Abstract. The oce
package makes it easy
to read, summarize and plot data from a variety of Oceanographic
instruments, isolating the researcher from the quirky data formats that
are common in this field. It also provides functions for working with
basic seawater properties such as the equation of state, and with
derived quantities such as the buoyancy frequency. Although simple
enough to be used in a teaching context, oce
is powerful
enough for a research setting. These things are illustrated here in a
general context, with further detail being provided by other vignettes,
by the documentation for the package’s functions and datasets, in Kelley
(2018) and in Kelley et al. (2022).
Oceanographers must deal with measurements made by a wide variety of instruments, a task that is complicated by a tendency of instrument manufacturers to invent new data formats to suite new measurement capabilities. Many manufacturers provide software for scanning data files and producing overview plots, but it is of somewhat limited use to researchers who work with several instrument types at the same time, and who need to move beyond standardized engineering plots to specialized scientific plots and statistical analyses.
The need to scan diverse data files was one motivation for the
creation of oce
, but an equal goal was to make it easy to
work with the data once they are in the system. This was accomplished
partly by the provision of specialized and generic (overloaded)
functions to work with the data, and partly by providing accessor
methods that make it convenient to reach inside the data objects (see
next section).
As illustrated in Figure 1, each oce
object contains
three slots:
data
, a list containing the actual data, e.g., for a
CTD object (see also the vignette about CTD
data), this will contain pressure
,
temperature
, etc.metadata
, a list containing information about the data,
such as units, data-quality flags, sampling locations, etc.processingLog
, a list that documents how the object was
created and possibly changed thereafter.For detailed analysis, users may want to access data within
oce objects. While it is possible to descend through the object using
the “slot” and “list” notation (e.g. d@data$salinity
yields
the salinity within an oce object named d
), this approach
is not recommended, for two reasons. First, direct access makes a user’s
code brittle to changes in the object structure (which are sometimes
necessary to track changes in measurement technologies, or in analysis
methods). And, second, direct access is limited to what is actually
stored in the file, and this might not be what the user wants – for
example, a record might contain electrical conductivity but not
salinity.
The preferred approach is to use the [[
notation, which
derives from the generic “Extract” method for accessing parts of an
object. It permits accessing not just the data stored in the object, but
also quantities that can be computed from such data (e.g. salinity, if
temperature, pressure and conductivity are stored).
A general introduction is provided by
and details are provided for individual object classes with e.g.
for the ctd
class.
The notation is simple. For example, suppose that d
is
an object that stores salinity
in either its
data
slot or metadata
slot. Then,
will extract the salinity if it is present in the object, or it will compute salinity, if the object’s contents are sufficient to compute salinity.
The [[
method first looks in the metadata
slot, but if the item is not found there, it proceeds to look in the
data
slot. This two-step scheme is helpful because it frees
the user from having to know where data are stored. For example, in a
ctd
object, latitude
might be stored in the
metadata
(for a CTD profile) or in the data
slot (for the slantwise sampling of a glider).
The [[
notation is helpful for several reasons, among
which two are prominent.
It can give valuable performance enhancements in some cases,
e.g. the data in the landsat
class are stored in two
byte-level arrays, which yields a marked improvement over the 8-byte
arrays that R generally uses on a 64-bit machine. The [[
assembles these byte-scale chunks into a conventional R numerical
matrix, which can be quite convenient for users who wish to operate on
the data without learning how to assemble the bytes.
It provides a uniformity of notation that can be helpful to
users. In oce
, objects derived from data files tend to hold
just the information in those files, not derived information. For
example, For example, CTD datasets often provide in-situ temperature but
not potential temperature (since the latter is not measured). The
in-situ temperature is found with e.g. d[["temperature"]]
,
and so it seems natural to write d[["theta"]]
to get the
potential temperature. If this is done, then the ctd
version of [[
first looks in the data slot, and returns
theta
if it is found there, as may sometimes be the case
(depending on the choice of the person who created the dataset).
However, if it is not found, then [[
calls
swTheta()
to calculate the value, and returns the
result.
Finally, it is also possible to extract the entirety of either the
metadata
or data
slot, e.g.
yields the full data slot, which is a list with elements that can be
accessed in the conventional way, e.g. for a ctd
object,
and
retrieves the temperature.
