Aesthetics and Settings

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.title[
# Aesthetics and Settings
]
.author[
### Nicholas Sim
]
.date[
### 26 March 2024
]

---

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# Introduction

---
### Topics

* Aesthetics such as `color`, `fill`, `shape`, and `size`

* Setting vs Aesthetic

* Passing a setting into `aes()` (common mistake)

* Set colors, size, etc.

---
### Required Libraries

```r
library(tidyverse)
library(gapminder)
```
---
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# Aesthetic

---
### What is an Aesthetic?

Ref: Chapter3 KH

An aesthetic is a mapping of one or more variables from a data frame to some elements in a visualisation.

Some aesthetics are mandatory. For instance, we must specify the variable to be shown on the plot as an aesthetic.

Some aesthetics are optional, but are used to help create a visualisation that tells a sharper data story. Optional aesthetics include  `color`, `fill`, `shape`, and `size`.

For this presentation, we will declare these optional aesthetics **locally** than globally, as we may not want them to be applied throughout our plot.

---
### The Color Aesthetic

The **color** aesthetic controls the color of the border, such as the borders of the bars in a barplot. For a scatter plot, the color aesthetic can be used to change the color of the scatter points because by default, these points do not have a hollow interior (and thus, without an border).

The `color` aesthetic is useful for distinguishing various categories in a qualitative variable. For instance, we may use the color aesthetic to differentiate the color of the scatter points for Asia, Europe, Sub-Saharan Africa, etc. or for males versus females, marital status, and so on, in a barplot.

---
### Example: The Color Aesthetic

Using the `mtcars` data frame, let's plot miles per gallon against displacement (i.e. weight), and use the number of cylinders as a color aesthetic.

```r
mtcars$cyl <- as.factor(mtcars$cyl) # change the class of cyl into factors
ggplot(data = mtcars, mapping = aes (x = disp, y= mpg)) + 
  geom_point(mapping = aes(color = cyl))
```

---
### The Fill Aesthestic

In terms of applications, the **fill** aesthetic serves the same purpose as the `color` aesthetic.

The main difference is that the `fill` aesthetic fills a <span style="color:red">hollow interior</span> with colors (representing a variable from the data frame), while the `color` aesthetic controls the color of the outline/border/exterior.

For example, the bars in a barplot have a white hollow interior by default. To differentiate the bars with colors, we should use the `fill` aesthetic and not the `color` aesthetic.

---
### The Fill Aesthestic

The `fill` aesthetic is usually used to differentiate colors in plots such as the histogram, bar chart, column chart, etc. where there are hollow interiors.

By contrast, scatter points by default have a solid (i.e. non-hollow) interior. As such, the `color` aesthetic, not the `fill` aesthetic, should be utilized to differentiate the color of the scatter points.

That being said, there could be scatter points with a hollow interior. If so, their colors should be differentiated by using the `fill` aesthetic.

---
### Example: The Fill Aesthetic

Let's map the `fill` aesthetic to the number of cylinders.

```r
ggplot(data = mtcars, mapping = aes (x = disp, y= mpg)) + 
  geom_point(mapping = aes(fill = cyl))
```

---
### The Shape and Size Aesthestics

For a qualitative (categorical) variable, we may differentiate its values by using a `shape` aesthetic. The `shape` aesthetic differentiates the different values in a variable by using different shapes.

For a quantitative (numerical) variable, we may differentiate its values by using a `size` aesthetic. The `size` aesthetic differentiates different numerical values by using larger-sized displays for larger values.

---
### Example: The Shape Aesthetic

Let's map the `shape` aesthetic to number of cylinders.

```r
ggplot(data = mtcars, mapping = aes (x = disp, y= mpg)) + 
  geom_point(mapping = aes(shape = cyl))
```

---
### Example: The Size Aesthetic

Let's map the `size` aesthetic to the number of cylinders.

```r
ggplot(data = mtcars, mapping = aes (x = disp, y= mpg)) + 
  geom_point(mapping = aes(size = cyl))
```

---
### Recap

The `color`, `fill`, and `shape` aesthetics can be used to display information about classes (non-ordered categories).

By contrast, the `size` aesthetic can only be used to display information about quantity.

---
### Exercise (Try on your own)

Using the `iris` dataset, construct a scatter plot that plots sepal length against petal length.  Declare species as a *local* color aesthetic. Add an OLS regression line (without confidence bands) and use `theme_classic()`. You should observe the plot below.

