2023-04-20_intro_vis_ggplot2/PRESENTATION/JRom_intro_ggplot2_examples.html

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class: center, middle, inverse, title-slide

.title[
# Data visualization basics
]
.subtitle[
## – examples in {ggplot2}
]
.author[
### <em>Julia Romanowska</em>
]
.date[
### April 20, 2023
]

---


class: inverse, left, middle

## <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M64 96c0-35.3 28.7-64 64-64H512c35.3 0 64 28.7 64 64V352H512V96H128V352H64V96zM0 403.2C0 392.6 8.6 384 19.2 384H620.8c10.6 0 19.2 8.6 19.2 19.2c0 42.4-34.4 76.8-76.8 76.8H76.8C34.4 480 0 445.6 0 403.2zM281 209l-31 31 31 31c9.4 9.4 9.4 24.6 0 33.9s-24.6 9.4-33.9 0l-48-48c-9.4-9.4-9.4-24.6 0-33.9l48-48c9.4-9.4 24.6-9.4 33.9 0s9.4 24.6 0 33.9zM393 175l48 48c9.4 9.4 9.4 24.6 0 33.9l-48 48c-9.4 9.4-24.6 9.4-33.9 0s-9.4-24.6 0-33.9l31-31-31-31c-9.4-9.4-9.4-24.6 0-33.9s24.6-9.4 33.9 0z"/></svg> {ggplot2} basics

---

### A plot in {ggplot2} consists of several layers:

--

1. data

2. aesthetics (`aes`) ([original documentation](https://ggplot2.tidyverse.org/reference/index.html#section-aesthetics))

3. `geom`s ([original documentation](https://ggplot2.tidyverse.org/reference/index.html#section-geoms))

4. `scale`s ([original documentation](https://ggplot2.tidyverse.org/reference/index.html#section-scales))

5. `theme` ([original documentation](https://ggplot2.tidyverse.org/reference/index.html#section-themes))

???

1. data
    - strict format: tidy data!
    - one row per datapoint
    - all grouping must be included in the data

2. aesthetics (`aes`) ([original documentation](https://ggplot2.tidyverse.org/reference/index.html#section-aesthetics))
    - how to map the data to the graph?
    - which column is *x*, *y*, ...
    - which column provides grouping (based on the grouping, one can either connect points into lines, split a graph into several facets, or color differently each group)

3. `geom`s ([original documentation](https://ggplot2.tidyverse.org/reference/index.html#section-geoms))
    - how to visualize the data?
    - points (`geom_point`), lines (`geom_line`), bars (`geom_bar`), etc.
    - `geom`s are connected to `stat`s that conduct any necessary pre-processing of data (e.g., `geom_histogram` would first calculate the number of observations in each bin through `stat_bin`)

4. `scale`s ([original documentation](https://ggplot2.tidyverse.org/reference/index.html#section-scales))
    - any type of data representation on the plot
    - coordinates: `scale_x_continuous`, `scale_x_discrete`, `scale_x_date`, etc.
    - colors: `scale_color_manual`, `scale_colour_brewer`, `scale_fill_continuous`, etc.

5. `theme` ([original documentation](https://ggplot2.tidyverse.org/reference/index.html#section-themes))
    - visual aspects
    - size and types of fonts
    - positioning of axes, legends, etc.

Each layer is added to the previous one with a `+` sign. The result can be saved to an object and added upon later.

---

class: inverse, left, middle

## Example plot with {ggplot2}

---

## Data

&lt;div style="float: right; position: fixed; right: 10px; top: 10px;"&gt;
&lt;img style="float: right; position: relative; width: 120px" src="libs/figs/palmer_penguins_logo.png" alt="palmer penguins R package logo"/&gt;&lt;br&gt;
&lt;/div&gt;

[palmerpenguins](https://allisonhorst.github.io/palmerpenguins/)

&lt;br&gt;


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

???

I will be using this simple dataset with morphological data of penguins living on a certain island.

--

&gt; **IMPORTANT** start with tidy data!

