,
            2,     3,   302) |>
  ggplot() +
  aes(x = var1,
      y = var2) +
* geom_point(size = 4)
```
]
 
.panel2-classic_v_semantic-auto[
![](asa-cowy-ggregions_files/figure-html/classic_v_semantic_auto_04_output-1.png)
]

---
count: false

``` r
tribble(~var1, ~var2, ~var3,
            1,     4,   500,
            2,     3,   302) |>
  ggplot() +
  aes(x = var1,
      y = var2) +
  geom_point(size = 4) +
* aes(color = var3)
```
]
 
.panel2-classic_v_semantic-auto[
![](asa-cowy-ggregions_files/figure-html/classic_v_semantic_auto_05_output-1.png)
]

---

``` r
*library(ggregions)
```

---
count: false

``` r
library(ggregions)

*tribble(~place, ~var2,
*      "Texas",     500,
*     "North Carolina",     204)
```

---
count: false

``` r
library(ggregions)

tribble(~place, ~var2,
       "Texas",     500,
      "North Carolina",     204) |>
*ggplot()
```

---
count: false

``` r
library(ggregions)

tribble(~place, ~var2,
       "Texas",     500,
      "North Carolina",     204) |>
ggplot() +
* aes(state_name = place)
```

---
count: false

``` r
library(ggregions)

tribble(~place, ~var2,
       "Texas",     500,
      "North Carolina",     204) |>
ggplot() +
  aes(state_name = place) +
* geom_state()
```

---
count: false

``` r
library(ggregions)

tribble(~place, ~var2,
       "Texas",     500,
      "North Carolina",     204) |>
ggplot() +
  aes(state_name = place) +
  geom_state() +
* aes(fill = var2)
```

---
class: inverse

## Semantic

## Positional

## Encoding

---
class: inverse

## Semantic (*meaning* of words or symbols)

## Positional

## Encoding

### 'normal' ggplot2 positional encoding

### `aes(x = c(1, 2), y = c(4, 2))`

### S.P.E.

### `aes(state_name = c("Texas", "Indiana"))`

---

#  Determinitic  (contrast w/ LLM color selection)

#  General (will accept "Texas" or "TX" or 05)

# Semantic

# Positional

# Encoding

---
class: inverse

## Semantic (*meaning* of words or symbols)

## Positional

## Encoding

### 'normal' ggplot2 positional encoding

### `aes(x = c(1, 2), y = c(4, 2))`

### S.P.E.

### `aes(state_name = c("Texas", "Indiana"))`

---
class: inverse

## Semantic (*meaning* of words or symbols)

## Positional

## Encoding

### 'normal' ggplot2 positional encoding

### `aes(x = c(1, 2), y = c(4, 2))`

### S.P.E.

### `aes(region = c("Texas", "Indiana"))`

---
class: inverse, center, middle

# Does {ggregions} deliver?

---

``` r
*library(ggplot2)
```
]
 
.panel2-usmapdata2-auto[

]

---
count: false

``` r
library(ggplot2)

*us_income
```
]
 
.panel2-usmapdata2-auto[

```
# A tibble: 52 × 5
   GEOID NAME                 variable income   moe
   <chr> <chr>                <chr>     <dbl> <dbl>
 1 01    Alabama              income    24476   136
 2 02    Alaska               income    32940   508
 3 04    Arizona              income    27517   148
 4 05    Arkansas             income    23789   165
 5 06    California           income    29454   109
 6 08    Colorado             income    32401   109
 7 09    Connecticut          income    35326   195
 8 10    Delaware             income    31560   247
 9 11    District of Columbia income    43198   681
10 12    Florida              income    25952    70
# ℹ 42 more rows
```
]

---
count: false

``` r
library(ggplot2)

us_income |>
* ggplot()
```
]
 
.panel2-usmapdata2-auto[
![](asa-cowy-ggregions_files/figure-html/usmapdata2_auto_03_output-1.png)
]

---
count: false

``` r
library(ggplot2)

us_income |>
  ggplot() +
* aes(region = NAME)
```
]
 
.panel2-usmapdata2-auto[
![](asa-cowy-ggregions_files/figure-html/usmapdata2_auto_04_output-1.png)
]

---
count: false

``` r
library(ggplot2)

us_income |>
  ggplot() +
  aes(region = NAME) +
* geom_region()
```
]
 
.panel2-usmapdata2-auto[
![](asa-cowy-ggregions_files/figure-html/usmapdata2_auto_05_output-1.png)
]

---
count: false

``` r
library(ggplot2)

us_income |>
  ggplot() +
  aes(region = NAME) +
  geom_region() +
* aes(fill = income)
```
]
 
.panel2-usmapdata2-auto[
![](asa-cowy-ggregions_files/figure-html/usmapdata2_auto_06_output-1.png)
]

---

``` r
*library(ggregions)
```
]
 
.panel2-usmapdata3-auto[

]

---
count: false

``` r
library(ggregions)

