{ggplot2} and extensions geoms

# {ggplot2} and extensions geoms
### Gina Reynolds

---

- ggplot2
- ggforce
- ggbump
- ggpointdensity
- ggrepel
- packcircles
- waffle

---

# 'Nouns':  The geom_*() line up

---
class: inverse, center, middle
name: cc

# continous x and y

---

```r
*ggplot(data = gapminder_2002)
```
]
 
.right-output-continuous_continuous_build-auto[
<img src="geoms_files/figure-html/continuous_continuous_build_auto_1_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
* aes(x = gdpPercap/1000)
```
]
 
.right-output-continuous_continuous_build-auto[
<img src="geoms_files/figure-html/continuous_continuous_build_auto_2_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
* aes(y = lifeExp)
```
]
 
.right-output-continuous_continuous_build-auto[
<img src="geoms_files/figure-html/continuous_continuous_build_auto_3_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
* coord_cartesian(xlim = c(0,50), ylim = c(35,90))
```
]
 
.right-output-continuous_continuous_build-auto[
<img src="geoms_files/figure-html/continuous_continuous_build_auto_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
* geom_point()
```
]
 
.right-output-continuous_continuous_build-auto[
<img src="geoms_files/figure-html/continuous_continuous_build_auto_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* theme_minimal()
```
]
 
.right-output-continuous_continuous_build-auto[
<img src="geoms_files/figure-html/continuous_continuous_build_auto_6_output-1.png" width="100%" />
]

---

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
  geom_rug() +  
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_1_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* geom_tile(width = 3, height = 3, fill = "green", alpha = .25) +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_2_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* geom_segment(aes(xend = 0, yend = 40), alpha = .2) +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_3_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* geom_curve(aes(xend = 0, yend = 40), linetype = "dotted") +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* geom_rect(aes(xmin = 20, xmax = gdpPercap/1000, ymin = 40, ymax = lifeExp), alpha = .02, fill = "magenta", color = "grey") +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* geom_text(aes(label = country)) +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* geom_label(aes(label = country)) +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* stat_density_2d(aes(fill = ..density..), geom = "raster",  contour = FALSE, alpha = .5) +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_8_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* geom_density2d() +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_9_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggalt::stat_bkde2d(bandwidth=c(0.5, 4), aes(fill = ..level..), geom = "polygon") +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_10_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggalt::geom_bkde2d(bandwidth=c(0.5, 4)) +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_11_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* geom_smooth() +  #
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_12_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* geom_smooth(method = lm) +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_13_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* geom_hex(alpha = .5,color = "white") +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_14_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* geom_hex(bins = 12, alpha = .9, color = "white") +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_15_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggforce::geom_voronoi_segment() +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_16_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggforce::geom_voronoi_tile(fill = "green", color = "white", alpha = .5) +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_17_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggforce::geom_voronoi_tile(aes(fill = continent, group = -1L), color = "white", alpha = .5, normalize = TRUE) +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_18_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggforce::geom_voronoi_tile(aes(fill = continent, group = -1L), colour = 'black', max.radius = 3, alpha = .5) +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_19_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggforce::geom_delaunay_tile(fill = "magenta", alpha = 0.3, colour = 'black') +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_20_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggforce::geom_delaunay_segment2(aes(color = continent, group = -1L), size = 3, lineend = 'round') +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_21_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggforce::geom_bspline_closed(alpha = 0.5, fill = "cadetblue") +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_22_output-1.png" width="100%" />
]

---
class: split-40
count: false

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggforce::geom_diagonal(aes(xend = 20, yend = lifeExp - 3), color = "goldenrod4") +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_24_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggbump::geom_sigmoid(aes(group = country, xend = 20, yend = lifeExp - 3), color = "firebrick") +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_25_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* geom_path(color = "plum4") +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_26_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* geom_polygon(alpha = 0.5, fill = "plum4") +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_27_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggalt::geom_encircle(s_shape=0.5, expand=0.1, colour="purple") +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_28_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggalt::geom_encircle(aes(color = continent), s_shape=0.5, expand=0.1) +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_29_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggalt::geom_encircle(s_shape=1, expand=0.1, colour="purple") +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_30_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggalt::geom_encircle(aes(color = continent), s_shape=1, expand=0.1) +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_31_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggforce::geom_mark_hull() +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_32_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggforce::geom_mark_hull(concavity = 7) +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_33_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggforce::geom_mark_hull(aes(fill = continent)) +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_34_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggforce::geom_mark_hull(aes(fill = continent, filter = continent == "Europe")) +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_35_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggforce::geom_mark_circle() +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_36_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggforce::geom_mark_circle(aes(fill = continent)) +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_37_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggforce::geom_mark_ellipse(aes(fill = continent, filter = continent == "Europe")) +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_38_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggforce::geom_mark_rect() +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_39_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggforce::geom_mark_rect(aes(fill = continent)) +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_40_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  
  aes(x = gdpPercap/1000) +  
  aes(y = lifeExp) +  
  coord_cartesian(xlim = c(0,50), ylim = c(35,90)) +  
  geom_point() +  
* ggpointdensity::geom_pointdensity() +
  theme_minimal()  
```
]
 
.right-output-continuous_continuous-rotate[
<img src="geoms_files/figure-html/continuous_continuous_rotate_41_output-1.png" width="100%" />
]

---

x and y dense

---

```r
*set.seed(2022)
```
]
 
.right-output-dense-auto[

]

---
class: split-40
count: false

```r
set.seed(2022)  
*tibble(my_x = rnorm(2000),
*      my_y = my_x + rnorm(2000))
```
]
 
.right-output-dense-auto[

```
  # A tibble: 2,000 x 2
       my_x    my_y
      <dbl>   <dbl>
   1  0.900  0.355 
   2 -1.17  -0.387 
   3 -0.897 -0.599 
   4 -1.44   0.0747
   5 -0.331  0.582 
   6 -2.90  -2.70  
   7 -1.06   1.40  
   8  0.278 -0.248 
   9  0.749 -0.0528
  10  0.242  0.926 
  # … with 1,990 more rows
```
]

---
class: split-40
count: false

```r
set.seed(2022)  
tibble(my_x = rnorm(2000),  
       my_y = my_x + rnorm(2000)) %>%  
* ggplot()
```
]
 
.right-output-dense-auto[
<img src="geoms_files/figure-html/dense_auto_3_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
set.seed(2022)  
tibble(my_x = rnorm(2000),  
       my_y = my_x + rnorm(2000)) %>%  
  ggplot() +  
* aes(x = my_x)
```
]
 
.right-output-dense-auto[
<img src="geoms_files/figure-html/dense_auto_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
set.seed(2022)  
tibble(my_x = rnorm(2000),  
       my_y = my_x + rnorm(2000)) %>%  
  ggplot() +  
  aes(x = my_x) +  
* aes(y = my_y)
```
]
 
.right-output-dense-auto[
<img src="geoms_files/figure-html/dense_auto_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
set.seed(2022)  
tibble(my_x = rnorm(2000),  
       my_y = my_x + rnorm(2000)) %>%  
  ggplot() +  
  aes(x = my_x) +  
  aes(y = my_y) +  
* ggpointdensity::geom_pointdensity()
```
]
 
.right-output-dense-auto[
<img src="geoms_files/figure-html/dense_auto_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
set.seed(2022)  
tibble(my_x = rnorm(2000),  
       my_y = my_x + rnorm(2000)) %>%  
  ggplot() +  
  aes(x = my_x) +  
  aes(y = my_y) +  
  ggpointdensity::geom_pointdensity() +  
* scale_color_viridis_c()
```
]
 
.right-output-dense-auto[
<img src="geoms_files/figure-html/dense_auto_7_output-1.png" width="100%" />
]

---

## Questions

- How is geom_tile() different from geom_point()? Look at the help.

- What aesthetics are required for geom_rect?

- How many geometric layers are in the final plot?

- Does order of the geom_*() statements matter to the end result?  How?

---
name: single_series

## Single series

---

```r
gapminder %>%  
  filter(country == "New Zealand") %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = pop * gdpPercap) +  
  aes(group = country) +  
  geom_point() +  
  geom_col() +  
  theme_minimal()  
```
]
 
.right-output-time_series-rotate[
<img src="geoms_files/figure-html/time_series_rotate_1_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country == "New Zealand") %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = pop * gdpPercap) +  
  aes(group = country) +  
  geom_point() +  
* geom_segment(aes(xend = year, yend = 0)) +
  theme_minimal()  
```
]
 
.right-output-time_series-rotate[
<img src="geoms_files/figure-html/time_series_rotate_2_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country == "New Zealand") %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = pop * gdpPercap) +  
  aes(group = country) +  
  geom_point() +  
* geom_segment(aes(xend = year, yend = 0), arrow = arrow()) +
  theme_minimal()  
```
]
 
.right-output-time_series-rotate[
<img src="geoms_files/figure-html/time_series_rotate_3_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country == "New Zealand") %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = pop * gdpPercap) +  
  aes(group = country) +  
  geom_point() +  
* geom_line() +
  theme_minimal()  
```
]
 
.right-output-time_series-rotate[
<img src="geoms_files/figure-html/time_series_rotate_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country == "New Zealand") %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = pop * gdpPercap) +  
  aes(group = country) +  
  geom_point() +  
* ggforce::geom_link2(aes(color = year, size = year, group = 1), lineend = 'round', alpha = .5) +
  theme_minimal()  
```
]
 
.right-output-time_series-rotate[
<img src="geoms_files/figure-html/time_series_rotate_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country == "New Zealand") %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = pop * gdpPercap) +  
  aes(group = country) +  
  geom_point() +  
* geom_line(aes(color = year, group = 1)) +
  theme_minimal()  
```
]
 
.right-output-time_series-rotate[
<img src="geoms_files/figure-html/time_series_rotate_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country == "New Zealand") %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = pop * gdpPercap) +  
  aes(group = country) +  
  geom_point() +  
* geom_area(alpha = .4, fill = "plum1", color = "plum1") +
  theme_minimal()  
```
]
 
.right-output-time_series-rotate[
<img src="geoms_files/figure-html/time_series_rotate_7_output-1.png" width="100%" />
]

---

```r
set.seed(2022)  
tibble(my_x = 1990:2010,  
       my_y = rnorm(21)) %>%  
  ggplot() +  
  aes(x = my_x) +  
  aes(y = my_y) +  
  geom_point() +  
  aes(color = my_y) +  
  geom_line() +  
  scale_color_viridis_c()  
```
]
 
.right-output-jagged-rotate[
<img src="geoms_files/figure-html/jagged_rotate_1_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
set.seed(2022)  
tibble(my_x = 1990:2010,  
       my_y = rnorm(21)) %>%  
  ggplot() +  
  aes(x = my_x) +  
  aes(y = my_y) +  
  geom_point() +  
  aes(color = my_y) +  
* geom_smooth() +
  scale_color_viridis_c()  
```
]
 
.right-output-jagged-rotate[
<img src="geoms_files/figure-html/jagged_rotate_2_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
set.seed(2022)  
tibble(my_x = 1990:2010,  
       my_y = rnorm(21)) %>%  
  ggplot() +  
  aes(x = my_x) +  
  aes(y = my_y) +  
  geom_point() +  
  aes(color = my_y) +  
* ggforce::geom_link() +
  scale_color_viridis_c()  
```
]
 
.right-output-jagged-rotate[
<img src="geoms_files/figure-html/jagged_rotate_3_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
set.seed(2022)  
tibble(my_x = 1990:2010,  
       my_y = rnorm(21)) %>%  
  ggplot() +  
  aes(x = my_x) +  
  aes(y = my_y) +  
  geom_point() +  
  aes(color = my_y) +  
* ggforce::geom_bspline() +
  scale_color_viridis_c()  
```
]
 
.right-output-jagged-rotate[
<img src="geoms_files/figure-html/jagged_rotate_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
set.seed(2022)  
tibble(my_x = 1990:2010,  
       my_y = rnorm(21)) %>%  
  ggplot() +  
  aes(x = my_x) +  
  aes(y = my_y) +  
  geom_point() +  
  aes(color = my_y) +  
* ggforce::geom_bezier(aes(group = 1)) +
  scale_color_viridis_c()  
```
]
 
.right-output-jagged-rotate[
<img src="geoms_files/figure-html/jagged_rotate_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
set.seed(2022)  
tibble(my_x = 1990:2010,  
       my_y = rnorm(21)) %>%  
  ggplot() +  
  aes(x = my_x) +  
  aes(y = my_y) +  
  geom_point() +  
  aes(color = my_y) +  
* ggalt::geom_xspline(spline_shape = 0.4) +
  scale_color_viridis_c()  
```
]
 
.right-output-jagged-rotate[
<img src="geoms_files/figure-html/jagged_rotate_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
set.seed(2022)  
tibble(my_x = 1990:2010,  
       my_y = rnorm(21)) %>%  
  ggplot() +  
  aes(x = my_x) +  
  aes(y = my_y) +  
  geom_point() +  
  aes(color = my_y) +  
* ggalt::geom_xspline(spline_shape = 0.8) +
  scale_color_viridis_c()  
```
]
 
.right-output-jagged-rotate[
<img src="geoms_files/figure-html/jagged_rotate_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
set.seed(2022)  
tibble(my_x = 1990:2010,  
       my_y = rnorm(21)) %>%  
  ggplot() +  
  aes(x = my_x) +  
  aes(y = my_y) +  
  geom_point() +  
  aes(color = my_y) +  
* ggalt::geom_xspline(spline_shape = 0.1) +
  scale_color_viridis_c()  
```
]
 
.right-output-jagged-rotate[
<img src="geoms_files/figure-html/jagged_rotate_8_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
set.seed(2022)  
tibble(my_x = 1990:2010,  
       my_y = rnorm(21)) %>%  
  ggplot() +  
  aes(x = my_x) +  
  aes(y = my_y) +  
  geom_point() +  
  aes(color = my_y) +  
* ggalt::geom_xspline(spline_shape = -1, size = 0.5) +
  scale_color_viridis_c()  
```
]
 
.right-output-jagged-rotate[
<img src="geoms_files/figure-html/jagged_rotate_9_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
set.seed(2022)  
tibble(my_x = 1990:2010,  
       my_y = rnorm(21)) %>%  
  ggplot() +  
  aes(x = my_x) +  
  aes(y = my_y) +  
  geom_point() +  
  aes(color = my_y) +  
* ggforce::geom_link2(aes(size = my_y, group = 1), lineend = 'round', alpha = .5) +
  scale_color_viridis_c()  
```
]
 
.right-output-jagged-rotate[
<img src="geoms_files/figure-html/jagged_rotate_10_output-1.png" width="100%" />
]

---

```r
*gapminder
```
]
 
.right-output-polar_bar-auto[

```
  # A tibble: 1,704 x 6
     country     continent  year lifeExp      pop gdpPercap
     <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
   1 Afghanistan Asia       1952    28.8  8425333      779.
   2 Afghanistan Asia       1957    30.3  9240934      821.
   3 Afghanistan Asia       1962    32.0 10267083      853.
   4 Afghanistan Asia       1967    34.0 11537966      836.
   5 Afghanistan Asia       1972    36.1 13079460      740.
   6 Afghanistan Asia       1977    38.4 14880372      786.
   7 Afghanistan Asia       1982    39.9 12881816      978.
   8 Afghanistan Asia       1987    40.8 13867957      852.
   9 Afghanistan Asia       1992    41.7 16317921      649.
  10 Afghanistan Asia       1997    41.8 22227415      635.
  # … with 1,694 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
* filter(continent == "Oceania")
```
]
 
.right-output-polar_bar-auto[

```
  # A tibble: 24 x 6
     country   continent  year lifeExp      pop gdpPercap
     <fct>     <fct>     <int>   <dbl>    <int>     <dbl>
   1 Australia Oceania    1952    69.1  8691212    10040.
   2 Australia Oceania    1957    70.3  9712569    10950.
   3 Australia Oceania    1962    70.9 10794968    12217.
   4 Australia Oceania    1967    71.1 11872264    14526.
   5 Australia Oceania    1972    71.9 13177000    16789.
   6 Australia Oceania    1977    73.5 14074100    18334.
   7 Australia Oceania    1982    74.7 15184200    19477.
   8 Australia Oceania    1987    76.3 16257249    21889.
   9 Australia Oceania    1992    77.6 17481977    23425.
  10 Australia Oceania    1997    78.8 18565243    26998.
  # … with 14 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
* mutate(pop_millions = pop/1000000)
```
]
 
.right-output-polar_bar-auto[

```
  # A tibble: 24 x 7
     country   continent  year lifeExp      pop gdpPercap pop_millions
     <fct>     <fct>     <int>   <dbl>    <int>     <dbl>        <dbl>
   1 Australia Oceania    1952    69.1  8691212    10040.         8.69
   2 Australia Oceania    1957    70.3  9712569    10950.         9.71
   3 Australia Oceania    1962    70.9 10794968    12217.        10.8 
   4 Australia Oceania    1967    71.1 11872264    14526.        11.9 
   5 Australia Oceania    1972    71.9 13177000    16789.        13.2 
   6 Australia Oceania    1977    73.5 14074100    18334.        14.1 
   7 Australia Oceania    1982    74.7 15184200    19477.        15.2 
   8 Australia Oceania    1987    76.3 16257249    21889.        16.3 
   9 Australia Oceania    1992    77.6 17481977    23425.        17.5 
  10 Australia Oceania    1997    78.8 18565243    26998.        18.6 
  # … with 14 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) %>%  
* arrange(-pop)
```
]
 
.right-output-polar_bar-auto[

```
  # A tibble: 24 x 7
     country   continent  year lifeExp      pop gdpPercap pop_millions
     <fct>     <fct>     <int>   <dbl>    <int>     <dbl>        <dbl>
   1 Australia Oceania    2007    81.2 20434176    34435.         20.4
   2 Australia Oceania    2002    80.4 19546792    30688.         19.5
   3 Australia Oceania    1997    78.8 18565243    26998.         18.6
   4 Australia Oceania    1992    77.6 17481977    23425.         17.5
   5 Australia Oceania    1987    76.3 16257249    21889.         16.3
   6 Australia Oceania    1982    74.7 15184200    19477.         15.2
   7 Australia Oceania    1977    73.5 14074100    18334.         14.1
   8 Australia Oceania    1972    71.9 13177000    16789.         13.2
   9 Australia Oceania    1967    71.1 11872264    14526.         11.9
  10 Australia Oceania    1962    70.9 10794968    12217.         10.8
  # … with 14 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) %>%  
  arrange(-pop) %>%  
* ggplot()
```
]
 
.right-output-polar_bar-auto[
<img src="geoms_files/figure-html/polar_bar_auto_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) %>%  
  arrange(-pop) %>%  
  ggplot() +  
* aes(x = year)
```
]
 
.right-output-polar_bar-auto[
<img src="geoms_files/figure-html/polar_bar_auto_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) %>%  
  arrange(-pop) %>%  
  ggplot() +  
  aes(x = year) +  
* aes(y = pop_millions)
```
]
 
.right-output-polar_bar-auto[
<img src="geoms_files/figure-html/polar_bar_auto_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) %>%  
  arrange(-pop) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = pop_millions) +  
* geom_col(width = 4, position = "dodge")
```
]
 