**each row is an observation, each column is a variable**

---


count: false
 

.panel1-penguins_species_simple-auto[

```r
*ggplot(data = penguins)
```
]
 
.panel2-penguins_species_simple-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species_simple_auto_01_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_species_simple-auto[

```r
ggplot(data = penguins) +
* aes(
* 	x = bill_length_mm,
*   y = bill_depth_mm
* )
```
]
 
.panel2-penguins_species_simple-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species_simple_auto_02_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_species_simple-auto[

```r
ggplot(data = penguins) +
  aes(
  	x = bill_length_mm,
    y = bill_depth_mm
  ) +
* geom_point()
```
]
 
.panel2-penguins_species_simple-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species_simple_auto_03_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_species_simple-auto[

```r
ggplot(data = penguins) +
  aes(
  	x = bill_length_mm,
    y = bill_depth_mm
  ) +
  geom_point() +
* geom_smooth(method = "lm")
```
]
 
.panel2-penguins_species_simple-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species_simple_auto_04_output-1.png)&lt;!-- --&gt;
]

&lt;style&gt;
.panel1-penguins_species_simple-auto {
  color: black;
  width: 51.5789473684211%;
  hight: 32%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
.panel2-penguins_species_simple-auto {
  color: black;
  width: 46.421052631579%;
  hight: 32%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
.panel3-penguins_species_simple-auto {
  color: black;
  width: NA%;
  hight: 33%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
&lt;/style&gt;


---


count: false
 

.panel1-penguins_species-auto[

```r
*ggplot(data = penguins)
```
]
 
.panel2-penguins_species-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species_auto_01_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_species-auto[

```r
ggplot(data = penguins) +
* aes(
* 	x = bill_length_mm,
*   y = bill_depth_mm
* )
```
]
 
.panel2-penguins_species-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species_auto_02_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_species-auto[

```r
ggplot(data = penguins) +
  aes(
  	x = bill_length_mm,
    y = bill_depth_mm
  ) +
* geom_point(
*   aes(color = species,
*   shape = species),
*     size = 2
*  )
```
]
 
.panel2-penguins_species-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species_auto_03_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_species-auto[

```r
ggplot(data = penguins) +
  aes(
  	x = bill_length_mm,
    y = bill_depth_mm
  ) +
  geom_point(
    aes(color = species,
    shape = species),
      size = 2
   ) +
* geom_smooth(
* 	method = "lm",
*   se = FALSE,
*   aes(color = species)
* )
```
]
 
.panel2-penguins_species-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species_auto_04_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_species-auto[

```r
ggplot(data = penguins) +
  aes(
  	x = bill_length_mm,
    y = bill_depth_mm
  ) +
  geom_point(
    aes(color = species,
    shape = species),
      size = 2
   ) +
  geom_smooth(
  	method = "lm",
    se = FALSE,
    aes(color = species)
  )
*penguins_default &lt;- last_plot()
```
]
 
.panel2-penguins_species-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species_auto_05_output-1.png)&lt;!-- --&gt;
]

&lt;style&gt;
.panel1-penguins_species-auto {
  color: black;
  width: 51.5789473684211%;
  hight: 32%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
.panel2-penguins_species-auto {
  color: black;
  width: 46.421052631579%;
  hight: 32%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
.panel3-penguins_species-auto {
  color: black;
  width: NA%;
  hight: 33%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
&lt;/style&gt;


???

Basics of ggplot2:

- layers
- 'aesthetics' + data + geom

---

class: inverse, left, middle

## _...back to theory..._

---

## Bar chart

---


count: false
 

.panel1-penguins_barchart-auto[

```r
*ggplot(penguins)
```
]
 
.panel2-penguins_barchart-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_barchart_auto_01_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_barchart-auto[

```r
ggplot(penguins) +
* geom_bar(aes(island))
```
]
 
.panel2-penguins_barchart-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_barchart_auto_02_output-1.png)&lt;!-- --&gt;
]

&lt;style&gt;
.panel1-penguins_barchart-auto {
  color: black;
  width: 51.5789473684211%;
  hight: 32%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
.panel2-penguins_barchart-auto {
  color: black;
  width: 46.421052631579%;
  hight: 32%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
.panel3-penguins_barchart-auto {
  color: black;
  width: NA%;
  hight: 33%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
&lt;/style&gt;


---

## Line chart

---


count: false
 

.panel1-penguins_linechart-auto[

```r
*penguins
```
]
 
.panel2-penguins_linechart-auto[

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

---
count: false
 

.panel1-penguins_linechart-auto[

```r
penguins %&gt;%
* count(year, sex)
```
]
 
.panel2-penguins_linechart-auto[

```
# A tibble: 9 × 3
   year sex        n
  &lt;int&gt; &lt;fct&gt;  &lt;int&gt;
1  2007 female    51
2  2007 male      52
3  2007 &lt;NA&gt;       7
4  2008 female    56
5  2008 male      57
6  2008 &lt;NA&gt;       1
7  2009 female    58
8  2009 male      59
9  2009 &lt;NA&gt;       3
```
]