Question: What happens if you declare species as a *global* color aesthetic?

```
## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"
```

---
class: center, middle, inverse
# Setting

---
### Setting versus Aesthetic

Ref: Chapter 3 KH
 
**Aesthetics** are characteristics on a plot (e.g.  `color`, `fill`, `shape`, and `size`) that are determined by some variables in the data frame.

**Settings** are parameters that are not mapped to variables but determined by the user.

For example, instead of differentiating the colors of the scatter points based on a variable in the data frame, we may set the colors of these points to, red, blue, etc.

---
###  The `iris` Dataset

To elaborate this difference, let's use the `iris` dataset. Here is a quick look at the dataset

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---
###  Setting a Color

Let's consider a scatter plot of sepal length against petal length.

```r
ggplot(iris, aes(x = Petal.Length, y = Sepal.Length)) + geom_point()
```

---
###  Setting a Color

Let's set the color of the points to "red". Notice that `color = "red"` is not passed into the `aes()` function. By doing so, the color is set to red rather being determined by some variable in the iris dataset.

```r
# Don't pass color through aes()
ggplot(iris, aes(x = Petal.Length, y = Sepal.Length)) + geom_point(color = "red") 
```

---
###  Setting the Size 
We may set the size of the points. Again, notice that `size = 2"` is not passed into the `aes()` function.

```r
# Don't pass color, size through aes()
ggplot(iris, aes(x = Petal.Length, y = Sepal.Length)) + geom_point(color = "red", size = 2)
```

---
###  Setting the Shape 
We may also set the shape of the points.

```r
# Don't pass color, size, shape through aes()
ggplot(iris, aes(x = Petal.Length, y = Sepal.Length)) + geom_point(color = "red", size=2, shape = 2) 
```

---
class: center, middle, inverse
#  Confusing a Setting as an Aesthetic
---
###  Passing a Setting into `aes()`

What will happen if we mistakenly pass the option, `color = "red"`, into  `aes()`?

Recall that `aes()` maps the data to features in the visualization.

If we pass (say) the option, `color = Region`, into `aes()`, *ggplot* will use attempt to use the `Region` variable as a `color` aesthetic.

By analogy, if we pass `color = "red"` into `aes()`, *ggplot* will attempt to use the values from a variable, `red`, as a `color` aesthetic.

However, there is no variable, `red`, in our data frame!

---
###  Passing a Setting into `aes()`

Recall the **recycling** property, which was discussed in Seminar 1 part 3.
 
If we combine a variable with a shorter length (i.e. fewer number of elements) with another longer variable, R will recycle the elements in the shorter vector to match its length with the longer vector's.

For example, if we try to "column" combine a vector with 5 observations with a data frame with 10 rows, R will recycle the values in the shorter vector to form a vector with 10 observations, before combining the shorter vector as a new column in the data frame.

---
###  Passing a Setting into `aes()`

From the concept of recycling, let's understand what may happen if we mistakenly pass `color = "red"` into `aes()` (red should be a color setting, not an aesthetic).

Since there is no variable "red", R will "construct" a new variable "red" with a single value. As "red" only has one value, passing `color = "red"`into `aes()` will result in only one color being displayed, but the color is not red.

---
###  Example: Passing in a Setting as an Aesthetic

To illustrate, let's pass the setting `color = "red"` into  `aes()`. Notice that the scatter points have a different color now. Although there is only one color, it is not red.

```r
# R created a variable with one value "red".
ggplot(iris, aes(x=Petal.Length, y=Sepal.Length)) + geom_point(aes(color = "red"))
```

---
###  Exercise

Let's explore if greater income inequality is associated with higher economic growth. For this task, load the `tidyverse` and `gapminder`. Using `read_csv()` function, import `WDI_Data.csv` into R and save it into a data frame, named `df`.

Do remove all the `NA`s  by passing the data frame `df` into `na.omit()` and saving the output.

.pull-left[
Construct a scatter plot with income inequality as the ``y` variable and economic growth as the `x` variable. In the dataset, income inequality is captured by `Gini` and growth is captured by `GDP.Growth`. Use `Region` as a `color` aesthetic and a trendline based on OLS regression.
]

.pull-right[

<img src="AestheticsSettings_files/figure-html/unnamed-chunk-11-1.png" style="display: block; margin: auto;" />
]