In addition to the above, [[
has a special entry that
can reveal not just the data in the object, but also the data that can
be computed from the object. It returns a list with items named
metadata
, metadataDerived
, data
and dataDerived
. The Derived
items are
specific not just to the object class, but also to the data stored
within the particular object. For example,
reveals that the square of the buoyancy frequency can be retrieved
with ctd[["N2"]]
, but if say the temperature were removed,
then "N2"
would not have been listed.
Exercise 1. Show that removing temperature from the
built-in "ctd"
dataset makes [["?"]]
indicate
that N2 is no longer available.
There are several schemes for modifying the data within
oce
objects. For example, and by analogy with the
[[
notation of the previous section, the following
will store the excess over freezing temperature into the
ctd
object.
Further information on this notation is provided by
The above works only within the data
slot. To store
within the metadata
slot, consider using e.g.
sets the “scientist”.
For archival work, it is important to store reasons for changing
things in an object. Two functions are provided for this purpose:
oceSetData
and oceSetMetadata
. For example, a
better way to change the scientist might be to write
ctd <- oceSetMetadata(ctd,
name = "scientist",
value = "Dalhousie Oceanography 4120/5120 Class of 2003",
note = "give credit where it's due"
)
and a better way to store temperatureAboveFreezing
would
be
ctd <- oceSetData(ctd,
name = "temperatureAboveFreezing",
value = ctd[["temperature"]] - swTFreeze(ctd),
unit = list(unit = expression(degree * C), scale = "ITS-90"),
originalName = "-",
note = "add temperatureAboveFreezing, for ice-related calculations"
)
which illustrates that this notation, as opposed to the
[[<-
notation, permits the specification of a unit and
an originalName
, both of which, together with the
note
, are displayed by summary(ctd)
.
The uniformity of the various oce
objects helps users
build skill in examining and modifying objects. Fine-scale control is
provided throughout oce
, but the best way to learn is to
start with simplest tools and their defaults. For example, the following
will read a CTD file named "station1.cnv"
, summarize the
contents, and plot an overview of the data, with profiles, a TS diagram,
and a map (Figure 2).
The reader should stop now and try this on a file of their own. The
pattern will work with a fairly wide variety of file types, because
read.oce()
examines the file name and contents to try to
discover what it is. For an example, if read.oce()
is given
the name of a file created by an Acoustic Doppler Profiler, it will
return an object inheriting from class "adp"
, so the
summary()
and plot()
calls will be tailored to
that type, e.g. the graph will show images of time-distance variation in
each of the measured velocity components.
Notes on oce function names.
As just illustrated, the general function to read a dataset ends
in .oce
, and the name is a signal that the returned object
is of class oce
. Depending on the file contents,
d
will also have an additional class, e.g. if the file
contains CTD data, then the object would inherit from two classes,
oce
and ctd
, with the second being used to
tailor the graphics by passing control to the ctd
variant
of the generic plot
function (use
help("plot,ctd-method")
to learn more).
Generally, oce
functions employ a “camel case”
naming convention, in which a function that is described by several
words is named by stringing the words together, capitalizing the first
letter of second and subsequent words. For example,
ctdFindProfiles()
locates individual profiles within a
ctd
object that holds data acquired during a cyclic raising
and lowering of the CTD instrument.
Function names begin with oce
in cases where a more
natural name would be in conflict with a function in the base R system
or a package commonly used by Oceanographers. For example,
oceContour()
is a function that provides an alternative to
contour()
in the graphics
package.
The oce
package provides many functions for dealing with
seawater properties. Perhaps the most used is swRho(S,T,p)
,
which computes seawater density ρ = ρ(S, T, p),
where S is salinity, T is in-situ temperature in
∘C (on the ITS-90 scale),
and p is seawater pressure,
i.e. the excess over atmospheric pressure, in dbar. (This and similar
functions starts with the letters sw
to designate that they
relate to seawater properties.) The result is a number in the order of
1000kg/m3. For many purposes,
Oceanographers prefer to use the density anomaly σ = ρ − 1000kg/m3, provided with
swSigma(salinity,temperature,pressure)
, or its adiabatic
cousin σθ,
provided with swSigmaTheta()
.
