Chop DAS data into effort segments
Usage
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,
strata.files = NULL,
distance.method = c("greatcircle", "lawofcosines", "haversine", "vincenty"),
seg0.drop = FALSE,
comment.drop = FALSE,
event.touse = NULL,
num.cores = NULL,
...
)Arguments
- x
an object of class
das_df, or a data frame that can be coerced to classdas_df- ...
arguments passed to the specified chopping function, such as
seg.kmorseg.min.km- 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
das_process, e.g. 'Bft', 'SwellHght', etc. Ifmethod == "condition", then these also are the conditions which trigger segment chopping when they change. Only the following conditions can be used for chopping: 'Bft', 'SwellHght', 'RainFog', 'HorizSun', 'VertSun', 'Glare', 'Vis', 'Course', 'SpdKt'- 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
NULL(the default), then no effort segments are not classified by strata.- 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
FALSE- 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
FALSE- event.touse
character vector of events to use to determine segment lengths; overrides
comment.drop. IfNULL(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- num.cores
Number of CPUs to over which to distribute computations. Defaults to
NULL, which uses one fewer than the number of cores reported bydetectCores. Using 1 core likely will be faster for smaller datasets
Value
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 indas_sightforreturn.formatis "default"randpicks: see
das_chop_equallength;NULLif using "condition" method
Details
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.
See also
Internal functions called by das_effort:
das_chop_condition, das_chop_equallength,
das_chop_section, das_segdata
Examples
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
)
#> $segdata
#> segnum section_id section_sub_id file stlin endlin lat1
#> lat 1 1 1 das_sample.das 2 20 39.32033
#> lat7 2 2 1 das_sample.das 23 33 39.37617
#> lat1 3 2 2 das_sample.das 33 43 39.42950
#> lat8 4 3 1 das_sample.das 59 69 39.56800
#> lat11 5 3 2 das_sample.das 69 70 39.66082
#> lat2 6 3 3 das_sample.das 70 75 39.66133
#> lat3 7 3 4 das_sample.das 75 78 39.67350
#> lat4 8 3 5 das_sample.das 78 84 39.69050
#> lat5 9 3 6 das_sample.das 84 85 39.73915
#> lat6 10 3 7 das_sample.das 85 90 39.75114
#> lat9 11 4 1 das_sample.das 99 108 39.94517
#> lat12 12 4 2 das_sample.das 108 121 39.96900
#> lat10 13 5 1 das_sample.das 127 147 40.15217
#> lat13 14 6 1 das_sample.das 150 160 40.26867
#> lat14 15 6 2 das_sample.das 160 164 40.32033
#> lat15 16 7 1 das_sample.das 167 174 40.38250
