This is a shortcut function to run all of the most important flagging functions on a TADA dataset. See ?function documentation for TADA_FlagResultUnit, TADA_FlagFraction, TADA_FindQCActivities, and TADA_FlagSpeciation for more information.
Arguments
- .data
A TADA dataframe
- remove_na
Boolean, Determines whether to keep TADA.ResultMeasureValues that are NA. Defaults to TRUE.
- clean
Boolean. Determines whether to keep the Invalid rows in the dataset following each flagging function. Defaults to TRUE.
Value
A TADA dataframe with the following flagging columns:TADA.ResultUnit.Flag, TADA.MethodSpeciation.Flag, TADA.SampleFraction.Flag, and TADA.ActivityType.Flag
Examples
# Load example dataset
data(Data_6Tribes_5y)
# Run flagging functions, keeping all rows
Data_6Tribes_5y_ALL <- TADA_RunKeyFlagFunctions(Data_6Tribes_5y, remove_na = FALSE, clean = FALSE)
#> [1] "Rows with invalid result value units have been flagged but retained. Review these rows before proceeding and/or set clean = 'invalid_only' or 'both'."
#> [1] "Rows with invalid sample fractions have been flagged but retained. Review these rows before proceeding and/or set clean = TRUE."
#> [1] "Rows with invalid speciations have been flagged but retained. Review these rows before proceeding and/or set clean = 'invalid_only' or 'both'."
# Run flagging functions, removing NA's and Invalid rows
Data_6Tribes_5y_CLEAN <- TADA_RunKeyFlagFunctions(Data_6Tribes_5y, remove_na = TRUE, clean = TRUE)
#> [1] "Quality control samples have been removed or were not present in the input dataframe. Returning dataframe with TADA.ActivityType.Flag column for tracking."