---
title: "Oce"
author: "Dan Kelley and Clark Richards"
date: "`r Sys.Date()`"
output:
  rmarkdown::html_vignette:
    toc: true
    number_sections: true
    fig_caption: yes
    fig_width: 5
    fig_height: 5
    dpi: 72
    dev.args: list(pointsize=11)
vignette: >
  %\VignetteIndexEntry{Oce}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

<!-- HOW TO BUILD THE VIGNETTE. -->
<!-- 1. edit this file in vignettes/oce.Rmd -->
<!-- 2. run devtools::build_vignettes() -->
<!-- 3. build oce -->


```{r, echo = FALSE}
knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
```

**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 practical context.  Worked
exercises are provided to help readers take early steps towards using the `oce`
package in their research.

# Introduction

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. Although manufacturers often provide
software for scanning data files and producing overview plots, this software is
of limited use to researchers who work with several instrument types at the
same time, and who need to move beyond engineering plots to scientific plots
and statistical analysis.

# Architecture of oce objects

![**Figure 1.** Basic layout of a CTD object.  All `oce` objects contain slots named `data`, `metadata`, and `processingLog`, with the contents depending on the type of data.](ctd-object.png)

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, this will
  contain `pressure`, `temperature`, etc.
* `metadata`, a list containing information about the data, such as units,
  quality flags, sampling locations, etc.
* `processingLog`, a list that documents how the object was created and
  possibly changed thereafter.

# Accessing data in oce objects

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. It is better to use the `[[` notation, which derives from the generic "Extract" method for accessing parts of an object. A general introduction is provided by
```{r eval=FALSE}
?`[[,oce-method`
```
and detaile are provided for individual object classes with e.g.
```{r eval=FALSE}
?`[[,ctd-method`
```
for the `ctd` class. The notation is very simple.  For example, suppose that `d` is an object that stores `salinity` in either its `data` slot or `metadata` slot. Then,
```{r eval=FALSE}
S <- d[['salinity']]
```
will extract the 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, e.g. in a `ctd` object it might be stored in the `metadata` (for a conventional CTD cast) or in the `data` slot (for the slantwise sampling of a glider).

In addition to accessing data within an object, the `[[` notation also permits the extraction of *derived* information. There are two reasons for this.

1. 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.
2. 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.
```{r eval=FALSE}
data <- d[['data']]
```
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,
```{r eval=FALSE}
data$temperature
```
retrieves the temperature. For obvious reasons, the method of derived-quantity access (e.g. the `theta` of the example above) will not work.

# Modifying data in oce objects

There are several schemes for modifying the data within `oce` objects. By analogy with the `[[` notation of the previous section, e.g. the following 
```{r eval=FALSE}
data(ctd)
ctd[["temperatureAboveFreezing"]] <- ctd[["temperature"]] - swTFreeze(ctd)
```
will store the excess over freezing temperature into the `ctd` object.

Further information on this notation is provided by
```{r eval=FALSE}
?"[[<-,oce-method"
```

The above works only within the `data` slot. To store within the `metadata` slot, consider using e.g.
```{r extractExample, eval=FALSE}
ctd[["metadata"]]$scientist <- "Dalhousie Oceanography 4120/5120 Class of 2003"
```
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
```{r eval=FALSE}
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
```{r eval=FALSE}
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)`.


# Oce functions

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).
```{r eval=FALSE}
library(oce)
d <- read.oce("station1.cnv")
summary(d)
plot(d)
```
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.**

1. 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).

2. 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 second and subsequent words. For example,
   `ctdAddColumn()` takes a `ctd` object, and returns a new `ctd` object that
   matches the original except for the added column (and an added entry in the
   `processingLog` to indicate the addition).

3. 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.