Most of the functions can use either the UNESCO or GSW (TEOS-10)
formulation of seawater properties, with the choice set by an argument
called eos
. It should be noted that oce
uses
the gsw
package for GSW calculations.
Caution: the results obtained with
eos="gsw"
in oce
functions may differ from the
results obtained when using the gsw
functions directly, due
to unit conventions. For example, swCSTp(..., eos="gsw")
reports conductivity ratio for consistency with the UNESCO formulation,
however the underlying gsw
function
gsw::gsw_C_from_SP()
reports conductivity in mS/cm.
A partial list of seawater functions is as follows:
swAlpha()
for thermal expansion coefficient α = −ρ0−1∂ρ/∂TswAlphaOverBeta()
for α/βswBeta()
for haline compression coefficient β = ρ0−1∂ρ/∂SswConductivity()
for conductivity from S, T and pswDepth()
for depth from p and latitudeswDynamicHeight()
for dynamic heightswLapseRate()
for adiabatic lapse rateswN2()
for buoyancy frequencyswRho()
for density ρ from S, T and pswSCTp()
for salinity from conductivity, temperature
and pressureswSTrho()
for salinity from temperature and
densityswSigma()
for ρ − 1000,kg/m3swSigmaT()
for σ with p set to zero and temperature
unalteredswSigmaTheta()
forσ with p set to zero and temperature
altered adiabaticallyswSoundSpeed()
for speed of soundswSpecificHeat()
for specific heatswSpice()
, spiciness0()
,
spiciness1()
and spiciness2()
for quantities
used in watermass researchswTFreeze()
for freezing temperatureswTSrho()
for temperature from salinity and
densityswTheta()
for potential temperature θswViscosity()
for viscosityDetails and examples are provided in the documentation of these functions.
The following exercise may be of help to readers who prefer to learn by doing. (Answers are provided at the end of this document.)
Exercise 2. a. What is the density of a seawater parcel at pressure 100 dbar, with salinity 34 PSU and temperature 10∘C? b. What temperature would the parcel have if raised adiabatically to the surface? c. What density would it have if raised adiabatically to the surface? d. What density would it have if lowered about 100m, increasing the pressure to 200 dbar? e. Draw a blank T-S diagram with S from 30 to 40 PSU and T from -2 to 20∘C.
The read.oce
function recognizes a wide variety of CTD
data formats, and the associated plot
function can produce
many types of graphical display. In addition, there are several
functions that aid in the analysis of such data. See the ctd vignette for more on dealing with CTD
data.
The commands
will plot a summary diagram containing sections of T, S, and σθ, along with a
chart indicating station locations. In addition to such overview
diagrams, the section
variant of the generic
plot
function can also create individual plots of
individual properties (use help("plot,section-method")
to
learn more).
Some section datasets are supplied in a pre-gridded format, but it is
also common to have different pressure levels at each station. For such
cases, sectionGrid()
may be used, e.g. the following
produces Figure 4. The ship was travelling westward from the
Mediterranean towards North America, taking 124 stations in total; the
stationId
value selects the last few stations of the
section, during which the ship headed toward the northwest, crossing
isobaths (and perhaps, the Gulf Stream) at nearly right angles.
library(oce)
#> Loading required package: gsw
data(section)
GS <- subset(section, 102 <= stationId & stationId <= 124)
GSg <- sectionGrid(GS, p = seq(0, 1600, 25))
plot(GSg, which = c(1, 99), map.xlim = c(-85, -(64 + 13 / 60)))
Exercise 3. Draw a T-S diagram for the section data,
using black symbols to the east of 30W and gray symbols to the west,
thus highlighting Mediterranean-derived waters. Use
handleFlags()
(see using
data-quality flags) to discard questionable data, and use the
accessor function [[
.
Exercise 4. Plot dynamic height and geostrophic
velocity across the Gulf Stream. (Hint: use the
swDynamicHeight()
function.)
Oce has several functions that facilitate the drawing of maps. A
variety of projections are provided, with the mathematics of projection
being handled behind the scenes with the sf
package. An
introduction to drawing maps is provided with ?mapPlot
, and
the map projection vignette
provides much more detail.
