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Plot SEC Composition Distribution

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

Creates a composition plot showing copolymer or blend composition as a function of molecular weight or elution time.

Usage

plot_sec_composition(
  data,
  composition_col = "composition_a",
  x_axis = c("mw", "retention"),
  mw_col = "mw",
  sample_id = NULL,
  show_average = TRUE,
  component_names = NULL,
  show_distribution = TRUE,
  show_points = FALSE,
  y_limits = c(0, 1),
  log_mw = TRUE,
  ...
)

Arguments

data

A data frame containing SEC results with composition data, typically from step_sec_composition().

composition_col

Name of the composition measure column. Default is "composition_a". Should contain values from 0 to 1.

x_axis

Variable for x-axis: "mw" (molecular weight, default) or "retention" (elution time).

mw_col

Name of molecular weight column. Default is "mw".

sample_id

Column name containing sample identifiers.

show_average

Logical. Show average composition line? Default is TRUE.

component_names

Named character vector with component labels. E.g., c(a = "Styrene", b = "Acrylate"). Default shows "Component A" and "Component B".

show_distribution

Logical. Show composition distribution as lines? Default is TRUE.

show_points

Logical. Show individual data points? Default is FALSE.

y_limits

Numeric vector of length 2 for y-axis limits. Default is c(0, 1) for fraction scale.

log_mw

Logical. Use log scale for MW on x-axis? Default is TRUE when x_axis = "mw".

...

Additional arguments passed to ggplot2::geom_line().

Value

A ggplot2 object.

Details

Composition plots are essential for characterizing copolymers and blends:

Uniform copolymers: Horizontal line across all MW values indicates consistent composition throughout the distribution.

Compositional drift: Slope indicates composition varies with chain length, common in batch polymerization where monomer ratios change over time.

Blend separation: Multiple distinct compositions indicate blend separation or block copolymer structure.

The plot works with output from step_sec_composition() which calculates weight fraction from UV/RI detector signals and known response factors.

See also

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

Examples

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

# Process copolymer data with composition calculation
processed <- recipe(~., data = sec_copolymer) |>
  step_measure_input_long(ri_signal, location = vars(elution_time), col_name = "ri") |>
  step_measure_input_long(uv_254_signal, location = vars(elution_time), col_name = "uv") |>
  step_sec_baseline() |>
  step_sec_composition(
    uv_col = "uv",
    ri_col = "ri",
    component_a_uv = 1.0,
    component_a_ri = 0.185,
    component_b_uv = 0.01,
    component_b_ri = 0.084,
    output_col = "styrene_frac"
  ) |>
  step_sec_conventional_cal(standards = ps_standards) |>
  prep() |>
  bake(new_data = NULL)

# Basic composition plot vs MW
plot_sec_composition(
  processed,
  composition_col = "styrene_frac"
)

# Composition vs retention time with custom component names
plot_sec_composition(
  processed,
  composition_col = "styrene_frac",
  x_axis = "retention",
  component_names = c(a = "Styrene", b = "Acrylate")
)

# Customize appearance
plot_sec_composition(processed, composition_col = "styrene_frac") +
  theme_bw() +
  labs(title = "Styrene-Acrylate Copolymer Composition")
} # }