cv01_dfclean {CleaningValidation} | R Documentation |
Clean and preprocess residue data for stability and capability analysis
Description
This function ensures data type and no missing data in residue_col, cleaning_event_col, usl_col of data their type. Furthermore, it changes cleaning_event_col to time ordered factor. It cleans and pre-processes the residue data for stability and capability analysis, ensuring that it meets the necessary criteria for analysis.
Usage
cv01_dfclean(data, residue_col, cleaning_event_col, usl_col)
Arguments
data |
A data frame containing one of drug active-ingredient residue (DAR), cleaning agent residue (CAR), or microbial bioburden residue (Mic) data. |
residue_col |
The name of the column containing the numeric residue data. |
cleaning_event_col |
The name of the column containing the Cleaning Event data. |
usl_col |
The name of the column containing the numeric upper specification limit (USL) data. |
Value
A cleaned and pre-processed data frame such that all variables have no missing values, its CleaningEvent is time-ordered categorical variable, and Residue and USL are numeric.
Author(s)
Chan, Mohamed, Lou, Wendy, Yang, Xiande [xiande.yang at gmail.com]
Examples
# Assume Eq_DAR, Eq_CAR, and Eq_Mic are loaded datasets
# Clean and preprocess residue data for Eq_DAR
Eq_DAR <- cv01_dfclean(data = Eq_DAR, residue_col = "DAR", usl_col = "USL",
cleaning_event_col = "CleaningEvent")
# Clean and preprocess residue data for Eq_CAR
Eq_CAR <- cv01_dfclean(data = Eq_CAR, residue_col = "CAR", usl_col = "USL",
cleaning_event_col = "CleaningEvent")
# Clean and preprocess residue data for Eq_Mic
Eq_Mic <- cv01_dfclean(data = Eq_Mic, residue_col = "Mic", usl_col = "USL",
cleaning_event_col = "CleaningEvent")