#> lat16 17 7 2 das_sample.das 174 181 40.42965
#> lat17 18 8 1 das_sample.das 188 199 40.52200
#> lat18 19 9 1 das_sample.das 232 240 40.98717
#> lat19 20 10 1 das_sample.das 242 259 41.02383
#> lon1 DateTime1 lat2 lon2 DateTime2
#> lat -137.6043 2013-01-13 06:27:39 39.36716 -137.5817 2013-01-13 06:46:25
#> lat7 -137.5978 2013-01-13 06:58:04 39.42950 -137.5715 2013-01-13 07:20:02
#> lat1 -137.5715 2013-01-13 07:20:02 39.51933 -137.5277 2013-01-13 07:57:05
#> lat8 -137.4530 2013-01-13 09:22:13 39.66082 -137.4132 2013-01-13 09:59:38
#> lat11 -137.4132 2013-01-13 09:59:38 39.66133 -137.4130 2013-01-13 09:59:50
#> lat2 -137.4130 2013-01-13 09:59:50 39.67350 -137.4080 2013-01-13 10:04:35
#> lat3 -137.4080 2013-01-13 10:04:35 39.69050 -137.4010 2013-01-13 10:11:09
#> lat4 -137.4010 2013-01-13 10:11:09 39.73915 -137.4031 2013-01-13 10:30:28
#> lat5 -137.4031 2013-01-13 10:30:28 39.75114 -137.4091 2013-01-13 10:35:14
#> lat6 -137.4091 2013-01-13 10:35:14 39.75433 -137.4107 2013-01-13 10:36:27
#> lat9 -137.3692 2013-01-13 11:51:51 39.96900 -137.3542 2013-01-13 12:02:29
#> lat12 -137.3542 2013-01-13 12:02:29 40.12745 -137.2488 2013-01-13 13:16:38
#> lat10 -137.1737 2013-01-13 13:50:07 40.26617 -137.1348 2013-01-13 14:38:13
#> lat13 -137.1268 2013-01-13 14:59:19 40.32033 -137.1108 2013-01-13 15:20:26
#> lat14 -137.1108 2013-01-13 15:20:26 40.37596 -137.0915 2013-01-13 15:43:08
#> lat15 -137.0977 2013-01-13 15:58:41 40.42965 -137.0745 2013-01-13 16:20:02
#> lat16 -137.0745 2013-01-13 16:20:02 40.45133 -137.0628 2013-01-13 16:29:50
#> lat17 -137.0533 2013-01-13 16:59:54 40.52533 -137.0515 2013-01-13 17:01:21
#> lat18 -135.5980 2013-01-14 11:24:32 41.01582 -135.5782 2013-01-14 11:37:24
#> lat19 -135.5743 2013-01-14 11:40:38 41.04599 -135.5595 2013-01-14 11:50:29
#> mlat mlon mDateTime dist year month day mtime
#> lat 39.34377 -137.5930 2013-01-13 06:37:02 5.5577 2013 1 13 06:37:02
#> lat7 39.40288 -137.5848 2013-01-13 07:09:03 6.3431 2013 1 13 07:09:03
#> lat1 39.47435 -137.5493 2013-01-13 07:38:33 10.6674 2013 1 13 07:38:33
#> lat8 39.61433 -137.4327 2013-01-13 09:40:55 10.8651 2013 1 13 09:40:55
#> lat11 39.66108 -137.4131 2013-01-13 09:59:44 0.0574 2013 1 13 09:59:44
#> lat2 39.66744 -137.4106 2013-01-13 10:02:12 1.4189 2013 1 13 10:02:12
#> lat3 39.68200 -137.4045 2013-01-13 10:07:52 1.9817 2013 1 13 10:07:52
#> lat4 39.71506 -137.3921 2013-01-13 10:20:48 5.6822 2013 1 13 10:20:48
#> lat5 39.74517 -137.4062 2013-01-13 10:32:51 1.4286 2013 1 13 10:32:51
#> lat6 39.75274 -137.4100 2013-01-13 10:35:50 0.3747 2013 1 13 10:35:50
#> lat9 39.95710 -137.3617 2013-01-13 11:57:10 2.9404 2013 1 13 11:57:10