---
count: false
 

.panel1-penguins_linechart-auto[

```r
penguins %&gt;%
  count(year, sex) %&gt;%
* ggplot(aes(x = year, y = n))
```
]
 
.panel2-penguins_linechart-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_linechart_auto_03_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_linechart-auto[

```r
penguins %&gt;%
  count(year, sex) %&gt;%
  ggplot(aes(x = year, y = n)) +
* geom_line(aes(color = sex))
```
]
 
.panel2-penguins_linechart-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_linechart_auto_04_output-1.png)&lt;!-- --&gt;
]

&lt;style&gt;
.panel1-penguins_linechart-auto {
  color: black;
  width: 51.5789473684211%;
  hight: 32%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
.panel2-penguins_linechart-auto {
  color: black;
  width: 46.421052631579%;
  hight: 32%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
.panel3-penguins_linechart-auto {
  color: black;
  width: NA%;
  hight: 33%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
&lt;/style&gt;


---

## Heatmap

---


count: false
 

.panel1-penguins_heatmap-auto[

```r
*penguins
```
]
 
.panel2-penguins_heatmap-auto[

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

---
count: false
 

.panel1-penguins_heatmap-auto[

```r
penguins %&gt;%
* count(species, island)
```
]
 
.panel2-penguins_heatmap-auto[

```
# A tibble: 5 × 3
  species   island        n
  &lt;fct&gt;     &lt;fct&gt;     &lt;int&gt;
1 Adelie    Biscoe       44
2 Adelie    Dream        56
3 Adelie    Torgersen    52
4 Chinstrap Dream        68
5 Gentoo    Biscoe      124
```
]

---
count: false
 

.panel1-penguins_heatmap-auto[

```r
penguins %&gt;%
  count(species, island) %&gt;%
*ggplot(aes(x = species, y = island))
```
]
 
.panel2-penguins_heatmap-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_heatmap_auto_03_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_heatmap-auto[

```r
penguins %&gt;%
  count(species, island) %&gt;%
ggplot(aes(x = species, y = island)) +
* geom_tile(aes(fill = n))
```
]
 
.panel2-penguins_heatmap-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_heatmap_auto_04_output-1.png)&lt;!-- --&gt;
]

&lt;style&gt;
.panel1-penguins_heatmap-auto {
  color: black;
  width: 51.5789473684211%;
  hight: 32%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
.panel2-penguins_heatmap-auto {
  color: black;
  width: 46.421052631579%;
  hight: 32%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
.panel3-penguins_heatmap-auto {
  color: black;
  width: NA%;
  hight: 33%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
&lt;/style&gt;


---

## Parallel coordinates

### [{ggbump} package](https://github.com/davidsjoberg/ggbump)

---


count: false
 

.panel1-penguins_parallel-auto[

```r
*penguins
```
]
 
.panel2-penguins_parallel-auto[

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

---
count: false
 

.panel1-penguins_parallel-auto[

```r
penguins %&gt;%
* group_by(species)
```
]
 
.panel2-penguins_parallel-auto[

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

---
count: false
 

.panel1-penguins_parallel-auto[

```r
penguins %&gt;%
  group_by(species) %&gt;%
* summarise(
*   across(bill_length_mm:body_mass_g, ~ mean(.x, na.rm = TRUE))
* )
```
]
 
.panel2-penguins_parallel-auto[

```
# A tibble: 3 × 5
  species   bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
  &lt;fct&gt;              &lt;dbl&gt;         &lt;dbl&gt;             &lt;dbl&gt;       &lt;dbl&gt;
1 Adelie              38.8          18.3              190.       3701.
2 Chinstrap           48.8          18.4              196.       3733.
3 Gentoo              47.5          15.0              217.       5076.
```
]

---
count: false
 

.panel1-penguins_parallel-auto[

```r
penguins %&gt;%
  group_by(species) %&gt;%
  summarise(
    across(bill_length_mm:body_mass_g, ~ mean(.x, na.rm = TRUE))
  ) %&gt;%
* pivot_longer(
*   bill_length_mm:body_mass_g,
*   names_to = "measurement",
*   values_to = "value"
* )
```
]
 
