featurette

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background-image: url(https://images.unsplash.com/photo-1528830639624-fc16697bb8e7?q=80&w=1470&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D)
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# <span style="color:burlywood">faceting in ggplot2 with reference data layer</span>

### <span style="color:burlywood">.tiny[Gina Reynolds | 2024-02-07 | Image credit: Georgi Petrov, Upsplash]</span>

---

### <span style="color:burlywood">Small multiples (facets) are a powerful tool.</span>
--

### <span style="color:burlywood">They break up and display data by category, so patterns within each category can be appreciated.</span>
--

### <span style="color:burlywood">But a simple facet means that comparison to the whole becomes more of an exercise in mental gymnastics.</span>

### <span style="color:burlywood">A solution is to present a reference layer of all the data beneath the featured category data.  Let's look at the syntax in ggplot2.</span>

---

count: false

.panel1-feature-auto[

```r
*library(tidyverse)
```
]
 
.panel2-feature-auto[

]

---
count: false

.panel1-feature-auto[

```r
library(tidyverse)

*palmerpenguins::penguins
```
]
 
.panel2-feature-auto[

```
## # A tibble: 344 × 8
##    species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
##    <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
##  1 Adelie  Torgersen           39.1          18.7               181        3750
##  2 Adelie  Torgersen           39.5          17.4               186        3800
##  3 Adelie  Torgersen           40.3          18                 195        3250
##  4 Adelie  Torgersen           NA            NA                  NA          NA
##  5 Adelie  Torgersen           36.7          19.3               193        3450
##  6 Adelie  Torgersen           39.3          20.6               190        3650
##  7 Adelie  Torgersen           38.9          17.8               181        3625
##  8 Adelie  Torgersen           39.2          19.6               195        4675
##  9 Adelie  Torgersen           34.1          18.1               193        3475
## 10 Adelie  Torgersen           42            20.2               190        4250
## # ℹ 334 more rows
## # ℹ 2 more variables: sex <fct>, year <int>
```
]

---
count: false

.panel1-feature-auto[

```r
library(tidyverse)

palmerpenguins::penguins |>
* ggplot(aes(x = bill_length_mm, y = bill_depth_mm))
```
]
 
.panel2-feature-auto[
![](facet-ref-shadow_files/figure-html/feature_auto_03_output-1.png)
]

---
count: false

.panel1-feature-auto[

```r
library(tidyverse)

palmerpenguins::penguins |>
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) +
* facet_wrap(~ species)
```
]
 
.panel2-feature-auto[
![](facet-ref-shadow_files/figure-html/feature_auto_04_output-1.png)
]

---
count: false

.panel1-feature-auto[

```r
library(tidyverse)

palmerpenguins::penguins |>
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) +
  facet_wrap(~ species) +
* ggstamp::theme_void_fill("linen")
```
]
 
.panel2-feature-auto[
![](facet-ref-shadow_files/figure-html/feature_auto_05_output-1.png)
]

---
count: false

.panel1-feature-auto[

```r
library(tidyverse)

palmerpenguins::penguins |>
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) +
  facet_wrap(~ species) +
  ggstamp::theme_void_fill("linen") +
# plot all data in each species facet
* geom_point(data =
*              . %>% select(-species),
*            size = 3, shape = 21, stroke = .7,
*            color = "burlywood4", fill= "white",
*            alpha = .7)
```
]
 
.panel2-feature-auto[
![](facet-ref-shadow_files/figure-html/feature_auto_06_output-1.png)
]

---
count: false

.panel1-feature-auto[

```r
library(tidyverse)

palmerpenguins::penguins |>
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) +
  facet_wrap(~ species) +
  ggstamp::theme_void_fill("linen") +
# plot all data in each species facet
  geom_point(data =
               . %>% select(-species),
             size = 3, shape = 21, stroke = .7,
             color = "burlywood4", fill= "white",
             alpha = .7) +
# use global data and plot each species in its facet
* geom_point(size = 3, shape = 21, stroke = .2,
*            color = "white", fill = "burlywood4")
```
]
 
.panel2-feature-auto[
![](facet-ref-shadow_files/figure-html/feature_auto_07_output-1.png)
]

---

class: center, middle

# <span style="color:burlywood">Discussion</span>

---

### In the first layer, *all* data is displayed.  This is because we've facetted by species, a variable we've remove via . %>% select(-species)

```r
  geom_point(
*   data = . %>% select(-species),
    size = 3, shape = 21, stroke = .7,
    color = "burlywood4", fill= "white",
    alpha = .7
    )
```

---

### The second layer, shows the category of interest, because the data it uses is the *globally declared data*, which contains the species faceting variable.

### Colors selection draws our attention to this layer.  A point with shape = 21, is a circle with a perimiter so a fill color and boarder color may be specified.

```r
  geom_point(
    size = 3, shape = 21, stroke = .2,
*   color = "white", fill = "burlywood4"
  ) 
```

---