*tribble(    ~place, ~var2,
*            "Texas",     500,
*   "North Carolina",     204)
```
]
 
.panel2-usmapdata3-auto[

```
# A tibble: 2 × 2
  place           var2
  <chr>          <dbl>
1 Texas            500
2 North Carolina   204
```
]

---
count: false

``` r
library(ggregions)

tribble(    ~place, ~var2,
             "Texas",     500,
    "North Carolina",     204) |>
*ggplot()
```
]
 
.panel2-usmapdata3-auto[
![](asa-cowy-ggregions_files/figure-html/usmapdata3_auto_03_output-1.png)
]

---
count: false

``` r
library(ggregions)

tribble(    ~place, ~var2,
             "Texas",     500,
    "North Carolina",     204) |>
ggplot() +
* aes(region = place)
```
]
 
.panel2-usmapdata3-auto[
![](asa-cowy-ggregions_files/figure-html/usmapdata3_auto_04_output-1.png)
]

---
count: false

``` r
library(ggregions)

tribble(    ~place, ~var2,
             "Texas",     500,
    "North Carolina",     204) |>
ggplot() +
  aes(region = place) +
* geom_state()
```
]
 
.panel2-usmapdata3-auto[
![](asa-cowy-ggregions_files/figure-html/usmapdata3_auto_05_output-1.png)
]

---
count: false

``` r
library(ggregions)

tribble(    ~place, ~var2,
             "Texas",     500,
    "North Carolina",     204) |>
ggplot() +
  aes(region = place) +
  geom_state() +
* aes(fill = var2)
```
]
 
.panel2-usmapdata3-auto[
![](asa-cowy-ggregions_files/figure-html/usmapdata3_auto_06_output-1.png)
]

---

> ## [ggregions-generated] APIs feels like they actually puts the "question" at the center. - Emily Riederer

---
class: inverse, center, middle

# How does it work?

---

## Effortlessly write new region-specific geom_* functions.

---

### Step 1. prepare data
.panel1-us_states_ref-auto[

``` r
*library(dplyr)
```
]
 
.panel2-us_states_ref-auto[

]

---
count: false
 
## Example with geographic regions: US states

### Step 1. prepare data
.panel1-us_states_ref-auto[

``` r
library(dplyr)

# Step 1. prep some of their geo reference data
*usmapdata::us_map()
```
]
 
.panel2-us_states_ref-auto[

```
Simple feature collection with 52 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -2584074 ymin: -2602555 xmax: 2516258 ymax: 731628.1
Projected CRS: NAD27 / US National Atlas Equal Area
First 10 features:
   fips abbr                 full                           geom
1    02   AK               Alaska MULTIPOLYGON (((-2390688 -2...
2    01   AL              Alabama MULTIPOLYGON (((1091785 -13...
3    05   AR             Arkansas MULTIPOLYGON (((482022.2 -9...
4    04   AZ              Arizona MULTIPOLYGON (((-1386064 -1...
5    06   CA           California MULTIPOLYGON (((-1716581 -1...
6    08   CO             Colorado MULTIPOLYGON (((-787705.6 -...
7    09   CT          Connecticut MULTIPOLYGON (((2156162 -83...
8    11   DC District of Columbia MULTIPOLYGON (((1950799 -40...
9    10   DE             Delaware MULTIPOLYGON (((2037480 -28...
10   12   FL              Florida MULTIPOLYGON (((1853163 -20...
```
]

---
count: false
 
## Example with geographic regions: US states

### Step 1. prepare data
.panel1-us_states_ref-auto[

``` r
library(dplyr)

# Step 1. prep some of their geo reference data
usmapdata::us_map() |>
* select(state_name = full,  # id col
*        state_abbr = abbr,
*        state_fips = fips,
*        geometry = geom)
```
]
 
.panel2-us_states_ref-auto[

```
Simple feature collection with 52 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -2584074 ymin: -2602555 xmax: 2516258 ymax: 731628.1
Projected CRS: NAD27 / US National Atlas Equal Area
First 10 features:
             state_name state_abbr state_fips                       geometry
1                Alaska         AK         02 MULTIPOLYGON (((-2390688 -2...
2               Alabama         AL         01 MULTIPOLYGON (((1091785 -13...
3              Arkansas         AR         05 MULTIPOLYGON (((482022.2 -9...
4               Arizona         AZ         04 MULTIPOLYGON (((-1386064 -1...
5            California         CA         06 MULTIPOLYGON (((-1716581 -1...
6              Colorado         CO         08 MULTIPOLYGON (((-787705.6 -...
7           Connecticut         CT         09 MULTIPOLYGON (((2156162 -83...
8  District of Columbia         DC         11 MULTIPOLYGON (((1950799 -40...
9              Delaware         DE         10 MULTIPOLYGON (((2037480 -28...
10              Florida         FL         12 MULTIPOLYGON (((1853163 -20...
```
]

---
count: false
 
## Example with geographic regions: US states

### Step 1. prepare data
.panel1-us_states_ref-auto[

``` r
library(dplyr)