# Calculation of seawater properties

The `oce` package provides many functions for dealing with seawater properties.
Perhaps the most used is `swRho(S,T,p)`, which computes seawater density
$\rho=\rho(S, T, p)$, where $S$ is salinity,  $T$ is *in-situ* temperature in
$^\circ$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 $1000$kg/m$^3$.  For many purposes,
Oceanographers prefer to use the density anomaly $\sigma=\rho-1000$kg/m$^3$,
provided with `swSigma(salinity,temperature,pressure)`, or its adiabatic cousin
$\sigma_\theta$, 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 $\alpha=-\rho_0^{-1}\partial\rho/\partial T$
* `swAlphaOverBeta()` for $\alpha/\beta$
* `swBeta()` for haline compression coefficient $\beta=\rho_0^{-1}\partial\rho/\partial S$
* `swConductivity()` for conductivity from $S$, $T$ and $p$
* `swDepth()` for depth from $p$ and latitude
* `swDynamicHeight()` for dynamic height
* `swLapseRate()` for adiabatic lapse rate
* `swN2()` for buoyancy frequency
* `swRho()` for density $\rho$ from $S$, $T$ and $p$
* `swSCTp()` for salinity from conductivity, temperature and pressure
* `swSTrho()` for salinity from temperature and density
* `swSigma()` for $\rho-1000$\,kg/m$^3$
* `swSigmaT()` for $\sigma$ with $p$ set to zero and temperature unaltered
* `swSigmaTheta()` for$\sigma$ with $p$ set to zero and temperature altered adiabatically
* `swSoundSpeed()` for speed of sound
* `swSpecificHeat()` for specific heat
* `swSpice()` for a quantity used in double-diffusive research
* `swTFreeze()` for freezing temperature
* `swTSrho()` for temperature from salinity and density
* `swTheta()` for potential temperature $\theta$
* `swViscosity()` for viscosity

Details 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 1.** a. What is the density of a seawater parcel at pressure
100 dbar, with salinity 34 PSU and temperature 10$^\circ$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$^\circ$C.



# CTD data

## Example with pre-trimmed data

Many of the object types supported by `oce` come with built-in data.  For an
example, `data(ctd)` yields a CTD profile that has been trimmed to just the
downcast portion of the sampling.  (See the next section to learn how to do
this trimming.)  A summary and plot (Figure 2) are created as follows.

```{r fig.cap="**Figure 2.** An overview of a ctd dataset.", fig.width=6, fig.height=6, dpi=72, dev.args=list(pointsize=14), message=FALSE}
library(oce)
data(ctd)
summary(ctd)
plot(ctd)
```

Accessing the data within this `ctd` object can be done directly, e.g.
`ctd@data$pressure` holds the pressure record, but it is usually better to use
an accessor function that is provided with `oce`. This function is named `[[`,
and it takes a character string as an argument, e.g. `ctd[["pressure"]]` yields
the pressure column. The accessor notation is preferable to direct access
because it is simpler for the user. For example, several `oce` objects store
the data in single-byte or two-byte chunks, to match the raw format used by the
instruments, and the accessor function takes care of translating these values
to what are sometimes called "science" units.

**Exercise 2.** Plot a profile of $\sigma_\theta$ and $N^2$, for the data in
the pycnocline. (Hint: use `subset()`.)

## Example with raw data

Practicing Oceanographers may be wondering how the CTD cast used in the
preceding section was trimmed of equilibration-phase and upcast-phase data.
Spurious data from these phases must be trimmed as a first step in processing.
For example, consider the following code.
```{r fig.cap="**Figure 3.** Scanwise plot of the `ctdRaw` sample data set.  Note the spike at the start, the equilibration phase before the downcast, and the spurious freshening signal near the start of the upcast.", fig.width=5, fig.height=3, dpi=72, dev.args=list(pointsize=12)}
data(ctdRaw)
plotScan(ctdRaw)
```

This produces a two-panel plot (Figure 3) of the data as a time-series,
revealing not just the desired downcast, but also an earlier equilibration
phase and a later upcast.  The x-axis in Figure 3 is the scan number, which is
a convenient index for extraction of the downcast portion of the profile by an
essentially manual method, e.g. proceeding with a sequence of commands such as

```{r eval=FALSE}
plotScan(ctdTrim(ctdRaw, "range",
                 parameters=list(item="scan", from=140, to=250)))
plotScan(ctdTrim(ctdRaw, "range",
                 parameters=list(item="scan", from=150, to=250)))
```

This method of making decisions based on plotted information is probably the
most robust method of trimming data.  However, for quick work, users may be
satisfied with the results of automatic downcast detection, e.g.
```{r eval=FALSE}
ctdTrimmed <- ctdTrim(ctdRaw)
```

It should be noted that `ctdTrim()` inserts entries into the object's log file,
so that the details of how the trimming was done are recorded together with the
data.

