featurette

class: inverse, left, bottom
background-image: url(https://images.unsplash.com/photo-1561336313-0bd5e0b27ec8?q=80&w=1470&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D)
background-size: cover
# .Large[# Dec 6th]
## .small[A [{ggsample}](https://github.com/EvaMaeRey/ggsample) feature]
#### .tiny[Gina Reynolds | 2023-10-25 |Image credit: René Porter, Upsplash]
???
Title

---

count: false

.panel1-feature-auto[

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

]

---
count: false

.panel1-feature-auto[

```r
# tidytuesday data
library(tidyverse)
*library(ggsample)
```
]
 
.panel2-feature-auto[

]

---
count: false

.panel1-feature-auto[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

*mtcars
```
]
 
.panel2-feature-auto[

```
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
```
]

---
count: false

.panel1-feature-auto[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

mtcars %>%
* ggplot()
```
]
 
.panel2-feature-auto[
![](ggsample_files/figure-html/feature_auto_04_output-1.png)
]

---
count: false

.panel1-feature-auto[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

mtcars %>%
  ggplot() +
* aes(x = wt, y = mpg)
```
]
 
.panel2-feature-auto[
![](ggsample_files/figure-html/feature_auto_05_output-1.png)
]

---
count: false

.panel1-feature-auto[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

mtcars %>%
  ggplot() +
  aes(x = wt, y = mpg) +
* geom_point(alpha = .5)
```
]
 
.panel2-feature-auto[
![](ggsample_files/figure-html/feature_auto_06_output-1.png)
]

---
count: false

.panel1-feature-auto[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

mtcars %>%
  ggplot() +
  aes(x = wt, y = mpg) +
  geom_point(alpha = .5) +
* theme_gray(base_size = 18)
```
]
 
.panel2-feature-auto[
![](ggsample_files/figure-html/feature_auto_07_output-1.png)
]

---
count: false

.panel1-feature-auto[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

mtcars %>%
  ggplot() +
  aes(x = wt, y = mpg) +
  geom_point(alpha = .5) +
  theme_gray(base_size = 18) +
* geom_smooth(method = lm, se = F)
```
]
 
.panel2-feature-auto[
![](ggsample_files/figure-html/feature_auto_08_output-1.png)
]

---
count: false

.panel1-feature-auto[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

mtcars %>%
  ggplot() +
  aes(x = wt, y = mpg) +
  geom_point(alpha = .5) +
  theme_gray(base_size = 18) +
  geom_smooth(method = lm, se = F) +
* ggxmean::geom_lm_formula(size = 8)
```
]
 
.panel2-feature-auto[
![](ggsample_files/figure-html/feature_auto_09_output-1.png)
]

---
count: false

.panel1-feature-auto[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

mtcars %>%
  ggplot() +
  aes(x = wt, y = mpg) +
  geom_point(alpha = .5) +
  theme_gray(base_size = 18) +
  geom_smooth(method = lm, se = F) +
  ggxmean::geom_lm_formula(size = 8) +
* coord_cartesian(xlim = c(1,6),
*                 ylim = c(5,35))
```
]
 
.panel2-feature-auto[
![](ggsample_files/figure-html/feature_auto_10_output-1.png)
]

---

## Demoing:

- ### facet_sample(n_sampled = 15, n_facets = 4)

- ### facet_sample_prop(n_facets = 9, prop = .3)

- ### facet_scramble(n_facets = 4)

- ### facet_bootstrap(n_facets = 9) + geom_count()

- ### facet_bootstrap(n_facets = **1**) + geom_count() 
--
*look at individual samples*

### Background... mostly borrowing from The facet_* functions mostly borrow from the ggplot2 extension vignette [(extending existing facet function)](https://github.com/tidyverse/ggplot2/blob/5f518d02af27160ab98fed736a472321d72d10d2/vignettes/extending-ggplot2.Rmd#L1028)

---

count: false

.panel1-feature1-rotate[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

mtcars %>%
  ggplot() +
  aes(x = wt, y = mpg) +
  geom_point(alpha = .5) +
  theme_gray(base_size = 20) +
  geom_smooth(method = lm, se = F) +
  ggxmean::geom_lm_formula(size = 5) +
  coord_cartesian(xlim = c(1,6),
                  ylim = c(5,35)) +
* facet_sample(n_sampled = 15, n_facets = 4)
```
]
 
.panel2-feature1-rotate[
![](ggsample_files/figure-html/feature1_rotate_01_output-1.png)
]

---
count: false

.panel1-feature1-rotate[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

mtcars %>%
  ggplot() +
  aes(x = wt, y = mpg) +
  geom_point(alpha = .5) +
  theme_gray(base_size = 20) +
  geom_smooth(method = lm, se = F) +
  ggxmean::geom_lm_formula(size = 5) +
  coord_cartesian(xlim = c(1,6),
                  ylim = c(5,35)) +
* facet_sample_prop(n_facets = 9, prop = .3)
```
]
 
.panel2-feature1-rotate[
![](ggsample_files/figure-html/feature1_rotate_02_output-1.png)
]