.right-output-polar_bar-auto[
<img src="geoms_files/figure-html/polar_bar_auto_8_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) %>%  
  arrange(-pop) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = pop_millions) +  
  geom_col(width = 4, position = "dodge") +  
* coord_polar()
```
]
 
.right-output-polar_bar-auto[
<img src="geoms_files/figure-html/polar_bar_auto_9_output-1.png" width="100%" />
]

---
class: split-40
count: false

---
class: split-40
count: false

---
class: split-40
count: false

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) %>%  
  arrange(-pop) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = pop_millions) +  
  geom_col(width = 4, position = "dodge") +  
  coord_polar() +  
  scale_y_continuous(limits = c(-10, 25)) +  
  scale_x_continuous(breaks = seq(1952, 2007, by = 5),  
                     limits = c(1950, 2010)) +  
  aes(fill = country) +  
* geom_text(data = . %>% filter(country == "Australia"),
*           aes(label = year,
*               y = pop_millions + 3),
*           fontface = "bold",
*           color = "navy")
```
]
 
.right-output-polar_bar-auto[
<img src="geoms_files/figure-html/polar_bar_auto_13_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) %>%  
  arrange(-pop) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = pop_millions) +  
  geom_col(width = 4, position = "dodge") +  
  coord_polar() +  
  scale_y_continuous(limits = c(-10, 25)) +  
  scale_x_continuous(breaks = seq(1952, 2007, by = 5),  
                     limits = c(1950, 2010)) +  
  aes(fill = country) +  
  geom_text(data = . %>% filter(country == "Australia"),  
            aes(label = year,  
                y = pop_millions + 3),  
            fontface = "bold",  
            color = "navy") +  
* theme_void()
```
]
 
.right-output-polar_bar-auto[
<img src="geoms_files/figure-html/polar_bar_auto_14_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) %>%  
  arrange(-pop) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = pop_millions) +  
  geom_col(width = 4, position = "dodge") +  
  coord_polar() +  
  scale_y_continuous(limits = c(-10, 25)) +  
  scale_x_continuous(breaks = seq(1952, 2007, by = 5),  
                     limits = c(1950, 2010)) +  
  aes(fill = country) +  
  geom_text(data = . %>% filter(country == "Australia"),  
            aes(label = year,  
                y = pop_millions + 3),  
            fontface = "bold",  
            color = "navy") +  
  theme_void() +  
* theme(legend.position = c(.8,.8))
```
]
 
.right-output-polar_bar-auto[
<img src="geoms_files/figure-html/polar_bar_auto_15_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) %>%  
  arrange(-pop) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = pop_millions) +  
  geom_col(width = 4, position = "dodge") +  
  coord_polar() +  
  scale_y_continuous(limits = c(-10, 25)) +  
  scale_x_continuous(breaks = seq(1952, 2007, by = 5),  
                     limits = c(1950, 2010)) +  
  aes(fill = country) +  
  geom_text(data = . %>% filter(country == "Australia"),  
            aes(label = year,  
                y = pop_millions + 3),  
            fontface = "bold",  
            color = "navy") +  
  theme_void() +  
  theme(legend.position = c(.8,.8)) +  
* labs(fill = NULL)
```
]
 
.right-output-polar_bar-auto[
<img src="geoms_files/figure-html/polar_bar_auto_16_output-1.png" width="100%" />
]

---

```r
*gapminder
```
]
 
.right-output-pie_donut-auto[

---
class: split-40
count: false

```r
gapminder %>%  
* filter(year == 2002)
```
]
 
.right-output-pie_donut-auto[

```
  # A tibble: 142 x 6
     country     continent  year lifeExp       pop gdpPercap
     <fct>       <fct>     <int>   <dbl>     <int>     <dbl>
   1 Afghanistan Asia       2002    42.1  25268405      727.
   2 Albania     Europe     2002    75.7   3508512     4604.
   3 Algeria     Africa     2002    71.0  31287142     5288.
   4 Angola      Africa     2002    41.0  10866106     2773.
   5 Argentina   Americas   2002    74.3  38331121     8798.
   6 Australia   Oceania    2002    80.4  19546792    30688.
   7 Austria     Europe     2002    79.0   8148312    32418.
   8 Bahrain     Asia       2002    74.8    656397    23404.
   9 Bangladesh  Asia       2002    62.0 135656790     1136.
  10 Belgium     Europe     2002    78.3  10311970    30486.
  # … with 132 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
* count(continent)
```
]
 
.right-output-pie_donut-auto[

```
  # A tibble: 5 x 2
    continent     n
    <fct>     <int>
  1 Africa       52
  2 Americas     25
  3 Asia         33
  4 Europe       30
  5 Oceania       2
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  count(continent) %>%  
* mutate(text_pos_y = sum(n) - cumsum(n))
```
]
 
.right-output-pie_donut-auto[

```
  # A tibble: 5 x 3
    continent     n text_pos_y
    <fct>     <int>      <int>
  1 Africa       52         90
  2 Americas     25         65
  3 Asia         33         32
  4 Europe       30          2
  5 Oceania       2          0
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  count(continent) %>%  
  mutate(text_pos_y = sum(n) - cumsum(n)) %>%  
* mutate(text_pos_y = (lag(text_pos_y,
*                          default = sum(n)) +
*                      text_pos_y) / 2)
```
]
 
.right-output-pie_donut-auto[

```
  # A tibble: 5 x 3
    continent     n text_pos_y
    <fct>     <int>      <dbl>
  1 Africa       52      116  
  2 Americas     25       77.5
  3 Asia         33       48.5
  4 Europe       30       17  
  5 Oceania       2        1
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  count(continent) %>%  
  mutate(text_pos_y = sum(n) - cumsum(n)) %>%  
  mutate(text_pos_y = (lag(text_pos_y,  
                           default = sum(n)) +  
                       text_pos_y) / 2) %>%  
* ggplot()
```
]
 
.right-output-pie_donut-auto[
<img src="geoms_files/figure-html/pie_donut_auto_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  count(continent) %>%  
  mutate(text_pos_y = sum(n) - cumsum(n)) %>%  
  mutate(text_pos_y = (lag(text_pos_y,  
                           default = sum(n)) +  
                       text_pos_y) / 2) %>%  
  ggplot() +  
* aes(x = 1)
```
]
 
.right-output-pie_donut-auto[
<img src="geoms_files/figure-html/pie_donut_auto_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  count(continent) %>%  
  mutate(text_pos_y = sum(n) - cumsum(n)) %>%  
  mutate(text_pos_y = (lag(text_pos_y,  
                           default = sum(n)) +  
                       text_pos_y) / 2) %>%  
  ggplot() +  
  aes(x = 1) +  
* aes(y = n)
```
]
 
.right-output-pie_donut-auto[
<img src="geoms_files/figure-html/pie_donut_auto_8_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  count(continent) %>%  
  mutate(text_pos_y = sum(n) - cumsum(n)) %>%  
  mutate(text_pos_y = (lag(text_pos_y,  
                           default = sum(n)) +  
                       text_pos_y) / 2) %>%  
  ggplot() +  
  aes(x = 1) +  
  aes(y = n) +  
* geom_col(width = 2)
```
]
 
.right-output-pie_donut-auto[
<img src="geoms_files/figure-html/pie_donut_auto_9_output-1.png" width="100%" />
]

---
class: split-40
count: false

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  count(continent) %>%  
  mutate(text_pos_y = sum(n) - cumsum(n)) %>%  
  mutate(text_pos_y = (lag(text_pos_y,  
                           default = sum(n)) +  
                       text_pos_y) / 2) %>%  
  ggplot() +  
  aes(x = 1) +  
  aes(y = n) +  
  geom_col(width = 2) +  
  aes(fill = continent) +  
* geom_text(aes(label = continent,
*               y = text_pos_y,
*               x = 2.3 + c(.3,.1,.2,.3,0)),
*           color = "navy",
*           fontface = "bold")
```
]
 
.right-output-pie_donut-auto[
<img src="geoms_files/figure-html/pie_donut_auto_11_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  count(continent) %>%  
  mutate(text_pos_y = sum(n) - cumsum(n)) %>%  
  mutate(text_pos_y = (lag(text_pos_y,  
                           default = sum(n)) +  
                       text_pos_y) / 2) %>%  
  ggplot() +  
  aes(x = 1) +  
  aes(y = n) +  
  geom_col(width = 2) +  
  aes(fill = continent) +  
  geom_text(aes(label = continent,  
                y = text_pos_y,  
                x = 2.3 + c(.3,.1,.2,.3,0)),  
            color = "navy",  
            fontface = "bold") +  
* coord_polar(theta = "y")
```
]
 
.right-output-pie_donut-auto[
<img src="geoms_files/figure-html/pie_donut_auto_12_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  count(continent) %>%  
  mutate(text_pos_y = sum(n) - cumsum(n)) %>%  
  mutate(text_pos_y = (lag(text_pos_y,  
                           default = sum(n)) +  
                       text_pos_y) / 2) %>%  
  ggplot() +  
  aes(x = 1) +  
  aes(y = n) +  
  geom_col(width = 2) +  
  aes(fill = continent) +  
  geom_text(aes(label = continent,  
                y = text_pos_y,  
                x = 2.3 + c(.3,.1,.2,.3,0)),  
            color = "navy",  
            fontface = "bold") +  
  coord_polar(theta = "y") +  
* theme_void()
```
]
 
.right-output-pie_donut-auto[
<img src="geoms_files/figure-html/pie_donut_auto_13_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  count(continent) %>%  
  mutate(text_pos_y = sum(n) - cumsum(n)) %>%  
  mutate(text_pos_y = (lag(text_pos_y,  
                           default = sum(n)) +  
                       text_pos_y) / 2) %>%  
  ggplot() +  
  aes(x = 1) +  
  aes(y = n) +  
  geom_col(width = 2) +  
  aes(fill = continent) +  
  geom_text(aes(label = continent,  
                y = text_pos_y,  
                x = 2.3 + c(.3,.1,.2,.3,0)),  
            color = "navy",  
            fontface = "bold") +  
  coord_polar(theta = "y") +  
  theme_void() +  
* scale_x_continuous(limits = c(-3, 2.8))
```
]
 
.right-output-pie_donut-auto[
<img src="geoms_files/figure-html/pie_donut_auto_14_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  count(continent) %>%  
  mutate(text_pos_y = sum(n) - cumsum(n)) %>%  
  mutate(text_pos_y = (lag(text_pos_y,  
                           default = sum(n)) +  
                       text_pos_y) / 2) %>%  
  ggplot() +  
  aes(x = 1) +  
  aes(y = n) +  
  geom_col(width = 2) +  
  aes(fill = continent) +  
  geom_text(aes(label = continent,  
                y = text_pos_y,  
                x = 2.3 + c(.3,.1,.2,.3,0)),  
            color = "navy",  
            fontface = "bold") +  
  coord_polar(theta = "y") +  
  theme_void() +  
  scale_x_continuous(limits = c(-3, 2.8)) +  
* theme(legend.position = "none")
```
]
 
.right-output-pie_donut-auto[
<img src="geoms_files/figure-html/pie_donut_auto_15_output-1.png" width="100%" />
]

---

```r
*gapminder
```
]
 
.right-output-circle_pack-auto[

---
class: split-40
count: false

```r
gapminder %>%  
* filter(continent == "Americas")
```
]
 
.right-output-circle_pack-auto[

```
  # A tibble: 300 x 6
     country   continent  year lifeExp      pop gdpPercap
     <fct>     <fct>     <int>   <dbl>    <int>     <dbl>
   1 Argentina Americas   1952    62.5 17876956     5911.
   2 Argentina Americas   1957    64.4 19610538     6857.
   3 Argentina Americas   1962    65.1 21283783     7133.
   4 Argentina Americas   1967    65.6 22934225     8053.
   5 Argentina Americas   1972    67.1 24779799     9443.
   6 Argentina Americas   1977    68.5 26983828    10079.
   7 Argentina Americas   1982    69.9 29341374     8998.
   8 Argentina Americas   1987    70.8 31620918     9140.
   9 Argentina Americas   1992    71.9 33958947     9308.
  10 Argentina Americas   1997    73.3 36203463    10967.
  # … with 290 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
* filter(year == 2002)
```
]
 
.right-output-circle_pack-auto[

```
  # A tibble: 25 x 6
     country            continent  year lifeExp       pop gdpPercap
     <fct>              <fct>     <int>   <dbl>     <int>     <dbl>
   1 Argentina          Americas   2002    74.3  38331121     8798.
   2 Bolivia            Americas   2002    63.9   8445134     3413.
   3 Brazil             Americas   2002    71.0 179914212     8131.
   4 Canada             Americas   2002    79.8  31902268    33329.
   5 Chile              Americas   2002    77.9  15497046    10779.
   6 Colombia           Americas   2002    71.7  41008227     5755.
   7 Costa Rica         Americas   2002    78.1   3834934     7723.
   8 Cuba               Americas   2002    77.2  11226999     6341.
   9 Dominican Republic Americas   2002    70.8   8650322     4564.
  10 Ecuador            Americas   2002    74.2  12921234     5773.
  # … with 15 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
* select(country, pop)
```
]
 
.right-output-circle_pack-auto[

```
  # A tibble: 25 x 2
     country                  pop
     <fct>                  <int>
   1 Argentina           38331121
   2 Bolivia              8445134
   3 Brazil             179914212
   4 Canada              31902268
   5 Chile               15497046
   6 Colombia            41008227
   7 Costa Rica           3834934
   8 Cuba                11226999
   9 Dominican Republic   8650322
  10 Ecuador             12921234
  # … with 15 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  select(country, pop) ->  
*prep
```
]
 
.right-output-circle_pack-auto[

]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  select(country, pop) ->  
prep

*packcircles::circleProgressiveLayout(prep$pop,
*                                        sizetype = 'area')
```
]
 
.right-output-circle_pack-auto[

```
               x          y    radius
  1   -3493.0180     0.0000 3493.0180
  2    1639.5639     0.0000 1639.5639
  3    2732.7743 -9142.0260 7567.5936
  4    1150.7522  4801.4069 3186.6608
  5    5273.8171  1302.3806 2221.0049
  6   10562.3302 -1160.6508 3612.9384
  7   -4573.1172 -4469.2048 1104.8518
  8   -7453.2262 -3646.6547 1890.4139
  9   -8636.6596  -299.9554 1659.3622
  10  -7908.2513  3314.7888 2028.0425
  11  -4769.2005  4746.5765 1422.1250
  12  -3084.4567  7593.9564 1886.3390
  13   -133.6785  9366.9797 1556.1460
  14   2869.9023  9116.0827 1457.8956
  15   4409.2334  7302.3944  920.9708
  16  10986.7220  8154.0495 5711.4249
  17  -5728.2786 -6555.5701 1279.9580
  18  -7982.0775 -6463.5726  975.7177
  19  -6893.5704  6556.3419 1368.6094
  20  -6631.4420 10836.0023 2919.0711
  21  -2614.0309 10551.5165 1108.4001
  22  -1083.9386 11293.7595  592.2196
  23 -18526.1513 -6598.5236 9569.2196
  24 -11041.9619   913.3993 1034.6512
  25   4129.4010 13162.9819 2780.4686
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  select(country, pop) ->  
prep

packcircles::circleProgressiveLayout(prep$pop,  
                                         sizetype = 'area') ->  
*pack
```
]
 
.right-output-circle_pack-auto[

]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  select(country, pop) ->  
prep

packcircles::circleProgressiveLayout(prep$pop,  
                                         sizetype = 'area') ->  
pack

*pack
```
]
 
.right-output-circle_pack-auto[

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  select(country, pop) ->  
prep

packcircles::circleProgressiveLayout(prep$pop,  
                                         sizetype = 'area') ->  
pack

pack %>%  
* packcircles::circleLayoutVertices(npoints = 50)
```
]
 