Once the profile has been trimmed, one may wish to use `ctd.decimate()` to smooth
the data and interpolate the smoothed results to uniformly-spaced pressure
values.

Taking these things together, a quick visual examination of a CTD file takes
just one line of code:
```{r eval=FALSE}
plot(ctdDecimate(ctdTrim(read.ctd("stn123.cnv"))))
```

## Example with WOCE archive data

The package has a harder time scanning the headers of data files in the WOCE
archive format than it does in the Seabird format illustrated in the previous
examples. This is mainly because front-line researchers tend to work in the
Seabird format, and partly because the WOCE format is odd. For example, the
first line of a WOCE file is of the form `CTD,20060609WHPOSIODAM` (or
`BOTTLE,...`).  Scanning the item to the left of the comma is not difficult
(although there are variants to the two shown, e.g. `CTDO` sometimes occurs).
The part to the right of the comma is more difficult.  The first part is a date
(`yyyymmdd`) so that is no problem.  But then things start to get tricky.  In
the example provided, this text contains the division of the institute (WHPO),
the institute itself (SIO), and initial of the investigator (DAM). The problem
is that no dividers separate these items, and that there seem to be no
standards for the item lengths. Rather than spend a great deal of time coding
special cases (e.g. scanning to see if the string `WHOI` occurs in the header
line), the approach taken with `oce` is to ignore such issues relating to
quirky headers, on the assumption that users can scan human-written headers
with high skill.

Quite commonly, CTD profiles taken during a cruise are collected together in a
sequence of files in a given directory.  For a real-world example, one might
visit the website mentioned in the code provided below, download and expand the
zip file, enter the directory thus formed, and run the code to get an overall
TS plot for all the CTD stations of this cruise. (Caution: the link seems to
change from time to time.)

```{r eval=FALSE}
library(oce)
# http://cchdo.ucsd.edu/data/7971/ar18_58JH19941029_ct1.zip
# setwd("~/Downloads/ar18_58JH19941029_ct1")
files <- system("ls *.csv", intern=TRUE)
for (i in 1:length(files)) {
    x <- read.ctd(files[i])
    if (i == 1) {
        plotTS(x, Slim=c(31, 35.5), Tlim=c(-2, 10), type='o')
    } else {
        points(x[["salinity"]], x[["potential temperature"]])
        lines(x[["salinity"]], x[["potential temperature"]])
    }
}
```
The `[[` notation is explained in Section 3, but this example conveys the gist,
that it permits accessing data, or derived data, from within an object.

In the above, lines connect the points within a given profile. This can be a
useful method for a quick scan looking for outliers. Another is to colour-code
the profiles, although this gets confusing with large datasets, in which case
the method of the following exercise might be useful.

**Exercise 3. (advanced)** Make a multi-file plot summarizing the TS
relationship, with each file showing the overall relationship in gray and the
individual profile in black.