.panel2-penguins_parallel-auto[

```
# A tibble: 12 × 3
   species   measurement        value
   &lt;fct&gt;     &lt;chr&gt;              &lt;dbl&gt;
 1 Adelie    bill_length_mm      38.8
 2 Adelie    bill_depth_mm       18.3
 3 Adelie    flipper_length_mm  190. 
 4 Adelie    body_mass_g       3701. 
 5 Chinstrap bill_length_mm      48.8
 6 Chinstrap bill_depth_mm       18.4
 7 Chinstrap flipper_length_mm  196. 
 8 Chinstrap body_mass_g       3733. 
 9 Gentoo    bill_length_mm      47.5
10 Gentoo    bill_depth_mm       15.0
11 Gentoo    flipper_length_mm  217. 
12 Gentoo    body_mass_g       5076. 
```
]

---
count: false
 

.panel1-penguins_parallel-auto[

```r
penguins %&gt;%
  group_by(species) %&gt;%
  summarise(
    across(bill_length_mm:body_mass_g, ~ mean(.x, na.rm = TRUE))
  ) %&gt;%
  pivot_longer(
    bill_length_mm:body_mass_g,
    names_to = "measurement",
    values_to = "value"
  ) %&gt;%
* group_by(measurement)
```
]
 
.panel2-penguins_parallel-auto[

```
# A tibble: 12 × 3
# Groups:   measurement [4]
   species   measurement        value
   &lt;fct&gt;     &lt;chr&gt;              &lt;dbl&gt;
 1 Adelie    bill_length_mm      38.8
 2 Adelie    bill_depth_mm       18.3
 3 Adelie    flipper_length_mm  190. 
 4 Adelie    body_mass_g       3701. 
 5 Chinstrap bill_length_mm      48.8
 6 Chinstrap bill_depth_mm       18.4
 7 Chinstrap flipper_length_mm  196. 
 8 Chinstrap body_mass_g       3733. 
 9 Gentoo    bill_length_mm      47.5
10 Gentoo    bill_depth_mm       15.0
11 Gentoo    flipper_length_mm  217. 
12 Gentoo    body_mass_g       5076. 
```
]

---
count: false
 

.panel1-penguins_parallel-auto[

```r
penguins %&gt;%
  group_by(species) %&gt;%
  summarise(
    across(bill_length_mm:body_mass_g, ~ mean(.x, na.rm = TRUE))
  ) %&gt;%
  pivot_longer(
    bill_length_mm:body_mass_g,
    names_to = "measurement",
    values_to = "value"
  ) %&gt;%
  group_by(measurement) %&gt;%
* mutate(
*   rank = rank(value)
* )
```
]
 
.panel2-penguins_parallel-auto[

```
# A tibble: 12 × 4
# Groups:   measurement [4]
   species   measurement        value  rank
   &lt;fct&gt;     &lt;chr&gt;              &lt;dbl&gt; &lt;dbl&gt;
 1 Adelie    bill_length_mm      38.8     1
 2 Adelie    bill_depth_mm       18.3     2
 3 Adelie    flipper_length_mm  190.      1
 4 Adelie    body_mass_g       3701.      1
 5 Chinstrap bill_length_mm      48.8     3
 6 Chinstrap bill_depth_mm       18.4     3
 7 Chinstrap flipper_length_mm  196.      2
 8 Chinstrap body_mass_g       3733.      2
 9 Gentoo    bill_length_mm      47.5     2
10 Gentoo    bill_depth_mm       15.0     1
11 Gentoo    flipper_length_mm  217.      3
12 Gentoo    body_mass_g       5076.      3
```
]

---
count: false
 

.panel1-penguins_parallel-auto[

```r
penguins %&gt;%
  group_by(species) %&gt;%
  summarise(
    across(bill_length_mm:body_mass_g, ~ mean(.x, na.rm = TRUE))
  ) %&gt;%
  pivot_longer(
    bill_length_mm:body_mass_g,
    names_to = "measurement",
    values_to = "value"
  ) %&gt;%
  group_by(measurement) %&gt;%
  mutate(
    rank = rank(value)
  ) %&gt;%
* ungroup()
```
]
 
.panel2-penguins_parallel-auto[

```
# A tibble: 12 × 4
   species   measurement        value  rank
   &lt;fct&gt;     &lt;chr&gt;              &lt;dbl&gt; &lt;dbl&gt;
 1 Adelie    bill_length_mm      38.8     1
 2 Adelie    bill_depth_mm       18.3     2
 3 Adelie    flipper_length_mm  190.      1
 4 Adelie    body_mass_g       3701.      1
 5 Chinstrap bill_length_mm      48.8     3
 6 Chinstrap bill_depth_mm       18.4     3
 7 Chinstrap flipper_length_mm  196.      2
 8 Chinstrap body_mass_g       3733.      2
 9 Gentoo    bill_length_mm      47.5     2
10 Gentoo    bill_depth_mm       15.0     1
11 Gentoo    flipper_length_mm  217.      3
12 Gentoo    body_mass_g       5076.      3
```
]

