Geom specific, data, aesthetic mapping, unmapped aesthetics

# Geom specific, data, aesthetic mapping, unmapped aesthetics
### Gina Reynolds, July 2019

---

# The conditional mood: geom specific data, aesthetic mapping, and unmapped aesthetics

The conditional mood is about "if".  "If I see flowers, then I pick them."

In ggplot, we also observe such conditionality.  Data and aesthetic mappings may be tied to *specific* geometric objects.  Also aesthetics that don't do any representation (unmapped aesthetics) may also be specified on a geom-by-geom basis. These specifications are "local" to a geom, rather than globally defined like the data declarations and aesthetic mapping statements we have seen before.

---

## Part i.  Going *local* with data

Let's look at tying some data to a specific geometric object in the next example.  In this example you'll also see how a 'variable' can be created *on the fly*.

---

```r
*gapminder
```
]
 
.right-panel-local_data-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(year == 2002)
```
]
 
.right-panel-local_data-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) ->  
*gm_2002
```
]
 
.right-panel-local_data-auto[

]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) ->  
gm_2002

*gm_2002
```
]
 
.right-panel-local_data-auto[

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) ->  
gm_2002

gm_2002 %>%  
* filter(continent == "Oceania")
```
]
 
.right-panel-local_data-auto[

```
  # A tibble: 2 x 6
    country     continent  year lifeExp      pop gdpPercap
    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
  1 Australia   Oceania    2002    80.4 19546792    30688.
  2 New Zealand Oceania    2002    79.1  3908037    23190.
```
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) ->  
gm_2002

gm_2002 %>%  
  filter(continent == "Oceania") ->  
*gm_2002_europe
```
]
 
.right-panel-local_data-auto[

]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) ->  
gm_2002

gm_2002 %>%  
  filter(continent == "Oceania") ->  
gm_2002_europe

*ggplot(data = gm_2002)   # global data
```
]
 
.right-panel-local_data-auto[
<img src="local_files/figure-html/local_data_auto_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) ->  
gm_2002

gm_2002 %>%  
  filter(continent == "Oceania") ->  
gm_2002_europe

ggplot(data = gm_2002) +  # global data
* aes(x = gdpPercap)
```
]
 
.right-panel-local_data-auto[
<img src="local_files/figure-html/local_data_auto_8_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) ->  
gm_2002

gm_2002 %>%  
  filter(continent == "Oceania") ->  
gm_2002_europe

ggplot(data = gm_2002) +  # global data
  aes(x = gdpPercap) +  
* aes(y = lifeExp)
```
]
 
.right-panel-local_data-auto[
<img src="local_files/figure-html/local_data_auto_9_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) ->  
gm_2002

gm_2002 %>%  
  filter(continent == "Oceania") ->  
gm_2002_europe

ggplot(data = gm_2002) +  # global data
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
* geom_point()
```
]
 
.right-panel-local_data-auto[
<img src="local_files/figure-html/local_data_auto_10_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) ->  
gm_2002

gm_2002 %>%  
  filter(continent == "Oceania") ->  
gm_2002_europe

ggplot(data = gm_2002) +  # global data
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
* aes(color = continent)
```
]
 
.right-panel-local_data-auto[
<img src="local_files/figure-html/local_data_auto_11_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) ->  
gm_2002

gm_2002 %>%  
  filter(continent == "Oceania") ->  
gm_2002_europe

ggplot(data = gm_2002) +  # global data
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
  aes(color = continent) +  
# xend and yend are required for geom_segment
# like creating a column with a single value, 0
* aes(xend = 0)
```
]
 
.right-panel-local_data-auto[
<img src="local_files/figure-html/local_data_auto_12_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) ->  
gm_2002

gm_2002 %>%  
  filter(continent == "Oceania") ->  
gm_2002_europe

ggplot(data = gm_2002) +  # global data
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
  aes(color = continent) +  
# xend and yend are required for geom_segment
# like creating a column with a single value, 0
  aes(xend = 0) +  
# like creating a column with a single value, 0
* aes(yend = 0)
```
]
 
.right-panel-local_data-auto[
<img src="local_files/figure-html/local_data_auto_13_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) ->  
gm_2002

gm_2002 %>%  
  filter(continent == "Oceania") ->  
gm_2002_europe

ggplot(data = gm_2002) +  # global data
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
  aes(color = continent) +  
# xend and yend are required for geom_segment
# like creating a column with a single value, 0
  aes(xend = 0) +  
# like creating a column with a single value, 0
  aes(yend = 0) +  
# geom specific data
* geom_segment(data = gm_2002_europe)
```
]
 
.right-panel-local_data-auto[
<img src="local_files/figure-html/local_data_auto_14_output-1.png" width="100%" />
]

---

### Adjusting within call

---

class: split-40
count: false
 
#### Manipulating data locally
.left-panel-local_data_filter-1[

