Check for results with suspect result Measure Qualifier Codes — TADA

This function checks for and flags or removes samples denoted as suspect based on the 'MeasureQualifierCode' column. The function will flag suspect samples as "Suspect" and passing samples as "Pass". This function also flags censored data as "Over-Detect" or "Non-Detect" for later use in the censored data function, TADA_SimpleCensoredMethods.

Usage

TADA_FlagMeasureQualifierCode(
  .data,
  clean = FALSE,
  flaggedonly = FALSE,
  define = TRUE
)

Arguments

.data: TADA dataframe which must include the column 'MeasureQualifierCode'
clean: Boolean argument with options "TRUE" or "FALSE". The default is clean = "FALSE" which does not remove any rows of data. When clean = "TRUE", any rows of data flagged as "Suspect" based on the MeasureQualifierCode will be removed.
flaggedonly: Boolean argument; the default is flaggedonly = FALSE. When flaggedonly = TRUE, the function will filter the dataframe to show only the rows of data flagged as Suspect.
define: Boolean argument; the default is define = TRUE. When define = TRUE, the function will add an additional column (TADA.MeasureQualifierCode.Def) providing all available definitions for the MethodQualifierCodes for each result. When define = FALSE, no additional column is added.

Value

This function adds the column "TADA.MeasureQualifierCode.Flag" to the dataframe which flags suspect samples based on the "MeasureQualifierCode" column. When clean = "FALSE", all suspect data are kept in the dataframe. When clean = "TRUE", all suspect data are removed from the dataframe. When flaggedonly = TRUE, the dataframe is filtered to show only the suspect data. When flaggedonly = FALSE, the full, cleaned dataframe is returned. The default is clean = FALSE and flaggedonly = FALSE.

Examples

# Load example dataset:
data(Data_6Tribes_5y)

# Flag and keep all suspect samples:
MeasureQualifierCode_flagged <- TADA_FlagMeasureQualifierCode(Data_6Tribes_5y)

# Flag suspect samples and filter to suspect data only:
MeasureQualifierCode_flags_only <- TADA_FlagMeasureQualifierCode(Data_6Tribes_5y, flaggedonly = TRUE)

# Remove all suspect samples:
MeasureQualifierCode_clean <- TADA_FlagMeasureQualifierCode(Data_6Tribes_5y, clean = TRUE)

# Remove all suspect samples and DO NOT include a new column with
# qualifier definitions (TADA.MeasureQualifierCode.Def):
MeasureQualifierCode_clean_nodefs <- TADA_FlagMeasureQualifierCode(Data_6Tribes_5y, clean = TRUE, define = FALSE)