---
count: false
 

.panel1-penguins_parallel-auto[

```r
penguins %&gt;%
  group_by(species) %&gt;%
  summarise(
    across(bill_length_mm:body_mass_g, ~ mean(.x, na.rm = TRUE))
  ) %&gt;%
  pivot_longer(
    bill_length_mm:body_mass_g,
    names_to = "measurement",
    values_to = "value"
  ) %&gt;%
  group_by(measurement) %&gt;%
  mutate(
    rank = rank(value)
  ) %&gt;%
  ungroup() %&gt;%
* ggplot(
*   aes(x = measurement, y = rank)
* )
```
]
 
.panel2-penguins_parallel-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_parallel_auto_08_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_parallel-auto[

```r
penguins %&gt;%
  group_by(species) %&gt;%
  summarise(
    across(bill_length_mm:body_mass_g, ~ mean(.x, na.rm = TRUE))
  ) %&gt;%
  pivot_longer(
    bill_length_mm:body_mass_g,
    names_to = "measurement",
    values_to = "value"
  ) %&gt;%
  group_by(measurement) %&gt;%
  mutate(
    rank = rank(value)
  ) %&gt;%
  ungroup() %&gt;%
  ggplot(
    aes(x = measurement, y = rank)
  ) +
* geom_bump(aes(color = species, group = species))
```
]
 
.panel2-penguins_parallel-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_parallel_auto_09_output-1.png)&lt;!-- --&gt;
]

&lt;style&gt;
.panel1-penguins_parallel-auto {
  color: black;
  width: 51.5789473684211%;
  hight: 32%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
.panel2-penguins_parallel-auto {
  color: black;
  width: 46.421052631579%;
  hight: 32%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
.panel3-penguins_parallel-auto {
  color: black;
  width: NA%;
  hight: 33%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
&lt;/style&gt;


---

class: left, middle

## ...many other geoms and extensions: https://exts.ggplot2.tidyverse.org/gallery/

---

class: inverse, left, middle

## _...back to theory..._

---


count: false
 

.panel1-penguins_species2-auto[

```r
*penguins_default
```
]
 
.panel2-penguins_species2-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species2_auto_01_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_species2-auto[

```r
penguins_default +
* facet_grid(rows = vars(species))
```
]
 
.panel2-penguins_species2-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species2_auto_02_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_species2-auto[

```r
penguins_default +
  facet_grid(rows = vars(species))
*penguins_facet &lt;- last_plot()
```
]
 
.panel2-penguins_species2-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species2_auto_03_output-1.png)&lt;!-- --&gt;
]

&lt;style&gt;
.panel1-penguins_species2-auto {
  color: black;
  width: 51.5789473684211%;
  hight: 32%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
.panel2-penguins_species2-auto {
  color: black;
  width: 46.421052631579%;
  hight: 32%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
.panel3-penguins_species2-auto {
  color: black;
  width: NA%;
  hight: 33%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
&lt;/style&gt;


---

[{ggiraph} package](https://davidgohel.github.io/ggiraph/)

.pull-left[

```r
penguins_facet_interactive &lt;- penguins_facet +
  geom_point_interactive(
    aes(
      color = species,
      shape = species,
      tooltip = paste0(sex, ", living on ", island)
    )
  )
girafe(ggobj = penguins_facet_interactive)
```
]
.pull-right[
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]

---

class: inverse, left, middle

## _...back to theory..._

---


count: false
 

.panel1-penguins_species3-auto[

```r
*penguins_facet
```
]
 
.panel2-penguins_species3-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species3_auto_01_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_species3-auto[

```r
penguins_facet +
* scale_color_manual(
*   values = c("darkorange",
*              "darkorchid",
*              "cyan4")
*   )
```
]
 
.panel2-penguins_species3-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species3_auto_02_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_species3-auto[

```r
penguins_facet +
  scale_color_manual(
    values = c("darkorange",
               "darkorchid",
               "cyan4")
    ) +
* labs(title =
*   "Penguin species differentation")
```
]
 
.panel2-penguins_species3-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species3_auto_03_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_species3-auto[

```r
penguins_facet +
  scale_color_manual(
    values = c("darkorange",
               "darkorchid",
               "cyan4")
    ) +
  labs(title =
    "Penguin species differentation") +
* labs(subtitle =
*   "based on bill depth and bill length")
```
]
 