# Section plots

The commands
```{r eval=FALSE}
data(section)
plot(section, which=c(1, 2, 3, 99))
```
will plot a summary diagram containing sections of $T$, $S$, and
$\sigma_\theta$, 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.

```{r fig.cap="**Figure 4.** Portion of the CTD section designated A03, showing the Gulf Sream region.  The square on the cruise track corresponds to zero distance on the section.", fig.width=4, fig.height=4, dpi=72, dev.args=list(pointsize=12)}
library(oce)
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 4.** 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()` to discard questionable
data, and use the accessor function `[[`.

**Exercise 5.** Plot dynamic height and geostrophic velocity across the Gulf
Stream. (Hint: use the `swDynamicHeight()` function.)

# Maps

## About projections

There are several ways to handle map projections in R and other systems. Oce
uses the `rgdal` package to calculate the details of map projections.  A wide
group of functions is provided for plotting maps.  The first step is to call
`mapPlot()` to construct the map, after which points can be added with
`mapPoints()`, lines with `mapLines()`, etc. For example, the following plots a
world coastline in the Winkel Tripel projection, with the cruise track
for the `section` dataset in red.
```{r fig.cap="**Figure 5.** World in Robinson projection, with the `section` profile sites indicated.", fig.width=4, fig.height=2.6, dpi=72}
library(oce)
data(coastlineWorld)
par(mar=rep(0, 4))
mapPlot(coastlineWorld, projection="+proj=robin", col="lightgray")
data(section)
lon <- section[["longitude", "byStation"]]
lat <- section[["latitude", "byStation"]]
mapLines(lon, lat, col='red', cex=0.5)
```
```{r echo=FALSE}
data(coastlineWorld)
par(mar=rep(1, 4))
```

There are far too many projections to illustrate here. See
<http://dankelley.github.io/r/2015/04/03/oce-proj.html>
for a blog item that provides examples of the available projections. Note that
some have a problem with spurious horizontal lines. This can result from
coastline segments that cross the edge of the plotting area, and getting rid of
this is tricky enough to be the heart of the longest-lived bug in the `oce`
issue list, i.e. <https://github.com/dankelley/oce/issues/388>. In some instances the function `coastlineCut()` can help, but it is provisional and subject to change.


## Popular world-view projections

A few common projections are worth highlighting. In addition to the Robinson
projection shown in Figure 5, it is also common to employ Mollweide
(`projection="+proj=moll"`), Eckert IV (`projection="+eck4"`) or Winkel Tripel
(`projection="+proj=wintri"`) for world-wide views. An attractive feature of
the Winkel Tripel projection is familiarity, since it is used by the National
Geographical Society, but its lack of a formal inverse can cause problems for
plotting in `oce` unless the operating system has a recent version of the
`gdal` library that is used for calculating projections.

Before moving on to other projections, it may be helpful to show how to plot
gridded data with projection, illustrated in Figure 6 with bathymetry near
eastern Canada.

```{r fig.cap="**Figure 6.** World bathymetry in Molleweide projection.", fig.width=5, fig.height=2.7, dpi=72, dev.args=list(pointsize=10)}
par(mar=c(1.5, 1, 1.5, 1))
data(topoWorld)
topo <- decimate(topoWorld, 2) # coarsen grid: 4X faster plot
lon <- topo[["longitude"]]
lat <- topo[["latitude"]]
z <- topo[["z"]]
cm <- colormap(name="gmt_globe")
drawPalette(colormap=cm)
mapPlot(coastlineWorld, projection="+proj=moll", grid=FALSE, col="lightgray")
mapImage(lon, lat, z, colormap=cm)
```

## Popular mid-latitude projections

Mid-latitude regions are sometimes represented with Lambert Conformal Conic projection,
e.g. Figure 7 was produced as follows.
```{r fig.cap="**Figure 7.** North Atlantic in Lambert Conformal Conic projection.", fig.width=5, fig.height=3, dpi=72}
par(mar=c(2, 2, 1, 1))
lonlim <- c(-80, 0)
latlim <- c(20, 60)
mapPlot(coastlineWorld, projection="+proj=lcc +lat_1=30 +lat_2=50 +lon_0=-40",
        col="lightgray", longitudelim=lonlim, latitudelim=latlim)
```

Other common mid-latitude projections include Mercator and Albers Equal-Area
Conic, produced as follows.
```{r , fig.keep="none"}
mapPlot(coastlineWorld, projection="+proj=merc",
        col="lightgray", longitudelim=lonlim, latitudelim=latlim)
mapPlot(coastlineWorld, projection="+proj=aea +lat_1=30 +lat_2=70 +lon_0=-40",
        col="lightgray", longitudelim=lonlim, latitudelim=latlim)
```

## Popular high-latitude projections

High-latitude regions are often represented with stereopolar projections, e.g.
Figure 8 was produced with the following.
```{r fig.cap="**Figure 8.** Arctic coastlines in stereopolar projection.", fig.width=3, fig.height=3, dpi=72}
par(mar=c(2, 2, 1, 1))
mapPlot(coastlineWorld, projection="+proj=stere +lat_0=90",
        col="lightgray", longitudelim=c(-80,0), latitudelim=c(70, 110))
```



# Sea-level data

## Time-domain analysis

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).