.right-output-circle_pack-auto[

```
                  x             y id
  1         0.00000  0.000000e+00  1
  2       -27.54349  4.377912e+02  1
  3      -109.73958  8.686783e+02  1
  4      -245.29200  1.285866e+03  1
  5      -432.06299  1.682774e+03  1
  6      -667.10708  2.053144e+03  1
  7      -946.71746  2.391135e+03  1
  8     -1266.48453  2.691417e+03  1
  9     -1621.36536  2.949253e+03  1
  10    -2005.76327  3.160577e+03  1
  11    -2413.61608  3.322058e+03  1
  12    -2838.49170  3.431147e+03  1
  13    -3273.68959  3.486125e+03  1
  14    -3712.34642  3.486125e+03  1
  15    -4147.54431  3.431147e+03  1
  16    -4572.41993  3.322058e+03  1
  17    -4980.27273  3.160577e+03  1
  18    -5364.67064  2.949253e+03  1
  19    -5719.55147  2.691417e+03  1
  20    -6039.31854  2.391135e+03  1
  21    -6318.92893  2.053144e+03  1
  22    -6553.97301  1.682774e+03  1
  23    -6740.74401  1.285866e+03  1
  24    -6876.29642  8.686783e+02  1
  25    -6958.49251  4.377912e+02  1
  26    -6986.03600 -1.123440e-12  1
  27    -6958.49251 -4.377912e+02  1
  28    -6876.29642 -8.686783e+02  1
  29    -6740.74401 -1.285866e+03  1
  30    -6553.97301 -1.682774e+03  1
  31    -6318.92893 -2.053144e+03  1
  32    -6039.31854 -2.391135e+03  1
  33    -5719.55147 -2.691417e+03  1
  34    -5364.67064 -2.949253e+03  1
  35    -4980.27273 -3.160577e+03  1
  36    -4572.41993 -3.322058e+03  1
  37    -4147.54431 -3.431147e+03  1
  38    -3712.34642 -3.486125e+03  1
  39    -3273.68959 -3.486125e+03  1
  40    -2838.49170 -3.431147e+03  1
  41    -2413.61608 -3.322058e+03  1
  42    -2005.76327 -3.160577e+03  1
  43    -1621.36536 -2.949253e+03  1
  44    -1266.48453 -2.691417e+03  1
  45     -946.71746 -2.391135e+03  1
  46     -667.10708 -2.053144e+03  1
  47     -432.06299 -1.682774e+03  1
  48     -245.29200 -1.285866e+03  1
  49     -109.73958 -8.686783e+02  1
  50      -27.54349 -4.377912e+02  1
  51        0.00000 -8.555427e-13  1
  52     3279.12771  0.000000e+00  2
  53     3266.19926  2.054918e+02  2
  54     3227.61780  4.077430e+02  2
  55     3163.99178  6.035637e+02  2
  56     3076.32462  7.898659e+02  2
  57     2965.99888  9.637115e+02  2
  58     2834.75447  1.122359e+03  2
  59     2684.66119  1.263306e+03  2
  60     2518.08610  1.384330e+03  2
  61     2337.65620  1.483522e+03  2
  62     2146.21695  1.559318e+03  2
  63     1946.78749  1.610523e+03  2
  64     1742.51292  1.636329e+03  2
  65     1536.61479  1.636329e+03  2
  66     1332.34023  1.610523e+03  2
  67     1132.91076  1.559318e+03  2
  68      941.47152  1.483522e+03  2
  69      761.04161  1.384330e+03  2
  70      594.46652  1.263306e+03  2
  71      444.37324  1.122359e+03  2
  72      313.12883  9.637115e+02  2
  73      202.80310  7.898659e+02  2
  74      115.13594  6.035637e+02  2
  75       51.50991  4.077430e+02  2
  76       12.92845  2.054918e+02  2
  77        0.00000 -5.273240e-13  2
  78       12.92845 -2.054918e+02  2
  79       51.50991 -4.077430e+02  2
  80      115.13594 -6.035637e+02  2
  81      202.80310 -7.898659e+02  2
  82      313.12883 -9.637115e+02  2
  83      444.37324 -1.122359e+03  2
  84      594.46652 -1.263306e+03  2
  85      761.04161 -1.384330e+03  2
  86      941.47152 -1.483522e+03  2
  87     1132.91076 -1.559318e+03  2
  88     1332.34023 -1.610523e+03  2
  89     1536.61479 -1.636329e+03  2
  90     1742.51292 -1.636329e+03  2
  91     1946.78749 -1.610523e+03  2
  92     2146.21695 -1.559318e+03  2
  93     2337.65620 -1.483522e+03  2
  94     2518.08610 -1.384330e+03  2
  95     2684.66119 -1.263306e+03  2
  96     2834.75447 -1.122359e+03  2
  97     2965.99888 -9.637115e+02  2
  98     3076.32462 -7.898659e+02  2
  99     3163.99178 -6.035637e+02  2
  100    3227.61780 -4.077430e+02  2
  101    3266.19926 -2.054918e+02  2
  102    3279.12771 -4.015773e-13  2
  103   10300.36784 -9.142026e+03  3
  104   10240.69510 -8.193555e+03  3
  105   10062.61797 -7.260042e+03  3
  106    9768.94482 -6.356209e+03  3
  107    9364.30706 -5.496310e+03  3
  108    8855.08607 -4.693906e+03  3
  109    8249.31257 -3.961652e+03  3
  110    7556.53995 -3.311095e+03  3
  111    6787.69368 -2.752495e+03  3
  112    5954.89890 -2.294663e+03  3
  113    5071.28929 -1.944817e+03  3
  114    4150.79990 -1.708475e+03  3
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  901   -8504.89324 -7.287398e+03 18
  902   -8397.51794 -7.346428e+03 18
  903   -8283.59089 -7.391535e+03 18
  904   -8164.90880 -7.422008e+03 18
  905   -8043.34336 -7.437365e+03 18
  906   -7920.81171 -7.437365e+03 18
  907   -7799.24626 -7.422008e+03 18
  908   -7680.56417 -7.391535e+03 18
  909   -7566.63713 -7.346428e+03 18
  910   -7459.26183 -7.287398e+03 18
  911   -7360.13165 -7.215376e+03 18
  912   -7270.80992 -7.131497e+03 18
  913   -7192.70531 -7.037085e+03 18
  914   -7127.04957 -6.933628e+03 18
  915   -7074.87814 -6.822758e+03 18
  916   -7037.01377 -6.706224e+03 18
  917   -7014.05363 -6.585862e+03 18
  918   -7006.35981 -6.463573e+03 18
  919   -5524.96104  6.556342e+03 19
  920   -5535.75293  6.727874e+03 19
  921   -5567.95842  6.896701e+03 19
  922   -5621.06960  7.060161e+03 19
  923   -5694.24888  7.215675e+03 19
  924   -5786.34218  7.360790e+03 19
  925   -5895.89712  7.493220e+03 19
  926   -6021.18597  7.610874e+03 19
  927   -6160.23285  7.711897e+03 19
  928   -6310.84489  7.794697e+03 19
  929   -6470.64687  7.857967e+03 19
  930   -6637.11860  7.900709e+03 19
  931   -6807.63474  7.922251e+03 19
  932   -6979.50612  7.922251e+03 19
  933   -7150.02226  7.900709e+03 19
  934   -7316.49399  7.857967e+03 19
  935   -7476.29597  7.794697e+03 19
  936   -7626.90801  7.711897e+03 19
  937   -7765.95489  7.610874e+03 19
  938   -7891.24374  7.493220e+03 19
  939   -8000.79868  7.360790e+03 19
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  941   -8166.07126  7.060161e+03 19
  942   -8219.18244  6.896701e+03 19
  943   -8251.38793  6.727874e+03 19
  944   -8262.17982  6.556342e+03 19
  945   -8251.38793  6.384810e+03 19
  946   -8219.18244  6.215983e+03 19
  947   -8166.07126  6.052523e+03 19
  948   -8092.89198  5.897009e+03 19
  949   -8000.79868  5.751894e+03 19
  950   -7891.24374  5.619464e+03 19
  951   -7765.95489  5.501810e+03 19
  952   -7626.90801  5.400787e+03 19
  953   -7476.29597  5.317987e+03 19
  954   -7316.49399  5.254717e+03 19
  955   -7150.02226  5.211974e+03 19
  956   -6979.50612  5.190433e+03 19
  957   -6807.63474  5.190433e+03 19
  958   -6637.11860  5.211974e+03 19
  959   -6470.64687  5.254717e+03 19
  960   -6310.84489  5.317987e+03 19
  961   -6160.23285  5.400787e+03 19
  962   -6021.18597  5.501810e+03 19
  963   -5895.89712  5.619464e+03 19
  964   -5786.34218  5.751894e+03 19
  965   -5694.24888  5.897009e+03 19
  966   -5621.06960  6.052523e+03 19
  967   -5567.95842  6.215983e+03 19
  968   -5535.75293  6.384810e+03 19
  969   -5524.96104  6.556342e+03 19
  970   -3712.37092  1.083600e+04 20
  971   -3735.38867  1.120186e+04 20
  972   -3804.07891  1.156195e+04 20
  973   -3917.35835  1.191058e+04 20
  974   -4073.44051  1.224228e+04 20
  975   -4269.86389  1.255179e+04 20
  976   -4503.53077  1.283424e+04 20
  977   -4770.75607  1.308519e+04 20
  978   -5067.32551  1.330066e+04 20
  979   -5388.56200  1.347726e+04 20
  980   -5729.39945  1.361220e+04 20
  981   -6084.46264  1.370337e+04 20
  982   -6448.15203  1.374931e+04 20
  983   -6814.73202  1.374931e+04 20
  984   -7178.42141  1.370337e+04 20
  985   -7533.48460  1.361220e+04 20
  986   -7874.32205  1.347726e+04 20
  987   -8195.55854  1.330066e+04 20
  988   -8492.12798  1.308519e+04 20
  989   -8759.35328  1.283424e+04 20
  990   -8993.02016  1.255179e+04 20
  991   -9189.44354  1.224228e+04 20
  992   -9345.52570  1.191058e+04 20
  993   -9458.80515  1.156195e+04 20
  994   -9527.49538  1.120186e+04 20
  995   -9550.51313  1.083600e+04 20
  996   -9527.49538  1.047015e+04 20
  997   -9458.80515  1.011006e+04 20
  998   -9345.52570  9.761421e+03 20
  999   -9189.44354  9.429729e+03 20
  1000  -8993.02016  9.120215e+03 20
  1001  -8759.35328  8.837761e+03 20
  1002  -8492.12798  8.586819e+03 20
  1003  -8195.55854  8.371349e+03 20
  1004  -7874.32205  8.194748e+03 20
  1005  -7533.48460  8.059801e+03 20
  1006  -7178.42141  7.968636e+03 20
  1007  -6814.73202  7.922691e+03 20
  1008  -6448.15203  7.922691e+03 20
  1009  -6084.46264  7.968636e+03 20
  1010  -5729.39945  8.059801e+03 20
  1011  -5388.56200  8.194748e+03 20
  1012  -5067.32551  8.371349e+03 20
  1013  -4770.75607  8.586819e+03 20
  1014  -4503.53077  8.837761e+03 20
  1015  -4269.86389  9.120215e+03 20
  1016  -4073.44051  9.429729e+03 20
  1017  -3917.35835  9.761421e+03 20
  1018  -3804.07891  1.011006e+04 20
  1019  -3735.38867  1.047015e+04 20
  1020  -3712.37092  1.083600e+04 20
  1021  -1505.63083  1.055152e+04 21
  1022  -1514.37090  1.069044e+04 21
  1023  -1540.45326  1.082716e+04 21
  1024  -1583.46658  1.095955e+04 21
  1025  -1642.73252  1.108549e+04 21
  1026  -1717.31641  1.120302e+04 21
  1027  -1806.04203  1.131027e+04 21
  1028  -1907.51011  1.140555e+04 21
  1029  -2020.12045  1.148737e+04 21
  1030  -2142.09711  1.155443e+04 21
  1031  -2271.51645  1.160567e+04 21
  1032  -2406.33745  1.164028e+04 21
  1033  -2544.43390  1.165773e+04 21
  1034  -2683.62793  1.165773e+04 21
  1035  -2821.72438  1.164028e+04 21
  1036  -2956.54538  1.160567e+04 21
  1037  -3085.96472  1.155443e+04 21
  1038  -3207.94138  1.148737e+04 21
  1039  -3320.55172  1.140555e+04 21
  1040  -3422.01980  1.131027e+04 21
  1041  -3510.74542  1.120302e+04 21
  1042  -3585.32931  1.108549e+04 21
  1043  -3644.59525  1.095955e+04 21
  1044  -3687.60857  1.082716e+04 21
  1045  -3713.69093  1.069044e+04 21
  1046  -3722.43100  1.055152e+04 21
  1047  -3713.69093  1.041260e+04 21
  1048  -3687.60857  1.027587e+04 21
  1049  -3644.59525  1.014349e+04 21
  1050  -3585.32931  1.001754e+04 21
  1051  -3510.74542  9.900015e+03 21
  1052  -3422.01980  9.792764e+03 21
  1053  -3320.55172  9.697480e+03 21
  1054  -3207.94138  9.615663e+03 21
  1055  -3085.96472  9.548606e+03 21
  1056  -2956.54538  9.497365e+03 21
  1057  -2821.72438  9.462749e+03 21
  1058  -2683.62793  9.445304e+03 21
  1059  -2544.43390  9.445304e+03 21
  1060  -2406.33745  9.462749e+03 21
  1061  -2271.51645  9.497365e+03 21
  1062  -2142.09711  9.548606e+03 21
  1063  -2020.12045  9.615663e+03 21
  1064  -1907.51011  9.697480e+03 21
  1065  -1806.04203  9.792764e+03 21
  1066  -1717.31641  9.900015e+03 21
  1067  -1642.73252  1.001754e+04 21
  1068  -1583.46658  1.014349e+04 21
  1069  -1540.45326  1.027587e+04 21
  1070  -1514.37090  1.041260e+04 21
  1071  -1505.63083  1.055152e+04 21
  1072   -491.71902  1.129376e+04 22
  1073   -496.38885  1.136798e+04 22
  1074   -510.32469  1.144104e+04 22
  1075   -533.30676  1.151177e+04 22
  1076   -564.97263  1.157906e+04 22
  1077   -604.82290  1.164186e+04 22
  1078   -652.22910  1.169916e+04 22
  1079   -706.44363  1.175007e+04 22
  1080   -766.61148  1.179379e+04 22
  1081   -831.78376  1.182962e+04 22
  1082   -900.93268  1.185699e+04 22
  1083   -972.96771  1.187549e+04 22
  1084  -1046.75282  1.188481e+04 22
  1085  -1121.12437  1.188481e+04 22
  1086  -1194.90947  1.187549e+04 22
  1087  -1266.94450  1.185699e+04 22
  1088  -1336.09342  1.182962e+04 22
  1089  -1401.26571  1.179379e+04 22
  1090  -1461.43355  1.175007e+04 22
  1091  -1515.64808  1.169916e+04 22
  1092  -1563.05429  1.164186e+04 22
  1093  -1602.90456  1.157906e+04 22
  1094  -1634.57042  1.151177e+04 22
  1095  -1657.55249  1.144104e+04 22
  1096  -1671.48833  1.136798e+04 22
  1097  -1676.15816  1.129376e+04 22
  1098  -1671.48833  1.121953e+04 22
  1099  -1657.55249  1.114648e+04 22
  1100  -1634.57042  1.107575e+04 22
  1101  -1602.90456  1.100846e+04 22
  1102  -1563.05429  1.094566e+04 22
  1103  -1515.64808  1.088836e+04 22
  1104  -1461.43355  1.083745e+04 22
  1105  -1401.26571  1.079373e+04 22
  1106  -1336.09342  1.075790e+04 22
  1107  -1266.94450  1.073053e+04 22
  1108  -1194.90947  1.071203e+04 22
  1109  -1121.12437  1.070271e+04 22
  1110  -1046.75282  1.070271e+04 22
  1111   -972.96771  1.071203e+04 22
  1112   -900.93268  1.073053e+04 22
  1113   -831.78376  1.075790e+04 22
  1114   -766.61148  1.079373e+04 22
  1115   -706.44363  1.083745e+04 22
  1116   -652.22910  1.088836e+04 22
  1117   -604.82290  1.094566e+04 22
  1118   -564.97263  1.100846e+04 22
  1119   -533.30676  1.107575e+04 22
  1120   -510.32469  1.114648e+04 22
  1121   -496.38885  1.121953e+04 22
  1122   -491.71902  1.129376e+04 22
  1123  -8956.93169 -6.598524e+03 23
  1124  -9032.38785 -5.399182e+03 23
  1125  -9257.56632 -4.218755e+03 23
  1126  -9628.91592 -3.075859e+03 23
  1127 -10140.58024 -1.988517e+03 23
  1128 -10784.49002 -9.738774e+02 23
  1129 -11550.49042 -4.794200e+01 23
  1130 -12426.50116  7.746868e+02 23
  1131 -13398.70703  1.481036e+03 23
  1132 -14451.77576  2.059965e+03 23
  1133 -15569.09982  2.502345e+03 23
  1134 -16733.05835  2.801199e+03 23
  1135 -17925.29503  2.951813e+03 23
  1136 -19127.00757  2.951813e+03 23
  1137 -20319.24425  2.801199e+03 23
  1138 -21483.20279  2.502345e+03 23
  1139 -22600.52685  2.059965e+03 23
  1140 -23653.59558  1.481036e+03 23
  1141 -24625.80145  7.746868e+02 23
  1142 -25501.81219 -4.794200e+01 23
  1143 -26267.81259 -9.738774e+02 23
  1144 -26911.72237 -1.988517e+03 23
  1145 -27423.38668 -3.075859e+03 23
  1146 -27794.73628 -4.218755e+03 23
  1147 -28019.91476 -5.399182e+03 23
  1148 -28095.37091 -6.598524e+03 23
  1149 -28019.91476 -7.797865e+03 23
  1150 -27794.73628 -8.978292e+03 23
  1151 -27423.38668 -1.012119e+04 23
  1152 -26911.72237 -1.120853e+04 23
  1153 -26267.81259 -1.222317e+04 23
  1154 -25501.81219 -1.314911e+04 23
  1155 -24625.80145 -1.397173e+04 23
  1156 -23653.59558 -1.467808e+04 23
  1157 -22600.52685 -1.525701e+04 23
  1158 -21483.20279 -1.569939e+04 23
  1159 -20319.24425 -1.599825e+04 23
  1160 -19127.00757 -1.614886e+04 23
  1161 -17925.29503 -1.614886e+04 23
  1162 -16733.05835 -1.599825e+04 23
  1163 -15569.09982 -1.569939e+04 23
  1164 -14451.77576 -1.525701e+04 23
  1165 -13398.70703 -1.467808e+04 23
  1166 -12426.50116 -1.397173e+04 23
  1167 -11550.49042 -1.314911e+04 23
  1168 -10784.49002 -1.222317e+04 23
  1169 -10140.58024 -1.120853e+04 23
  1170  -9628.91592 -1.012119e+04 23
  1171  -9257.56632 -8.978292e+03 23
  1172  -9032.38785 -7.797865e+03 23
  1173  -8956.93169 -6.598524e+03 23
  1174 -10007.31062  9.133993e+02 24
  1175 -10015.46915  1.043075e+03 24
  1176 -10039.81609  1.170707e+03 24
  1177 -10079.96746  1.294280e+03 24
  1178 -10135.29006  1.411846e+03 24
  1179 -10204.91142  1.521552e+03 24
  1180 -10287.73356  1.621667e+03 24
  1181 -10382.45034  1.710612e+03 24
  1182 -10487.56800  1.786984e+03 24
  1183 -10601.42879  1.849580e+03 24
  1184 -10722.23704  1.897411e+03 24
  1185 -10848.08755  1.929724e+03 24
  1186 -10976.99557  1.946009e+03 24
  1187 -11106.92815  1.946009e+03 24
  1188 -11235.83617  1.929724e+03 24
  1189 -11361.68668  1.897411e+03 24
  1190 -11482.49494  1.849580e+03 24
  1191 -11596.35572  1.786984e+03 24
  1192 -11701.47339  1.710612e+03 24
  1193 -11796.19016  1.621667e+03 24
  1194 -11879.01231  1.521552e+03 24
  1195 -11948.63366  1.411846e+03 24
  1196 -12003.95626  1.294280e+03 24
  1197 -12044.10764  1.170707e+03 24
  1198 -12068.45458  1.043075e+03 24
  1199 -12076.61311  9.133993e+02 24
  1200 -12068.45458  7.837231e+02 24
  1201 -12044.10764  6.560920e+02 24
  1202 -12003.95626  5.325188e+02 24
  1203 -11948.63366  4.149523e+02 24
  1204 -11879.01231  3.052466e+02 24
  1205 -11796.19016  2.051318e+02 24
  1206 -11701.47339  1.161868e+02 24
  1207 -11596.35572  3.981436e+01 24
  1208 -11482.49494 -2.278113e+01 24
  1209 -11361.68668 -7.061250e+01 24
  1210 -11235.83617 -1.029254e+02 24
  1211 -11106.92815 -1.192103e+02 24
  1212 -10976.99557 -1.192103e+02 24
  1213 -10848.08755 -1.029254e+02 24
  1214 -10722.23704 -7.061250e+01 24
  1215 -10601.42879 -2.278113e+01 24
  1216 -10487.56800  3.981436e+01 24
  1217 -10382.45034  1.161868e+02 24
  1218 -10287.73356  2.051318e+02 24
  1219 -10204.91142  3.052466e+02 24
  1220 -10135.29006  4.149523e+02 24
  1221 -10079.96746  5.325188e+02 24
  1222 -10039.81609  6.560920e+02 24
  1223 -10015.46915  7.837231e+02 24
  1224 -10007.31062  9.133993e+02 24
  1225   6909.86962  1.316298e+04 25
  1226   6887.94479  1.351147e+04 25
  1227   6822.51609  1.385446e+04 25
  1228   6714.61534  1.418654e+04 25
  1229   6565.94423  1.450248e+04 25
  1230   6378.84737  1.479730e+04 25
  1231   6156.27541  1.506634e+04 25
  1232   5901.73842  1.530537e+04 25
  1233   5619.25061  1.551061e+04 25
  1234   5313.26699  1.567883e+04 25
  1235   4988.61309  1.580736e+04 25
  1236   4650.40890  1.589420e+04 25
  1237   4303.98811  1.593796e+04 25
  1238   3954.81398  1.593796e+04 25
  1239   3608.39319  1.589420e+04 25
  1240   3270.18901  1.580736e+04 25
  1241   2945.53511  1.567883e+04 25
  1242   2639.55148  1.551061e+04 25
  1243   2357.06368  1.530537e+04 25
  1244   2102.52669  1.506634e+04 25
  1245   1879.95472  1.479730e+04 25
  1246   1692.85787  1.450248e+04 25
  1247   1544.18675  1.418654e+04 25
  1248   1436.28601  1.385446e+04 25
  1249   1370.85730  1.351147e+04 25
  1250   1348.93248  1.316298e+04 25
  1251   1370.85730  1.281450e+04 25
  1252   1436.28601  1.247151e+04 25
  1253   1544.18675  1.213942e+04 25
  1254   1692.85787  1.182348e+04 25
  1255   1879.95472  1.152866e+04 25
  1256   2102.52669  1.125962e+04 25
  1257   2357.06368  1.102059e+04 25
  1258   2639.55148  1.081535e+04 25
  1259   2945.53511  1.064714e+04 25
  1260   3270.18901  1.051860e+04 25
  1261   3608.39319  1.043176e+04 25
  1262   3954.81398  1.038800e+04 25
  1263   4303.98811  1.038800e+04 25
  1264   4650.40890  1.043176e+04 25
  1265   4988.61309  1.051860e+04 25
  1266   5313.26699  1.064714e+04 25
  1267   5619.25061  1.081535e+04 25
  1268   5901.73842  1.102059e+04 25
  1269   6156.27541  1.125962e+04 25
  1270   6378.84737  1.152866e+04 25
  1271   6565.94423  1.182348e+04 25
  1272   6714.61534  1.213942e+04 25
  1273   6822.51609  1.247151e+04 25
  1274   6887.94479  1.281450e+04 25
  1275   6909.86962  1.316298e+04 25
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  select(country, pop) ->  
prep

packcircles::circleProgressiveLayout(prep$pop,  
                                         sizetype = 'area') ->  
pack

pack %>%  
  packcircles::circleLayoutVertices(npoints = 50) ->  
*circle_outlines
```
]
 