#> lat12 40.04864 -137.3024 2013-01-13 12:39:33 19.7686 2013 1 13 12:39:33
#> lat10 40.20895 -137.1531 2013-01-13 14:14:10 13.0922 2013 1 13 14:14:10
#> lat13 40.29448 -137.1187 2013-01-13 15:09:52 5.8993 2013 1 13 15:09:52
#> lat14 40.34833 -137.1019 2013-01-13 15:31:47 6.4018 2013 1 13 15:31:47
#> lat15 40.40618 -137.0864 2013-01-13 16:09:21 5.5964 2013 1 13 16:09:21
#> lat16 40.44053 -137.0687 2013-01-13 16:24:56 2.6020 2013 1 13 16:24:56
#> lat17 40.52365 -137.0524 2013-01-13 17:00:37 0.4016 2013 1 13 17:00:37
#> lat18 41.00151 -135.5881 2013-01-14 11:30:58 3.5940 2013 1 14 11:30:58
#> lat19 41.03500 -135.5671 2013-01-14 11:45:33 2.7600 2013 1 14 11:45:33
#> Cruise Mode EffType ESWsides maxdistBft maxdistSwellHght maxdistVis
#> lat 1000 C S 2 3 3 6.0
#> lat7 1000 C S 2 3 3 6.0
#> lat1 1000 C S 2 3 3 5.5
#> lat8 1000 C S 2 3 3 5.5
#> lat11 1000 C S 2 3 3 6.0
#> lat2 1000 C S 2 2 3 6.0
#> lat3 1000 C S 2 2 3 5.5
#> lat4 1000 C S 2 2 3 4.5
#> lat5 1000 C S 2 2 3 3.5
#> lat6 1000 C S 2 2 3 2.5
#> lat9 1000 C S 2 3 3 5.8
#> lat12 1000 C S 2 3 3 6.0
#> lat10 1000 C S 2 3 3 6.0
#> lat13 1000 C S 2 3 3 6.0
#> lat14 1000 C S 2 2 3 6.0
#> lat15 1000 C S 2 3 3 6.0
#> lat16 1000 C S 2 2 3 6.0
#> lat17 1000 C S 2 2 3 6.0
#> lat18 1000 C S 2 2 1 4.0
#> lat19 1000 C S 2 2 1 4.0
#>
#> $sightinfo
#> segnum mlat mlon Event DateTime year Lat Lon
#> 1 1 39.34377 -137.5930 S 2013-01-13 06:46:02 2013 39.36617 -137.5820
#> 2 3 39.47435 -137.5493 S 2013-01-13 07:56:22 2013 39.51767 -137.5285
#> 3 4 39.61433 -137.4327 t 2013-01-13 09:34:27 2013 39.59733 -137.4400
#> 4 13 40.20895 -137.1531 t 2013-01-13 14:02:55 2013 40.18283 -137.1622
#> 5 13 40.20895 -137.1531 S 2013-01-13 14:37:56 2013 40.26567 -137.1350
#> 6 15 40.34833 -137.1019 t 2013-01-13 15:36:47 2013 40.36050 -137.0978
#> 7 17 40.44053 -137.0687 S 2013-01-13 16:29:50 2013 40.45133 -137.0628
#> 8 18 40.52365 -137.0524 S 2013-01-13 17:00:45 2013 40.52400 -137.0522
#> 9 18 40.52365 -137.0524 S 2013-01-13 17:00:45 2013 40.52400 -137.0522
#> 10 19 41.00151 -135.5881 F 2013-01-14 11:25:32 2013 40.98950 -135.5965
#> 11 20 41.03500 -135.5671 S 2013-01-14 11:47:51 2013 41.04017 -135.5635
#> 12 20 41.03500 -135.5671 S 2013-01-14 11:47:51 2013 41.04017 -135.5635
#> 13 20 41.03500 -135.5671 S 2013-01-14 11:49:14 2013 41.04333 -135.5615
#> 14 20 41.03500 -135.5671 S 2013-01-14 11:49:14 2013 41.04333 -135.5615
#> OnEffort Cruise Mode OffsetGMT EffType ESWsides Course SpdKt Bft SwellHght
#> 1 TRUE 1000 C 5 S 2 25 10.2 3 3
#> 2 TRUE 1000 C 5 S 2 26 9.7 3 3
#> 3 TRUE 1000 C 5 S 2 27 9.0 3 3
#> 4 TRUE 1000 C 5 S 2 20 9.3 3 3
#> 5 TRUE 1000 C 5 S 2 20 9.3 3 3