```{r fig.cap="**Figure 9.** Sea-level timeseries measured in 2003 in Halifax Harbour.", fig.width=5, fig.height=4, dpi=72, dev.args=list(pointsize=16)}
library(oce)
data(sealevel)
plot(sealevel)
```

**Exercise 6.** Illustrate Halifax sea-level variations during Hurricane Juan,
near the end of September, 2003.

## Tidal-harmonic analysis

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 7.** 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`.)

# Acoustic Doppler Profiler data

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
(use `help("plot,adp-method")` to learn more).
```{r fig.cap="**Figure 10.** Measurements made with a bottom-mounted ADP in the St Lawrence Estuary. The line near the surface indicates pressure measured by the ADP.", fig.width=4, fig.height=2, dpi=72}
library(oce)
data(adp)
plot(adp, which=1)
lines(adp[['time']], adp[['pressure']], lwd=2)
```

# Handling data-quality flags

## About flags
Hydrographic and some other data may be accompanied by data-quality flags.  For
example, salinity is subject to a variety of instrumental and other errors, and
so archives of its value commonly contain quality flags.  These flags may
derive from statistical checks, from the judgement of a human analyst, or a
combination of the two.

Some flags are in the form of numerical values (typically integers) but others
may be expressed as character strings. A frustrating aspect of oceanographic
analysis is that different data archiving agencies employ different systems for
flags. For example, the [World Hydrographic
Programme](https://www.nodc.noaa.gov/woce/woce_v3/wocedata_1/whp/exchange/exchange_format_desc.htm)
designates good bottle/CTD data with a flag value of 2, whereas the [World
Ocean
Database](http://data.nodc.noaa.gov/woa/WOD/CODES/TXT/Definition_of_Quality_Flags.txt)
uses 0 for good data.  It is also common to indicate bad data by setting values
to non-physical values, e.g. -999, -99.99, or similar, and sometimes these
coded values may contradict more formal flags, when both are present.

Dealing with flags often requires ad hoc analysis methods, and for this reason,
the `oce` package offers tools for dealing with flags in a variety of ways.
The first thing to realize is that only certain data types contain flags, and
only certain data objects will contain flags. For example, hydrographic data
are quite likely to be provided with data-quality flags, but these are never
provided with coastline data.

Data-quality flags are stored in the `flags` entry in the `metadata` slot of
`oce` objects. This entry is a list, containing items with names corresponding
to names of data elements that are stored in the `data` slot. Direct
manipulation is possible, although it is more convenient to use the `[[` and
`[[<-` access methods provided in `oce`, or, for some tasks, the `handleFlags`
function provided by `oce`.

## Retrieving and changing flags

An example should clarify a working procedure. The `section` dataset provided
with `oce` holds many `ctd` objects that each have data-quality flags. 
The first few salinity flags for the 100th station may be found as follows.
```{r}
data(section)
stn <- section[["station", 100]]
head(stn[["salinityFlag"]])
```
Note that these data use the WHP scheme, i.e. good data are indicated with 2,
whereas questionable data are indicated with 3.

Before discussing how such flags might be handled, it is important to note that
it is often necessary to alter such flags. For example, some datasets will have
flags set to indicate good data, even though the data are set to -999, which is
one of several old-fashioned missing-value codes. The `section` dataset does
not contain any such cases, but the situation may be simulated as follows.
```{r}
# fake second datum
stn[["salinity"]][2] <- -999
```
With this, the setting of flags might be done with
```{r}
stn[["salinityFlag"]] <- ifelse(stn[["salinity"]] < 0, 3, stn[["salinityFlag"]])
```
with a check being done with
```{r}
head(stn[["salinityFlag"]])
```

It should be obvious to the reader that this procedure could be extended in
more sophisticated ways, e.g. with schemes searching for pressure inversions,
large departures from a TS relationship, loops on TS diagrams, etc. It can be
useful to combine automatic schemes with interactive plotting, to identify bad
data.


**Exercise 8.** Manipulating flags. The dataset provided with oce using
```r
library(oce)
d <- read.oce(system.file("extdata", "d201211_0011.cnv", package="oce"))
```
contains a column named `flags` that consists of a 3-digit number. The first
digit is a flag for temperature, the second for conductivity, and the third for
oxygen. Compute the individual flags and add them to the metadata of the `ctd` object.

## Using flags to manipulate data

A conservative approach is to set data to the missing-value code `NA` if the
data-quality flag indicates a problem. For example, in the WHP scheme, values
other than 2 indicate unchecked, bad or suspect data, and those data could be
set to the missing-value code as follows, where `stn2` is the new version:
```{r}
stn2 <- stn
stn2[["salinity"]] <- ifelse(stn2[["salinityFlag"]]!=2, NA, stn2[["salinity"]])
head(stn2[["salinityFlag"]])
```

This can be expressed more compactly with `handleFlags`, e.g.
```{r}
stn3 <- stn