```r
gapminder %>%  
  filter(year == 2002) ->  
gm_2002

ggplot(data = gm_2002) +  # global data
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
  aes(color = continent) +  
# xend and yend are required for geom_segment
# like creating a column with a single value, 0
  aes(xend = 0) +  
# like creating a column with a single value, 0
  aes(yend = 0) +  
# geom specific data
* geom_segment(data = gm_2002 %>% filter(continent == "Oceania"))
```
]
 
.right-panel-local_data_filter-1[
<img src="local_files/figure-html/local_data_filter_1_1_output-1.png" width="100%" />
]

---

class: split-40
count: false
 
#### Referring to global data locally using period (.)
.left-panel-local_data_from_global-1[

```r
*gapminder %>%
* filter(year == 2002) %>%
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point() +  
  aes(color = continent) +  
# xend and yend are required for geom_segment
# like creating a column with a single value, 0
  aes(xend = 0) +  
# like creating a column with a single value, 0
  aes(yend = 0) +  
# geom specific data
* geom_segment(data = . %>% filter(continent == "Oceania"))
```
]
 
.right-panel-local_data_from_global-1[
<img src="local_files/figure-html/local_data_from_global_1_1_output-1.png" width="100%" />
]

---

## Part ii. geom specific aesthetic representation

So far we have seen aesthetic "mapping" (representation) applied globally --- where aes() is an independent statement.  In this case aesthetic representation is applied to all geoms.  However, we can be specific about which geoms should take on the aesthetic representation, if we use aes() within the geom_*() statement.  This local aesthetic mapping declaration will overwrite

An example is shown below.

---

```r
ggplot(data = gapminder_2002_europe) +  
# global aesthetics
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  aes(xend = 0) +  # required aes for segment
  aes(yend = 0) +  # required aes for segment
  geom_segment() +  
# geom specific aesthetics
  geom_point()   
```
]
 
.right-panel-geom_specific_aes_mapping-rotate[
<img src="local_files/figure-html/geom_specific_aes_mapping_rotate_1_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002_europe) +  
# global aesthetics
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  aes(xend = 0) +  # required aes for segment
  aes(yend = 0) +  # required aes for segment
  geom_segment() +  
# geom specific aesthetics
* geom_point(aes(color = gdpPercap))
```
]
 
.right-panel-geom_specific_aes_mapping-rotate[
<img src="local_files/figure-html/geom_specific_aes_mapping_rotate_2_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002_europe) +  
# global aesthetics
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  aes(xend = 0) +  # required aes for segment
  aes(yend = 0) +  # required aes for segment
  geom_segment() +  
# geom specific aesthetics
* geom_point(aes(size = gdpPercap))
```
]
 
.right-panel-geom_specific_aes_mapping-rotate[
<img src="local_files/figure-html/geom_specific_aes_mapping_rotate_3_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002_europe) +  
# global aesthetics
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  aes(xend = 0) +  # required aes for segment
  aes(yend = 0) +  # required aes for segment
  geom_segment() +  
# geom specific aesthetics
* geom_point(aes(size = pop))
```
]
 
.right-panel-geom_specific_aes_mapping-rotate[
<img src="local_files/figure-html/geom_specific_aes_mapping_rotate_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002_europe) +  
# global aesthetics
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  aes(xend = 0) +  # required aes for segment
  aes(yend = 0) +  # required aes for segment
  geom_segment() +  
# geom specific aesthetics
* geom_point(aes(color = gdpPercap, size = pop))
```
]
 
.right-panel-geom_specific_aes_mapping-rotate[
<img src="local_files/figure-html/geom_specific_aes_mapping_rotate_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002_europe) +  
# global aesthetics
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  aes(xend = 0) +  # required aes for segment
  aes(yend = 0) +  # required aes for segment
  geom_segment() +  
# geom specific aesthetics
* geom_point(aes(color = gdpPercap, x = gdpPercap/2))
```
]
 
