Gallery

library(ggswim)
library(ggplot2)
library(dplyr)

This gallery will showcase different datasets and ways to interact with ggswim.

Single-Row Dataset

Some common swimmer plot data structures may involve single-row observations where each record or lane corresponds to a single status classification and contains all associated points of interest. Let’s look at an example of what we mean by this:

single_row_data <- tibble::tribble(
  ~"record", ~"status", ~"start_time", ~"end_time", ~"m1", ~"m1_time", ~"m2", ~"m2_time", ~"m2_name",
  1, "status1", 0, 5, "m1", 3, "❌", 5, "Negative",
  2, "status1", -2, 7, "m1", 4, "✅", 6, "Positive",
  3, "status2", 2, 15, "m2", 10, "❌", 15, "Negative"
)

single_row_data |>
  rmarkdown::paged_table()

In this dataset, we have a combination of statuses and markers where each record is a single row observation. Notice that we have markers defined as both points and label value/name pairs.

In this example, ggswim can handle the dataset quite easily and with the convenience of inheriting single_row_data for the data parameter in each add_marker() call:

ggswim(
  data = single_row_data,
  mapping = aes(x = start_time, xend = end_time, y = record, colour = status),
  linewidth = 2
) +
  scale_color_brewer(name = "Lanes", palette = "Dark2") +
  new_scale_color() +
  add_marker(
    mapping = aes(x = m1_time, y = record, colour = m1),
    size = 5
  ) +
  add_marker(
    mapping = aes(x = m2_time, y = record, label_vals = m2, label_names = m2_name),
    fill = NA, label.size = NA, size = 5
  ) +
  scale_color_brewer(name = "Markers", palette = "Set2") +
  theme_ggswim()

Note: add_marker() still needs aes() parameters defined.