oce (version 1.0-1)

handleFlags,adp-method: Handle Flags in adp Objects

Description

Data-quality flags are stored in the metadata slot of oce-class objects in a list named flags. The present function (a generic that has specialized versions for various data classes) provides a way to manipulate the core data based on the data-quality flags. For example, a common operation is to replace suspicious or erroneous data with NA.

If metadata$flags in the object supplied as the first argument is empty, then that object is returned, unaltered. Otherwise, handleFlags analyses the data-quality flags within the object, in relation to the flags argument, and interprets the action argument to select an action to be applied to matched data.

Usage

# S4 method for adp
handleFlags(object, flags = NULL, actions = NULL,
  debug = getOption("oceDebug"))

Arguments

object

A adp object, i.e. one inheriting from adp-class.

flags

A list specifying flag values upon which actions will be taken. This can take two forms. In the first, the list has named elements each containing a vector of integers. For example, salinities flagged with values of 1 or 3 through 9 would be specified by flags=list(salinity=c(1,3:9)). Several data items can be specified, e.g. flags=list(salinity=c(1,3:9), temperature=c(1,3:9)) indicates that the actions are to take place for both salinity and temperature. In the second form, flags is a list with unnamed vectors, and this means to apply the actions to all the data entries; thus, flags=list(c(1,3:9)) means to apply not just to salinity and temperature, but also to everything else that is in the data slot. If flags is not provided, then defaultFlags is called, to try to determine a conservative default.

actions

An optional list that contains items with names that match those in the flags argument. If actions is not supplied, the default will be to set all values identified by flags to NA; this can also be specified by specifying actions=list("NA"). It is also possible to specify functions that calculate replacement values. These are provided with object as the single argument, and must return a replacement for the data item in question. See “Details” for the default that is used if actions is not supplied.

debug

An optional integer specifying the degree of debugging, with value 0 meaning to skip debugging and 1 or higher meaning to print some information about the arguments and the data. It is usually a good idea to set this to 1 for initial work with a dataset, to see which flags are being handled for each data item. If not supplied, this defaults to the value of getOption("oceDebug").

Details

If flags and actions are not provided, the default is to consider a flag value of 1 to indicate bad data, and 0 to indicate good data. Note that it only makes sense to use velocity (v) flags, because other flags are, at least for some instruments, stored as raw quantities, and such quantities may not be set to NA.

See Also

Other functions relating to data-quality flags: defaultFlags, handleFlags,argo-method, handleFlags,ctd-method, handleFlags,section-method, handleFlags, initializeFlagScheme,ctd-method, initializeFlagScheme,oce-method, initializeFlagScheme,section-method, initializeFlagScheme, initializeFlags,adp-method, initializeFlags,oce-method, initializeFlags, setFlags,adp-method, setFlags,ctd-method, setFlags,oce-method, setFlags

Other things related to adp data: [[,adp-method, [[<-,adp-method, adp-class, adpEnsembleAverage, adp, as.adp, beamName, beamToXyzAdp, beamToXyzAdv, beamToXyz, beamUnspreadAdp, binmapAdp, enuToOtherAdp, enuToOther, plot,adp-method, read.ad2cp, read.adp.nortek, read.adp.rdi, read.adp.sontek.serial, read.adp.sontek, read.adp, read.aquadoppHR, read.aquadoppProfiler, read.aquadopp, rotateAboutZ, setFlags,adp-method, subset,adp-method, summary,adp-method, toEnuAdp, toEnu, velocityStatistics, xyzToEnuAdp, xyzToEnu

Examples

# NOT RUN {
# Flag low "goodness" or high "error beam" values.
library(oce)
data(adp)
# Same as Example 2 of ?'setFlags,adp-method'
v <- adp[["v"]]
i2 <- array(FALSE, dim=dim(v))
g <- adp[["g", "numeric"]]
# Thresholds on percent "goodness" and error "velocity"
G <- 25
V4 <- 0.45
for (k in 1:3)
    i2[,,k] <- ((g[,,k]+g[,,4]) < G) | (v[,,4] > V4)
adpQC <- initializeFlags(adp, "v", 2)
adpQC <- setFlags(adpQC, "v", i2, 3)
adpClean <- handleFlags(adpQC, flags=list(3), actions=list("NA"))
# Demonstrate (subtle) change graphically.
par(mfcol=c(2, 1))
plot(adp, which="u1")
plot(adpClean, which="u1")

# }