Autoplot Methods for Measure Objects — autoplot-measure • measure

Create ggplot2 visualizations of spectral/chromatographic data stored in measure objects.

Usage

# S3 method for class 'measure_tbl'
autoplot(object, ...)

# S3 method for class 'measure_list'
autoplot(object, summary = FALSE, max_spectra = 50, alpha = 0.3, ...)

# S3 method for class 'recipe'
autoplot(object, n_samples = 10, which = c("before_after", "summary"), ...)

Arguments

object: A measure_tbl, measure_list, or recipe object.
...: Additional arguments passed to specific plot types.
summary: Logical. If TRUE, add mean +/- SD ribbon. Default FALSE.
max_spectra: Maximum number of individual spectra to plot. Default 50. Set to NULL for no limit.
alpha: Transparency for individual spectrum lines. Default 0.3.
n_samples: Number of samples to show in before/after comparison. Default 10.
which: Which comparison to show: "before_after" (default) shows side-by-side before/after comparison, "summary" shows summary statistics (mean +/- SD) for the processed data.

Value

A ggplot2 object.

Details

For measure_tbl (single spectrum):

Plots location vs value as a line

For measure_list (multiple spectra):

Plots all spectra with optional summary ribbon
Use summary = TRUE for mean +/- SD ribbon
Use max_spectra to limit number of individual lines

For recipe:

Shows before/after comparison of preprocessing
Requires a prepped recipe
Use n_samples to control number of samples shown

Examples

if (FALSE) { # \dontrun{
library(ggplot2)

# Single spectrum
spec <- new_measure_tbl(location = 1:100, value = sin(1:100 / 10) + rnorm(100, sd = 0.1))
autoplot(spec)

# Multiple spectra with summary
rec <- recipe(water ~ ., data = meats_long) |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  prep()
baked <- bake(rec, new_data = NULL)
autoplot(baked$.measures, summary = TRUE)

# Recipe before/after comparison
rec <- recipe(water ~ ., data = meats_long) |>
  update_role(id, new_role = "id") |>
  step_measure_input_long(transmittance, location = vars(channel)) |>
  step_measure_snv() |>
  prep()
autoplot(rec, n_samples = 10)
} # }