---
count: false

.panel1-feature1-rotate[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

mtcars %>%
  ggplot() +
  aes(x = wt, y = mpg) +
  geom_point(alpha = .5) +
  theme_gray(base_size = 20) +
  geom_smooth(method = lm, se = F) +
  ggxmean::geom_lm_formula(size = 5) +
  coord_cartesian(xlim = c(1,6),
                  ylim = c(5,35)) +
* facet_scramble(n_facets = 4)
```
]
 
.panel2-feature1-rotate[
![](ggsample_files/figure-html/feature1_rotate_03_output-1.png)
]

---
count: false

.panel1-feature1-rotate[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

mtcars %>%
  ggplot() +
  aes(x = wt, y = mpg) +
  geom_point(alpha = .5) +
  theme_gray(base_size = 20) +
  geom_smooth(method = lm, se = F) +
  ggxmean::geom_lm_formula(size = 5) +
  coord_cartesian(xlim = c(1,6),
                  ylim = c(5,35)) +
* facet_bootstrap(n_facets = 9) + geom_count()
```
]
 
.panel2-feature1-rotate[
![](ggsample_files/figure-html/feature1_rotate_04_output-1.png)
]

---
count: false

.panel1-feature1-rotate[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

mtcars %>%
  ggplot() +
  aes(x = wt, y = mpg) +
  geom_point(alpha = .5) +
  theme_gray(base_size = 20) +
  geom_smooth(method = lm, se = F) +
  ggxmean::geom_lm_formula(size = 5) +
  coord_cartesian(xlim = c(1,6),
                  ylim = c(5,35)) +
* facet_bootstrap(n_facets = 1) + geom_count()
```
]
 
.panel2-feature1-rotate[
![](ggsample_files/figure-html/feature1_rotate_05_output-1.png)
]

---
count: false

.panel1-feature1-rotate[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

mtcars %>%
  ggplot() +
  aes(x = wt, y = mpg) +
  geom_point(alpha = .5) +
  theme_gray(base_size = 20) +
  geom_smooth(method = lm, se = F) +
  ggxmean::geom_lm_formula(size = 5) +
  coord_cartesian(xlim = c(1,6),
                  ylim = c(5,35)) +
* facet_bootstrap(n_facets = 1) + geom_count()
```
]
 
.panel2-feature1-rotate[
![](ggsample_files/figure-html/feature1_rotate_06_output-1.png)
]

---
count: false

.panel1-feature1-rotate[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

mtcars %>%
  ggplot() +
  aes(x = wt, y = mpg) +
  geom_point(alpha = .5) +
  theme_gray(base_size = 20) +
  geom_smooth(method = lm, se = F) +
  ggxmean::geom_lm_formula(size = 5) +
  coord_cartesian(xlim = c(1,6),
                  ylim = c(5,35)) +
* facet_bootstrap(n_facets = 1) + geom_count()
```
]
 
.panel2-feature1-rotate[
![](ggsample_files/figure-html/feature1_rotate_07_output-1.png)
]

---
count: false

.panel1-feature1-rotate[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

mtcars %>%
  ggplot() +
  aes(x = wt, y = mpg) +
  geom_point(alpha = .5) +
  theme_gray(base_size = 20) +
  geom_smooth(method = lm, se = F) +
  ggxmean::geom_lm_formula(size = 5) +
  coord_cartesian(xlim = c(1,6),
                  ylim = c(5,35)) +
* facet_bootstrap(n_facets = 1) + geom_count()
```
]
 
.panel2-feature1-rotate[
![](ggsample_files/figure-html/feature1_rotate_08_output-1.png)
]

---
count: false

.panel1-feature1-rotate[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

mtcars %>%
  ggplot() +
  aes(x = wt, y = mpg) +
  geom_point(alpha = .5) +
  theme_gray(base_size = 20) +
  geom_smooth(method = lm, se = F) +
  ggxmean::geom_lm_formula(size = 5) +
  coord_cartesian(xlim = c(1,6),
                  ylim = c(5,35)) +
* facet_bootstrap(n_facets = 1) + geom_count()
```
]
 
.panel2-feature1-rotate[
![](ggsample_files/figure-html/feature1_rotate_09_output-1.png)
]

---
count: false

.panel1-feature1-rotate[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

mtcars %>%
  ggplot() +
  aes(x = wt, y = mpg) +
  geom_point(alpha = .5) +
  theme_gray(base_size = 20) +
  geom_smooth(method = lm, se = F) +
  ggxmean::geom_lm_formula(size = 5) +
  coord_cartesian(xlim = c(1,6),
                  ylim = c(5,35)) +
* facet_bootstrap(n_facets = 1) + geom_count()
```
]
 
.panel2-feature1-rotate[
![](ggsample_files/figure-html/feature1_rotate_10_output-1.png)
]

---
count: false

.panel1-feature1-rotate[

```r
# tidytuesday data
library(tidyverse)
library(ggsample)

mtcars %>%
  ggplot() +
  aes(x = wt, y = mpg) +
  geom_point(alpha = .5) +
  theme_gray(base_size = 20) +
  geom_smooth(method = lm, se = F) +
  ggxmean::geom_lm_formula(size = 5) +
  coord_cartesian(xlim = c(1,6),
                  ylim = c(5,35)) +
* facet_bootstrap(n_facets = 1) + geom_count()
```
]
 
.panel2-feature1-rotate[
![](ggsample_files/figure-html/feature1_rotate_11_output-1.png)
]

---

### Contribute

- https://github.com/EvaMaeRey/ggsample

---

### Check out flipbookr, used to build this featurette

- https://github.com/EvaMaeRey/flipbookr
- discussion: https://github.com/EvaMaeRey/flipbookr/blob/master/docs/draft_jasa_submission.pdf

---

### Check out more featurettes

- https://EvaMaeRey.github.io/featurette