stn3[["salinity"]][2] <- -999
stn3 <- handleFlags(stn3, list(salinity=c(1,3:9)))
```

A summary of the salinities follows
```{r}
head(data.frame(stnS=stn[["salinity"]], stn2S=stn2[["salinity"]], stn3S=stn3[["salinity"]]))
```



#  Working in non-English languages

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).
```{r fig.keep="none"}
library(oce)
Sys.setenv(LANGUAGE="fr")
data(ctd)
plot(ctd)
```
```{r echo=FALSE}
Sys.setenv(LANGUAGE="en")
```

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.

# Extending oce

## Initializing oce objects

Oce handles a wide variety of data types, creating sub-classes the inherit from
the base class that is named `oce`. However, even if a dataset does not fit
within these sub-classes, it is still possible to use some of the features of
`oce`, such as (FILL IN ... accessors, plotting, summaries ??).

For example, suppose that there is a dataset that contains two variables, `mm`
and `ss`, that have been measured at times `t`. Suppose that the data are
recorded at a fixed location. To illustrate how to deal with such data, we
first create some fake data:
```{r}
lon <- -60
lat <- -30
t <- seq(as.POSIXct("2017-08-30"), as.POSIXct("2017-09-01"), by="15 min")
tsec <- as.numeric(t)
u <- sin(tsec * 2 * pi / (3600 * 12.4206))
v <- 0.5 * cos(tsec * 2 * pi / (3600 * 12.4206))
```
```{r echo=FALSE, eval=FALSE}
plot(t, u, type="l")
lines(t, v, col=2)
```

These may be inserted into an `oce` object as follows. First, create the object.
```{R}
o <- new("oce")
```
This contains slots without data:
```{R}
str(o)
```

As noted previously, it's possible to insert `t`, `u`, and `v` directly into
the `data` slot, but it's better to use `oceSetData` to do that, because it 
can set up units, and it leaves a trail in the processing log.
```{R}
o <- oceSetData(o, "t", t, list(unit=expression(s), scale=""))
o <- oceSetData(o, "u", u, list(unit=expression(m/s), scale=""))
o <- oceSetData(o, "v", v, list(unit=expression(m/s), scale=""))
```

Astute readers will realize that these data are akin to tidal velocities, and
so might agree with the author that `lon` and `lat` belong in the `metadata`
slot, not the `data` slot. 
```{R}
o <- oceSetMetadata(o, "longitude", lon)
o <- oceSetMetadata(o, "latitude", lat)
```

At this stage, `o` is a full-fledged `oce` object. It may be summarized with
```{r}
summary(o)
```
and individual data components may be extracted with the `[[` notation
```{r}
o[["latitude"]]                        # from metadata
head(o[["t"]])                         # from data
head(o[["u"]])
head(o[["v"]])
```

A crude plot can be done with
```{r fig.cap="**Figure 11.** Crude plot of constructed `oce` object.", fig.width=4, fig.height=4, dpi=72}
plot(o)
```

Note that the plot uses the `pairs` function to show how all elements of the
`data` slot vary with each other. To customize the plot, a new object class
should be created, as in the next section.


## Creating new oce object classes

Let's call this new object class `uv`. With this, inventing a new object
sub-class is as simple as invoking
```{r}
setClass("uv", contains="oce")
```

For most objects, it makes sense to define three specialized methods: one to
create new objects (called `"initialize"`), one to plot them (`"plot"`), and
one to summarize them (`"summary"`).

The initialization can be handled with
```{r}
setMethod(f="initialize",
          signature="uv",
          definition=function(.Object, time, u, v, longitude, latitude) {
              if (missing(time)) stop("must provide 'time'")
              ## repeat the above also for u, v, longitude, and latitude
              .Object@metadata$units$u <- list(unit=expression(m/s), scale="")
              .Object@metadata$units$v <- list(unit=expression(m/s), scale="")
              .Object@metadata$longitude <- longitude
              .Object@metadata$latitude <- latitude
              .Object@data$time <- time
              .Object@data$u <- u
              .Object@data$v <- v
              .Object@processingLog$time <- as.POSIXct(Sys.time())
              .Object@processingLog$value <- "create 'uv' object"
              return(.Object)
          })
```
This is fairly arcane, but simply copying and pasting this will be enough to get the
reader started. Let's try it:
```{r}
oo <- new("uv", t, u, v, lon, lat)
```

Next, customize `summary` for this new class:
```{r}
setMethod(f="summary",
          signature="uv",
          definition=function(object, ...) {
              cat("uv Summary\n-----------\n\n", ...)
              cat(paste("* Location:           ",
                        sprintf("%.5f N", object@metadata$latitude), ", ",
                        sprintf("%.5f E", object@metadata$longitude), "\n", sep=''))
              callNextMethod()
          })
```
The `callNextMethod` will summarize the entries in the `data` slot adequately for this case. Let's try it:
```{r}
summary(oo)
```

Finally, let's make a custom method for `plot`. This is usually the most
complicated part of handling any new object class, because there are usually
many ways to look at data. (Some `oce` objects have over a dozen specialized
plot styles.) Let's suppose there are only two desired plot styles: one showing
timeseries of `u` and `v` in a two panel plot, and the other showing how `u`
and `v` covary.

