| handleFlags,ctd-method {oce} | R Documentation |
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.
## S4 method for signature 'ctd'
handleFlags(object, flags = NULL, actions = NULL,
debug = getOption("oceDebug"))
object |
A |
flags |
A |
actions |
An optional |
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
|
1. 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,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 ctd data: [[,ctd-method,
[[<-,ctd-method, as.ctd,
cnvName2oceName, ctd-class,
ctdDecimate, ctdFindProfiles,
ctdRaw, ctdTrim,
ctd, 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
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)))