Ragged grids for ggplot2"
In ggragged: Ragged Grids for 'ggplot2'

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.dpi = 96,
  fig.width = 7.0,
  fig.height = 4.5
)

Introduction

ggragged extends the faceting system in ggplot2 to ragged grid layouts. In a ragged grid, panels are grouped into either rows or columns. Groups can have varying lengths, and panel placement within them is independent of other groups.

Here we showcase some examples of applying ragged grid layouts.

library(ggragged)

Example: Imbalanced rows

The motivation for creating ggragged came in the context of a phase I clinical trial---a dose-escalation study that enrolled cohorts of different sizes. For visual review of individual subject data, it would have been very useful to have cohorts clearly grouped together. The situation called for panels laid out in rows of uneven length.

Let's demonstrate with some publicly available data.

There are a number of pharmacokinetic datasets distributed with R. None of them quite match the structure for this example, so we first prepare a dataset with the required features. Using the built-in Theoph dataset as a starting point, we assign subjects into 3 imbalanced cohorts and simulate doubling doses between them:

data(Theoph)

Theoph2 <- transform(Theoph, Cohort = (as.numeric(Subject) - 1) %% 3 + 1)
Theoph2 <- transform(Theoph2, Subject = (as.numeric(Subject) - 1) %/% 3 + 1)
Theoph2 <- transform(Theoph2, Subject = sprintf("%d0%d", Cohort, Subject))
Theoph2 <- transform(Theoph2, conc = conc * 2^(Cohort - 1))
Theoph2 <- subset(Theoph2, Subject != "204")

with(Theoph2, table(Cohort, Subject))

We are interested in visualizing drug concentrations over time after a single dose for each subject. The standard tool for that is facet_wrap(). However, due to the imbalanced group sizes, the result here does not do a great job at communicating the cohort structure in the data:

p <- ggplot(Theoph2, aes(Time, conc)) + geom_line()
p + facet_wrap(vars(Subject, Cohort), labeller = label_both)

With facet_ragged_rows(), we can have each cohort distinctively laid out on a row of its own:

p + facet_ragged_rows(vars(Cohort), vars(Subject), labeller = label_both)

This plot also clearly shows the general escalation of concentration levels between cohorts, as all panels share the same y-axis range. If we instead wanted to focus on variation between subjects within cohorts, for example for data review purposes, we can set scales = "free_y" to let y-axis ranges vary between rows:

p + facet_ragged_rows(vars(Cohort), vars(Subject), labeller = label_both, scales = "free_y")

This effect would not be possible with facet_wrap(), where we could only have axis ranges vary between all panels, or none at all.

Example: Balanced columns

Ragged grid layouts can be useful in a balanced group setting, too. They can still clarify the nesting hierarchy between faceting variables in the panel layout. Let's look at an example.

The Gun dataset in package nlme records the results of an experiment on methods for firing naval guns. The experiment measured the number of rounds fired per minute. 9 teams of gunners of 3 physiques (3 teams each) each tested the 2 methods being compared:

data(Gun, package = "nlme")
with(Gun, table(Method, Team, Physique))

When visualizing these data, we could facet the plot by team to focus on the informative within-team differences in each panel. To also communicate the selection of teams by physique, we could show these data in a 3x3 grid with facet_grid(). For that we need a helper variable, a team identifier nested within physique:

p <- ggplot(Gun, aes(Method, rounds)) + geom_point()
p + facet_grid(vars(Team = substr(Team, 1, 2)), vars(Physique), labeller = label_both)

There are some issues with this presentation. The layout does not clearly communicate the fact that there were 9 separate teams. It also visually invites you to compare individual teams that happen to be grouped on the same row across physiques, which is not a particularly interesting comparison. facet_ragged_cols() can help:

p + facet_ragged_cols(vars(Team), vars(Physique), labeller = label_both)

Now each panel is labelled with the original team identifier, the labels clearly pointing out the 9 teams. The additional strips break up the rows of panels, visually signaling that comparisons across them are not particularly relevant. We can also see that the order of the teams (which in the data is not based on the alphanumeric team identifier, but rather on the rate of rounds fired) is preserved within physiques.