R: Handle Flags in CTD Objects

handleFlags,ctd-method {oce}

R Documentation

Handle Flags in CTD Objects

Description

Data-quality flags are stored in the metadata slot of oce 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 contents of the data slot, based on such data-quality flags. For example, a common operation is to replace erroneous data with NA.

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

Usage

## S4 method for signature 'ctd'
handleFlags(
  object,
  flags = NULL,
  actions = NULL,
  where = NULL,
  debug = getOption("oceDebug")
)

Arguments

`object`	a ctd object.
`flags`	A list specifying flag values upon which actions will be taken. This can take two forms. In the first form, the list has named elements each containing a vector of integers. For example, salinities flagged with values of 1 or 3:9 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 holding a single unnamed vector, and this means to apply the actions to all the data entries. For example, `flags=list(c(1,3:9))` means to apply not just to salinity and temperature, but to everything within the `data` slot. If `flags` is not provided, then `defaultFlags()` is called, to try to determine a reasonable 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.
`where`	an optional character value that permits the function to work with objects that store flags in e.g. `object@metadata$flags$where` instead of in `object@metadata$flags`, and data within `object@data$where` instead of within `object@data`. The default value of `NULL` means to look withing `object@metadata` itself, and this is the default within `oce`. (The purpose of `where` is to make `oce` extensible by other packages, which may choose to store data two levels deep in the `data` slot.)
`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")`.

References

https://www.nodc.noaa.gov/woce/woce_v3/wocedata_1/whp/exchange/exchange_format_desc.htm

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

Other things related to ctd data: CTD_BCD2014666_008_1_DN.ODF.gz, [[,ctd-method, [[<-,ctd-method, as.ctd(), cnvName2oceName(), ctd-class, ctd.cnv, ctdDecimate(), ctdFindProfiles(), ctdRaw, ctdTrim(), ctd, d200321-001.ctd, d201211_0011.cnv, initialize,ctd-method, initializeFlagScheme,ctd-method, oceNames2whpNames(), oceUnits2whpUnits(), plot,ctd-method, plotProfile(), plotScan(), plotTS(), read.ctd.itp(), read.ctd.odf(), read.ctd.sbe(), read.ctd.woce.other(), read.ctd.woce(), read.ctd(), setFlags,ctd-method, subset,ctd-method, summary,ctd-method, woceNames2oceNames(), woceUnit2oceUnit(), write.ctd()

Examples

library(oce)
data(section)
stn <- section[["station", 100]]
# 1. Default: anything not flagged as 2 is set to NA, to focus
# solely on 'good', in the World Hydrographic Program scheme.
STN1 <- handleFlags(stn, flags=list(c(1, 3:9)))
data.frame(old=stn[["salinity"]], new=STN1[["salinity"]], salinityFlag=stn[["salinityFlag"]])

# 2. Use bottle salinity, if it is good and ctd is bad
replace <- 2 == stn[["salinityBottleFlag"]] && 2 != stn[["salinityFlag"]]
S <- ifelse(replace, stn[["salinityBottle"]], stn[["salinity"]])
STN2 <- oceSetData(stn, "salinity", S)

# 3. Use smoothed TS relationship to nudge questionable data.
f <- function(x) {
  S <- x[["salinity"]]
  T <- x[["temperature"]]
  df <- 0.5 * length(S) # smooths a bit
  sp <- smooth.spline(T, S, df=df)
  0.5 * (S + predict(sp, T)$y)
}
par(mfrow=c(1,2))
STN3 <- handleFlags(stn, flags=list(salinity=c(1,3:9)), action=list(salinity=f))
plotProfile(stn, "salinity", mar=c(3, 3, 3, 1))
p <- stn[["pressure"]]
par(mar=c(3, 3, 3, 1))
plot(STN3[["salinity"]] - stn[["salinity"]], p, ylim=rev(range(p)))

# 4. Single-variable flags (vector specification)
data(section)
# Multiple-flag scheme: one per data item
A <- section[["station", 100]]
deep <- A[["pressure"]] > 1500
flag <- ifelse(deep, 7, 2)
for (flagName in names(A[["flags"]]))
    A[[paste(flagName, "Flag", sep="")]] <- flag
Af <- handleFlags(A)
expect_equal(is.na(Af[["salinity"]]), deep)

# 5. Single-variable flags (list specification)
B <- section[["station", 100]]
B[["flags"]] <- list(flag)
Bf <- handleFlags(B)
expect_equal(is.na(Bf[["salinity"]]), deep)