The following code graphs a built-in dataset of sea-level time series
(Figure 9). The top panel provides an overview of the entire data set.
The second panel is narrowed to the most recent month, which should
reveal spring-neap cycles if the tide is mixed. The third panel is a log
spectrum, with a few tidal constituents indicated. The
section
variant of the generic plot
function
provides other possibilities for plotting, including a cumulative
spectrum that can be quite informative (use
help("plot,sealevel-method")
to learn more).
Exercise 5. Illustrate Halifax sea-level variations during Hurricane Juan, near the end of September, 2003.
A preliminary version of tidal analysis is provided by the
tidem
function provided in this version of the package, but
readers are cautioned that the results are certain to change in a future
version. (The problems involve phase and the inference of satellite
nodes.)
Exercise 6. Plot the de-tided Halifax sea level
during Autumn 2003, to see whether Hurricane Juan is visible. (Hint: use
predict
with the results from tidem
.)
The following commands produce Figure 10, showing one velocity
component of currents measured in the St Lawrence Estuary Internal Wave
Experiment. This plot type is just one of many provided by the
adp
variant of the generic plot
function (see
?"plot,adp-method"
). See the adp
vignette for much more on acoustic Doppler profiler data.
Archives of CTD/bottle and Argo drifter measurements commonly supply data-quality flags that provide an indication of the trust to be put in individual data points. Oce has a flexible scheme for dealing with such flags, and also for inserting flags into these or any data type; see the using data-quality flags vignette for more..
Many of the oce
plotting functions produce axis labels
that can be displayed in languages other than English. At the time of
writing, French, German, Spanish, and Mandarin are supported in at least
a rudimentary way. Setting the language can be done at the general
system level, or within R, as indicated below (results not shown).
Most of the translated items were found by online dictionaries, and so they may be incorrect for oceanographic usage. Readers can help out in the translation effort, if they have knowledge of how nautical words such as Pitch and Roll and technical terms such as Absolute Salinity and Potential Temperature should be written in various languages.
The oce object structure can be used as a basis for new object types.
This has the advantage the basic operations of oce will carry over to
the new types. For example, the accessing operator [[
and
summary
function will work as expected, and so will such
aspects as the handling of data-quality flags and units. More details on
setting up classes that inherit from oce are provided in the subclassing vignette.
The present version of oce
can only handle data types
that the authors have been using in their research. New data types will
be added as the need arises in that work, but the authors would also be
happy to add other data types that are likely to prove useful to the
Oceanographic community. (The data types need not be restricted to
Physical Oceanography, but the authors will need some help in dealing
with other types of data, given their research focus.)
Two principles will guide the addition of data types and functions: (a) the need, as perceived by the authors or by other contributors and (b) the ease with which the additions can be made. One might call this development-by-triage, by analogy to the scheme used in Emergency Rooms to organize medical effort efficiently.
The site https://github.com/dankelley/oce
provides a
window on the development that goes on between the CRAN releases of the
package. Readers are requested to visit the site to report bugs, to
suggest new features, or just to see how oce
development is
coming along. Note that the development
branch is used by
the authors in their work, and is updated so frequently that it must be
considered unstable, at least in those spots being changed on a given
day. Official CRAN releases derive from the master
branch,
and are done when the code is considered to be of reasonable stability
and quality. This is all in a common pattern for open-source
software.
Exercise 1. Show that removing temperature from the
built-in "ctd"
dataset makes [["?"]]
indicate
that N2 is no longer available.
This may be demonstrated as below.
library(oce)
data(ctd)
"N2" %in% ctd[["?"]]$dataDerived
#> [1] TRUE
ctd@data$temperature <- NULL # erase temperature
"N2" %in% ctd[["?"]]$dataDerived
#> [1] FALSE
Exercise 2. Seawater properties. In the UNESCO system we may write
library(oce)
swRho(34, 10, 100, eos = "unesco")
#> [1] 1026.624
swTheta(34, 10, 100, eos = "unesco")
#> [1] 9.988599
swRho(34, swTheta(34, 10, 100, eos = "unesco"), 0, eos = "unesco")
#> [1] 1026.173
swRho(34, swTheta(34, 10, 100, 200, eos = "unesco"), 200, eos = "unesco")
#> [1] 1027.074
plotTS(as.ctd(c(30, 40), c(-2, 20), rep(0, 2)), eos = "unesco", grid = TRUE, col = "white")
and in the Gibbs SeaWater system, we use eos="gws"
and
must supply longitude
and latitude
arguments
to the sw*()
calls and also to the as.ctd()
call.