```{r}
setMethod(f="plot",
          signature=signature("uv"),
          definition=function(x, which=1, ...)
          {
              if (which == 1) {
                  oce.plot.ts(x[["time"]], x[["u"]], ylab="u [m/s]", ...)
              } else if (which == 2) {
                  oce.plot.ts(x[["time"]], x[["v"]], ylab="v [m/s]", ...)
              } else if (which == 3) {
                  plot(x[["u"]], x[["v"]], xlab="u [m/s]", ylab="v [m/s]", ...)
              }
          })
```

```{r echo=FALSE}
par(mar=c(3, 3, 1, 1), mgp=c(2, 0.7, 0)) # thin margins
```

An now, we can try it out
```{r fig.cap="**Figure 12.** Time-series u(t) plot of newly-constructed `uv` object.", fig.width=4, fig.height=4, dpi=72}
par(mfrow=c(2,1))
plot(oo, which=1)
plot(oo, which=2)
```
```{r fig.cap="**Figure 13.** Hodograph plot of newly-constructed `uv` object.", fig.width=4, fig.height=4, dpi=72}
plot(oo, which=3, asp=1)
```



# The future of oce

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.


# Development website

The site [http://github.com/dankelley/oce](http://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.

# Answers to exercises

**Exercise 1.** Seawater properties. In the UNESCO system we
may write
```{r fig.width=5, fig.height=5, fig.keep="none"}
library(oce)
swRho(34, 10, 100, eos="unesco")
swTheta(34, 10, 100, eos="unesco")
swRho(34, swTheta(34, 10, 100, eos="unesco"), 0, eos="unesco")
swRho(34, swTheta(34, 10, 100, 200, eos="unesco"), 200, eos="unesco")
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 2.** Plot a profile of $\sigma_\theta$ and $N^2$, for the data in
the pycnocline. (Hint: use `subset()`.)