#> 6 TRUE 1000 C 5 S 2 16 8.9 2 3
#> 7 TRUE 1000 C 5 S 2 25 8.9 2 3
#> 8 TRUE 1000 C 5 S 2 30 9.5 2 3
#> 9 TRUE 1000 C 5 S 2 30 9.5 2 3
#> 10 TRUE 1000 C 5 S 2 35 9.5 2 1
#> 11 TRUE 1000 C 5 S 2 23 9.6 2 1
#> 12 TRUE 1000 C 5 S 2 23 9.6 2 1
#> 13 TRUE 1000 C 5 S 2 23 9.6 2 1
#> 14 TRUE 1000 C 5 S 2 23 9.6 2 1
#> WindSpdKt RainFog HorizSun VertSun Glare Vis ObsL Rec ObsR ObsInd EffortDot
#> 1 10 1 NA NA NA 6.0 208 280 001 <NA> TRUE
#> 2 10 3 2 2 FALSE 5.5 125 208 280 <NA> TRUE
#> 3 10 1 2 2 FALSE 5.5 001 126 149 <NA> TRUE
#> 4 6 1 8 1 FALSE 6.0 280 001 126 <NA> TRUE
#> 5 6 1 8 1 FALSE 6.0 280 001 126 <NA> TRUE
#> 6 6 1 9 1 FALSE 6.0 125 208 280 <NA> TRUE
#> 7 6 1 8 2 FALSE 6.0 149 125 208 <NA> TRUE
#> 8 6 1 8 2 FALSE 6.0 126 149 125 <NA> TRUE
#> 9 6 1 8 2 FALSE 6.0 126 149 125 <NA> TRUE
#> 10 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> 11 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> 12 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> 13 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> 14 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> EventNum file_das line_num SightNo Subgroup SightNoDaily Obs ObsStd
#> 1 15 das_sample.das 15 1406 <NA> 20130113_1 208 TRUE
#> 2 35 das_sample.das 38 1407 <NA> 20130113_2 125 TRUE
#> 3 59 das_sample.das 65 <NA> <NA> <NA> 280 FALSE
#> 4 131 das_sample.das 137 <NA> <NA> <NA> 228 FALSE
#> 5 136 das_sample.das 142 1408 <NA> 20130113_3 280 TRUE
#> 6 153 das_sample.das 162 <NA> <NA> <NA> 231 FALSE
#> 7 167 das_sample.das 176 1409 <NA> 20130113_4 149 TRUE
#> 8 181 das_sample.das 193 1410 <NA> 20130113_5 125 TRUE
#> 9 181 das_sample.das 193 1410 <NA> 20130113_5 125 TRUE
#> 10 8 das_sample.das 238 <NA> <NA> <NA> 149 TRUE
#> 11 18 das_sample.das 248 1412 <NA> 20130114_1 149 TRUE
#> 12 18 das_sample.das 248 1412 <NA> 20130114_1 149 TRUE
#> 13 20 das_sample.das 252 1413 <NA> 20130114_2 208 TRUE
#> 14 20 das_sample.das 252 1413 <NA> 20130114_2 208 TRUE
#> Bearing Reticle DistNm Cue Method Photos Birds CalibSchool PhotosAerial
#> 1 309 2.8 1.06 3 4 N N <NA> <NA>
#> 2 326 0.4 2.97 3 4 Y N <NA> <NA>
#> 3 120 NA 0.03 NA NA <NA> <NA> <NA> <NA>
#> 4 300 NA 0.02 NA NA <NA> <NA> <NA> <NA>
#> 5 270 14.0 0.28 3 4 N N <NA> <NA>
#> 6 45 NA 0.05 NA NA <NA> <NA> <NA> <NA>
#> 7 344 0.2 3.68 3 4 Y Y <NA> <NA>
#> 8 70 1.4 1.66 3 4 Y N <NA> <NA>
#> 9 70 1.4 1.66 3 4 Y N <NA> <NA>
#> 10 309 1.7 1.47 NA NA <NA> <NA> <NA> <NA>
#> 11 359 0.3 3.28 2 4 Y N <NA> <NA>
#> 12 359 0.3 3.28 2 4 Y N <NA> <NA>
#> 13 38 0.8 2.23 3 4 Y N <NA> <NA>
#> 14 38 0.8 2.23 3 4 Y N <NA> <NA>
#> Biopsy Prob nSp Mixed SpCode SpCodeProb GsSchoolBest GsSchoolHigh
#> 1 <NA> FALSE 1 FALSE 018 <NA> NA NA
