Create SEC Results Object

Constructor for the sec_results class, which wraps processed SEC/GPC data and enables ggplot2's autoplot() functionality for automatic visualization.

Usage

sec_results(data, sample_id = NULL)

Arguments

data

A data frame containing processed SEC results with measure columns. Typically the output from bake() on a prepped SEC recipe.

sample_id

Optional. Column name containing sample identifiers. If NULL, auto-detection is attempted.

Value

An object of class sec_results (inherits from tbl_df).

Details

The sec_results class provides a unified interface for SEC/GPC data that enables:

• Automatic plot selection via autoplot()

• Integration with ggplot2 theming

• Summary statistics access

Expected Data Structure: The input data should contain measure columns (list columns with location and value components). Common measure columns include:

• ri, uv, mals - Detector signals

• mw - Molecular weight from calibration

• concentration - Concentration profile

• intrinsic_visc - Intrinsic viscosity

• rg - Radius of gyration

See also

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

Examples

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

# 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") |>
  step_sec_conventional_cal(standards = ps_standards) |>
  prep() |>
  bake(new_data = NULL)

# Wrap as sec_results
results <- sec_results(processed, sample_id = "sample_id")

# Use autoplot for automatic visualization
autoplot(results)
autoplot(results, type = "mwd")
autoplot(results, type = "chromatogram", normalize = TRUE)
} # }