---
title: "3.3 ggplot2: Facets, Themes, and Saving"
---

```{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/facets/"
)
library(tidyverse)
library(ggthemes)
data("mpg")
data("diamonds")
```

Facet to add a category, set a theme and a scale, then `ggsave()` the object.

## 1. Facets

Facets make a panel for each level of a categorical variable. That is often clearer than twenty colors on one panel.

`facet_wrap(~ class)` wraps one variable into a grid:

```{r}
#| label: facet-wrap
#| fig-height: 4.5
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point() +
  facet_wrap(~ class)
```

`facet_grid(rows ~ cols)` is a matrix of two categoricals:

```{r}
#| label: facet-grid
#| fig-height: 4.2
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point(alpha = 0.3) +
  facet_grid(drv ~ class)
```

A `.` means "no faceting on that side":

```{r}
#| label: facet-grid-rows
#| fig-height: 5
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point() +
  facet_grid(class ~ .)
```

```{r}
#| label: facet-grid-cols
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point() +
  facet_grid(. ~ drv)
```

## 2. A fixed color is not a mapping

To make every point blue, set `color` **outside** `aes()`:

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

The name is a character, so it is in quotes. Inside `aes()`, ggplot2 treats `"blue"` as a one-level categorical variable and draws a legend for it. That is a common mistake:

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

The same rule applies to histograms. `fill` is the interior; `color` is the outline:

```{r}
#| label: hist-outline
ggplot(data = mpg, mapping = aes(x = hwy)) +
  geom_histogram(bins = 25, fill = "white", color = "black")
```

## 3. Themes

A theme sets the background, grid, and fonts in one step. `theme_bw()` is a usual choice for a document. `theme_classic()` is quieter.

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

`ggthemes` adds more, including `theme_economist()` and `theme_fivethirtyeight()`. Load the package first.

## 4. Scales and colorblind palettes

A scale maps data values to visual values. You can set the ends of a continuous color scale, or log both axes:

```{r}
#| label: scale-continuous
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = cty)) +
  geom_point() +
  scale_color_continuous(low = "black", high = "white")
```

```{r}
#| label: scale-log
ggplot(data = diamonds, mapping = aes(x = carat, y = price)) +
  geom_point(alpha = 0.01) +
  scale_x_log10() +
  scale_y_log10()
```

For a plot you will share, use a colorblind-safe palette. `ggthemes::scale_color_colorblind()` and `scale_fill_colorblind()` are one option. viridis is another: colorful, perceptually even, and still readable in grayscale. Use `scale_color_viridis_d()` / `scale_fill_viridis_d()` for discrete groups and `_c` for continuous.

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

```{r}
#| label: viridis-fill
ggplot(data = mpg, mapping = aes(x = hwy, fill = drv)) +
  geom_histogram(position = "dodge", bins = 15) +
  theme_bw() +
  scale_fill_viridis_d()
```

## 5. Labels

`xlab()`, `ylab()`, and `ggtitle()` set the axis titles and the main title. The legend title is an argument on the scale (`name = ...`).

```{r}
#| label: labels
ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = class)) +
  geom_point() +
  theme_bw() +
  scale_color_discrete(name = "Car Class") +
  xlab("Displacement") +
  ylab("Highway") +
  ggtitle("Displacement vs Highway")
```

## 6. Saving a plot

A ggplot is an object. Assign it, print it, then export.

```{r}
#| label: save-object
pl <- ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color = class)) +
  geom_point() +
  theme_bw()

pl
```

In RStudio, **Export** in the Plots pane writes a file. In a script or Quarto document, `ggsave()` does the same. It overwrites without asking.

```{r}
#| label: ggsave-demo
#| eval: false
ggsave("hwy-vs-displ.pdf", plot = pl, width = 6, height = 4)
```

If you omit `plot`, `ggsave()` uses the last plot you printed. Put the file in a folder you already have, or create one first.

## 7. This week, in one place

By the end of Week 3 you should be able to:

- build a plot with `ggplot()`, `aes()`, and a `geom_*()`
- choose a geom from the variables: scatter (two quantitative), histogram or density (one quantitative), boxplot (one categorical and one quantitative)
- map a third variable with `color`, `fill`, or facets
- add a smooth, fix overplotting, change a theme and a scale
- label the plot and save it with `ggsave()`

Functions from this week include `ggplot()`, `aes()`, `geom_point()`, `geom_smooth()`, `geom_jitter()`, `geom_hex()`, `geom_histogram()`, `geom_density()`, `geom_boxplot()`, `facet_wrap()`, `facet_grid()`, `theme_bw()`, `scale_y_log10()`, `xlab()`, `ylab()`, `ggtitle()`, and `ggsave()`.

Chapter 3 of [R for Data Science](https://r4ds.had.co.nz/data-visualisation.html) and the [ggplot2 cheat sheet](https://rstudio.github.io/cheatsheets/data-visualization.pdf) are the usual references.

## 8. Practice

1. Boxplots of diamond `price` vs `cut`, log-`y`, faceted by `color`.

2. The same plot: orange boxes, `theme_bw()`.

3. Save that plot with `ggsave()`.