.right-panel-geom_specific_aes_mapping-rotate[
<img src="local_files/figure-html/geom_specific_aes_mapping_rotate_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002_europe) +  
# global aesthetics
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  aes(xend = 0) +  # required aes for segment
  aes(yend = 0) +  # required aes for segment
  geom_segment() +  
# geom specific aesthetics
* geom_point(aes(color = gdpPercap, y = 0))
```
]
 
.right-panel-geom_specific_aes_mapping-rotate[
<img src="local_files/figure-html/geom_specific_aes_mapping_rotate_7_output-1.png" width="100%" />
]

---

## Part iii. Being a dictator -- unmapped aesthetics (Imparative mode)

*Mapped* aesthetics contrast with unmapped, across-the-board, aesthetics for a geometric object.

geom_point(color = "blue"), is an imperative -- not an ask.  A dictator move.  "Do this everywhere." It is good to show a plot with two of the same geom layer, one with mapped aesthetics and the other without.

---

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
# Another geometric layer with aesthetics
# that don't do representation:
  geom_point()   
```
]
 
.right-panel-unmapped_aes-rotate[
<img src="local_files/figure-html/unmapped_aes_rotate_1_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
# Another geometric layer with aesthetics
# that don't do representation:
* geom_point(color = "plum4")
```
]
 
.right-panel-unmapped_aes-rotate[
<img src="local_files/figure-html/unmapped_aes_rotate_2_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
# Another geometric layer with aesthetics
# that don't do representation:
* geom_point(color = "steelblue")
```
]
 
.right-panel-unmapped_aes-rotate[
<img src="local_files/figure-html/unmapped_aes_rotate_3_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
# Another geometric layer with aesthetics
# that don't do representation:
* geom_point(color = "#0FF3F3")
```
]
 
.right-panel-unmapped_aes-rotate[
<img src="local_files/figure-html/unmapped_aes_rotate_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
# Another geometric layer with aesthetics
# that don't do representation:
* geom_point(size = 7)
```
]
 
.right-panel-unmapped_aes-rotate[
<img src="local_files/figure-html/unmapped_aes_rotate_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
# Another geometric layer with aesthetics
# that don't do representation:
* geom_point(alpha = .5)
```
]
 
.right-panel-unmapped_aes-rotate[
<img src="local_files/figure-html/unmapped_aes_rotate_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
# Another geometric layer with aesthetics
# that don't do representation:
* geom_point(color = "plum4", size = 7)
```
]
 
.right-panel-unmapped_aes-rotate[
<img src="local_files/figure-html/unmapped_aes_rotate_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
# Another geometric layer with aesthetics
# that don't do representation:
* geom_point(color = "plum4", size = 7, alpha = .5)
```
]
 
.right-panel-unmapped_aes-rotate[
<img src="local_files/figure-html/unmapped_aes_rotate_8_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder %>%  
  filter(year == 2002) %>%  
  ggplot() +  
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
# Another geometric layer with aesthetics
# that don't do representation:
* geom_point(color = "firebrick", size = 7, alpha = .8, shape = "square")
```
]
 
.right-panel-unmapped_aes-rotate[
<img src="local_files/figure-html/unmapped_aes_rotate_9_output-1.png" width="100%" />
]

---

# Combinations of local and global data, aesthetics

Now you know about *global* and *local* data, aesthetic representing variables, and overwriting defaults for aesthetics doing no variable representation.  Let's think about combining these.

---

Look at the code that follows.  What are your expectations for the result?  Move forward in the presentation to check if your expectations match the actual result.

---

```r
*ggplot(data = gapminder_2002)   # global data
```
]
 
.right-panel-all_conditional-auto[
<img src="local_files/figure-html/all_conditional_auto_1_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  # global data
* aes(x = gdpPercap)
```
]
 
.right-panel-all_conditional-auto[
<img src="local_files/figure-html/all_conditional_auto_2_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  # global data
  aes(x = gdpPercap) +  
* aes(y = lifeExp)
```
]
 
.right-panel-all_conditional-auto[
<img src="local_files/figure-html/all_conditional_auto_3_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  # global data
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
* geom_point(size = 5,
*            alpha = .7)
```
]
 
.right-panel-all_conditional-auto[
<img src="local_files/figure-html/all_conditional_auto_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  # global data
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point(size = 5,  
             alpha = .7) +  
* aes(color = continent)
```
]
 
.right-panel-all_conditional-auto[
<img src="local_files/figure-html/all_conditional_auto_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  # global data
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point(size = 5,  
             alpha = .7) +  
  aes(color = continent) +  
# xend and yend are required for geom_segment
# like creating a column with a single value, 0
* aes(xend = 0)
```
]
 
.right-panel-all_conditional-auto[
<img src="local_files/figure-html/all_conditional_auto_6_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  # global data
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point(size = 5,  
             alpha = .7) +  
  aes(color = continent) +  
# xend and yend are required for geom_segment
# like creating a column with a single value, 0
  aes(xend = 0) +  
# like creating a column with a single value, 0
* aes(yend = 0)
```
]
 
