TADA_FlagSpeciation: Check Method Speciation Validity

In USEPA/TADA: EPA TADA (Tools for Automated Data Analysis) R Package

Check Method Speciation Validity

Description

Function checks the validity of each characteristic-method speciation combination in the dataframe. When clean = "suspect_only", rows with Suspect characteristic-method speciation combinations are removed. Default is clean = "suspect_only". When flaggedonly = TRUE, dataframe is filtered to show only rows with "Suspect" or "NonStandardized" characteristic-method speciation combinations. Default is flaggedonly = FALSE.

Usage

TADA_FlagSpeciation(
  .data,
  clean = c("suspect_only", "nonstandardized_only", "both", "none"),
  flaggedonly = FALSE
)

Arguments

.data	TADA dataframe
clean	Character argument with options "suspect_only", "nonstandardized_only", "both", or "none." The default is clean = "suspect_only" which removes rows of data flagged as having "Suspect" characteristic-method speciation combinations. When clean = "nonstandardized_only", the function removes rows of data flagged as having "NonStandardized" characteristic-method speciation combinations. When clean = "both", the function removes rows of data flagged as either "Suspect" or "NonStandardized". And when clean = "none", the function does not remove any "Suspect" or "NonStandardized" rows of data.
flaggedonly	Boolean argument; filters to show only the "Suspect" characteristic-method speciation combinations from the dataframe when flaggedonly = TRUE. Default is flaggedonly = FALSE.

Details

The “Not Reviewed” value within "TADA.MethodSpeciation.Flag" means that the EPA WQX team has not yet reviewed the combinations (see https://cdx.epa.gov/wqx/download/DomainValues/QAQCCharacteristicValidation.CSV). The WQX team plans to review and update these new combinations quarterly.

Value

This function adds TADA.MethodSpeciation.Flag to the dataframe. This column flags each TADA.CharacteristicName and MethodSpeciationName combination in your dataframe as either "NonStandardized", "Suspect", "Pass", or "Not Reviewed". When clean = "none" and flaggedonly = TRUE, the dataframe is filtered to show only the "Suspect" and "NonStandardized data; the column TADA.MethodSpeciation.Flag is still appended. When clean = "suspect_only" and flaggedonly = FALSE, "Suspect" rows are removed from the dataframe, but "NonStandardized" rows are retained. When clean = "nonstandardized_only" and flaggedonly = FALSE, "NonStandardized" rows are removed, but "Suspect" rows are retained. The default is clean = "suspect_only" and flaggedonly = FALSE.

Examples

# Load example dataset:
data(Data_Nutrients_UT)

# Remove data with Suspect characteristic-method speciation combinations
# from dataframe,
# but retain "NonStandardized" combinations flagged in new column
# 'TADA.MethodSpeciation.Flag':
SuspectSpeciation_clean <- TADA_FlagSpeciation(Data_Nutrients_UT)

# Remove data with "NonStandardized" characteristic-method speciation
# combinations
# from dataframe but retain Suspect combinations flagged in new column
# 'TADA.MethodSpeciation.Flag':
NonstandardSpeciation_clean <- TADA_FlagSpeciation(Data_Nutrients_UT,
  clean = "nonstandardized_only"
)

# Remove both "Suspect" and "NonStandardized" characteristic-method
# speciation combinations
# from dataframe:
Speciation_clean <- TADA_FlagSpeciation(Data_Nutrients_UT, clean = "both")

# Flag, but do not remove, data with "Suspect" or "NonStandardized"
# characteristic-method speciation
# combinations in new column titled "TADA.MethodSpeciation.Flag":
SuspectSpeciation_flags <- TADA_FlagSpeciation(Data_Nutrients_UT,
  clean = "none"
)

# Show only Suspect characteristic-method speciation combinations:
SuspectSpeciation_flaggedonly <- TADA_FlagSpeciation(Data_Nutrients_UT,
  clean = "nonstandardized_only", flaggedonly = TRUE
)

# Show only "NonStandardized" characteristic-method speciation combinations:
NonstandardSpeciation_flaggedonly <- TADA_FlagSpeciation(Data_Nutrients_UT,
  clean = "suspect_only", flaggedonly = TRUE
)

USEPA/TADA documentation built on April 12, 2025, 1:47 p.m.