.right-output-circle_pack-auto[

]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  select(country, pop) ->  
prep

packcircles::circleProgressiveLayout(prep$pop,  
                                         sizetype = 'area') ->  
pack

pack %>%  
  packcircles::circleLayoutVertices(npoints = 50) ->  
circle_outlines

*circle_outlines
```
]
 
.right-output-circle_pack-auto[

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  select(country, pop) ->  
prep

packcircles::circleProgressiveLayout(prep$pop,  
                                         sizetype = 'area') ->  
pack

pack %>%  
  packcircles::circleLayoutVertices(npoints = 50) ->  
circle_outlines

circle_outlines %>%  
* ggplot()
```
]
 
.right-output-circle_pack-auto[
<img src="geoms_files/figure-html/circle_pack_auto_12_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  select(country, pop) ->  
prep

packcircles::circleProgressiveLayout(prep$pop,  
                                         sizetype = 'area') ->  
pack

pack %>%  
  packcircles::circleLayoutVertices(npoints = 50) ->  
circle_outlines

circle_outlines %>%  
  ggplot() +  
* aes(x = x, y = y)
```
]
 
.right-output-circle_pack-auto[
<img src="geoms_files/figure-html/circle_pack_auto_13_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  select(country, pop) ->  
prep

packcircles::circleProgressiveLayout(prep$pop,  
                                         sizetype = 'area') ->  
pack

pack %>%  
  packcircles::circleLayoutVertices(npoints = 50) ->  
circle_outlines

circle_outlines %>%  
  ggplot() +  
  aes(x = x, y = y) +  
* geom_polygon(colour = "black", alpha = 0.6)
```
]
 
.right-output-circle_pack-auto[
<img src="geoms_files/figure-html/circle_pack_auto_14_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  select(country, pop) ->  
prep

packcircles::circleProgressiveLayout(prep$pop,  
                                         sizetype = 'area') ->  
pack

pack %>%  
  packcircles::circleLayoutVertices(npoints = 50) ->  
circle_outlines

circle_outlines %>%  
  ggplot() +  
  aes(x = x, y = y) +  
  geom_polygon(colour = "black", alpha = 0.6) +  
* aes(group = id)
```
]
 
.right-output-circle_pack-auto[
<img src="geoms_files/figure-html/circle_pack_auto_15_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  select(country, pop) ->  
prep

packcircles::circleProgressiveLayout(prep$pop,  
                                         sizetype = 'area') ->  
pack

pack %>%  
  packcircles::circleLayoutVertices(npoints = 50) ->  
circle_outlines

circle_outlines %>%  
  ggplot() +  
  aes(x = x, y = y) +  
  geom_polygon(colour = "black", alpha = 0.6) +  
  aes(group = id) +  
* aes(fill = factor(id))
```
]
 
.right-output-circle_pack-auto[
<img src="geoms_files/figure-html/circle_pack_auto_16_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  select(country, pop) ->  
prep

packcircles::circleProgressiveLayout(prep$pop,  
                                         sizetype = 'area') ->  
pack

pack %>%  
  packcircles::circleLayoutVertices(npoints = 50) ->  
circle_outlines

circle_outlines %>%  
  ggplot() +  
  aes(x = x, y = y) +  
  geom_polygon(colour = "black", alpha = 0.6) +  
  aes(group = id) +  
  aes(fill = factor(id)) +  
* geom_text(data = cbind(prep, pack),
*           aes(x, y, size = pop, label = country,
*               group = NULL, fill = NULL))
```
]
 
.right-output-circle_pack-auto[
<img src="geoms_files/figure-html/circle_pack_auto_17_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  select(country, pop) ->  
prep

packcircles::circleProgressiveLayout(prep$pop,  
                                         sizetype = 'area') ->  
pack

pack %>%  
  packcircles::circleLayoutVertices(npoints = 50) ->  
circle_outlines

circle_outlines %>%  
  ggplot() +  
  aes(x = x, y = y) +  
  geom_polygon(colour = "black", alpha = 0.6) +  
  aes(group = id) +  
  aes(fill = factor(id)) +  
  geom_text(data = cbind(prep, pack),  
            aes(x, y, size = pop, label = country,  
                group = NULL, fill = NULL)) +  
* theme(legend.position = "none")
```
]
 
.right-output-circle_pack-auto[
<img src="geoms_files/figure-html/circle_pack_auto_18_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  select(country, pop) ->  
prep

packcircles::circleProgressiveLayout(prep$pop,  
                                         sizetype = 'area') ->  
pack

pack %>%  
  packcircles::circleLayoutVertices(npoints = 50) ->  
circle_outlines

circle_outlines %>%  
  ggplot() +  
  aes(x = x, y = y) +  
  geom_polygon(colour = "black", alpha = 0.6) +  
  aes(group = id) +  
  aes(fill = factor(id)) +  
  geom_text(data = cbind(prep, pack),  
            aes(x, y, size = pop, label = country,  
                group = NULL, fill = NULL)) +  
  theme(legend.position = "none") +  
* coord_equal()
```
]
 
.right-output-circle_pack-auto[
<img src="geoms_files/figure-html/circle_pack_auto_19_output-1.png" width="100%" />
]

---

```r
*gapminder
```
]
 
.right-output-treemapify-auto[

---
class: split-40
count: false

```r
gapminder %>%  
* filter(continent == "Americas")
```
]
 
.right-output-treemapify-auto[

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
* filter(year == 2002)
```
]
 
.right-output-treemapify-auto[

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
* mutate(pop_millions = pop/1000000)
```
]
 
.right-output-treemapify-auto[

```
  # A tibble: 25 x 7
     country            continent  year lifeExp       pop gdpPercap pop_millions
     <fct>              <fct>     <int>   <dbl>     <int>     <dbl>        <dbl>
   1 Argentina          Americas   2002    74.3  38331121     8798.        38.3 
   2 Bolivia            Americas   2002    63.9   8445134     3413.         8.45
   3 Brazil             Americas   2002    71.0 179914212     8131.       180.  
   4 Canada             Americas   2002    79.8  31902268    33329.        31.9 
   5 Chile              Americas   2002    77.9  15497046    10779.        15.5 
   6 Colombia           Americas   2002    71.7  41008227     5755.        41.0 
   7 Costa Rica         Americas   2002    78.1   3834934     7723.         3.83
   8 Cuba               Americas   2002    77.2  11226999     6341.        11.2 
   9 Dominican Republic Americas   2002    70.8   8650322     4564.         8.65
  10 Ecuador            Americas   2002    74.2  12921234     5773.        12.9 
  # … with 15 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  mutate(pop_millions = pop/1000000) %>%  
* ggplot()
```
]
 
.right-output-treemapify-auto[
<img src="geoms_files/figure-html/treemapify_auto_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  mutate(pop_millions = pop/1000000) %>%  
  ggplot() +  
* aes(fill = pop_millions, area = pop_millions)
```
]
 
.right-output-treemapify-auto[
<img src="geoms_files/figure-html/treemapify_auto_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  mutate(pop_millions = pop/1000000) %>%  
  ggplot() +  
  aes(fill = pop_millions, area = pop_millions) +  
* treemapify::geom_treemap()
```
]
 
.right-output-treemapify-auto[
<img src="geoms_files/figure-html/treemapify_auto_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  mutate(pop_millions = pop/1000000) %>%  
  ggplot() +  
  aes(fill = pop_millions, area = pop_millions) +  
  treemapify::geom_treemap() +  
* treemapify::geom_treemap_text(aes(label = country),
*   fontface = "italic",
*                               colour = "white",
*                               place = "topleft",
*                               grow = F)
```
]
 
.right-output-treemapify-auto[
<img src="geoms_files/figure-html/treemapify_auto_8_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Americas") %>%  
  filter(year == 2002) %>%  
  mutate(pop_millions = pop/1000000) %>%  
  ggplot() +  
  aes(fill = pop_millions, area = pop_millions) +  
  treemapify::geom_treemap() +  
  treemapify::geom_treemap_text(aes(label = country),  
    fontface = "italic",  
                                colour = "white",  
                                place = "topleft",  
                                grow = F) +  
* scale_fill_viridis_c(trans = "log10", begin = .2, option = "inferno",
*                      end = .7, breaks = c(1, 3, 10, 30, 100))
```
]
 
.right-output-treemapify-auto[
<img src="geoms_files/figure-html/treemapify_auto_9_output-1.png" width="100%" />
]

---
class: middle, center, inverse

# Singe value per category

---

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Europe") %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  aes(y = reorder(country, lifeExp)) +  
  geom_point() +  
  geom_col() +  
  theme_minimal()  
```
]
 
.right-output-single_value-rotate[
<img src="geoms_files/figure-html/single_value_rotate_1_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Europe") %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  aes(y = reorder(country, lifeExp)) +  
  geom_point() +  
* geom_segment(aes(xend = 0, yend = country)) +
  theme_minimal()  
```
]
 
.right-output-single_value-rotate[
<img src="geoms_files/figure-html/single_value_rotate_2_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  filter(continent == "Europe") %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  aes(y = reorder(country, lifeExp)) +  
  geom_point() +  
* geom_hline(aes(yintercept = country), linetype = "dotted") +
  theme_minimal()  
```
]
 
.right-output-single_value-rotate[
<img src="geoms_files/figure-html/single_value_rotate_3_output-1.png" width="100%" />
]

---

```r
# install.packages("waffle", repos = "https://cinc.rud.is")
```

---

```r
# special install
*gapminder
```
]
 
.right-output-waffle-auto[

---
class: split-40
count: false

```r
# special install
gapminder %>%  
* filter(year == 2002)
```
]
 
.right-output-waffle-auto[

---
class: split-40
count: false

```r
# special install
gapminder %>%  
  filter(year == 2002) %>%  
* count(continent)
```
]
 
.right-output-waffle-auto[

```
  # A tibble: 5 x 2
    continent     n
    <fct>     <int>
  1 Africa       52
  2 Americas     25
  3 Asia         33
  4 Europe       30
  5 Oceania       2
```
]

---
class: split-40
count: false

```r
# special install
gapminder %>%  
  filter(year == 2002) %>%  
  count(continent) %>%  
* ggplot()
```
]
 
.right-output-waffle-auto[
<img src="geoms_files/figure-html/waffle_auto_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
# special install
gapminder %>%  
  filter(year == 2002) %>%  
  count(continent) %>%  
  ggplot() +  
* waffle::geom_waffle(aes(fill = continent, values = n),
*   n_rows = 20, size = 0.33, colour = "white", flip = T)
```
]
 
.right-output-waffle-auto[
<img src="geoms_files/figure-html/waffle_auto_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
# special install
gapminder %>%  
  filter(year == 2002) %>%  
  count(continent) %>%  
  ggplot() +  
  waffle::geom_waffle(aes(fill = continent, values = n),  
    n_rows = 20, size = 0.33, colour = "white", flip = T) +  
* coord_equal()
```
]
 
.right-output-waffle-auto[
<img src="geoms_files/figure-html/waffle_auto_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
# special install
gapminder %>%  
  filter(year == 2002) %>%  
  count(continent) %>%  
  ggplot() +  
  waffle::geom_waffle(aes(fill = continent, values = n),  
    n_rows = 20, size = 0.33, colour = "white", flip = T) +  
  coord_equal() +  
* facet_wrap(~continent, strip.position = "bottom")
```
]
 
.right-output-waffle-auto[
<img src="geoms_files/figure-html/waffle_auto_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
# special install
gapminder %>%  
  filter(year == 2002) %>%  
  count(continent) %>%  
  ggplot() +  
  waffle::geom_waffle(aes(fill = continent, values = n),  
    n_rows = 20, size = 0.33, colour = "white", flip = T) +  
  coord_equal() +  
  facet_wrap(~continent, strip.position = "bottom") +  
* theme_void()
```
]
 
.right-output-waffle-auto[
<img src="geoms_files/figure-html/waffle_auto_8_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
# special install
gapminder %>%  
  filter(year == 2002) %>%  
  count(continent) %>%  
  ggplot() +  
  waffle::geom_waffle(aes(fill = continent, values = n),  
    n_rows = 20, size = 0.33, colour = "white", flip = T) +  
  coord_equal() +  
  facet_wrap(~continent, strip.position = "bottom") +  
  theme_void() +  
* theme(legend.position = "none")
```
]
 
.right-output-waffle-auto[
<img src="geoms_files/figure-html/waffle_auto_9_output-1.png" width="100%" />
]

---
class: inverse, center, middle
name: mult_series

## a few series

---

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(gdp = pop * gdpPercap) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = gdp) +  
  geom_point() +  
  geom_line(aes(linetype = country, color = country)) +  
  theme_minimal()  
```
]
 
.right-output-time_series_multi-rotate[
<img src="geoms_files/figure-html/time_series_multi_rotate_1_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(gdp = pop * gdpPercap) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = gdp) +  
  geom_point() +  
* ggforce::geom_bspline(aes(linetype = country)) +
  theme_minimal()  
```
]
 
.right-output-time_series_multi-rotate[
<img src="geoms_files/figure-html/time_series_multi_rotate_2_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(gdp = pop * gdpPercap) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = gdp) +  
  geom_point() +  
* geom_col(aes(fill = country), position = "dodge", alpha = .4) +
  theme_minimal()  
```
]
 
.right-output-time_series_multi-rotate[
<img src="geoms_files/figure-html/time_series_multi_rotate_3_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(gdp = pop * gdpPercap) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = gdp) +  
  geom_point() +  
* geom_col(aes(fill = country), alpha = .4) +
  theme_minimal()  
```
]
 
.right-output-time_series_multi-rotate[
<img src="geoms_files/figure-html/time_series_multi_rotate_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(gdp = pop * gdpPercap) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = gdp) +  
  geom_point() +  
* geom_area(aes(fill = country), alpha = .4) +
  theme_minimal()  
```
]
 
.right-output-time_series_multi-rotate[
<img src="geoms_files/figure-html/time_series_multi_rotate_5_output-1.png" width="100%" />
]

---

---
class: inverse, center, middle

# Many series

---

```r
*gapminder
```
]
 
.right-output-aes_group-auto[

---
class: split-40
count: false

```r
gapminder %>%  
* filter(continent %in% c("Americas", "Oceania"))
```
]
 
.right-output-aes_group-auto[

```
  # A tibble: 324 x 6
     country   continent  year lifeExp      pop gdpPercap
     <fct>     <fct>     <int>   <dbl>    <int>     <dbl>
   1 Argentina Americas   1952    62.5 17876956     5911.
   2 Argentina Americas   1957    64.4 19610538     6857.
   3 Argentina Americas   1962    65.1 21283783     7133.
   4 Argentina Americas   1967    65.6 22934225     8053.
   5 Argentina Americas   1972    67.1 24779799     9443.
   6 Argentina Americas   1977    68.5 26983828    10079.
   7 Argentina Americas   1982    69.9 29341374     8998.
   8 Argentina Americas   1987    70.8 31620918     9140.
   9 Argentina Americas   1992    71.9 33958947     9308.
  10 Argentina Americas   1997    73.3 36203463    10967.
  # … with 314 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent %in% c("Americas", "Oceania")) %>%  
* ggplot()
```
]
 
.right-output-aes_group-auto[
<img src="geoms_files/figure-html/aes_group_auto_3_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent %in% c("Americas", "Oceania")) %>%  
  ggplot() +  
* aes(x = year)
```
]
 
.right-output-aes_group-auto[
<img src="geoms_files/figure-html/aes_group_auto_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent %in% c("Americas", "Oceania")) %>%  
  ggplot() +  
  aes(x = year) +  
* aes(y = lifeExp)
```
]
 
.right-output-aes_group-auto[
<img src="geoms_files/figure-html/aes_group_auto_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent %in% c("Americas", "Oceania")) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
* geom_point(color = "grey60")
```
]
 
.right-output-aes_group-auto[
<img src="geoms_files/figure-html/aes_group_auto_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent %in% c("Americas", "Oceania")) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
  geom_point(color = "grey60") +  
* geom_line(color = "grey60")
```
]
 
.right-output-aes_group-auto[
<img src="geoms_files/figure-html/aes_group_auto_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent %in% c("Americas", "Oceania")) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
  geom_point(color = "grey60") +  
  geom_line(color = "grey60") +  
* aes(color = country)
```
]
 
.right-output-aes_group-auto[
<img src="geoms_files/figure-html/aes_group_auto_8_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent %in% c("Americas", "Oceania")) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
  geom_point(color = "grey60") +  
  geom_line(color = "grey60") +  
  aes(color = country) +  
* scale_color_discrete(guide = F)
```
]
 
.right-output-aes_group-auto[
<img src="geoms_files/figure-html/aes_group_auto_9_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent %in% c("Americas", "Oceania")) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
  geom_point(color = "grey60") +  
  geom_line(color = "grey60") +  
  aes(color = country) +  
  scale_color_discrete(guide = F) +  
* aes(color = NULL)
```
]
 
.right-output-aes_group-auto[
<img src="geoms_files/figure-html/aes_group_auto_10_output-1.png" width="100%" />
]

---
class: split-40
count: false

---
class: split-40
count: false

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent %in% c("Americas", "Oceania")) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
  geom_point(color = "grey60") +  
  geom_line(color = "grey60") +  
  aes(color = country) +  
  scale_color_discrete(guide = F) +  
  aes(color = NULL) +  
  aes(group = country) +  
  geom_point(data = . %>%  # using global data
               filter(country %in%  # filtering to
                       c("New Zealand","Chile")),  # NZ and  Chile
             color = "plum4",  
             size = 4) +  
* geom_line(data = . %>%
*             filter(country %in%
*                      c("New Zealand","Chile")),
*           color = "plum4",
*           size = 2)
```
]
 