#> 2 <NA> FALSE 1 FALSE 076 <NA> 8.00000 14.00
#> 3 <NA> NA NA NA LV <NA> 1.00000 NA
#> 4 <NA> NA NA NA DC <NA> 1.00000 NA
#> 5 <NA> FALSE 1 FALSE 037 <NA> 10.66667 20.00
#> 6 <NA> NA NA NA DC <NA> 1.00000 NA
#> 7 <NA> FALSE 1 FALSE 016 <NA> 46.66667 79.00
#> 8 <NA> FALSE 2 TRUE 013 <NA> 41.75000 72.75
#> 9 <NA> FALSE 2 TRUE 016 <NA> 41.75000 72.75
#> 10 <NA> NA NA NA <NA> <NA> NA NA
#> 11 <NA> FALSE 2 TRUE 018 <NA> 151.50000 249.00
#> 12 <NA> FALSE 2 TRUE 277 <NA> 151.50000 249.00
#> 13 <NA> TRUE 2 TRUE 016 016 21.25000 37.75
#> 14 <NA> TRUE 2 TRUE 277 016 21.25000 37.75
#> GsSchoolLow GsSpBest GsSpHigh GsSpLow CourseSchool TurtleJFR TurtleAge
#> 1 42.333333 NA NA 42.333333 NA <NA> <NA>
#> 2 5.666667 8.00000 14.0000 5.666667 NA <NA> <NA>
#> 3 NA 1.00000 NA NA NA <NA> A
#> 4 NA 1.00000 NA NA NA <NA> J
#> 5 10.666667 10.66667 20.0000 10.666667 NA <NA> <NA>
#> 6 NA 1.00000 NA NA NA <NA> A
#> 7 46.666667 46.66667 79.0000 46.666667 NA <NA> <NA>
#> 8 41.750000 30.06000 52.3800 30.060000 NA <NA> <NA>
#> 9 41.750000 11.69000 20.3700 11.690000 NA <NA> <NA>
#> 10 NA NA NA NA NA <NA> <NA>
#> 11 151.500000 128.77500 211.6500 128.775000 NA <NA> <NA>
#> 12 151.500000 22.72500 37.3500 22.725000 NA <NA> <NA>
#> 13 21.250000 15.08750 26.8025 15.087500 NA <NA> <NA>
#> 14 21.250000 6.16250 10.9475 6.162500 NA <NA> <NA>
#> TurtleCapt PerpDistKm included
#> 1 <NA> 1.52563078 TRUE
#> 2 <NA> 3.07580701 TRUE
#> 3 N 0.04811637 FALSE
#> 4 N 0.03207758 FALSE
#> 5 <NA> 0.51856000 TRUE
#> 6 <NA> 0.06547809 FALSE
#> 7 <NA> 1.87856781 TRUE
#> 8 <NA> 2.88891582 TRUE
#> 9 <NA> 2.88891582 TRUE
#> 10 <NA> 2.11573325 TRUE
#> 11 <NA> 0.10601569 TRUE
#> 12 <NA> 0.10601569 TRUE
#> 13 <NA> 2.54265727 TRUE
#> 14 <NA> 2.54265727 TRUE
#>
#> $randpicks
#> NULL
#>
# Using "section" method
das_effort(y.proc, method = "section", num.cores = 1)
#> $segdata
#> segnum section_id section_sub_id file stlin endlin lat1
#> lat 1 1 1 das_sample.das 2 20 39.32033
#> lat1 2 2 1 das_sample.das 23 43 39.37617
#> lat2 3 3 1 das_sample.das 59 90 39.56800
#> lat3 4 4 1 das_sample.das 99 121 39.94517
#> lat4 5 5 1 das_sample.das 127 147 40.15217
#> lat5 6 6 1 das_sample.das 150 164 40.26867
#> lat6 7 7 1 das_sample.das 167 181 40.38250
#> lat7 8 8 1 das_sample.das 188 199 40.52200
#> lat8 9 9 1 das_sample.das 232 240 40.98717
#> lat9 10 10 1 das_sample.das 242 259 41.02383
#> lon1 DateTime1 lat2 lon2 DateTime2
#> lat -137.6043 2013-01-13 06:27:39 39.36716 -137.5817 2013-01-13 06:46:25
#> lat1 -137.5978 2013-01-13 06:58:04 39.51933 -137.5277 2013-01-13 07:57:05
#> lat2 -137.4530 2013-01-13 09:22:13 39.75433 -137.4107 2013-01-13 10:36:27
