---
title: "3.1 ggplot2: The Grammar of Graphics"
---

```{r}
#| label: setup
#| include: false
knitr::opts_chunk$set(
  echo = TRUE,
  message = FALSE,
  warning = FALSE,
  fig.width = 6,
  fig.height = 3.6,
  fig.align = "center",
  dpi = 120,
  error = TRUE,
  fig.path = "_figs/grammar/"
)
library(tidyverse)
```

A plot is data, a mapping, and a geom. `ggplot()` is the canvas; `+` adds a layer.

## 1. Why we plot

Numbers do not tell the whole story. Anscombe's quartet is four data sets with the same means, variances, and correlation. The scatterplots are not the same: one is a line, one is a curve, one is a line with an outlier, one is a vertical stack with an outlier. The Datasaurus dozen is the same idea with a T-Rex in it.

Plot when you explore and when you explain. Do not add decoration that does not carry information.

R has three plotting systems:

- Base R: start a blank plot, then add pieces (`plot()`, `lines()`, `rug()`)
- `lattice`: one large function call
- `ggplot2`: layers that follow a grammar of graphics

This course uses `ggplot2`. It comes with `library(tidyverse)`. You do not load it again.

```{r}
#| label: load-mpg
data("mpg")
```

`mpg` is a tibble of cars: engine size (`displ`), city and highway mpg (`cty`, `hwy`), drive (`drv`), class, and more. Type `?mpg`.

A Base R scatterplot with a loess curve and rugs takes several unconnected calls. The ggplot2 version is one chain:

```{r}
#| label: mpg-smooth-rug
ggplot(mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point() +
  geom_smooth(se = FALSE, method = loess) +
  geom_rug() +
  xlab("Displacement") +
  ylab("Highway")
```

`qplot()` is a short ggplot2 helper for a quick look. We will write `ggplot()` so the layers stay visible.

## 2. The grammar, in four pieces

The grammar of graphics treats a plot as layers. You can get most of the work done with four of them:

- **data**: a data frame
- **aesthetic mappings**: which column is `x`, `y`, `color`, `shape`, `size`, `alpha`, `fill`, … Created with `aes()`
- **geoms**: the marks, `geom_point()`, `geom_histogram()`, `geom_boxplot()`, …
- **facets**: a panel per level of a categorical variable, `facet_wrap()` or `facet_grid()`

There are also stats (binning for a histogram), positions (jitter, dodge, stack), and coordinate systems. We will change those when we need them.

`ggplot()` makes the canvas. It needs `data` (a data frame) and, usually, `mapping = aes(...)`.

```{r}
#| label: empty-canvas
ggplot(data = mpg, mapping = aes(x = displ, y = hwy))
```

That draws axes and an empty panel. There is no geom yet.

Add a layer with `+` at the **end** of the line, then the next function on the next line. A `+` at the start of a line will error.

```{r}
#| label: first-points
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point()
```

You can pipe the data in. The first argument of `ggplot()` is then filled, so you only write the mapping:

```{r}
#| label: pipe-ggplot
mpg |>
  ggplot(mapping = aes(x = displ, y = cty)) +
  geom_point()
```

Larger `displ` goes with smaller `cty`: a negative relationship.

## 3. Which variable is `x`

You choose the axes from the question. The response usually goes on `y`. The explanatory variable goes on `x`.

"Plot A vs B" and "plot A as a function of B" both put B on `x` and A on `y`. Swapping the axes does not change the association. It does change what a reader thinks is doing the explaining.

```{r}
#| label: cty-hwy
ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point()
```

`cty` and `hwy` move together: a positive relationship.

## 4. A third variable: categorical

A categorical variable has a short list of groups. In `mpg`, `drv` is `f` (front), `r` (rear), or `4`.

Map it to `color` (usually clearer) or `shape` (harder once you have many levels):

```{r}
#| label: color-drv
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = drv)) +
  geom_point()
```

```{r}
#| label: shape-drv
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, shape = drv)) +
  geom_point()
```

Fifty states as fifty colors is a mess. Recode into fewer groups, or facet (note 3.3).

`size` and `alpha` have a natural order (small to large, faint to dark). A categorical variable usually does not. Mapping `drv` to `size` or `alpha` is legal and usually a bad idea.

## 5. A third variable: quantitative

A third number can go on `color` (a spectrum), `alpha` (transparency), or `size` (a bubble chart; worst when points overlap).

```{r}
#| label: color-cty
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = cty)) +
  geom_point()
```

```{r}
#| label: color-cty-viridis
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = cty)) +
  geom_point() +
  viridis::scale_color_viridis()
```

```{r}
#| label: alpha-cty
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, alpha = cty)) +
  geom_point()
```

```{r}
#| label: size-cty
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, size = cty)) +
  geom_point()
```

`shape` has a short list of symbols. Mapping a continuous variable to `shape` errors.

Stacking `color`, `size`, `alpha`, and `shape` at once is possible and usually unreadable. If you cannot say the sentence the plot is making, drop a mapping.

## 6. Practice

1. Load `iris` (`data(iris)`; `head(iris)`). Which columns are quantitative? Which is categorical?

2. Scatterplot petal length vs petal width, colored by species. What do you see?

3. Map a categorical column in `mpg` to `size` or `alpha`. Why is that a poor default?