.right-output-aes_group-auto[
<img src="geoms_files/figure-html/aes_group_auto_13_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent %in% c("Americas", "Oceania")) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
  geom_point(color = "grey60") +  
  geom_line(color = "grey60") +  
  aes(color = country) +  
  scale_color_discrete(guide = F) +  
  aes(color = NULL) +  
  aes(group = country) +  
  geom_point(data = . %>%  # using global data
               filter(country %in%  # filtering to
                       c("New Zealand","Chile")),  # NZ and  Chile
             color = "plum4",  
             size = 4) +  
  geom_line(data = . %>%  
              filter(country %in%  
                       c("New Zealand","Chile")),  
            color = "plum4",  
            size = 2) +  
* geom_text(data = . %>%
*             filter(country %in%
*                      c("New Zealand","Chile")) %>%
*             group_by(country) %>%
*             slice(3),
*           aes(label = country),
*           nudge_y = 2, hjust = .7,
*           color = "plum4", size = 5,
*           fontface = "bold")
```
]
 
.right-output-aes_group-auto[
<img src="geoms_files/figure-html/aes_group_auto_14_output-1.png" width="100%" />
]

---

```r
*gapminder
```
]
 
.right-output-difference_ribbon-auto[

---
class: split-40
count: false

```r
gapminder %>%  
* filter(continent == "Oceania")
```
]
 
.right-output-difference_ribbon-auto[

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
* mutate(pop_millions = pop/1000000)
```
]
 
.right-output-difference_ribbon-auto[

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) ->  
*gm_oceania
```
]
 
.right-output-difference_ribbon-auto[

]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) ->  
gm_oceania

*gm_oceania
```
]
 
.right-output-difference_ribbon-auto[

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) ->  
gm_oceania

gm_oceania %>%  
* select(-gdpPercap, -lifeExp, - pop)
```
]
 
.right-output-difference_ribbon-auto[

```
  # A tibble: 24 x 4
     country   continent  year pop_millions
     <fct>     <fct>     <int>        <dbl>
   1 Australia Oceania    1952         8.69
   2 Australia Oceania    1957         9.71
   3 Australia Oceania    1962        10.8 
   4 Australia Oceania    1967        11.9 
   5 Australia Oceania    1972        13.2 
   6 Australia Oceania    1977        14.1 
   7 Australia Oceania    1982        15.2 
   8 Australia Oceania    1987        16.3 
   9 Australia Oceania    1992        17.5 
  10 Australia Oceania    1997        18.6 
  # … with 14 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) ->  
gm_oceania

gm_oceania %>%  
  select(-gdpPercap, -lifeExp, - pop) %>%  
* pivot_wider(names_from = country,
*             values_from = pop_millions)
```
]
 
.right-output-difference_ribbon-auto[

```
  # A tibble: 12 x 4
     continent  year Australia `New Zealand`
     <fct>     <int>     <dbl>         <dbl>
   1 Oceania    1952      8.69          1.99
   2 Oceania    1957      9.71          2.23
   3 Oceania    1962     10.8           2.49
   4 Oceania    1967     11.9           2.73
   5 Oceania    1972     13.2           2.93
   6 Oceania    1977     14.1           3.16
   7 Oceania    1982     15.2           3.21
   8 Oceania    1987     16.3           3.32
   9 Oceania    1992     17.5           3.44
  10 Oceania    1997     18.6           3.68
  11 Oceania    2002     19.5           3.91
  12 Oceania    2007     20.4           4.12
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) ->  
gm_oceania

gm_oceania %>%  
  select(-gdpPercap, -lifeExp, - pop) %>%  
  pivot_wider(names_from = country,  
              values_from = pop_millions) ->  
*gm_oceania_pop_millions_wide
```
]
 
.right-output-difference_ribbon-auto[

]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) ->  
gm_oceania

gm_oceania %>%  
  select(-gdpPercap, -lifeExp, - pop) %>%  
  pivot_wider(names_from = country,  
              values_from = pop_millions) ->  
gm_oceania_pop_millions_wide

*gm_oceania   # global data
```
]
 
.right-output-difference_ribbon-auto[

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) ->  
gm_oceania

gm_oceania %>%  
  select(-gdpPercap, -lifeExp, - pop) %>%  
  pivot_wider(names_from = country,  
              values_from = pop_millions) ->  
gm_oceania_pop_millions_wide

gm_oceania %>%  # global data
* ggplot()
```
]
 
.right-output-difference_ribbon-auto[
<img src="geoms_files/figure-html/difference_ribbon_auto_10_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) ->  
gm_oceania

gm_oceania %>%  
  select(-gdpPercap, -lifeExp, - pop) %>%  
  pivot_wider(names_from = country,  
              values_from = pop_millions) ->  
gm_oceania_pop_millions_wide

gm_oceania %>%  # global data
  ggplot() +  
* aes(x = year)
```
]
 
.right-output-difference_ribbon-auto[
<img src="geoms_files/figure-html/difference_ribbon_auto_11_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) ->  
gm_oceania

gm_oceania %>%  
  select(-gdpPercap, -lifeExp, - pop) %>%  
  pivot_wider(names_from = country,  
              values_from = pop_millions) ->  
gm_oceania_pop_millions_wide

gm_oceania %>%  # global data
  ggplot() +  
  aes(x = year) +  
* aes(y = pop_millions)
```
]
 
.right-output-difference_ribbon-auto[
<img src="geoms_files/figure-html/difference_ribbon_auto_12_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) ->  
gm_oceania

gm_oceania %>%  
  select(-gdpPercap, -lifeExp, - pop) %>%  
  pivot_wider(names_from = country,  
              values_from = pop_millions) ->  
gm_oceania_pop_millions_wide

gm_oceania %>%  # global data
  ggplot() +  
  aes(x = year) +  
  aes(y = pop_millions) +  
* geom_line(aes(linetype = country),
*           color = "plum4")
```
]
 
.right-output-difference_ribbon-auto[
<img src="geoms_files/figure-html/difference_ribbon_auto_13_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) ->  
gm_oceania

gm_oceania %>%  
  select(-gdpPercap, -lifeExp, - pop) %>%  
  pivot_wider(names_from = country,  
              values_from = pop_millions) ->  
gm_oceania_pop_millions_wide

gm_oceania %>%  # global data
  ggplot() +  
  aes(x = year) +  
  aes(y = pop_millions) +  
  geom_line(aes(linetype = country),  
            color = "plum4") +  
* geom_ribbon(data = gm_oceania_pop_millions_wide,  # local data
*             aes(ymin = `New Zealand`,  # poorly named var like this one has space
*                 ymax = Australia,  # must be surrounded by back ticks
*                 y = NULL),  # without this geom ribbon is wondering about y equals year
*             fill = "plum4",
*             alpha = .1)
```
]
 
.right-output-difference_ribbon-auto[
<img src="geoms_files/figure-html/difference_ribbon_auto_14_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Oceania") %>%  
  mutate(pop_millions = pop/1000000) ->  
gm_oceania

gm_oceania %>%  
  select(-gdpPercap, -lifeExp, - pop) %>%  
  pivot_wider(names_from = country,  
              values_from = pop_millions) ->  
gm_oceania_pop_millions_wide

gm_oceania %>%  # global data
  ggplot() +  
  aes(x = year) +  
  aes(y = pop_millions) +  
  geom_line(aes(linetype = country),  
            color = "plum4") +  
  geom_ribbon(data = gm_oceania_pop_millions_wide,  # local data
              aes(ymin = `New Zealand`,  # poorly named var like this one has space
                  ymax = Australia,  # must be surrounded by back ticks
                  y = NULL),  # without this geom ribbon is wondering about y equals year
              fill = "plum4",  
              alpha = .1) +  
* scale_y_continuous(limits = c(0, 22))
```
]
 
.right-output-difference_ribbon-auto[
<img src="geoms_files/figure-html/difference_ribbon_auto_15_output-1.png" width="100%" />
]

---

```r
*gapminder
```
]
 
.right-output-growth-auto[

---
class: split-40
count: false

```r
gapminder %>%  
* filter(continent == "Europe")
```
]
 
.right-output-growth-auto[

```
  # A tibble: 360 x 6
     country continent  year lifeExp     pop gdpPercap
     <fct>   <fct>     <int>   <dbl>   <int>     <dbl>
   1 Albania Europe     1952    55.2 1282697     1601.
   2 Albania Europe     1957    59.3 1476505     1942.
   3 Albania Europe     1962    64.8 1728137     2313.
   4 Albania Europe     1967    66.2 1984060     2760.
   5 Albania Europe     1972    67.7 2263554     3313.
   6 Albania Europe     1977    68.9 2509048     3533.
   7 Albania Europe     1982    70.4 2780097     3631.
   8 Albania Europe     1987    72   3075321     3739.
   9 Albania Europe     1992    71.6 3326498     2497.
  10 Albania Europe     1997    73.0 3428038     3193.
  # … with 350 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
* filter(year %in% c(1972, 2002))
```
]
 
.right-output-growth-auto[

```
  # A tibble: 60 x 6
     country                continent  year lifeExp      pop gdpPercap
     <fct>                  <fct>     <int>   <dbl>    <int>     <dbl>
   1 Albania                Europe     1972    67.7  2263554     3313.
   2 Albania                Europe     2002    75.7  3508512     4604.
   3 Austria                Europe     1972    70.6  7544201    16662.
   4 Austria                Europe     2002    79.0  8148312    32418.
   5 Belgium                Europe     1972    71.4  9709100    16672.
   6 Belgium                Europe     2002    78.3 10311970    30486.
   7 Bosnia and Herzegovina Europe     1972    67.4  3819000     2860.
   8 Bosnia and Herzegovina Europe     2002    74.1  4165416     6019.
   9 Bulgaria               Europe     1972    70.9  8576200     6597.
  10 Bulgaria               Europe     2002    72.1  7661799     7697.
  # … with 50 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  filter(year %in% c(1972, 2002)) %>%  
* mutate(country = reorder(country, lifeExp, mean))
```
]
 
.right-output-growth-auto[

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  filter(year %in% c(1972, 2002)) %>%  
  mutate(country = reorder(country, lifeExp, mean)) ->  
*gap_72_02_europe
```
]
 
.right-output-growth-auto[

]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  filter(year %in% c(1972, 2002)) %>%  
  mutate(country = reorder(country, lifeExp, mean)) ->  
gap_72_02_europe

*gap_72_02_europe
```
]
 
.right-output-growth-auto[

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  filter(year %in% c(1972, 2002)) %>%  
  mutate(country = reorder(country, lifeExp, mean)) ->  
gap_72_02_europe

gap_72_02_europe %>%  
* select(-gdpPercap, -pop)
```
]
 
.right-output-growth-auto[

```
  # A tibble: 60 x 4
     country                continent  year lifeExp
     <fct>                  <fct>     <int>   <dbl>
   1 Albania                Europe     1972    67.7
   2 Albania                Europe     2002    75.7
   3 Austria                Europe     1972    70.6
   4 Austria                Europe     2002    79.0
   5 Belgium                Europe     1972    71.4
   6 Belgium                Europe     2002    78.3
   7 Bosnia and Herzegovina Europe     1972    67.4
   8 Bosnia and Herzegovina Europe     2002    74.1
   9 Bulgaria               Europe     1972    70.9
  10 Bulgaria               Europe     2002    72.1
  # … with 50 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  filter(year %in% c(1972, 2002)) %>%  
  mutate(country = reorder(country, lifeExp, mean)) ->  
gap_72_02_europe

gap_72_02_europe %>%  
  select(-gdpPercap, -pop) %>%  
* pivot_wider(names_from = year,
*             values_from = lifeExp)
```
]
 
.right-output-growth-auto[

```
  # A tibble: 30 x 4
     country                continent `1972` `2002`
     <fct>                  <fct>      <dbl>  <dbl>
   1 Albania                Europe      67.7   75.7
   2 Austria                Europe      70.6   79.0
   3 Belgium                Europe      71.4   78.3
   4 Bosnia and Herzegovina Europe      67.4   74.1
   5 Bulgaria               Europe      70.9   72.1
   6 Croatia                Europe      69.6   74.9
   7 Czech Republic         Europe      70.3   75.5
   8 Denmark                Europe      73.5   77.2
   9 Finland                Europe      70.9   78.4
  10 France                 Europe      72.4   79.6
  # … with 20 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  filter(year %in% c(1972, 2002)) %>%  
  mutate(country = reorder(country, lifeExp, mean)) ->  
gap_72_02_europe

gap_72_02_europe %>%  
  select(-gdpPercap, -pop) %>%  
  pivot_wider(names_from = year,  
              values_from = lifeExp) ->  
*gap_72_02_europe_wide
```
]
 
.right-output-growth-auto[

]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  filter(year %in% c(1972, 2002)) %>%  
  mutate(country = reorder(country, lifeExp, mean)) ->  
gap_72_02_europe

gap_72_02_europe %>%  
  select(-gdpPercap, -pop) %>%  
  pivot_wider(names_from = year,  
              values_from = lifeExp) ->  
gap_72_02_europe_wide

*ggplot(data = gap_72_02_europe)
```
]
 
.right-output-growth-auto[
<img src="geoms_files/figure-html/growth_auto_10_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  filter(year %in% c(1972, 2002)) %>%  
  mutate(country = reorder(country, lifeExp, mean)) ->  
gap_72_02_europe

gap_72_02_europe %>%  
  select(-gdpPercap, -pop) %>%  
  pivot_wider(names_from = year,  
              values_from = lifeExp) ->  
gap_72_02_europe_wide

ggplot(data = gap_72_02_europe) +  
* aes(x = lifeExp)
```
]
 
.right-output-growth-auto[
<img src="geoms_files/figure-html/growth_auto_11_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  filter(year %in% c(1972, 2002)) %>%  
  mutate(country = reorder(country, lifeExp, mean)) ->  
gap_72_02_europe

gap_72_02_europe %>%  
  select(-gdpPercap, -pop) %>%  
  pivot_wider(names_from = year,  
              values_from = lifeExp) ->  
gap_72_02_europe_wide

ggplot(data = gap_72_02_europe) +  
  aes(x = lifeExp) +  
* aes(y = country)
```
]
 
.right-output-growth-auto[
<img src="geoms_files/figure-html/growth_auto_12_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  filter(year %in% c(1972, 2002)) %>%  
  mutate(country = reorder(country, lifeExp, mean)) ->  
gap_72_02_europe

gap_72_02_europe %>%  
  select(-gdpPercap, -pop) %>%  
  pivot_wider(names_from = year,  
              values_from = lifeExp) ->  
gap_72_02_europe_wide

ggplot(data = gap_72_02_europe) +  
  aes(x = lifeExp) +  
  aes(y = country) +  
* geom_point()
```
]
 
.right-output-growth-auto[
<img src="geoms_files/figure-html/growth_auto_13_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  filter(year %in% c(1972, 2002)) %>%  
  mutate(country = reorder(country, lifeExp, mean)) ->  
gap_72_02_europe

gap_72_02_europe %>%  
  select(-gdpPercap, -pop) %>%  
  pivot_wider(names_from = year,  
              values_from = lifeExp) ->  
gap_72_02_europe_wide

ggplot(data = gap_72_02_europe) +  
  aes(x = lifeExp) +  
  aes(y = country) +  
  geom_point() +  
* aes(color = factor(year))
```
]
 
.right-output-growth-auto[
<img src="geoms_files/figure-html/growth_auto_14_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  filter(year %in% c(1972, 2002)) %>%  
  mutate(country = reorder(country, lifeExp, mean)) ->  
gap_72_02_europe

gap_72_02_europe %>%  
  select(-gdpPercap, -pop) %>%  
  pivot_wider(names_from = year,  
              values_from = lifeExp) ->  
gap_72_02_europe_wide

ggplot(data = gap_72_02_europe) +  
  aes(x = lifeExp) +  
  aes(y = country) +  
  geom_point() +  
  aes(color = factor(year)) +  
* geom_segment(data = gap_72_02_europe_wide,
*                aes(x = `1972`,  # poorly named columns ie starting w number
*                    xend = `2002`,  # need to be surouned by backtick
*                    yend = country),
*              color = "plum3",
*              size = 1.5)
```
]
 
.right-output-growth-auto[
<img src="geoms_files/figure-html/growth_auto_15_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  filter(year %in% c(1972, 2002)) %>%  
  mutate(country = reorder(country, lifeExp, mean)) ->  
gap_72_02_europe

gap_72_02_europe %>%  
  select(-gdpPercap, -pop) %>%  
  pivot_wider(names_from = year,  
              values_from = lifeExp) ->  
gap_72_02_europe_wide

ggplot(data = gap_72_02_europe) +  
  aes(x = lifeExp) +  
  aes(y = country) +  
  geom_point() +  
  aes(color = factor(year)) +  
  geom_segment(data = gap_72_02_europe_wide,  
                 aes(x = `1972`,  # poorly named columns ie starting w number
                     xend = `2002`,  # need to be surouned by backtick
                     yend = country),  
               color = "plum3",  
               size = 1.5) +  
* geom_point(size = 4)   # point layer above not really needed - just for showing logic
```
]
 
.right-output-growth-auto[
<img src="geoms_files/figure-html/growth_auto_16_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  filter(year %in% c(1972, 2002)) %>%  
  mutate(country = reorder(country, lifeExp, mean)) ->  
gap_72_02_europe

gap_72_02_europe %>%  
  select(-gdpPercap, -pop) %>%  
  pivot_wider(names_from = year,  
              values_from = lifeExp) ->  
gap_72_02_europe_wide

ggplot(data = gap_72_02_europe) +  
  aes(x = lifeExp) +  
  aes(y = country) +  
  geom_point() +  
  aes(color = factor(year)) +  
  geom_segment(data = gap_72_02_europe_wide,  
                 aes(x = `1972`,  # poorly named columns ie starting w number
                     xend = `2002`,  # need to be surouned by backtick
                     yend = country),  
               color = "plum3",  
               size = 1.5) +  
  geom_point(size = 4) +  # point layer above not really needed - just for showing logic
* scale_color_manual(values = c("plum3", "plum4"))
```
]
 
.right-output-growth-auto[
<img src="geoms_files/figure-html/growth_auto_17_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  filter(year %in% c(1972, 2002)) %>%  
  mutate(country = reorder(country, lifeExp, mean)) ->  
gap_72_02_europe

gap_72_02_europe %>%  
  select(-gdpPercap, -pop) %>%  
  pivot_wider(names_from = year,  
              values_from = lifeExp) ->  
gap_72_02_europe_wide

ggplot(data = gap_72_02_europe) +  
  aes(x = lifeExp) +  
  aes(y = country) +  
  geom_point() +  
  aes(color = factor(year)) +  
  geom_segment(data = gap_72_02_europe_wide,  
                 aes(x = `1972`,  # poorly named columns ie starting w number
                     xend = `2002`,  # need to be surouned by backtick
                     yend = country),  
               color = "plum3",  
               size = 1.5) +  
  geom_point(size = 4) +  # point layer above not really needed - just for showing logic
  scale_color_manual(values = c("plum3", "plum4")) +  
* theme(legend.position = c(.15,.85))
```
]
 