Although one may argue as to the limits of the pycnocline, for illustration let
us say it is in 5 dbar to 12 dbar range.  One way to do this is

```{r fig.width=5, fig.height=5, fig.keep="none"}
library(oce)
data(ctd)
pycnocline <- ctdTrim(ctd, "range",
                      parameters=list(item="pressure", from=5, to=12))
plotProfile(pycnocline, which="density+N2")
```

Another is
```{r fig.width=5, fig.height=5, fig.keep="none"}
library(oce)
data(ctd)
pycnocline <- subset(ctd, 5 <= pressure & pressure <= 12)
plotProfile(pycnocline, which="density+N2")
```

**Exercise 3.** Make a multi-file plot summarizing the TS relationship, with
each file showing the overall relationship in gray and the individual profile
in black. (This is an advanced exercise requiring some R skills.)

The code provided below creates 91 PNG files, with names `ar18_01.png`,
`ar18_02.png`, etc. Loading these in a view that permits quick paging through
this file list is an easy way to spot suspicious data, since each plot has the
station number at the top.  (Users trying this example should bear in mind that
this is a fairly large dataset, so the processing will take up to a minute.)

```{r eval=FALSE}
library(oce)
# http://cchdo.ucsd.edu/data/7971/ar18_58JH19941029_ct1.zip
# setwd("~/Downloads/ar18_58JH19941029_ct1")
files <- system("ls *.csv", intern=TRUE)
n <- length(files)
ctds <- vector("list", n) # to hold the CTD objects
station <- vector("list", n)
for (i in 1:n) {
    ctds[[i]] <- read.ctd(files[i])
    station[[i]] <- ctds[[i]][["station"]]
}
S <- unlist(lapply(1:n, function(i) ctds[[i]][["salinity"]]))
T <- unlist(lapply(1:n, function(i) ctds[[i]][["temperature"]]))
p <- unlist(lapply(1:n, function(i) ctds[[i]][["pressure"]]))
overall <- as.ctd(S, T, p)
png("ar18_%02d.png")
for (i in 1:n) {
    plotTS(overall, col='gray')
    lines(ctds[[i]][["salinity"]], ctds[[i]][["potential temperature"]])
    mtext(station[i], side=3, line=0)
}
dev.off()
```


**Exercise 4.** 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()` to discard questionable
data, and use the accessor function `[[`.

We will use the Gibbs SeaWater system, so `as.ctd()` needs location information.

```{r fig.width=5, fig.height=5, fig.keep="none"}
library(oce)
data(section)
s <- handleFlags(section)
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 5.** Plot dynamic height and geostrophic velocity across the Gulf
Stream. (Hint: use the `swDynamicHeight` function.)

(Try `?swDynamicHeight` for hints on smoothing.)
```{r fig.width=5, fig.height=3, fig.keep="none"}
library(oce)
data(section)
GS <- subset(section, 102 <= stationId & stationId <= 124)
dh <- swDynamicHeight(GS)
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 6.** 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.
```{r fig.width=7, fig.height=3, fig.keep="none"}
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 7.** 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`.)

```{r results="hide", fig.keep="none"}
library(oce)
data(sealevel)
m <- tidem(sealevel)
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.

**Exercise 8.** Manipulating flags.
```r
library(oce)
d <- read.oce(system.file("extdata", "d201211_0011.cnv", package="oce"))

## The following is in the .cnv file:
# Processing Notes:  Flag word has 3 columns: Temperature, Conductivity, and Oxygen
# Processing Notes:  Flag_code:  0 = no QC; 2 = good; 6 = interpolated, or replaced by dual sensor or upcast value;

flag <- d[['flag']]
flag1 <- floor(flag/100)
flag2 <- floor((flag-100*flag1)/10)
flag3 <- floor((flag-100*flag1-10*flag2))
d[["flags"]]$temperature <- flag1
d[["flags"]]$conductivity <- flag2
d[["flags"]]$oxygen <- flag3
```


