facet_wrap() allows for creating a multi-faceted plot. In this plot, each of the panels displays the data for one given level of a categorical predictor variable. This results often in a more readable figure than a larger, unique figure where all the data points overlap.

Let’s illustrate this with the following histogram of distribution. The dataframe for drawing this histogram is described below. It contains 3000 entries sorted into 6 groups (A, B, C, D, E and F) via the categorical variable groups:

# predictor variable
groups <- c(rep("A", 500), rep("B", 500), rep("C", 500), rep("D", 500), rep("E", 500), rep("F", 500))
# response variable
values <- c(rnorm(500, mean=50, sd=10), rnorm(500, mean=30, sd=5), rnorm(500, mean=40, sd=10), rnorm(500, mean=60, sd=20), rnorm(500, mean=70, sd=10), rnorm(500, mean=55, sd=15))
# dataframe
df <- data.frame(groups, values)

Here is the color-coded histogram of distribution, with one color for each level of groups:

ggplot(df, aes(values, fill=groups)) +
  geom_histogram(bins=50) +
  scale_fill_viridis_d()

Displaying the groups with colors is helpful to some extent. But here there is substantial overlap. Things would be clearer if we could isolate the groups from each other. And this is exactly what facet_wrap() allows us to do. The only thing that you must supply is the name of the variable that contains the groups (in our dataframe, it is groups) and put a ~ in front of it:

ggplot(df, aes(values, fill=groups)) +
  geom_histogram(bins=50) +
  scale_fill_viridis_d() +
  facet_wrap(~groups)

It is now much easier to assess the contribution of each of the levels of groups now that each of them is displayed separately.

From here, you can define whether you want these “mini-plots” to be separated in a grid with n columns (use ncol= n) or with n rows (use nrow= n). Here are two examples, one with 2 columns and one with only 1 column:

ggplot(df, aes(values, fill=groups)) +    # left plot, 2 columns
  geom_histogram(bins=50) +
  scale_fill_viridis_d() +
  facet_wrap(~groups, ncol=2)

ggplot(df, aes(values, fill=groups)) +    # right plot, 1 column
  geom_histogram(bins=50) +
  scale_fill_viridis_d() +
  facet_wrap(~groups, nrow=1)

Another useful option is dir = which allow to reorder the display of the categories or groups. On the left example above, group A is displayed first in the top left corner of the grid, then comes group B directly to the right, and so on until the row is complete (then starts a new row.). Use dir = v so that the groups will be dispatched vertically first. In the example below, group B comes under group A:

ggplot(df, aes(values, fill=groups)) +
  geom_histogram(bins=50) +
  scale_fill_viridis_d() +
  facet_wrap(~groups,  dir = "v")

By default in facet_wrap(), the scales of the X- and Y-axes are fixed. This means that all the mini-plots will have the same scales, making them easy to compare. You may override that setting with scales =. This argument has 4 options:

• "free": all the plots will have different scales on both axes based on the data content of the individual plots,
• "free_x": only the scale of the Y-axis will be fixed and common to all plots,
• "free_y": only the scale of the X-axis will be fixed and common to all plots,
• "fixed": both the X- and Y-axis are common to all plots (default).

Here are two examples, with free_y to the left and free to the right:

ggplot(df, aes(values, fill = groups)) +    # left plot, free_y
  geom_histogram(bins=50) +
  scale_fill_viridis_d() +
  facet_wrap(~groups, scales = "free_y")

ggplot(df, aes(values, fill = groups)) +    # right plot, free
  geom_histogram(bins=50) +
  scale_fill_viridis_d() +
  facet_wrap(~groups, scales = "free")

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