Exercise 3. Draw a T-S diagram for the section data,
using black symbols to the east of 30W and gray symbols to the west,
thus highlighting Mediterranean-derived waters. Use
handleFlags()
(see using
data-quality flags) to discard questionable data, and use the
accessor function [[
.
We will use the Gibbs SeaWater system, so as.ctd()
needs
location information.
library(oce)
data(section)
s <- handleFlags(section, flags = list(c(1, 3:9)))
ctd <- as.ctd(s[["salinity"]], s[["temperature"]], s[["pressure"]],
longitude = s[["longitude"]], latitude = s[["latitude"]]
)
col <- ifelse(s[["longitude"]] > -30, "black", "gray")
plotTS(ctd, col = col, eos = "gsw")
Exercise 4. Plot dynamic height and geostrophic
velocity across the Gulf Stream. (Hint: use the
swDynamicHeight
function.)
(Try ?swDynamicHeight
for hints on smoothing.)
library(oce)
data(section)
GS <- subset(section, 102 <= stationId & stationId <= 124)
dh <- swDynamicHeight(GS)
#> Warning in regularize.values(x, y, ties, missing(ties), na.rm = na.rm):
#> collapsing to unique 'x' values
par(mfrow = c(2, 1), mar = c(3, 3, 1, 1), mgp = c(2, 0.7, 0))
plot(dh$distance, dh$height, type = "l", xlab = "", ylab = "Dyn. Height [m]")
grid()
# 1e3 metres per kilometre
latMean <- mean(GS[["latitude"]])
f <- coriolis(latMean)
g <- gravity(latMean)
v <- diff(dh$height) / diff(dh$distance) * g / f / 1e3
plot(dh$distance[-1], v, type = "l", xlab = "Distance [km]", ylab = "Velocity [m/s]")
grid()
abline(h = 0, col = "red")
Exercise 5. Halifax sea-level during Hurricane Juan, near the end of September, 2003.
A web search will tell you that Hurricane Juan hit about midnight, 2003-sep-28. The first author can verify that the strongest winds occurred a bit after midnight – that was the time he moved to a room without windows, in fear of flying glass.
library(oce)
data(sealevel)
# Focus on 2003-Sep-28 to 29th, the time when Hurricane Juan caused flooding
plot(sealevel, which = 1, xlim = as.POSIXct(c("2003-09-24", "2003-10-05"), tz = "UTC"))
abline(v = as.POSIXct("2003-09-29 04:00:00", tz = "UTC"), col = "red")
mtext("Juan", at = as.POSIXct("2003-09-29 04:00:00", tz = "UTC"), col = "red")
Exercise 6. Plot the de-tided Halifax sea level
during Autumn 2003, to see whether Hurricane Juan is visible. (Hint: use
predict
with the results from tidem
.)
library(oce)
data(sealevel)
m <- tidem(sealevel)
#> Note: the tidal record is too short to fit for constituents: SA, PI1, S1, PSI1, GAM2, H1, H2, T2, R2
oce.plot.ts(sealevel[["time"]], sealevel[["elevation"]] - predict(m),
ylab = "Detided sealevel [m]",
xlim = c(as.POSIXct("2003-09-20"), as.POSIXct("2003-10-08"))
)
The spike reveals a surge of about 1.5m, on the 29th of September, 2003.
Kelley, Dan E. Oceanographic Analysis with R. 1st ed. 2018. New York, NY: Springer New York : Imprint: Springer, 2018. https://doi.org/10.1007/978-1-4939-8844-0.
Kelley, Dan E., Clark Richards, and Chantelle Layton. “Oce: An R Package for Oceanographic Analysis.” Journal of Open Source Software 7, no. 71 (March 3, 2022): 3594. https://doi.org/10.21105/joss.03594.