16-Faceting.Rmd

# Faceting

**Learning objectives:**

- Facet wrap
- Facet grid
- Controlling scales and space
- Missing faceting variables
- Grouping vs. faceting
- Continuous variables

## What is faceting?

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
library(tidyverse)
mpg2 <- subset(mpg, cyl != 5 & drv %in% c("4", "f") & class != "2seater")
```

Faceting breaks down a dataset into multiple plots that show different subsets of the data, often plotting the same variables in each plot.

There are three types of faceting functions:

- `facet_null()`: a single plot, the default
- `facet_wrap()`: “wraps” a 1d ribbon of panels into 2d
- `facet_grid()`: produces a 2d grid of panels defined by variables which form the rows and columns

![](https://ggplot2-book.org/diagrams/position-facets.png)

## Facet wrap

Useful if you have a single variable with many levels and want to arrange the plots in a spatially efficient way. For example, if you have multiple individuals within a study.

Useful arguments:

- `ncol` and `nrow` control how many columns or rows, respectively. Only one of these needs to be set.
- `as.table` controls whether the facets are laid out like a table (`TRUE`, the default), with highest values at the bottom-right, or a plot (`FALSE`), with the highest values at the top-right. 
- `dir` controls the direction of wrap: horizontal (`"h"`) or vertical (`"v"`).

```{r}
base <- ggplot(mpg2, aes(displ, hwy)) + 
  geom_blank() + 
  xlab(NULL) + 
  ylab(NULL)

mpg2%>%count(class)

base + facet_wrap(~class, ncol = 3) + labs(title = "ncol = 3")
base + facet_wrap(~class, ncol = 3, as.table = FALSE) + labs(title = "ncol = 3, as.table = FALSE")
base + facet_wrap(~class, nrow = 3) + labs(title = "nrow = 3")
base + facet_wrap(~class, nrow = 3, dir = "v") + labs (title = "nrow = 3, dir = \"v\"")
```

## Facet grid

`facet_grid()` uses a formula (`y ~ x`) to lay out plots in a 2-dimensional grid. This can be useful to compare across combinations of variables.

```{r}
base + facet_grid(. ~ cyl) + labs(title = ". ~ cyl")
base + facet_grid(drv ~ .) + labs(title = "drv ~ .")
base + facet_grid(drv ~ cyl) + labs(title = "drv ~ cyl")
```

Use multiple variables in the rows and columns by "adding" them: `a + b ~ c + d`
Variables specified on rows and columns will be crossed.

```{r}
base + facet_grid(drv ~ cyl + class) + labs(title = "drv ~ cyl + class")
```

## Controlling scales

The `scales` parameter can be used to control whether the position scales are the same in all panels (fixed) or allowed to vary between panels (free).

- `scales = "fixed"`: x and y scales are fixed across all panels.
- `scales = "free_x"`: the x scale is free, and the y scale is fixed.
- `scales = "free_y"`: the y scale is free, and the x scale is fixed.
- `scales = "free"`: x and y scales vary across panels.


```{r}
p <- ggplot(mpg2, aes(cty, hwy)) + 
  geom_abline() + # I think this defaults to a default of intercept = 0 and slope = 1?
  geom_jitter(width = 0.1, height = 0.1)

p + facet_wrap(~cyl) + labs(title = "default (fixed scales)")
p + facet_wrap(~cyl, scales = "free") + labs(title = "free scales")
```

Free scales can be especially useful when comparing multiple time series measured on different scales.

```{r}
economics_long%>%count(date)

ggplot(economics_long, aes(date, value)) + 
  geom_line() + 
  facet_wrap(~variable, scales = "free_y", ncol = 1)
```

## Controlling space

`facet_grid()` has an additional parameter: `space`. This takes the same values as `scales`, but when space is “free”, each column (or row) will have width (or height) proportional to the range of the scale for that column (or row).

This is most useful for categorical scales, where we can assign space proportionally based on the number of levels in each facet.

