Introduction to gghighlight

Hiroaki Yutani

2021-06-05

Motivation

Suppose we have data that has so many series that it is hard to identify them by their colours as the differences are so subtle.

set.seed(2)
d <- purrr::map_dfr(
  letters,
  ~ data.frame(
      idx = 1:400,
      value = cumsum(runif(400, -1, 1)),
      type = .,
      flag = sample(c(TRUE, FALSE), size = 400, replace = TRUE),
      stringsAsFactors = FALSE
    )
)
library(ggplot2)

ggplot(d) +
  geom_line(aes(idx, value, colour = type))

To filter the data to a reasonable number of lines, we can use dplyr’s filter().

library(dplyr, warn.conflicts = FALSE)

d_filtered <- d %>%
  group_by(type) %>% 
  filter(max(value) > 20) %>%
  ungroup()

ggplot(d_filtered) +
  geom_line(aes(idx, value, colour = type))

But, it seems not so handy. For example, what if we want to change the threshold in predicate (max(value) > 20) and highlight other series as well? It’s a bit tiresome to type all the code above again every time we replace 20 with some other value.

Besides, considering one of the main purposes of visualization is to get the overview of a data, it may not be good to simply filter out the unmatched data because the plot will lose its context.

Here comes gghighlight package, dplyr::filter() equivalent for ggplot2.

(If you are interested in more details behind the idea of highlighting, please read this post: Anatomy of gghighlight.)

gghighlight()

The main function of the gghighlight package is gghighlight(). For example, by using this function, we can highlight the lines whose max values are larger than 20 as seen below:

library(gghighlight)

ggplot(d) +
  geom_line(aes(idx, value, colour = type)) +
  gghighlight(max(value) > 20)
#> label_key: type

You can specify as many predicates as you like. For example, the following code highlights the data that satisfies both max(value) > 15 and mean(flag) > 0.55.

ggplot(d) +
  geom_line(aes(idx, value, colour = type)) +
  gghighlight(max(value) > 15, mean(flag) > 0.55)
#> label_key: type

Customization

As adding gghighlight() results in a ggplot object, it is fully customizable just as we usually do with ggplot2 like custom themes.

ggplot(d) +
  geom_line(aes(idx, value, colour = type)) +
  gghighlight(max(value) > 19) +
  theme_minimal()
#> label_key: type

The plot also can be facetted:

ggplot(d) +
  geom_line(aes(idx, value, colour = type)) +
  gghighlight(max(value) > 19) +
  theme_minimal() +
  facet_wrap(~ type)
#> label_key: type

There are also some options to control the way of highlighting. See “Options” section below.

Geoms

gghighlight() can highlight almost every geom. Here are some examples.

Bar

gghighlight() can highlight bars.

p <- ggplot(iris, aes(Sepal.Length, fill = Species)) +
  geom_histogram() +
  gghighlight()
#> label_key: Species

p
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Are you wondering if this is really highlighted? Yes, it is. But, the unhighlighted bars are all overwritten by the highlighted bars. This seems not so useful, until you see the facetted version:

p + facet_wrap(~ Species)
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Point

As is explained in Anatomy of gghighlight, lines and points typically have different semantics (group-wise or not). But, in most cases, you don’t need to be careful about the difference with gghighlight() because it automatically picks the right method of calculation.

set.seed(10)
d2 <- dplyr::slice_sample(d, n = 20)

ggplot(d2, aes(idx, value)) +
  geom_point() +
  gghighlight(value > 0, label_key = type)

More precisely, gghighlight() takes the following strategy:

  1. Calculate the group IDs from mapping.
    1. If group exists, use it.
    2. Otherwise, assign the group IDs based on the combination of the values of discrete variables.
  2. If the group IDs exists, evaluate the predicates in a grouped manner.
  3. If the group IDs doesn’t exist or the grouped calculation fails, evaluate the predicates in an ungrouped manner.

Note that, in this case, label_key = type is needed to show labels because gghighlight() chooses a discrete variable from the mapping, but aes(idx, value) consists of continuous variables only.

Non-logical predicate

To construct a predicate expression like below, we need to determine a threshold (in this example, 20). But it is difficult to choose a nice one before we draw plots.

max(value) > 20

So, gghighlight() allows predicates that return non-logical (e.g. numeric and character) results. The values are used for sorting data and the top max_highlight of rows/groups are highlighted:

ggplot(d, aes(idx, value, colour = type)) +
  geom_line() +
  gghighlight(max(value), max_highlight = 5L)
#> label_key: type

Labels

gghighlight() adds direct labels for some geoms. Currently, the following geoms are supported:

If you don’t want them to be labelled automatically, you can specify use_direct_label = FALSE

ggplot(d) +
  geom_line(aes(idx, value, colour = type)) +
  gghighlight(max(value) > 20, use_direct_label = FALSE)