cv01_dfclean {CleaningValidation}

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")