```{r}
mpg2$model <- reorder(mpg2$model, mpg2$cty)

mpg2$manufacturer <- reorder(mpg2$manufacturer, -mpg2$cty)

ggplot(mpg2, aes(cty, model)) + 
  geom_point() + 
  facet_grid(manufacturer ~ .) +
  theme(strip.text.y = element_text(angle = 0)) +
  labs(title = "fixed space")

ggplot(mpg2, aes(cty, model)) + 
  geom_point() + 
  facet_grid(manufacturer ~ ., space = "free") +
  theme(strip.text.y = element_text(angle = 0)) +
  labs(title = "free space only")

ggplot(mpg2, aes(cty, model)) + 
  geom_point() + 
  facet_grid(manufacturer ~ ., scales = "free") +
  theme(strip.text.y = element_text(angle = 0)) +
  labs(title = "free scales only")

ggplot(mpg2, aes(cty, model)) + 
  geom_point() + 
  facet_grid(manufacturer ~ ., scales = "free", space = "free") +
  theme(strip.text.y = element_text(angle = 0)) +
  labs(title = "free scales and space")
```


## Missing faceting variables

When you add a map layer that doesn't contain a variable, ggplot will display the map in every facet: missing faceting variables are treated like they have all values.

This is useful when you want to add annotations that make it easier to compare among facets.

```{r}
df1 <- data.frame(x = 1:3, y = 1:3, gender = c("f", "f", "m"))
df2 <- data.frame(x = 2, y = 2)

ggplot(df1, aes(x, y)) + 
  geom_point(data = df2, colour = "red", size = 2) + 
  geom_point() + 
  facet_wrap(~gender)
```

## Grouping vs. faceting

With faceting, each group is quite far apart in its own panel, and there is no overlap between the groups. **This is good if the groups overlap a lot, but it does make small differences harder to see.** 

When using aesthetics to differentiate groups, the groups are close together and may overlap, but **small differences are easier to see**.

```{r}
df <- data.frame(
  x = rnorm(120, c(0, 2, 4)),
  y = rnorm(120, c(1, 2, 1)),
  z = letters[1:3]
)

ggplot(df, aes(x, y)) + 
  geom_point(aes(colour = z))
```

```{r}
ggplot(df, aes(x, y)) + 
  geom_point(aes(color=z)) + 
  facet_wrap(~z)
```

You can help the viewer make comparisons across facets with some thoughtful annotation. 

In this example, we show the mean of every group in each panel.

```{r}
df_sum <- df %>% 
  group_by(z) %>% 
  summarise(x = mean(x), y = mean(y)) %>%
  rename(z2 = z)


ggplot(df, aes(x, y)) + 
  geom_point() + 
  geom_point(data = df_sum, aes(colour = z2), size = 4) + 
  facet_wrap(~z)
```

Alternatively, we could put all data points in each panel, and only colour the focal data.

```{r}
df2 <- dplyr::select(df, -z)

ggplot(df, aes(x, y)) + 
  geom_point(data = df2, colour = "grey70") +
  geom_point(aes(colour = z)) + 
  facet_wrap(~z)
```

## Continuous variables

To facet continuous variables, you must first discretise them. ggplot2 provides three helper functions to do so:

- Divide the data into `n` bins each of the same length: `cut_interval(x, n)`
- Divide the data into bins of width `width`: `cut_width(x, width)`.
- Divide the data into `n` bins each containing (approximately) the same number of points: `cut_number(x, n = 10)`

Because the faceting formula does not evaluate functions, you must first create a new variable containing the discretised data.


```{r}
# Bins of width 1
mpg2$disp_w <- cut_width(mpg2$displ, 1)
# Six bins of equal length
mpg2$disp_i <- cut_interval(mpg2$displ, 6)
# Six bins containing equal numbers of points
mpg2$disp_n <- cut_number(mpg2$displ, 6)

plot <- ggplot(mpg2, aes(cty, hwy)) +
  geom_point() +
  labs(x = NULL, y = NULL)
plot + facet_wrap(~disp_w, nrow = 1)
```

## Meeting Videos

### Cohort 1

`r knitr::include_url("https://www.youtube.com/embed/gKVGjht4N20")`