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Plot trajectories and footprints

Source: R/plot_track.R
plot_track.Rd

plot_track() visualizes trajectory and footprint data from codetrack R objects in various ways, allowing for the plotting of trajectories, footprints, or both combined, with customizable aesthetics.

Usage

plot_track(
  data,
  plot = "FootprintsTrajectories",
  colours = NULL,
  cex.f = NULL,
  shape.f = NULL,
  alpha.f = NULL,
  cex.t = NULL,
  alpha.t = NULL,
  plot.labels = NULL,
  labels = NULL,
  box.p = NULL,
  cex.l = NULL,
  alpha.l = NULL,
  arrow.t = FALSE,
  arrow.size = 0.15,
  seq.foot = FALSE
)

Arguments

data

A trackway R object, which is a list consisting of two elements:

  • Trajectories: A list of trajectories representing trackway midlines, interpolated by connecting the midpoints of successive left–right footprint pairs (i.e., footprints linked by pace lines). Includes columns X, Y, IMAGE, ID, and Side (set to "Medial").

  • Footprints: A list of data frames containing footprint coordinates and associated metadata, with a Side column ("R" or "L") and a missing marker ("Actual" or "Inferred").

plot

Type of plot to generate. Options are "FootprintsTrajectories" (default), "Trajectories", or "Footprints". Determines what elements are included in the plot.

colours

A vector of colors to be used for different trajectories If NULL, defaults to black. The length of this vector should match the number of trackways in the data.

cex.f

The size of the points representing footprints. Default is 2.5. If seq.foot = TRUE, this controls the text size of footprint sequence numbers.

shape.f

A vector of shapes to be used for representing footprints in different trackways. If NULL, defaults to 19 (solid circle). The length of this vector should match the number of trackways in the data.

alpha.f

The transparency of the points representing footprints. Default is 0.5.

cex.t

The thickness of the trajectory lines. Default is 0.5.

alpha.t

The transparency of the trajectory lines. Default is 1.

plot.labels

Logical indicating whether to add labels to each trackway. Default is FALSE.

labels

A vector of labels for each trackway. If NULL, labels are automatically generated from trackway names.

box.p

Padding around label boxes, used only if plot.labels is TRUE. Adjusts the spacing around the label text.

cex.l

The size of the labels. Default is 3.88.

alpha.l

The transparency of the labels. Default is 0.5.

arrow.t

Logical indicating whether to add an arrowhead at the end of each trajectory to verify direction. Default is FALSE.

arrow.size

Numeric controlling the arrowhead size (in inches, passed to grid::unit()). Default is 0.15.

seq.foot

Logical indicating whether to display the footprint sequence numbers instead of footprint points. Default is FALSE. This is useful to verify that footprints are in the correct order along each trackway. When TRUE, the size of the numbers is controlled by cex.f.

Value

A ggplot object that displays the specified plot type, including trajectories, footprints, or both, from trackway R objects. The ggplot2 package is used for plotting.

Details

The plot_track() function is designed as a diagnostic and exploratory tool. Its primary purpose is to display the raw spatial data (footprint coordinates and interpolated trajectories) that have been digitized, so that users can visually confirm data integrity before conducting quantitative analyses. This includes checking whether footprints are in the correct order, whether trackways are oriented consistently, and whether interpolated trajectories align with the raw footprint data.

Importantly, these plots are not intended to replace traditional ichnological illustrations. Hand-drawn maps and outlines often convey information that is not captured in raw coordinate plots, such as tridactyl morphology, manus/pes distinction, taxonomic attribution, or trackway orientation, and they frequently provide clearer and more communicative visual summaries of ichnological material.

By contrast, plot_track() focuses on plotting digitized data as they are, without additional interpretation, stylization, or symbolic annotation. The goal is to offer a reproducible, data-driven representation that complements, rather than supplants, traditional methods. Users are encouraged to treat these plots as quality-control visualizations that help detect potential errors or inconsistencies in the raw data prior to downstream analyses, while continuing to rely on classical ichnological illustrations for detailed morphological and taxonomic interpretation.

See also

tps_to_track

Author

Humberto G. Ferrón

humberto.ferron@uv.es

Macroevolution and Functional Morphology Research Group (www.macrofun.es)

Cavanilles Institute of Biodiversity and Evolutionary Biology

Calle Catedrático José Beltrán Martínez, nº 2

46980 Paterna - Valencia - Spain

Phone: +34 (9635) 44477

Examples

# Example 1: Basic Plot with Default Settings - MountTom Dataset
plot_track(MountTom)


# Example 2: Basic Plot with Default Settings - PaluxyRiver Dataset
plot_track(PaluxyRiver)


# Example 3: Plot Trajectories Only - MountTom Dataset
plot_track(MountTom, plot = "Trajectories")


# Example 4: Plot Footprints Only - PaluxyRiver Dataset
plot_track(PaluxyRiver, plot = "Footprints")


# Example 5: Custom Colors for Trajectories - MountTom Dataset
custom_colors <- c(
  "#008000", "#0000FF", "#FF0000", "#800080", "#FFA500", "#FFC0CB", "#FFFF00",
  "#00FFFF", "#A52A2A", "#FF00FF", "#808080", "#000000", "#006400", "#00008B",
  "#8B0000", "#FF8C00", "#008B8B", "#A9A9A9", "#000080", "#808000", "#800000",
  "#008080", "#FFD700"
)
plot_track(MountTom, colours = custom_colors)


# Example 6: Larger Footprints and wider Trajectories Lines - PaluxyRiver Dataset
plot_track(PaluxyRiver, cex.f = 5, cex.t = 2)


# Example 7: Semi-Transparent Footprints and Trajectories - MountTom Dataset
plot_track(MountTom, alpha.f = 0.5, alpha.t = 0.5)


# Example 8: Different Shapes for Footprints - PaluxyRiver Dataset
plot_track(PaluxyRiver, shape.f = c(16, 17))


# Example 9: Plot with Labels for Trajectories - MountTom Dataset
labels <- paste("Trackway", seq_along(MountTom[[1]]))
plot_track(MountTom, plot.labels = TRUE, labels = labels, cex.l = 4, box.p = 0.3, alpha.l = 0.7)


# Example 10: Custom Colors and Shapes for Footprints Only - PaluxyRiver Dataset
plot_track(PaluxyRiver, plot = "Footprints", colours = c("purple", "orange"), shape.f = c(15, 18))


# Example 11: Wider Line Size & Custom Colors for Trajectories Only - MountTom Dataset
plot_track(MountTom, plot = "Trajectories", cex.t = 1.5, colours = custom_colors)


# Example 12: Black Footprints and Trajectories with Labels - PaluxyRiver Dataset
plot_track(PaluxyRiver,
  colours = NULL, shape.f = c(16, 16), plot.labels = TRUE,
  labels = c("Saurpod", "Theropod"), cex.l = 2, alpha.l = 0.5
)


# Example 13: Add arrowheads to show trajectory direction
plot_track(MountTom, plot = "Trajectories", arrow.t = TRUE, arrow.size = 0.2)


# Example 14: Show footprint sequence numbers (quality control)
plot_track(MountTom, plot = "Footprints", seq.foot = TRUE)