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Create a tf-aware ggplot

Source: R/tf-ggplot.R
tf_ggplot.Rd

tf_ggplot() creates a ggplot object that can handle tf (functional data) aesthetics. It works similarly to ggplot() but automatically transforms tf objects into long-format data suitable for standard ggplot2 geoms.

Usage

tf_ggplot(
  data = NULL,
  mapping = aes(),
  ...,
  arg = NULL,
  interpolate = TRUE,
  type = NULL
)

Arguments

data

Default dataset to use for plot. If not provided, must be supplied in each layer added to the plot.

mapping

Default list of aesthetic mappings to use for plot. Can include tf-specific aesthetics like tf, tf_x, tf_y, tf_ymin, tf_ymax.

...

Other arguments passed to ggplot2 functions.

arg

Optional. Evaluation grid for tf objects. A numeric vector of arg values, or a single integer specifying the desired grid length (resolved to an equidistant grid over the tf domain). If NULL (default), uses the natural grid of the tf objects.

interpolate

Logical. Should tf objects be interpolated to the evaluation grid? Defaults to TRUE. In tf_mv trajectory plots this is ignored and interpolation is always used to pair components on a common argument grid.

type

Display mode for multivariate (tf_mv) aesthetics, mirroring tf::plot.tf_mv(): "trajectory" draws the planar curve x(t) vs y(t) (requires exactly 2 components), "facet" draws value-vs-arg with one group per curve and component (add ggplot2::facet_wrap() on .component). If NULL (default), resolves to "trajectory" for 2-component objects and "facet" otherwise. Ignored for univariate tf aesthetics.

Value

A tf_ggplot object that inherits from ggplot

Details

tf_ggplot supports the following tf-specific aesthetics:

  • tf: Maps a tf object to y aesthetic (shorthand for tf_y)

  • tf_x: Maps a tf object to x aesthetic

  • tf_y: Maps a tf object to y aesthetic

  • tf_ymin: Maps a tf object to ymin aesthetic (for ribbons)

  • tf_ymax: Maps a tf object to ymax aesthetic (for ribbons)

When tf aesthetics are used, the data is automatically transformed:

  • tf objects are evaluated on a common grid

  • Each function becomes multiple rows (one per evaluation point)

  • Group identifiers are created to maintain function identity

  • Non-tf columns are replicated appropriately

Examples

# Basic usage
data <- data.frame(
id = 1:10,
group = sample(c("A", "B"), 10, replace = TRUE)
)
data$f <- tf_rgp(10)

# Method 1: tf aesthetic in constructor
tf_ggplot(data, ggplot2::aes(tf = f, color = group)) + ggplot2::geom_line()


# Method 2: tf aesthetic in geom (equivalent)
tf_ggplot(data) + ggplot2::geom_line(ggplot2::aes(tf = f, color = group))


# Confidence bands
tf_ggplot(data) +
  ggplot2::geom_ribbon(
    ggplot2::aes(tf_ymin = mean(f) - sd(f), tf_ymax = mean(f) + sd(f)),
    alpha = 0.3
  ) +
  ggplot2::geom_line(ggplot2::aes(tf = mean(f)))