das_effort | R Documentation |
Chop DAS data into effort segments
das_effort(x, ...)
## S3 method for class 'data.frame'
das_effort(x, ...)
## S3 method for class 'das_df'
das_effort(
x,
method = c("condition", "equallength", "section"),
conditions = NULL,
strata.files = NULL,
distance.method = c("greatcircle", "lawofcosines", "haversine", "vincenty"),
seg0.drop = FALSE,
comment.drop = FALSE,
event.touse = NULL,
num.cores = NULL,
...
)
x |
an object of class |
... |
arguments passed to the specified chopping function, such as
|
method |
character; method to use to chop DAS data into effort segments Can be "condition", "equallength", "section", or any partial match thereof (case sensitive) |
conditions |
character vector of names of conditions to include in
segdata output. These values must be column names from the output of
|
strata.files |
list of path(s) of the CSV file(s) with points defining
each stratum. The CSV files must contain headers and be a closed polygon.
The list should be named; see the Details section. If |
distance.method |
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" |
seg0.drop |
logical; flag indicating whether or not to drop segments of
length 0 that contain no sighting (S, K, M, G, t) events. Default is
|
comment.drop |
logical; flag indicating if comments ("C" events) should
be ignored (i.e. position information should not be used) when segment
chopping. Default is |
event.touse |
character vector of events to use to determine segment
lengths; overrides |
num.cores |
Number of CPUs to over which to distribute computations.
Defaults to |
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.
If strata.files
is not NULL
, then the effort lines will be
split by the user-provided stratum (strata). In this case, a column
'stratum' will be added to the end of the segdata data frame with the
user-provided name of the stratum that the segment was in, or NA
if
the segment was not in any of the strata. If no name was provided for the
stratum in strata.files
, then the value will be "Stratum#", where
"#" is the index of the applicable stratum in strata.files
. While
the user can provide as many strata as they want, these strata can share
boundaries but they cannot overlap. See das_effort_strata
for
more details.
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.
List of three data frames:
segdata: one row for every segment, and columns for information including unique segment number (segnum), the corresponding effort section (section_id), the segment index within the corresponding effort section (section_sub_id), the starting and ending line of the segment in the DAS file (stlin, endlin), start/end/midpoint coordinates(lat1/lon1, lat2/lon2, and mlat/mlon, respectively), the start/end/midpoint date/time of the segment (DateTime1, DateTime2, and mDateTime, respectively; mDateTime is the average of DateTime1 and DateTime2), segment length (dist), conditions (e.g. Beaufort), and, if applicable, stratum (InStratumName).
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
for
return.format
is "default"
randpicks: see das_chop_equallength
; NULL
if
using "condition" method
Internal functions called by das_effort
:
das_chop_condition
, das_chop_equallength
,
das_chop_section
, das_segdata
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
)
# Using "section" method
das_effort(y.proc, method = "section", num.cores = 1)
# 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 and chop by strata
stratum.file <- system.file("das_sample_stratum.csv", package = "swfscDAS")
das_effort(
y.proc, method = "section", strata.files = list(Poly1 = stratum.file),
num.cores = 1
)
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