.right-output-growth-auto[
<img src="geoms_files/figure-html/growth_auto_18_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  filter(year %in% c(1972, 2002)) %>%  
  mutate(country = reorder(country, lifeExp, mean)) ->  
gap_72_02_europe

gap_72_02_europe %>%  
  select(-gdpPercap, -pop) %>%  
  pivot_wider(names_from = year,  
              values_from = lifeExp) ->  
gap_72_02_europe_wide

ggplot(data = gap_72_02_europe) +  
  aes(x = lifeExp) +  
  aes(y = country) +  
  geom_point() +  
  aes(color = factor(year)) +  
  geom_segment(data = gap_72_02_europe_wide,  
                 aes(x = `1972`,  # poorly named columns ie starting w number
                     xend = `2002`,  # need to be surouned by backtick
                     yend = country),  
               color = "plum3",  
               size = 1.5) +  
  geom_point(size = 4) +  # point layer above not really needed - just for showing logic
  scale_color_manual(values = c("plum3", "plum4")) +  
  theme(legend.position = c(.15,.85)) +  
* labs(color = NULL, y = NULL)
```
]
 
.right-output-growth-auto[
<img src="geoms_files/figure-html/growth_auto_19_output-1.png" width="100%" />
]

---
name: group

## The group aesthetic and geometric objects

---

Using geom_line(), you can use many of the same aesthetics as before as well as one special aesthetic: `group`.

> Lines and paths fall somewhere in between: each overall line is composed of a set of straight segments, but each segment represents two points. --- Hadley Wickham

Group is like a declaration that instead of plotting the entire data set all together, instead you will plot many small data sets, each defined by the categorical variable "mapped" to `group`.

Let's see how this works with the following example.

---
name: many_series
class: inverse, middle, center

# Many series

---

The geometric object `line` connects all points as it moves along the x axis.  If a group is not defined, the geom_line is hard to interpret, as seen before group is added.

We also see that mapping discrete data to the color or linetype aesthetics has a similar effect to declaring that there are groups in the data.

Another thing we observe in the last addition to the last plot is adding an additional geometric layer.  The point layer, because it follows the line layer, appears on top of the line layer.

---

```r
*gapminder
```
]
 
.right-output-time_series_heat-auto[

---
class: split-40
count: false

```r
gapminder %>%  
* filter(continent == "Europe")
```
]
 
.right-output-time_series_heat-auto[

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
* ggplot()
```
]
 
.right-output-time_series_heat-auto[
<img src="geoms_files/figure-html/time_series_heat_auto_3_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  ggplot() +  
* aes(x = factor(year))
```
]
 
.right-output-time_series_heat-auto[
<img src="geoms_files/figure-html/time_series_heat_auto_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  ggplot() +  
  aes(x = factor(year)) +  
* aes(y = country)
```
]
 
.right-output-time_series_heat-auto[
<img src="geoms_files/figure-html/time_series_heat_auto_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  ggplot() +  
  aes(x = factor(year)) +  
  aes(y = country) +  
* geom_tile(color = "grey80")
```
]
 
.right-output-time_series_heat-auto[
<img src="geoms_files/figure-html/time_series_heat_auto_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  ggplot() +  
  aes(x = factor(year)) +  
  aes(y = country) +  
  geom_tile(color = "grey80") +  
* aes(fill = lifeExp)
```
]
 
.right-output-time_series_heat-auto[
<img src="geoms_files/figure-html/time_series_heat_auto_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(continent == "Europe") %>%  
  ggplot() +  
  aes(x = factor(year)) +  
  aes(y = country) +  
  geom_tile(color = "grey80") +  
  aes(fill = lifeExp) +  
* scale_fill_viridis_c(option = "magma")
```
]
 
.right-output-time_series_heat-auto[
<img src="geoms_files/figure-html/time_series_heat_auto_8_output-1.png" width="100%" />
]

---

# ranks of a series

---

```r
*gapminder
```
]
 
.right-output-time_series_bump-auto[

---
class: split-40
count: false

```r
gapminder %>%  
* filter(country %in% c("Austria", "Belgium",
*                       "Norway", "Netherlands",
*                       "Spain", "Italy","France"))
```
]
 
.right-output-time_series_bump-auto[

```
  # A tibble: 84 x 6
     country continent  year lifeExp     pop gdpPercap
     <fct>   <fct>     <int>   <dbl>   <int>     <dbl>
   1 Austria Europe     1952    66.8 6927772     6137.
   2 Austria Europe     1957    67.5 6965860     8843.
   3 Austria Europe     1962    69.5 7129864    10751.
   4 Austria Europe     1967    70.1 7376998    12835.
   5 Austria Europe     1972    70.6 7544201    16662.
   6 Austria Europe     1977    72.2 7568430    19749.
   7 Austria Europe     1982    73.2 7574613    21597.
   8 Austria Europe     1987    74.9 7578903    23688.
   9 Austria Europe     1992    76.0 7914969    27042.
  10 Austria Europe     1997    77.5 8069876    29096.
  # … with 74 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Austria", "Belgium",  
                        "Norway", "Netherlands",  
                        "Spain", "Italy","France")) %>%  
* filter(year > 1980)
```
]
 
.right-output-time_series_bump-auto[

```
  # A tibble: 42 x 6
     country continent  year lifeExp      pop gdpPercap
     <fct>   <fct>     <int>   <dbl>    <int>     <dbl>
   1 Austria Europe     1982    73.2  7574613    21597.
   2 Austria Europe     1987    74.9  7578903    23688.
   3 Austria Europe     1992    76.0  7914969    27042.
   4 Austria Europe     1997    77.5  8069876    29096.
   5 Austria Europe     2002    79.0  8148312    32418.
   6 Austria Europe     2007    79.8  8199783    36126.
   7 Belgium Europe     1982    73.9  9856303    20980.
   8 Belgium Europe     1987    75.4  9870200    22526.
   9 Belgium Europe     1992    76.5 10045622    25576.
  10 Belgium Europe     1997    77.5 10199787    27561.
  # … with 32 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Austria", "Belgium",  
                        "Norway", "Netherlands",  
                        "Spain", "Italy","France")) %>%  
  filter(year > 1980) %>%  
* group_by(year)
```
]
 
.right-output-time_series_bump-auto[

```
  # A tibble: 42 x 6
  # Groups:   year [6]
     country continent  year lifeExp      pop gdpPercap
     <fct>   <fct>     <int>   <dbl>    <int>     <dbl>
   1 Austria Europe     1982    73.2  7574613    21597.
   2 Austria Europe     1987    74.9  7578903    23688.
   3 Austria Europe     1992    76.0  7914969    27042.
   4 Austria Europe     1997    77.5  8069876    29096.
   5 Austria Europe     2002    79.0  8148312    32418.
   6 Austria Europe     2007    79.8  8199783    36126.
   7 Belgium Europe     1982    73.9  9856303    20980.
   8 Belgium Europe     1987    75.4  9870200    22526.
   9 Belgium Europe     1992    76.5 10045622    25576.
  10 Belgium Europe     1997    77.5 10199787    27561.
  # … with 32 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Austria", "Belgium",  
                        "Norway", "Netherlands",  
                        "Spain", "Italy","France")) %>%  
  filter(year > 1980) %>%  
  group_by(year) %>%  
* arrange(lifeExp)
```
]
 
.right-output-time_series_bump-auto[

```
  # A tibble: 42 x 6
  # Groups:   year [6]
     country     continent  year lifeExp      pop gdpPercap
     <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
   1 Austria     Europe     1982    73.2  7574613    21597.
   2 Belgium     Europe     1982    73.9  9856303    20980.
   3 France      Europe     1982    74.9 54433565    20294.
   4 Austria     Europe     1987    74.9  7578903    23688.
   5 Italy       Europe     1982    75.0 56535636    16537.
   6 Belgium     Europe     1987    75.4  9870200    22526.
   7 Norway      Europe     1987    75.9  4186147    31541.
   8 Norway      Europe     1982    76.0  4114787    26299.
   9 Austria     Europe     1992    76.0  7914969    27042.
  10 Netherlands Europe     1982    76.0 14310401    21399.
  # … with 32 more rows
```
]

---
class: split-40
count: false

```
  # A tibble: 42 x 7
  # Groups:   year [6]
     country     continent  year lifeExp      pop gdpPercap rank_life_exp
     <fct>       <fct>     <int>   <dbl>    <int>     <dbl>         <int>
   1 Austria     Europe     1982    73.2  7574613    21597.             1
   2 Belgium     Europe     1982    73.9  9856303    20980.             2
   3 France      Europe     1982    74.9 54433565    20294.             3
   4 Austria     Europe     1987    74.9  7578903    23688.             1
   5 Italy       Europe     1982    75.0 56535636    16537.             4
   6 Belgium     Europe     1987    75.4  9870200    22526.             2
   7 Norway      Europe     1987    75.9  4186147    31541.             3
   8 Norway      Europe     1982    76.0  4114787    26299.             5
   9 Austria     Europe     1992    76.0  7914969    27042.             1
  10 Netherlands Europe     1982    76.0 14310401    21399.             6
  # … with 32 more rows
```
]

---
class: split-40
count: false

```
  # A tibble: 42 x 7
     country     continent  year lifeExp      pop gdpPercap rank_life_exp
     <fct>       <fct>     <int>   <dbl>    <int>     <dbl>         <int>
   1 Austria     Europe     1982    73.2  7574613    21597.             1
   2 Belgium     Europe     1982    73.9  9856303    20980.             2
   3 France      Europe     1982    74.9 54433565    20294.             3
   4 Austria     Europe     1987    74.9  7578903    23688.             1
   5 Italy       Europe     1982    75.0 56535636    16537.             4
   6 Belgium     Europe     1987    75.4  9870200    22526.             2
   7 Norway      Europe     1987    75.9  4186147    31541.             3
   8 Norway      Europe     1982    76.0  4114787    26299.             5
   9 Austria     Europe     1992    76.0  7914969    27042.             1
  10 Netherlands Europe     1982    76.0 14310401    21399.             6
  # … with 32 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Austria", "Belgium",  
                        "Norway", "Netherlands",  
                        "Spain", "Italy","France")) %>%  
  filter(year > 1980) %>%  
  group_by(year) %>%  
  arrange(lifeExp) %>%  
  mutate(rank_life_exp = 1:n()) %>%  
  ungroup() %>%  
* ggplot()
```
]
 
.right-output-time_series_bump-auto[
<img src="geoms_files/figure-html/time_series_bump_auto_8_output-1.png" width="100%" />
]

---
class: split-40
count: false

---
class: split-40
count: false

---
class: split-40
count: false

---
class: split-40
count: false

---
class: split-40
count: false

---
class: split-40
count: false

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Austria", "Belgium",  
                        "Norway", "Netherlands",  
                        "Spain", "Italy","France")) %>%  
  filter(year > 1980) %>%  
  group_by(year) %>%  
  arrange(lifeExp) %>%  
  mutate(rank_life_exp = 1:n()) %>%  
  ungroup() %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = rank_life_exp) +  
  geom_point(size = 6) +  
  aes(color = country) +  
  ggbump::geom_bump(size = 2, smooth = 8) +  
  scale_x_continuous(limits = c(1970, 2020),  
                     breaks = seq(1982, 2007,  
                                  by = 5)) +  
* geom_text(data = . %>%
*             filter(year == min(year)),
*           aes(x = year - 2.5, label = country),
*           size = 5, hjust = 1)
```
]
 
.right-output-time_series_bump-auto[
<img src="geoms_files/figure-html/time_series_bump_auto_15_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Austria", "Belgium",  
                        "Norway", "Netherlands",  
                        "Spain", "Italy","France")) %>%  
  filter(year > 1980) %>%  
  group_by(year) %>%  
  arrange(lifeExp) %>%  
  mutate(rank_life_exp = 1:n()) %>%  
  ungroup() %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = rank_life_exp) +  
  geom_point(size = 6) +  
  aes(color = country) +  
  ggbump::geom_bump(size = 2, smooth = 8) +  
  scale_x_continuous(limits = c(1970, 2020),  
                     breaks = seq(1982, 2007,  
                                  by = 5)) +  
  geom_text(data = . %>%  
              filter(year == min(year)),  
            aes(x = year - 2.5, label = country),  
            size = 5, hjust = 1) +  
* geom_text(data = . %>%
*             filter(year == max(year)),
*           aes(x = year + 2.5, label = country),
*           size = 5, hjust = 0)
```
]
 
.right-output-time_series_bump-auto[
<img src="geoms_files/figure-html/time_series_bump_auto_16_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Austria", "Belgium",  
                        "Norway", "Netherlands",  
                        "Spain", "Italy","France")) %>%  
  filter(year > 1980) %>%  
  group_by(year) %>%  
  arrange(lifeExp) %>%  
  mutate(rank_life_exp = 1:n()) %>%  
  ungroup() %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = rank_life_exp) +  
  geom_point(size = 6) +  
  aes(color = country) +  
  ggbump::geom_bump(size = 2, smooth = 8) +  
  scale_x_continuous(limits = c(1970, 2020),  
                     breaks = seq(1982, 2007,  
                                  by = 5)) +  
  geom_text(data = . %>%  
              filter(year == min(year)),  
            aes(x = year - 2.5, label = country),  
            size = 5, hjust = 1) +  
  geom_text(data = . %>%  
              filter(year == max(year)),  
            aes(x = year + 2.5, label = country),  
            size = 5, hjust = 0) +  
* scale_color_manual(values = paletteer::paletteer_d("basetheme::brutal", 7))
```
]
 
.right-output-time_series_bump-auto[
<img src="geoms_files/figure-html/time_series_bump_auto_17_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Austria", "Belgium",  
                        "Norway", "Netherlands",  
                        "Spain", "Italy","France")) %>%  
  filter(year > 1980) %>%  
  group_by(year) %>%  
  arrange(lifeExp) %>%  
  mutate(rank_life_exp = 1:n()) %>%  
  ungroup() %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = rank_life_exp) +  
  geom_point(size = 6) +  
  aes(color = country) +  
  ggbump::geom_bump(size = 2, smooth = 8) +  
  scale_x_continuous(limits = c(1970, 2020),  
                     breaks = seq(1982, 2007,  
                                  by = 5)) +  
  geom_text(data = . %>%  
              filter(year == min(year)),  
            aes(x = year - 2.5, label = country),  
            size = 5, hjust = 1) +  
  geom_text(data = . %>%  
              filter(year == max(year)),  
            aes(x = year + 2.5, label = country),  
            size = 5, hjust = 0) +  
  scale_color_manual(values = paletteer::paletteer_d("basetheme::brutal", 7)) +  
* theme_minimal()
```
]
 
.right-output-time_series_bump-auto[
<img src="geoms_files/figure-html/time_series_bump_auto_18_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Austria", "Belgium",  
                        "Norway", "Netherlands",  
                        "Spain", "Italy","France")) %>%  
  filter(year > 1980) %>%  
  group_by(year) %>%  
  arrange(lifeExp) %>%  
  mutate(rank_life_exp = 1:n()) %>%  
  ungroup() %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = rank_life_exp) +  
  geom_point(size = 6) +  
  aes(color = country) +  
  ggbump::geom_bump(size = 2, smooth = 8) +  
  scale_x_continuous(limits = c(1970, 2020),  
                     breaks = seq(1982, 2007,  
                                  by = 5)) +  
  geom_text(data = . %>%  
              filter(year == min(year)),  
            aes(x = year - 2.5, label = country),  
            size = 5, hjust = 1) +  
  geom_text(data = . %>%  
              filter(year == max(year)),  
            aes(x = year + 2.5, label = country),  
            size = 5, hjust = 0) +  
  scale_color_manual(values = paletteer::paletteer_d("basetheme::brutal", 7)) +  
  theme_minimal() +  
* theme(legend.position = "none")
```
]
 
.right-output-time_series_bump-auto[
<img src="geoms_files/figure-html/time_series_bump_auto_19_output-1.png" width="100%" />
]

---

```r
gapminder %>%  
  filter(country %in% c("Austria", "Belgium",  
                        "Norway", "Netherlands",  
                        "Spain", "Italy","France")) %>%  
  filter(year > 1980) %>%  
  group_by(year) %>%  
  arrange(lifeExp) %>%  
  mutate(rank_life_exp = 1:n()) %>%  
  ungroup() %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = rank_life_exp) +  
  aes(color = country) +  
* geom_line(size = 2, smooth = 8) +
* geom_point(size = 6, shape = 21, fill = "white", stroke = 2) +
  scale_x_continuous(limits = c(1970, 2020),  
                     breaks = seq(1982, 2007,  
                                  by = 5)) +  
  geom_text(data = . %>%  
              filter(year == min(year)),  
            aes(x = year - 2.5, label = country),  
            size = 5, hjust = 1) +  
  geom_text(data = . %>%  
              filter(year == max(year)),  
            aes(x = year + 2.5, label = country),  
            size = 5, hjust = 0) +  
  scale_color_manual(values = paletteer::paletteer_d("basetheme::brutal", 7)) +  
  theme_minimal() +  
  theme(legend.position = "none")  
```
]
 
.right-output-time_series_bump_line-1[
<img src="geoms_files/figure-html/time_series_bump_line_1_1_output-1.png" width="100%" />
]

---

```r
*gapminder
```
]
 
.right-output-slope_plot-auto[

---
class: split-40
count: false

```r
gapminder %>%  
* filter(country %in% c("Belgium",
*                       "Norway",
*                       "Spain"))
```
]
 
.right-output-slope_plot-auto[

```
  # A tibble: 36 x 6
     country continent  year lifeExp      pop gdpPercap
     <fct>   <fct>     <int>   <dbl>    <int>     <dbl>
   1 Belgium Europe     1952    68    8730405     8343.
   2 Belgium Europe     1957    69.2  8989111     9715.
   3 Belgium Europe     1962    70.2  9218400    10991.
   4 Belgium Europe     1967    70.9  9556500    13149.
   5 Belgium Europe     1972    71.4  9709100    16672.
   6 Belgium Europe     1977    72.8  9821800    19118.
   7 Belgium Europe     1982    73.9  9856303    20980.
   8 Belgium Europe     1987    75.4  9870200    22526.
   9 Belgium Europe     1992    76.5 10045622    25576.
  10 Belgium Europe     1997    77.5 10199787    27561.
  # … with 26 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Belgium",  
                        "Norway",  
                        "Spain")) %>%  
* filter(year > 1990)
```
]
 
.right-output-slope_plot-auto[

```
  # A tibble: 12 x 6
     country continent  year lifeExp      pop gdpPercap
     <fct>   <fct>     <int>   <dbl>    <int>     <dbl>
   1 Belgium Europe     1992    76.5 10045622    25576.
   2 Belgium Europe     1997    77.5 10199787    27561.
   3 Belgium Europe     2002    78.3 10311970    30486.
   4 Belgium Europe     2007    79.4 10392226    33693.
   5 Norway  Europe     1992    77.3  4286357    33966.
   6 Norway  Europe     1997    78.3  4405672    41283.
   7 Norway  Europe     2002    79.0  4535591    44684.
   8 Norway  Europe     2007    80.2  4627926    49357.
   9 Spain   Europe     1992    77.6 39549438    18603.
  10 Spain   Europe     1997    78.8 39855442    20445.
  11 Spain   Europe     2002    79.8 40152517    24835.
  12 Spain   Europe     2007    80.9 40448191    28821.
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Belgium",  
                        "Norway",  
                        "Spain")) %>%  
  filter(year > 1990) %>%  
* ggplot()
```
]
 
