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Plot SEC Chromatogram

Source: R/plot-functions.R
plot_sec_chromatogram.Rd

Creates a chromatogram plot from SEC data showing detector signal vs elution time/volume.

Usage

plot_sec_chromatogram(
  data,
  measures = NULL,
  sample_id = NULL,
  x_label = "Elution Time (min)",
  y_label = "Signal",
  normalize = FALSE,
  facet_by = c("none", "measure", "sample"),
  color_by = c("sample", "measure"),
  ...
)

Arguments

data

A data frame containing SEC results with measure columns, or a tibble from measure_sec_slice_table().

measures

Character vector of measure column names to plot. If NULL, plots all measure columns found.

sample_id

Column name containing sample identifiers. If NULL, attempts to auto-detect or uses row numbers.

x_label

Label for x-axis. Default is "Elution Time (min)".

y_label

Label for y-axis. Default is "Signal".

normalize

Logical. Normalize signals to 0-1 range for comparison? Default is FALSE.

facet_by

How to facet the plot. One of:

  • "none": All on single plot (default)

  • "measure": Separate panel per detector/measure

  • "sample": Separate panel per sample

color_by

What to map to color aesthetic. One of "sample" (default) or "measure".

...

Additional arguments passed to ggplot2::geom_line().

Value

A ggplot2 object.

Details

This is the fundamental SEC visualization showing raw or processed chromatographic data. Works with both:

See also

Other sec-visualization: autoplot.sec_results(), plot_sec(), plot_sec_calibration(), plot_sec_composition(), plot_sec_conformation(), plot_sec_multidetector(), plot_sec_mwd(), sec_results()

Examples

if (FALSE) { # \dontrun{
library(recipes)
library(measure)

# Process SEC data
processed <- recipe(~., data = sec_triple_detect) |>
  step_measure_input_long(
    ri_signal,
    location = vars(elution_time),
    col_name = "ri"
  ) |>
  step_sec_baseline(measures = "ri") |>
  prep() |>
  bake(new_data = NULL)

# Basic chromatogram
plot_sec_chromatogram(processed, measures = "ri")

# Normalized overlay of multiple samples
plot_sec_chromatogram(processed, measures = "ri", normalize = TRUE)

# Faceted by sample
plot_sec_chromatogram(processed, facet_by = "sample")
} # }