.panel2-penguins_species3-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species3_auto_04_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_species3-auto[

```r
penguins_facet +
  scale_color_manual(
    values = c("darkorange",
               "darkorchid",
               "cyan4")
    ) +
  labs(title =
    "Penguin species differentation") +
  labs(subtitle =
    "based on bill depth and bill length") +
* xlab("bill length (mm)")
```
]
 
.panel2-penguins_species3-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species3_auto_05_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_species3-auto[

```r
penguins_facet +
  scale_color_manual(
    values = c("darkorange",
               "darkorchid",
               "cyan4")
    ) +
  labs(title =
    "Penguin species differentation") +
  labs(subtitle =
    "based on bill depth and bill length") +
  xlab("bill length (mm)") +
* ylab("bill depth (mm)")
```
]
 
.panel2-penguins_species3-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species3_auto_06_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_species3-auto[

```r
penguins_facet +
  scale_color_manual(
    values = c("darkorange",
               "darkorchid",
               "cyan4")
    ) +
  labs(title =
    "Penguin species differentation") +
  labs(subtitle =
    "based on bill depth and bill length") +
  xlab("bill length (mm)") +
  ylab("bill depth (mm)") +
* theme_minimal()
```
]
 
.panel2-penguins_species3-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species3_auto_07_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_species3-auto[

```r
penguins_facet +
  scale_color_manual(
    values = c("darkorange",
               "darkorchid",
               "cyan4")
    ) +
  labs(title =
    "Penguin species differentation") +
  labs(subtitle =
    "based on bill depth and bill length") +
  xlab("bill length (mm)") +
  ylab("bill depth (mm)") +
  theme_minimal() +
* theme(
*   plot.title =
*     element_text(face = "bold"),
*   plot.subtitle =
*     element_text(face = "italic"))
```
]
 
.panel2-penguins_species3-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species3_auto_08_output-1.png)&lt;!-- --&gt;
]

---
count: false
 

.panel1-penguins_species3-auto[

```r
penguins_facet +
  scale_color_manual(
    values = c("darkorange",
               "darkorchid",
               "cyan4")
    ) +
  labs(title =
    "Penguin species differentation") +
  labs(subtitle =
    "based on bill depth and bill length") +
  xlab("bill length (mm)") +
  ylab("bill depth (mm)") +
  theme_minimal() +
  theme(
    plot.title =
      element_text(face = "bold"),
    plot.subtitle =
      element_text(face = "italic")) +
* theme(legend.position = "none")
```
]
 
.panel2-penguins_species3-auto[
![](JRom_intro_ggplot2_examples_files/figure-html/penguins_species3_auto_09_output-1.png)&lt;!-- --&gt;
]

&lt;style&gt;
.panel1-penguins_species3-auto {
  color: black;
  width: 51.5789473684211%;
  hight: 32%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
.panel2-penguins_species3-auto {
  color: black;
  width: 46.421052631579%;
  hight: 32%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
.panel3-penguins_species3-auto {
  color: black;
  width: NA%;
  hight: 33%;
  float: left;
  padding-left: 1%;
  font-size: 50%
}
&lt;/style&gt;


???

OK, that was the basic graph, but we can modify everything:

- change of color,
- split into sub-graphs,
- adding graph title (and subtitle, caption)
- adding axes titles
- styling


---

class: inverse, center, middle

# <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M64 96c0-35.3 28.7-64 64-64H512c35.3 0 64 28.7 64 64V352H512V96H128V352H64V96zM0 403.2C0 392.6 8.6 384 19.2 384H620.8c10.6 0 19.2 8.6 19.2 19.2c0 42.4-34.4 76.8-76.8 76.8H76.8C34.4 480 0 445.6 0 403.2zM281 209l-31 31 31 31c9.4 9.4 9.4 24.6 0 33.9s-24.6 9.4-33.9 0l-48-48c-9.4-9.4-9.4-24.6 0-33.9l48-48c9.4-9.4 24.6-9.4 33.9 0s9.4 24.6 0 33.9zM393 175l48 48c9.4 9.4 9.4 24.6 0 33.9l-48 48c-9.4 9.4-24.6 9.4-33.9 0s-9.4-24.6 0-33.9l31-31-31-31c-9.4-9.4-9.4-24.6 0-33.9s24.6-9.4 33.9 0z"/></svg> Hands-on

    </textarea>
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