.right-output-slope_plot-auto[
<img src="geoms_files/figure-html/slope_plot_auto_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Belgium",  
                        "Norway",  
                        "Spain")) %>%  
  filter(year > 1990) %>%  
  ggplot() +  
* aes(x = year)
```
]
 
.right-output-slope_plot-auto[
<img src="geoms_files/figure-html/slope_plot_auto_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Belgium",  
                        "Norway",  
                        "Spain")) %>%  
  filter(year > 1990) %>%  
  ggplot() +  
  aes(x = year) +  
* aes(y = lifeExp)
```
]
 
.right-output-slope_plot-auto[
<img src="geoms_files/figure-html/slope_plot_auto_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Belgium",  
                        "Norway",  
                        "Spain")) %>%  
  filter(year > 1990) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
* aes(color = country)
```
]
 
.right-output-slope_plot-auto[
<img src="geoms_files/figure-html/slope_plot_auto_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Belgium",  
                        "Norway",  
                        "Spain")) %>%  
  filter(year > 1990) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
  aes(color = country) +  
* geom_line(size = 2, smooth = 8)
```
]
 
.right-output-slope_plot-auto[
<img src="geoms_files/figure-html/slope_plot_auto_8_output-1.png" width="100%" />
]

---
class: split-40
count: false

---
class: split-40
count: false

---
class: split-40
count: false

---
class: split-40
count: false

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Belgium",  
                        "Norway",  
                        "Spain")) %>%  
  filter(year > 1990) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
  aes(color = country) +  
  geom_line(size = 2, smooth = 8) +  
  geom_label(aes(label = round(lifeExp, 1)),  
    color = "darkgrey",  
    stroke = "red",  
    check_overlap = T) +  
  scale_x_continuous(limits = c(1985, 2012),  
                     breaks = seq(1992, 2007,  
                                  by = 5)) +  
  geom_text(data = . %>%  
              filter(year == min(year)),  
            aes(x = year - 1.5, label = country),  
            size = 5, hjust = 1) +  
  geom_text(data = . %>%  
              filter(year == max(year)),  
            aes(x = year + 1.5, label = country),  
            size = 5, hjust = 0) +  
* scale_color_manual(values =
*                      paletteer::paletteer_d("basetheme::brutal", 7))
```
]
 
.right-output-slope_plot-auto[
<img src="geoms_files/figure-html/slope_plot_auto_13_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Belgium",  
                        "Norway",  
                        "Spain")) %>%  
  filter(year > 1990) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
  aes(color = country) +  
  geom_line(size = 2, smooth = 8) +  
  geom_label(aes(label = round(lifeExp, 1)),  
    color = "darkgrey",  
    stroke = "red",  
    check_overlap = T) +  
  scale_x_continuous(limits = c(1985, 2012),  
                     breaks = seq(1992, 2007,  
                                  by = 5)) +  
  geom_text(data = . %>%  
              filter(year == min(year)),  
            aes(x = year - 1.5, label = country),  
            size = 5, hjust = 1) +  
  geom_text(data = . %>%  
              filter(year == max(year)),  
            aes(x = year + 1.5, label = country),  
            size = 5, hjust = 0) +  
  scale_color_manual(values =  
                       paletteer::paletteer_d("basetheme::brutal", 7)) +  
* theme_minimal()
```
]
 
.right-output-slope_plot-auto[
<img src="geoms_files/figure-html/slope_plot_auto_14_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Belgium",  
                        "Norway",  
                        "Spain")) %>%  
  filter(year > 1990) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
  aes(color = country) +  
  geom_line(size = 2, smooth = 8) +  
  geom_label(aes(label = round(lifeExp, 1)),  
    color = "darkgrey",  
    stroke = "red",  
    check_overlap = T) +  
  scale_x_continuous(limits = c(1985, 2012),  
                     breaks = seq(1992, 2007,  
                                  by = 5)) +  
  geom_text(data = . %>%  
              filter(year == min(year)),  
            aes(x = year - 1.5, label = country),  
            size = 5, hjust = 1) +  
  geom_text(data = . %>%  
              filter(year == max(year)),  
            aes(x = year + 1.5, label = country),  
            size = 5, hjust = 0) +  
  scale_color_manual(values =  
                       paletteer::paletteer_d("basetheme::brutal", 7)) +  
  theme_minimal() +  
* theme(panel.grid.major.y = element_blank())
```
]
 
.right-output-slope_plot-auto[
<img src="geoms_files/figure-html/slope_plot_auto_15_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Belgium",  
                        "Norway",  
                        "Spain")) %>%  
  filter(year > 1990) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
  aes(color = country) +  
  geom_line(size = 2, smooth = 8) +  
  geom_label(aes(label = round(lifeExp, 1)),  
    color = "darkgrey",  
    stroke = "red",  
    check_overlap = T) +  
  scale_x_continuous(limits = c(1985, 2012),  
                     breaks = seq(1992, 2007,  
                                  by = 5)) +  
  geom_text(data = . %>%  
              filter(year == min(year)),  
            aes(x = year - 1.5, label = country),  
            size = 5, hjust = 1) +  
  geom_text(data = . %>%  
              filter(year == max(year)),  
            aes(x = year + 1.5, label = country),  
            size = 5, hjust = 0) +  
  scale_color_manual(values =  
                       paletteer::paletteer_d("basetheme::brutal", 7)) +  
  theme_minimal() +  
  theme(panel.grid.major.y = element_blank()) +  
* theme(panel.grid.minor.y = element_blank())
```
]
 
.right-output-slope_plot-auto[
<img src="geoms_files/figure-html/slope_plot_auto_16_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Belgium",  
                        "Norway",  
                        "Spain")) %>%  
  filter(year > 1990) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
  aes(color = country) +  
  geom_line(size = 2, smooth = 8) +  
  geom_label(aes(label = round(lifeExp, 1)),  
    color = "darkgrey",  
    stroke = "red",  
    check_overlap = T) +  
  scale_x_continuous(limits = c(1985, 2012),  
                     breaks = seq(1992, 2007,  
                                  by = 5)) +  
  geom_text(data = . %>%  
              filter(year == min(year)),  
            aes(x = year - 1.5, label = country),  
            size = 5, hjust = 1) +  
  geom_text(data = . %>%  
              filter(year == max(year)),  
            aes(x = year + 1.5, label = country),  
            size = 5, hjust = 0) +  
  scale_color_manual(values =  
                       paletteer::paletteer_d("basetheme::brutal", 7)) +  
  theme_minimal() +  
  theme(panel.grid.major.y = element_blank()) +  
  theme(panel.grid.minor.y = element_blank()) +  
* theme(panel.grid.minor.x = element_blank())
```
]
 
.right-output-slope_plot-auto[
<img src="geoms_files/figure-html/slope_plot_auto_17_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Belgium",  
                        "Norway",  
                        "Spain")) %>%  
  filter(year > 1990) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
  aes(color = country) +  
  geom_line(size = 2, smooth = 8) +  
  geom_label(aes(label = round(lifeExp, 1)),  
    color = "darkgrey",  
    stroke = "red",  
    check_overlap = T) +  
  scale_x_continuous(limits = c(1985, 2012),  
                     breaks = seq(1992, 2007,  
                                  by = 5)) +  
  geom_text(data = . %>%  
              filter(year == min(year)),  
            aes(x = year - 1.5, label = country),  
            size = 5, hjust = 1) +  
  geom_text(data = . %>%  
              filter(year == max(year)),  
            aes(x = year + 1.5, label = country),  
            size = 5, hjust = 0) +  
  scale_color_manual(values =  
                       paletteer::paletteer_d("basetheme::brutal", 7)) +  
  theme_minimal() +  
  theme(panel.grid.major.y = element_blank()) +  
  theme(panel.grid.minor.y = element_blank()) +  
  theme(panel.grid.minor.x = element_blank()) +  
* theme(panel.grid.major.x = element_blank())
```
]
 
.right-output-slope_plot-auto[
<img src="geoms_files/figure-html/slope_plot_auto_18_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Belgium",  
                        "Norway",  
                        "Spain")) %>%  
  filter(year > 1990) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
  aes(color = country) +  
  geom_line(size = 2, smooth = 8) +  
  geom_label(aes(label = round(lifeExp, 1)),  
    color = "darkgrey",  
    stroke = "red",  
    check_overlap = T) +  
  scale_x_continuous(limits = c(1985, 2012),  
                     breaks = seq(1992, 2007,  
                                  by = 5)) +  
  geom_text(data = . %>%  
              filter(year == min(year)),  
            aes(x = year - 1.5, label = country),  
            size = 5, hjust = 1) +  
  geom_text(data = . %>%  
              filter(year == max(year)),  
            aes(x = year + 1.5, label = country),  
            size = 5, hjust = 0) +  
  scale_color_manual(values =  
                       paletteer::paletteer_d("basetheme::brutal", 7)) +  
  theme_minimal() +  
  theme(panel.grid.major.y = element_blank()) +  
  theme(panel.grid.minor.y = element_blank()) +  
  theme(panel.grid.minor.x = element_blank()) +  
  theme(panel.grid.major.x = element_blank()) +  
* theme(axis.text.y = element_blank())
```
]
 
.right-output-slope_plot-auto[
<img src="geoms_files/figure-html/slope_plot_auto_19_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Belgium",  
                        "Norway",  
                        "Spain")) %>%  
  filter(year > 1990) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
  aes(color = country) +  
  geom_line(size = 2, smooth = 8) +  
  geom_label(aes(label = round(lifeExp, 1)),  
    color = "darkgrey",  
    stroke = "red",  
    check_overlap = T) +  
  scale_x_continuous(limits = c(1985, 2012),  
                     breaks = seq(1992, 2007,  
                                  by = 5)) +  
  geom_text(data = . %>%  
              filter(year == min(year)),  
            aes(x = year - 1.5, label = country),  
            size = 5, hjust = 1) +  
  geom_text(data = . %>%  
              filter(year == max(year)),  
            aes(x = year + 1.5, label = country),  
            size = 5, hjust = 0) +  
  scale_color_manual(values =  
                       paletteer::paletteer_d("basetheme::brutal", 7)) +  
  theme_minimal() +  
  theme(panel.grid.major.y = element_blank()) +  
  theme(panel.grid.minor.y = element_blank()) +  
  theme(panel.grid.minor.x = element_blank()) +  
  theme(panel.grid.major.x = element_blank()) +  
  theme(axis.text.y = element_blank()) +  
* theme(axis.ticks.y = element_blank())
```
]
 
.right-output-slope_plot-auto[
<img src="geoms_files/figure-html/slope_plot_auto_20_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Belgium",  
                        "Norway",  
                        "Spain")) %>%  
  filter(year > 1990) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
  aes(color = country) +  
  geom_line(size = 2, smooth = 8) +  
  geom_label(aes(label = round(lifeExp, 1)),  
    color = "darkgrey",  
    stroke = "red",  
    check_overlap = T) +  
  scale_x_continuous(limits = c(1985, 2012),  
                     breaks = seq(1992, 2007,  
                                  by = 5)) +  
  geom_text(data = . %>%  
              filter(year == min(year)),  
            aes(x = year - 1.5, label = country),  
            size = 5, hjust = 1) +  
  geom_text(data = . %>%  
              filter(year == max(year)),  
            aes(x = year + 1.5, label = country),  
            size = 5, hjust = 0) +  
  scale_color_manual(values =  
                       paletteer::paletteer_d("basetheme::brutal", 7)) +  
  theme_minimal() +  
  theme(panel.grid.major.y = element_blank()) +  
  theme(panel.grid.minor.y = element_blank()) +  
  theme(panel.grid.minor.x = element_blank()) +  
  theme(panel.grid.major.x = element_blank()) +  
  theme(axis.text.y = element_blank()) +  
  theme(axis.ticks.y = element_blank()) +  
* theme(legend.position = "none")
```
]
 
.right-output-slope_plot-auto[
<img src="geoms_files/figure-html/slope_plot_auto_21_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(country %in% c("Belgium",  
                        "Norway",  
                        "Spain")) %>%  
  filter(year > 1990) %>%  
  ggplot() +  
  aes(x = year) +  
  aes(y = lifeExp) +  
  aes(color = country) +  
  geom_line(size = 2, smooth = 8) +  
  geom_label(aes(label = round(lifeExp, 1)),  
    color = "darkgrey",  
    stroke = "red",  
    check_overlap = T) +  
  scale_x_continuous(limits = c(1985, 2012),  
                     breaks = seq(1992, 2007,  
                                  by = 5)) +  
  geom_text(data = . %>%  
              filter(year == min(year)),  
            aes(x = year - 1.5, label = country),  
            size = 5, hjust = 1) +  
  geom_text(data = . %>%  
              filter(year == max(year)),  
            aes(x = year + 1.5, label = country),  
            size = 5, hjust = 0) +  
  scale_color_manual(values =  
                       paletteer::paletteer_d("basetheme::brutal", 7)) +  
  theme_minimal() +  
  theme(panel.grid.major.y = element_blank()) +  
  theme(panel.grid.minor.y = element_blank()) +  
  theme(panel.grid.minor.x = element_blank()) +  
  theme(panel.grid.major.x = element_blank()) +  
  theme(axis.text.y = element_blank()) +  
  theme(axis.ticks.y = element_blank()) +  
  theme(legend.position = "none") +  
* labs(x = NULL, y = NULL)
```
]
 
.right-output-slope_plot-auto[
<img src="geoms_files/figure-html/slope_plot_auto_22_output-1.png" width="100%" />
]

---

[Racing bar chart](https://evamaerey.github.io/flipbooks/racing_bars/racing_barcharts.html#34) is concerned with ranks and values.

---

## Questions

geom_area() is a special case of what other geom_*()?  Use the help to answer this question.

---

Geoms is a long section consider because there are many geoms. Consider taking the [overview track](#local).

---
name: c

# Continuous Univariate

---

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  theme_minimal() +  
  geom_rug(col = "darkgrey") +  
  geom_histogram(alpha = .5, fill = "cadetblue")   
```
]
 
.right-output-univariate_continuous-rotate[
<img src="geoms_files/figure-html/univariate_continuous_rotate_1_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  theme_minimal() +  
  geom_rug(col = "darkgrey") +  
* geom_freqpoly(linetype = "dashed")
```
]
 
.right-output-univariate_continuous-rotate[
<img src="geoms_files/figure-html/univariate_continuous_rotate_2_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  theme_minimal() +  
  geom_rug(col = "darkgrey") +  
* geom_dotplot(fill = "blue", alpha = .5)
```
]
 
.right-output-univariate_continuous-rotate[
<img src="geoms_files/figure-html/univariate_continuous_rotate_3_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  theme_minimal() +  
  geom_rug(col = "darkgrey") +  
* geom_dotplot(binwidth = "dotdensity", alpha = .5)
```
]
 
.right-output-univariate_continuous-rotate[
<img src="geoms_files/figure-html/univariate_continuous_rotate_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  theme_minimal() +  
  geom_rug(col = "darkgrey") +  
* geom_histogram(col = "black", fill = "maroon4", alpha = .4, aes(y = ..density..))
```
]
 
.right-output-univariate_continuous-rotate[
<img src="geoms_files/figure-html/univariate_continuous_rotate_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  theme_minimal() +  
  geom_rug(col = "darkgrey") +  
* geom_density(linetype = "dotted", adjust = 1/4, size = 1.25)
```
]
 
.right-output-univariate_continuous-rotate[
<img src="geoms_files/figure-html/univariate_continuous_rotate_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  theme_minimal() +  
  geom_rug(col = "darkgrey") +  
* geom_density(linetype = "dashed", adjust = 1/2, size = 1.25)
```
]
 
.right-output-univariate_continuous-rotate[
<img src="geoms_files/figure-html/univariate_continuous_rotate_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  theme_minimal() +  
  geom_rug(col = "darkgrey") +  
* ggalt::geom_bkde(alpha = 0.3 )
```
]
 
.right-output-univariate_continuous-rotate[
<img src="geoms_files/figure-html/univariate_continuous_rotate_8_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  theme_minimal() +  
  geom_rug(col = "darkgrey") +  
* ggalt::geom_bkde(alpha = 0.3, bandwidth = .25, aes(fill = continent))
```
]
 
.right-output-univariate_continuous-rotate[
<img src="geoms_files/figure-html/univariate_continuous_rotate_9_output-1.png" width="100%" />
]

---

---
name: c

# Continuous - Discrete via facetting

---

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  theme_minimal() +  
  geom_rug(col = "darkgrey") +  
  geom_histogram(alpha = .5, fill = "cadetblue") +  
  facet_grid(continent ~ .)  
```
]
 
.right-output-geom_discrete_continuous_facet-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_facet_rotate_1_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  theme_minimal() +  
  geom_rug(col = "darkgrey") +  
* geom_freqpoly(linetype = "dashed") +
  facet_grid(continent ~ .)  
```
]
 
.right-output-geom_discrete_continuous_facet-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_facet_rotate_2_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  theme_minimal() +  
  geom_rug(col = "darkgrey") +  
* geom_dotplot(fill = "blue", alpha = .5) +
  facet_grid(continent ~ .)  
```
]
 
.right-output-geom_discrete_continuous_facet-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_facet_rotate_3_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  theme_minimal() +  
  geom_rug(col = "darkgrey") +  
* geom_dotplot(binwidth = "dotdensity", alpha = .5) +
  facet_grid(continent ~ .)  
```
]
 
.right-output-geom_discrete_continuous_facet-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_facet_rotate_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  theme_minimal() +  
  geom_rug(col = "darkgrey") +  
* geom_histogram(col = "black", fill = "maroon4", alpha = .4, aes(y = ..density..)) +
  facet_grid(continent ~ .)  
```
]
 
.right-output-geom_discrete_continuous_facet-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_facet_rotate_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  theme_minimal() +  
  geom_rug(col = "darkgrey") +  
* geom_density(linetype = "dotted", adjust = 1/4, size = 1.25) +
  facet_grid(continent ~ .)  
```
]
 
.right-output-geom_discrete_continuous_facet-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_facet_rotate_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = lifeExp) +  
  theme_minimal() +  
  geom_rug(col = "darkgrey") +  
* geom_density(linetype = "dashed", adjust = 1/2, size = 1.25) +
  facet_grid(continent ~ .)  
```
]
 
.right-output-geom_discrete_continuous_facet-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_facet_rotate_7_output-1.png" width="100%" />
]

---

---
class: inverse, center, middle
name: dc

## Discrete v. Continuous

---

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  ggplot() +  
  aes(y = fct_rev(continent)) +  
  aes(x = lifeExp) +  
  geom_point(size = .2) +  
  geom_boxplot(alpha = .1) +  
  theme_minimal() +  
  labs(y = NULL)  
```
]
 
.right-output-geom_discrete_continuous-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_rotate_1_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  ggplot() +  
  aes(y = fct_rev(continent)) +  
  aes(x = lifeExp) +  
  geom_point(size = .2) +  
* geom_boxplot(width = .1, outlier.shape = NA, alpha = 0.5) +
  theme_minimal() +  
  labs(y = NULL)  
```
]
 
