Chop DAS data into effort segments
das_effort(x, ...)# S3 method for data.frame
das_effort(x, ...)
# S3 method for das_df
das_effort(
x,
method = c("condition", "equallength", "section"),
conditions = NULL,
distance.method = c("greatcircle", "lawofcosines", "haversine", "vincenty"),
seg0.drop = FALSE,
comment.drop = FALSE,
event.touse = NULL,
num.cores = NULL,
...
)
an object of class das_df,
or a data frame that can be coerced to class das_df
arguments passed to the specified chopping function,
such as seg.km or seg.min.km
character; method to use to chop DAS data into effort segments Can be "condition", "equallength", "section", or any partial match thereof (case sensitive)
character vector of names of conditions to include in segdata output.
These values must be column names from the output of das_process,
e.g. 'Bft', 'SwellHght', etc.
If method == "condition", then these also are the conditions which
trigger segment chopping when they change.
character; method to use to calculate distance between lat/lon coordinates. Can be "greatcircle", "lawofcosines", "haversine", "vincenty", or any partial match thereof (case sensitive). Default is "greatcircle"
logical; flag indicating whether or not to drop segments
of length 0 that contain no sighting (S, K, M, G, t) events.
Default is FALSE
logical; flag indicating if comments ("C" events)
should be ignored (i.e. position information should not be used)
when segment chopping. Default is FALSE
character vector of events to use to determine
segment lengths; overrides comment.drop.
If NULL (the default), then all on effort events are used.
If used, this argument must include at least R, E, S, and A events,
and cannot include ? or 1:8 events
Number of CPUs to over which to distribute computations.
Defaults to NULL, which uses one fewer than the number of cores
reported by detectCores.
Using 1 core likely will be faster for smaller datasets
List of three data frames:
segdata: one row for every segment, and columns for information including unique segment number, start/end/midpoint coordinates, and conditions (e.g. Beaufort)
sightinfo: details for all sightings in x, including:
the unique segment number it is associated with, segment mid points (lat/lon),
the 'included' column described in the 'Details' section,
and the output information described in das_sight
randpicks: see das_chop_equallength;
NULL if using "condition" method
This is the top-level function for chopping processed DAS data
into modeling segments (henceforth 'segments'), and assigning sightings
and related information (e.g., weather conditions) to each segment.
This function returns data frames with all relevant information for the
effort segments and associated sightings ('segdata' and 'sightinfo', respectively).
Before chopping, the DAS data is filtered for events (rows) where either
the 'OnEffort' column is TRUE or the 'Event' column "E".
In other words, the data is filtered for continuous effort sections (henceforth 'effort sections'),
where effort sections run from "R" to "E" events (inclusive),
and then passed to the chopping function specified using method.
Note that while B events immediately preceding an R are on effort,
they are ignored during effort chopping.
In addition, all on effort events (other than ? and numeric events)
with NA DateTime, Lat, or Lon values are verbosely removed.
The following chopping methods are currently available:
"condition", "equallength", and "section.
When using the "condition" method, effort sections are chopped
into segments every time a condition changes,
thereby ensuring that the conditions are consistent across the entire segment.
See das_chop_condition for more details about this method,
including arguments that must be passed to it via the argument ...
The "equallength" method consists of
chopping effort sections into equal-length segments of length seg.km,
and doing a weighted average of the conditions for the length of that segment.
See das_chop_equallength for more details about this method,
including arguments that must be passed to it via the argument ...
The "section" method involves 'chopping' the effort into continuous effort sections,
i.e. each continuous effort section is a single effort segment.
See das_chop_section for more details about this method.
The distance between the lat/lon points of subsequent events
is calculated using the method specified in distance.method.
If "greatcircle", distance_greatcircle is used,
while distance is used otherwise.
See das_sight for how the sightings are processed.
The sightinfo data frame includes the column 'included',
which is used in das_effort_sight when summarizing
the number of sightings and animals for selected species.
das_effort_sight is a separate function to allow users to
personalize the included values as desired for their analysis.
By default, i.e. in the output of this function, 'included' is TRUE if:
the sighting was made when on effort,
by a standard observer (see das_sight),
and in a Beaufort sea state less than or equal to five.
# NOT RUN {
y <- system.file("das_sample.das", package = "swfscDAS")
y.proc <- das_process(y)
# Using "condition" method
das_effort(
y.proc, method = "condition", conditions = c("Bft", "SwellHght", "Vis"),
seg.min.km = 0.05, num.cores = 1
)
# }
# NOT RUN {
# Using "equallength" method
y.rand <- system.file("das_sample_randpicks.csv", package = "swfscDAS")
das_effort(
y.proc, method = "equallength", seg.km = 10, randpicks.load = y.rand,
num.cores = 1
)
# Using "section" method
das_effort(y.proc, method = "section", num.cores = 1)
# }
# NOT RUN {
# }
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