# Step 1. prep some of their geo reference data
usmapdata::us_map() |>
  select(state_name = full,  # id col
         state_abbr = abbr,
         state_fips = fips,
         geometry = geom) ->
*us_states_ref
```
]
 
.panel2-us_states_ref-auto[

]

---

### Step 1b

``` r
options(ggregions.ref.regions = us_states_ref)
```

---

### Step 2. Plot w/ aes(region = my_var) + geom_region()

``` r
ggplot(data = us_income) + 
  aes(region = NAME) + 
  geom_region() + 
  aes(fill = income)
```

![](asa-cowy-ggregions_files/figure-html/unnamed-chunk-6-1.png)

---

### Option 2. Step 1b. write user-facing functions with ggregion 'write' utilities

When you anticipate multiple regional types

``` r
library(ggregions)

geom_state <- write_geom_region(us_states_ref)
stamp_state <- write_stamp_region(us_states_ref)
geom_state_text <- write_geom_region_text(us_states_ref)
stamp_state_text <- write_stamp_region_text(us_states_ref)
```

``` r
geom_county <- write_geom_region(us_county_ref)
stamp_county <- write_stamp_region(us_county_ref)
geom_county_text <- write_geom_region_text(us_county_ref)
stamp_county_text <- write_stamp_region_text(us_county_ref)
```

---

## Option 2. Step 2. Plot!

``` r
ggplot(data = us_income) + 
  aes(region = NAME) + 
* geom_state() +  # scope is locked-in
  aes(fill = income)
```

![](asa-cowy-ggregions_files/figure-html/unnamed-chunk-9-1.png)

---

### Step 2b. (optional) Inspect user-facing function

``` r
geom_state
```

```
function (mapping = aes(), data = NULL, stat = StatRegion, position = "identity", 
    na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ref_data = us_states_ref, 
    ...) 
{
    c(layer_sf(geom = GeomSf, data = data, mapping = mapping, 
        stat = stat, position = position, show.legend = show.legend, 
        inherit.aes = inherit.aes, params = rlang::list2(na.rm = na.rm, 
            ref_data = ref_data, ...)), coord_sf(crs = st_crs_mod(ref_data)))
}
<environment: namespace:ggregions>
```

---

#### Steb 2bb (for the truely curious) Inspect StatRegions compute

``` r
ggregions:::StatRegion$compute_panel
```

```
<ggproto method>
  <Wrapper function>
    function (...) 
compute_panel(...)

<Inner function (f)>
    function (data, scales, ref_data, keep = NULL, drop = NULL, stamp = F) 
{
    ref_data$id <- ref_data[1][[1]]
    if (!is.null(keep)) {
        ref_data <- dplyr::filter(ref_data, id %in% keep)
    }
    if (!is.null(drop)) {
        ref_data <- dplyr::filter(ref_data, !(id %in% drop))
    }
    if (!stamp) {
        ref_data_long <- pivot_longer(mutate(ref_data, across(-geometry, 
            as.character)), cols = -c(geometry, id), names_to = ".id_type", 
            values_to = "region")
        length(ref_data_long$region) == length(unique(ref_data_long$region))
        ref_data_long <- ggplot2::StatSfCoordinates$compute_group(ggplot2::StatSf$compute_panel(ref_data_long, 
            coord = ggplot2::CoordSf), coord = ggplot2::CoordSf)
        out <- dplyr::inner_join(ref_data_long, data, by = join_by(region))
        inner_join(sf::st_drop_geometry(ref_data), out, by = join_by(id))
    }
    else {
        ggplot2::StatSfCoordinates$compute_group(ggplot2::StatSf$compute_panel(ref_data, 
            coord = ggplot2::CoordSf), coord = ggplot2::CoordSf)
    }
}
```

---

### What does `ggregions::write_geom_region()` do?

``` r
ggregions::write_geom_region
```

```
function (ref_data) 
{
    modified_fun <- geom_region
    formals(modified_fun)$ref_data <- substitute(ref_data)
    return(modified_fun)
}
<bytecode: 0x117758710>
<environment: namespace:ggregions>
```

``` r
ggregions:::geom_region
```

```
function (mapping = aes(), data = NULL, stat = StatRegion, position = "identity", 
    na.rm = FALSE, show.legend = NA, inherit.aes = TRUE, ref_data = getOption("ggregions.ref.regions", 
        ref_data_us), ...) 
{
    c(layer_sf(geom = GeomSf, data = data, mapping = mapping, 
        stat = stat, position = position, show.legend = show.legend, 
        inherit.aes = inherit.aes, params = rlang::list2(na.rm = na.rm, 
            ref_data = ref_data, ...)), coord_sf(crs = st_crs_mod(ref_data)))
}
<bytecode: 0x117757440>
<environment: namespace:ggregions>
```

---

``` r
*library(ggplot2)
```
]
 
.panel2-usmapdata-auto[

]

---
count: false

``` r
library(ggplot2)
*library(usmapdata)
```
]
 
.panel2-usmapdata-auto[

]

---
count: false