.right-output-geom_discrete_continuous-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_rotate_2_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  ggplot() +  
  aes(y = fct_rev(continent)) +  
  aes(x = lifeExp) +  
  geom_point(size = .2) +  
* geom_jitter(width = .2, alpha = .5, color = "green") +
  theme_minimal() +  
  labs(y = NULL)  
```
]
 
.right-output-geom_discrete_continuous-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_rotate_3_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  ggplot() +  
  aes(y = fct_rev(continent)) +  
  aes(x = lifeExp) +  
  geom_point(size = .2) +  
* geom_violin(alpha = .1) +
  theme_minimal() +  
  labs(y = NULL)  
```
]
 
.right-output-geom_discrete_continuous-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_rotate_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  ggplot() +  
  aes(y = fct_rev(continent)) +  
  aes(x = lifeExp) +  
  geom_point(size = .2) +  
* ggforce::geom_sina(col = "magenta", alpha = .5) +
  theme_minimal() +  
  labs(y = NULL)  
```
]
 
.right-output-geom_discrete_continuous-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_rotate_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  ggplot() +  
  aes(y = fct_rev(continent)) +  
  aes(x = lifeExp) +  
  geom_point(size = .2) +  
* stat_summary(fun.y = mean, geom = "point", col = "goldenrod2", size = 8) +
  theme_minimal() +  
  labs(y = NULL)  
```
]
 
.right-output-geom_discrete_continuous-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_rotate_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  ggplot() +  
  aes(y = fct_rev(continent)) +  
  aes(x = lifeExp) +  
  geom_point(size = .2) +  
* ggridges::geom_density_ridges(alpha = .2, fill = "blue") +
  theme_minimal() +  
  labs(y = NULL)  
```
]
 
.right-output-geom_discrete_continuous-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_rotate_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  ggplot() +  
  aes(y = fct_rev(continent)) +  
  aes(x = lifeExp) +  
  geom_point(size = .2) +  
* ggridges::geom_density_ridges(scale = .7, alpha = .2, fill = "blue") +
  theme_minimal() +  
  labs(y = NULL)  
```
]
 
.right-output-geom_discrete_continuous-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_rotate_8_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  ggplot() +  
  aes(y = fct_rev(continent)) +  
  aes(x = lifeExp) +  
  geom_point(size = .2) +  
* ggridges::geom_density_ridges(scale = 1.5, alpha = .2, fill = "blue") +
  theme_minimal() +  
  labs(y = NULL)  
```
]
 
.right-output-geom_discrete_continuous-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_rotate_9_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  ggplot() +  
  aes(y = fct_rev(continent)) +  
  aes(x = lifeExp) +  
  geom_point(size = .2) +  
* ggridges::geom_density_ridges_gradient(aes(fill = stat(x))) +
  theme_minimal() +  
  labs(y = NULL)  
```
]
 
.right-output-geom_discrete_continuous-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_rotate_10_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  ggplot() +  
  aes(y = fct_rev(continent)) +  
  aes(x = lifeExp) +  
  geom_point(size = .2) +  
* ggridges::stat_density_ridges(quantile_lines = TRUE, quantiles = c(0.025, 0.975), alpha = 0.5, fill = "plum4") +
  theme_minimal() +  
  labs(y = NULL)  
```
]
 
.right-output-geom_discrete_continuous-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_rotate_11_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  ggplot() +  
  aes(y = fct_rev(continent)) +  
  aes(x = lifeExp) +  
  geom_point(size = .2) +  
* ggridges::geom_density_ridges(jittered_points = TRUE, position = "raincloud", alpha = .5, scale = 0.9) +
  theme_minimal() +  
  labs(y = NULL)  
```
]
 
.right-output-geom_discrete_continuous-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_rotate_12_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  ggplot() +  
  aes(y = fct_rev(continent)) +  
  aes(x = lifeExp) +  
  geom_point(size = .2) +  
* ggridges::geom_density_ridges(stat = "binline", bins = 20, scale = 0.95, draw_baseline = FALSE) +
  theme_minimal() +  
  labs(y = NULL)  
```
]
 
.right-output-geom_discrete_continuous-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_rotate_13_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  ggplot() +  
  aes(y = fct_rev(continent)) +  
  aes(x = lifeExp) +  
  geom_point(size = .2) +  
* ggridges::geom_density_ridges(scale = 2, alpha = .5) + ggridges::scale_fill_cyclical(values = c("blue", "green")) +
  theme_minimal() +  
  labs(y = NULL)  
```
]
 
.right-output-geom_discrete_continuous-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_rotate_14_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  ggplot() +  
  aes(y = fct_rev(continent)) +  
  aes(x = lifeExp) +  
  geom_point(size = .2) +  
* geom_point(aes(color = continent), position = position_nudge(x = 0, y = .2), size = .5, alpha = 0.8) + geom_boxplot(width = .1, outlier.shape = NA, alpha = 0.5) +
  theme_minimal() +  
  labs(y = NULL)  
```
]
 
.right-output-geom_discrete_continuous-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_rotate_15_output-1.png" width="100%" />
]

---

```r
gapminder %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  ggplot() +  
  aes(x = continent) +  
  aes(y = lifeExp) +  
  aes(alpha = year) +  
  ggbeeswarm::geom_quasirandom(aes(x = continent, y = lifeExp))   
```
]
 
.right-output-geom_discrete_continuous_bee-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_bee_rotate_1_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  ggplot() +  
  aes(x = continent) +  
  aes(y = lifeExp) +  
  aes(alpha = year) +  
* ggbeeswarm::geom_quasirandom(aes(color = continent))
```
]
 
.right-output-geom_discrete_continuous_bee-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_bee_rotate_2_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  ggplot() +  
  aes(x = continent) +  
  aes(y = lifeExp) +  
  aes(alpha = year) +  
* ggbeeswarm::geom_quasirandom(aes(color = higher_income), dodge.width = .8)
```
]
 
.right-output-geom_discrete_continuous_bee-rotate[
<img src="geoms_files/figure-html/geom_discrete_continuous_bee_rotate_3_output-1.png" width="100%" />
]

---
class: inverse, center, middle
name: dd

## Discrete v. Discrete

---

```r
gapminder_2002 %>%  
  mutate(seventy_plus = lifeExp >= 75) %>%  
  mutate(age_category =  
           case_when(seventy_plus == T ~  
                       "Seventy +",  
                     seventy_plus == F ~  
                       "< Seventy")) %>%  
  ggplot() +  
  aes(x = continent) +  
  aes(y = age_category) +  
  geom_jitter(col = "blue", alpha = .3, width = .35, height = .35) +  
  geom_count(col = "magenta", alpha = .6) +  
  theme_minimal()  
```
]
 
.right-output-discrete2x-rotate[
<img src="geoms_files/figure-html/discrete2x_rotate_1_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder_2002 %>%  
  mutate(seventy_plus = lifeExp >= 75) %>%  
  mutate(age_category =  
           case_when(seventy_plus == T ~  
                       "Seventy +",  
                     seventy_plus == F ~  
                       "< Seventy")) %>%  
  ggplot() +  
  aes(x = continent) +  
  aes(y = age_category) +  
  geom_jitter(col = "blue", alpha = .3, width = .35, height = .35) +  
* geom_text(data = . %>% group_by(age_category, continent) %>% count, aes(label = n)) +
  theme_minimal()  
```
]
 
.right-output-discrete2x-rotate[
<img src="geoms_files/figure-html/discrete2x_rotate_2_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder_2002 %>%  
  mutate(seventy_plus = lifeExp >= 75) %>%  
  mutate(age_category =  
           case_when(seventy_plus == T ~  
                       "Seventy +",  
                     seventy_plus == F ~  
                       "< Seventy")) %>%  
  ggplot() +  
  aes(x = continent) +  
  aes(y = age_category) +  
  geom_jitter(col = "blue", alpha = .3, width = .35, height = .35) +  
* geom_label(data = . %>% group_by(age_category, continent) %>% count, aes(label = n)) +
  theme_minimal()  
```
]
 
.right-output-discrete2x-rotate[
<img src="geoms_files/figure-html/discrete2x_rotate_3_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder_2002 %>%  
  mutate(seventy_plus = lifeExp >= 75) %>%  
  mutate(age_category =  
           case_when(seventy_plus == T ~  
                       "Seventy +",  
                     seventy_plus == F ~  
                       "< Seventy")) %>%  
  ggplot() +  
  aes(x = continent) +  
  aes(y = age_category) +  
  geom_jitter(col = "blue", alpha = .3, width = .35, height = .35) +  
* geom_point(position = ggforce::position_jitternormal()) +
  theme_minimal()  
```
]
 
.right-output-discrete2x-rotate[
<img src="geoms_files/figure-html/discrete2x_rotate_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder_2002 %>%  
  mutate(seventy_plus = lifeExp >= 75) %>%  
  mutate(age_category =  
           case_when(seventy_plus == T ~  
                       "Seventy +",  
                     seventy_plus == F ~  
                       "< Seventy")) %>%  
  ggplot() +  
  aes(x = continent) +  
  aes(y = age_category) +  
  geom_jitter(col = "blue", alpha = .3, width = .35, height = .35) +  
* geom_point(position = ggforce::position_jitternormal(sd_x = 0.15, sd_y = 0.01)) +
  theme_minimal()  
```
]
 
.right-output-discrete2x-rotate[
<img src="geoms_files/figure-html/discrete2x_rotate_5_output-1.png" width="100%" />
]

---

```r
gapminder_2002 %>%  
  mutate(seventyfive_plus = lifeExp >= 75) %>%  
  mutate(age_category =  
           case_when(seventyfive_plus == T ~  
                       "75+",  
                     seventyfive_plus == F ~  
                       "<75")) %>%  
  ggplot() +  
  aes(x = age_category) +  
  aes(fill = continent) +  
  geom_point(stat = "count") +  
  geom_bar(position = "dodge", alpha = .6) +  
  theme_minimal()  
```
]
 
.right-output-discrete2x2-rotate[
<img src="geoms_files/figure-html/discrete2x2_rotate_1_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder_2002 %>%  
  mutate(seventyfive_plus = lifeExp >= 75) %>%  
  mutate(age_category =  
           case_when(seventyfive_plus == T ~  
                       "75+",  
                     seventyfive_plus == F ~  
                       "<75")) %>%  
  ggplot() +  
  aes(x = age_category) +  
  aes(fill = continent) +  
  geom_point(stat = "count") +  
* geom_bar(alpha = .6) +
  theme_minimal()  
```
]
 
.right-output-discrete2x2-rotate[
<img src="geoms_files/figure-html/discrete2x2_rotate_2_output-1.png" width="100%" />
]

---

# Mosaic

---

```r
*gapminder
```
]
 
.right-output-mosaic-auto[

---
class: split-40
count: false

```r
gapminder %>%  
* filter(year == 2002)
```
]
 
.right-output-mosaic-auto[

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
* mutate(higher_income = gdpPercap > 20000)
```
]
 
.right-output-mosaic-auto[

```
  # A tibble: 142 x 7
     country     continent  year lifeExp       pop gdpPercap higher_income
     <fct>       <fct>     <int>   <dbl>     <int>     <dbl> <lgl>        
   1 Afghanistan Asia       2002    42.1  25268405      727. FALSE        
   2 Albania     Europe     2002    75.7   3508512     4604. FALSE        
   3 Algeria     Africa     2002    71.0  31287142     5288. FALSE        
   4 Angola      Africa     2002    41.0  10866106     2773. FALSE        
   5 Argentina   Americas   2002    74.3  38331121     8798. FALSE        
   6 Australia   Oceania    2002    80.4  19546792    30688. TRUE         
   7 Austria     Europe     2002    79.0   8148312    32418. TRUE         
   8 Bahrain     Asia       2002    74.8    656397    23404. TRUE         
   9 Bangladesh  Asia       2002    62.0 135656790     1136. FALSE        
  10 Belgium     Europe     2002    78.3  10311970    30486. TRUE         
  # … with 132 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
* group_by(continent, higher_income)
```
]
 
.right-output-mosaic-auto[

```
  # A tibble: 142 x 7
  # Groups:   continent, higher_income [8]
     country     continent  year lifeExp       pop gdpPercap higher_income
     <fct>       <fct>     <int>   <dbl>     <int>     <dbl> <lgl>        
   1 Afghanistan Asia       2002    42.1  25268405      727. FALSE        
   2 Albania     Europe     2002    75.7   3508512     4604. FALSE        
   3 Algeria     Africa     2002    71.0  31287142     5288. FALSE        
   4 Angola      Africa     2002    41.0  10866106     2773. FALSE        
   5 Argentina   Americas   2002    74.3  38331121     8798. FALSE        
   6 Australia   Oceania    2002    80.4  19546792    30688. TRUE         
   7 Austria     Europe     2002    79.0   8148312    32418. TRUE         
   8 Bahrain     Asia       2002    74.8    656397    23404. TRUE         
   9 Bangladesh  Asia       2002    62.0 135656790     1136. FALSE        
  10 Belgium     Europe     2002    78.3  10311970    30486. TRUE         
  # … with 132 more rows
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  group_by(continent, higher_income) %>%  
* summarise(count = n())
```
]
 
.right-output-mosaic-auto[

```
  # A tibble: 8 x 3
  # Groups:   continent [5]
    continent higher_income count
    <fct>     <lgl>         <int>
  1 Africa    FALSE            52
  2 Americas  FALSE            23
  3 Americas  TRUE              2
  4 Asia      FALSE            26
  5 Asia      TRUE              7
  6 Europe    FALSE            13
  7 Europe    TRUE             17
  8 Oceania   TRUE              2
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  group_by(continent, higher_income) %>%  
  summarise(count = n()) %>%  
* mutate(num_in_continents = sum(count),
*        prop = count/sum(count))
```
]
 
.right-output-mosaic-auto[

```
  # A tibble: 8 x 5
  # Groups:   continent [5]
    continent higher_income count num_in_continents  prop
    <fct>     <lgl>         <int>             <int> <dbl>
  1 Africa    FALSE            52                52 1    
  2 Americas  FALSE            23                25 0.92 
  3 Americas  TRUE              2                25 0.08 
  4 Asia      FALSE            26                33 0.788
  5 Asia      TRUE              7                33 0.212
  6 Europe    FALSE            13                30 0.433
  7 Europe    TRUE             17                30 0.567
  8 Oceania   TRUE              2                 2 1
```
]

---
class: split-40
count: false

```
  # A tibble: 8 x 5
    continent higher_income count num_in_continents  prop
    <fct>     <lgl>         <int>             <int> <dbl>
  1 Africa    FALSE            52                52 1    
  2 Americas  FALSE            23                25 0.92 
  3 Americas  TRUE              2                25 0.08 
  4 Asia      FALSE            26                33 0.788
  5 Asia      TRUE              7                33 0.212
  6 Europe    FALSE            13                30 0.433
  7 Europe    TRUE             17                30 0.567
  8 Oceania   TRUE              2                 2 1
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  group_by(continent, higher_income) %>%  
  summarise(count = n()) %>%  
  mutate(num_in_continents = sum(count),  
         prop = count/sum(count)) %>%  
  ungroup() %>%  
* ggplot()
```
]
 
.right-output-mosaic-auto[
<img src="geoms_files/figure-html/mosaic_auto_8_output-1.png" width="100%" />
]

---
class: split-40
count: false

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  group_by(continent, higher_income) %>%  
  summarise(count = n()) %>%  
  mutate(num_in_continents = sum(count),  
         prop = count/sum(count)) %>%  
  ungroup() %>%  
  ggplot() +  
  aes(x = continent, y = prop) +  
* geom_bar(stat = "identity",
*          position = "fill",
*          color = "lightgrey")
```
]
 
.right-output-mosaic-auto[
<img src="geoms_files/figure-html/mosaic_auto_10_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  group_by(continent, higher_income) %>%  
  summarise(count = n()) %>%  
  mutate(num_in_continents = sum(count),  
         prop = count/sum(count)) %>%  
  ungroup() %>%  
  ggplot() +  
  aes(x = continent, y = prop) +  
  geom_bar(stat = "identity",  
           position = "fill",  
           color = "lightgrey") +  
* facet_grid(~continent, scales = "free_x", space = "free_x")
```
]
 
.right-output-mosaic-auto[
<img src="geoms_files/figure-html/mosaic_auto_11_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  group_by(continent, higher_income) %>%  
  summarise(count = n()) %>%  
  mutate(num_in_continents = sum(count),  
         prop = count/sum(count)) %>%  
  ungroup() %>%  
  ggplot() +  
  aes(x = continent, y = prop) +  
  geom_bar(stat = "identity",  
           position = "fill",  
           color = "lightgrey") +  
  facet_grid(~continent, scales = "free_x", space = "free_x") +  
* aes(width = num_in_continents)
```
]
 
.right-output-mosaic-auto[
<img src="geoms_files/figure-html/mosaic_auto_12_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  group_by(continent, higher_income) %>%  
  summarise(count = n()) %>%  
  mutate(num_in_continents = sum(count),  
         prop = count/sum(count)) %>%  
  ungroup() %>%  
  ggplot() +  
  aes(x = continent, y = prop) +  
  geom_bar(stat = "identity",  
           position = "fill",  
           color = "lightgrey") +  
  facet_grid(~continent, scales = "free_x", space = "free_x") +  
  aes(width = num_in_continents) +  
* aes(fill = higher_income)
```
]
 
.right-output-mosaic-auto[
<img src="geoms_files/figure-html/mosaic_auto_13_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  group_by(continent, higher_income) %>%  
  summarise(count = n()) %>%  
  mutate(num_in_continents = sum(count),  
         prop = count/sum(count)) %>%  
  ungroup() %>%  
  ggplot() +  
  aes(x = continent, y = prop) +  
  geom_bar(stat = "identity",  
           position = "fill",  
           color = "lightgrey") +  
  facet_grid(~continent, scales = "free_x", space = "free_x") +  
  aes(width = num_in_continents) +  
  aes(fill = higher_income) +  
* theme(panel.spacing.x = unit(0, "npc"))
```
]
 
.right-output-mosaic-auto[
<img src="geoms_files/figure-html/mosaic_auto_14_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  mutate(higher_income = gdpPercap > 20000) %>%  
  group_by(continent, higher_income) %>%  
  summarise(count = n()) %>%  
  mutate(num_in_continents = sum(count),  
         prop = count/sum(count)) %>%  
  ungroup() %>%  
  ggplot() +  
  aes(x = continent, y = prop) +  
  geom_bar(stat = "identity",  
           position = "fill",  
           color = "lightgrey") +  
  facet_grid(~continent, scales = "free_x", space = "free_x") +  
  aes(width = num_in_continents) +  
  aes(fill = higher_income) +  
  theme(panel.spacing.x = unit(0, "npc")) +  
* theme(strip.text = element_blank())
```
]
 
.right-output-mosaic-auto[
<img src="geoms_files/figure-html/mosaic_auto_15_output-1.png" width="100%" />
]

---

# Cartography, Networks, Animation...

Are in other code-movies in the [ggplot2 grammar guide](https://evamaerey.github.io/ggplot2_grammar_guide/about).

---