Creates a summary table of SEC analysis results with key metrics for each sample.
Usage
measure_sec_summary_table(
data,
mw_col = NULL,
include_mw = TRUE,
include_fractions = TRUE,
include_purity = TRUE,
sample_id = NULL,
additional_cols = NULL,
digits = 2
)Arguments
- data
A data frame containing SEC results.
- mw_col
Column name containing molecular weight averages (list column with Mn, Mw, Mz, dispersity).
- include_mw
Logical. Include molecular weight averages? Default is
TRUE.- include_fractions
Logical. Include MW fractions if available? Default is
TRUE.- include_purity
Logical. Include purity metrics (HMWS, monomer, LMWS) if available? Default is
TRUE.- sample_id
Column name for sample identifiers.
- additional_cols
Character vector of additional columns to include in the summary.
- digits
Number of decimal places for numeric columns. Default is 2.
Value
A tibble with one row per sample containing:
- sample_id
Sample identifier
- Mn
Number-average molecular weight
- Mw
Weight-average molecular weight
- Mz
Z-average molecular weight
- dispersity
Polydispersity index (Mw/Mn)
- purity_hmws
Percent high MW species (if available)
- purity_monomer
Percent monomer (if available)
- purity_lmws
Percent low MW species (if available)
Details
This function creates a publication-ready summary table of SEC results. It automatically detects and includes available metrics.
Typical Summary Metrics:
Molecular weight averages: Mn, Mw, Mz
Dispersity (PDI): Mw/Mn
Purity metrics: %HMWS, %Monomer, %LMWS
MW fractions: % above/below cutoffs
Recovery: % mass balance
See also
Other sec-export:
measure_sec_compare(),
measure_sec_report(),
measure_sec_slice_table()
Examples
if (FALSE) { # \dontrun{
# Generate summary after SEC processing
summary_tbl <- measure_sec_summary_table(
processed_data,
sample_id = "sample_name"
)
# Print formatted table
print(summary_tbl)
# Export to Excel
writexl::write_xlsx(summary_tbl, "sec_summary.xlsx")
} # }