.right-panel-all_conditional-auto[
<img src="local_files/figure-html/all_conditional_auto_7_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
ggplot(data = gapminder_2002) +  # global data
  aes(x = gdpPercap) +  
  aes(y = lifeExp) +  
  geom_point(size = 5,  
             alpha = .7) +  
  aes(color = continent) +  
# xend and yend are required for geom_segment
# like creating a column with a single value, 0
  aes(xend = 0) +  
# like creating a column with a single value, 0
  aes(yend = 0) +  
# geom specific data
* geom_segment(
*   data = gapminder_2002_europe,
*   aes(size = gdpPercap,
*       alpha = pop),
*   color = "orange"
*   )
```
]
 
.right-panel-all_conditional-auto[
<img src="local_files/figure-html/all_conditional_auto_8_output-1.png" width="100%" />
]

---

# Advanced aes --- conflicting desires for color and fill, for example

---

```r
gapminder_2002 %>%  
  count(continent) %>%  
  ggplot() +  
  aes(x = continent) +  
  aes(y = n) +  
  geom_col(aes(color = continent))   
```
]
 
.right-panel-aes_after-rotate[
<img src="local_files/figure-html/aes_after_rotate_1_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder_2002 %>%  
  count(continent) %>%  
  ggplot() +  
  aes(x = continent) +  
  aes(y = n) +  
* geom_col(aes(color = continent, fill = after_scale(color)))
```
]
 
.right-panel-aes_after-rotate[
<img src="local_files/figure-html/aes_after_rotate_2_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder_2002 %>%  
  count(continent) %>%  
  ggplot() +  
  aes(x = continent) +  
  aes(y = n) +  
* geom_col(aes(color = continent, fill = after_scale(alpha(color, .4))))
```
]
 
.right-panel-aes_after-rotate[
<img src="local_files/figure-html/aes_after_rotate_3_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder_2002 %>%  
  count(continent) %>%  
  ggplot() +  
  aes(x = continent) +  
  aes(y = n) +  
* geom_col(aes(color = continent), fill = NA)
```
]
 
.right-panel-aes_after-rotate[
<img src="local_files/figure-html/aes_after_rotate_4_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder_2002 %>%  
  count(continent) %>%  
  ggplot() +  
  aes(x = continent) +  
  aes(y = n) +  
* geom_col(aes(color = continent, fill = NULL))
```
]
 
.right-panel-aes_after-rotate[
<img src="local_files/figure-html/aes_after_rotate_5_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder_2002 %>%  
  count(continent) %>%  
  ggplot() +  
  aes(x = continent) +  
  aes(y = n) +  
* geom_col(aes(color = continent), fill = "snow1")
```
]
 
.right-panel-aes_after-rotate[
<img src="local_files/figure-html/aes_after_rotate_6_output-1.png" width="100%" />
]

---

# Even more control with stage

in the example we see how we can get transparency for outline.  alpha aes, usually refers to

---

```r
gapminder_2002 %>%  
  count(continent) %>%  
  ggplot() +  
  aes(x = continent) +  
  aes(y = n) +  
  geom_col()   
```
]
 
.right-panel-stage-rotate[
<img src="local_files/figure-html/stage_rotate_1_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder_2002 %>%  
  count(continent) %>%  
  ggplot() +  
  aes(x = continent) +  
  aes(y = n) +  
* geom_col(aes(color = continent))
```
]
 
.right-panel-stage-rotate[
<img src="local_files/figure-html/stage_rotate_2_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder_2002 %>%  
  count(continent) %>%  
  ggplot() +  
  aes(x = continent) +  
  aes(y = n) +  
* geom_col(aes(color = continent), size = 3)
```
]
 
.right-panel-stage-rotate[
<img src="local_files/figure-html/stage_rotate_3_output-1.png" width="100%" />
]

---
class: split-40
count: false

```r
gapminder_2002 %>%  
  count(continent) %>%  
  ggplot() +  
  aes(x = continent) +  
  aes(y = n) +  
* geom_col(aes(color = stage(start = continent, after_scale = alpha(color, 0.5))), size = 3)
```
]
 
.right-panel-stage-rotate[
<img src="local_files/figure-html/stage_rotate_4_output-1.png" width="100%" />
]