When you want to compare a continuous measurement across a handful of groups, the box plot is the compact summary that fits in a single panel. It draws the five-number summary, the median, the two quartiles, and the whiskers, for each group side by side, so the reader can scan across categories and see at once which group sits higher, which is more variable, and which has stragglers. Reach for it when the question is “does this number differ by group?” and the groups number in the low single digits. With twenty groups the boxes get too thin to read; with two groups you might prefer to overlay densities. In the sweet spot of three to six categories, the box plot is hard to beat.
hvtiPlotR does not provide a box-plot helper, so this chapter builds the plot directly with ggplot2’s (Wickham et al. 2026) geom_boxplot() and styles it with the house theme theme_hv_manuscript() so it stays visually consistent with the rest of the book. As with the other bare-ggplot recipes, you write the ggplot() call yourself and finish with + theme_hv_manuscript().
A box plot needs a categorical column for the x-axis and a continuous column for the y-axis. We use hvtiPlotR::sample_eda_data() and detect a numeric column and a categorical grouping column programmatically. The first numeric column here is year (the surgery year) and the first categorical is valve_morph (valve morphology: Bicuspid, Tricuspid, or Unicuspid).
dat <- sample_eda_data()
num <- names(dat)[vapply(dat, is.numeric, logical(1))][1]
grp <- names(dat)[vapply(dat, function(x) is.factor(x) || is.character(x), logical(1))][1]
c(numeric = num, group = grp) numeric group
"year" "valve_morph" The .data[[grp]] idiom is how ggplot2 reads a column whose name lives in a variable, so the same recipe runs against whatever columns the detection step selected.
The grouped box plot is the base case: one box per level of the categorical variable, all on a shared y-axis.
ggplot(dat, aes(x = .data[[grp]], y = .data[[num]])) +
geom_boxplot() +
labs(
title = "Numeric distribution by group",
x = grp,
y = num
) +
theme_hv_manuscript()
Each box packs a lot in. The line inside the box is the median; the box edges are the first and third quartiles (the 25th and 75th percentiles), so the box spans the middle half of the data, the interquartile range or IQR. The whiskers reach out to the most extreme points within 1.5 times the IQR of the box, and anything past the whiskers is drawn as an individual outlier point.
Read a box plot one box at a time, then across.
What a box plot does not show is the shape inside the box. Two groups can have identical boxes while one is a single clump and the other is two separated clusters. That blind spot is the reason for the next variation.
Overlaying the raw observations with geom_jitter() shows the underlying sample size and spread that a box plot alone hides. We turn off the box’s own outlier points (outlier.shape = NA) so they are not drawn twice, then scatter the points with a little horizontal jitter and low alpha so dense regions read as darker.
ggplot(dat, aes(x = .data[[grp]], y = .data[[num]])) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(width = 0.15, alpha = 0.3, size = 0.7) +
labs(
title = "Box plot with jittered observations",
x = grp,
y = num
) +
theme_hv_manuscript()
Now you can see how many patients sit behind each box, and whether the points under a box form one cloud or two. For the small group in this sample, the handful of points makes plain how little the box is summarising.
A box plot draws every box the same width regardless of how many patients it summarises, which can mislead. Setting varwidth = TRUE makes each box’s width proportional to the square root of its group size, so a thin box is a visual warning that it rests on few patients.
ggplot(dat, aes(x = .data[[grp]], y = .data[[num]])) +
geom_boxplot(varwidth = TRUE) +
labs(
title = "Box width scaled to group size",
x = grp,
y = num
) +
theme_hv_manuscript()
A grey box plot is honest but plain. Mapping fill to the group colours each box from a palette, which makes a busy panel easier to scan and ties the boxes to the same colours the group uses elsewhere in a figure. Because the x-axis already names the groups, the fill only reinforces them, so we drop the redundant legend with guide = "none" rather than spend panel space on it.
ggplot(dat, aes(x = .data[[grp]], y = .data[[num]], fill = .data[[grp]])) +
geom_boxplot() +
scale_fill_brewer(palette = "Set1", guide = "none") +
labs(
title = "Numeric distribution by group, filled",
x = grp,
y = num
) +
theme_hv_manuscript()
When the fill encodes a second variable the boxes are dodged by — not the x-axis group — the legend earns its place; keep it then, placed inside the panel so it does not crowd the boxes.