#> lat3 -137.3692 2013-01-13 11:51:51 40.12745 -137.2488 2013-01-13 13:16:38
#> lat4 -137.1737 2013-01-13 13:50:07 40.26617 -137.1348 2013-01-13 14:38:13
#> lat5 -137.1268 2013-01-13 14:59:19 40.37596 -137.0915 2013-01-13 15:43:08
#> lat6 -137.0977 2013-01-13 15:58:41 40.45133 -137.0628 2013-01-13 16:29:50
#> lat7 -137.0533 2013-01-13 16:59:54 40.52533 -137.0515 2013-01-13 17:01:21
#> lat8 -135.5980 2013-01-14 11:24:32 41.01582 -135.5782 2013-01-14 11:37:24
#> lat9 -135.5743 2013-01-14 11:40:38 41.04599 -135.5595 2013-01-14 11:50:29
#> mlat mlon mDateTime dist year month day mtime
#> lat 39.34377 -137.5930 2013-01-13 06:37:02 5.5577 2013 1 13 06:37:02
#> lat1 39.44767 -137.5625 2013-01-13 07:27:34 17.0106 2013 1 13 07:27:34
#> lat2 39.66117 -137.4131 2013-01-13 09:59:20 21.8086 2013 1 13 09:59:20
#> lat3 40.03679 -137.3101 2013-01-13 12:34:14 22.7090 2013 1 13 12:34:14
#> lat4 40.20895 -137.1531 2013-01-13 14:14:10 13.0922 2013 1 13 14:14:10
#> lat5 40.32254 -137.1102 2013-01-13 15:21:13 12.3011 2013 1 13 15:21:13
#> lat6 40.41714 -137.0810 2013-01-13 16:14:15 8.1984 2013 1 13 16:14:15
#> lat7 40.52365 -137.0524 2013-01-13 17:00:37 0.4016 2013 1 13 17:00:37
#> lat8 41.00151 -135.5881 2013-01-14 11:30:58 3.5940 2013 1 14 11:30:58
#> lat9 41.03500 -135.5671 2013-01-14 11:45:33 2.7600 2013 1 14 11:45:33
#> Cruise Mode EffType ESWsides avgBft avgSwellHght avgHorizSun avgVertSun
#> lat 1000 C S 2 3.000000 3 2.000000 3
#> lat1 1000 C S 2 3.000000 3 2.000000 2
#> lat2 1000 C S 2 2.500835 3 2.000000 2
#> lat3 1000 C S 2 3.000000 3 12.000000 12
#> lat4 1000 C S 2 3.000000 3 8.000000 1
#> lat5 1000 C S 2 2.479576 3 8.520424 1
#> lat6 1000 C S 2 2.682617 3 8.000000 2
#> lat7 1000 C S 2 2.000000 3 8.000000 2
#> lat8 1000 C S 2 2.000000 1 NA NA
#> lat9 1000 C S 2 2.000000 1 NA NA
#> avgGlare avgVis avgCourse avgSpdKt
#> lat 0 6.000000 23.56198 9.912395
#> lat1 0 5.686447 27.11868 9.476263
#> lat2 0 5.090748 95.65614 9.287725
#> lat3 0 5.974103 34.61155 9.428340
#> lat4 0 6.000000 20.43919 9.343919
#> lat5 0 6.000000 16.47958 9.091830
#> lat6 0 6.000000 25.00000 8.900000
#> lat7 0 6.000000 30.00000 9.500000
#> lat8 NA 4.000000 35.00000 9.500000
#> lat9 NA 4.000000 23.00000 9.600000
#>
#> $sightinfo
#> segnum mlat mlon Event DateTime year Lat Lon
#> 1 1 39.34377 -137.5930 S 2013-01-13 06:46:02 2013 39.36617 -137.5820
#> 2 2 39.44767 -137.5625 S 2013-01-13 07:56:22 2013 39.51767 -137.5285
#> 3 3 39.66117 -137.4131 t 2013-01-13 09:34:27 2013 39.59733 -137.4400
#> 4 5 40.20895 -137.1531 t 2013-01-13 14:02:55 2013 40.18283 -137.1622
#> 5 5 40.20895 -137.1531 S 2013-01-13 14:37:56 2013 40.26567 -137.1350
#> 6 6 40.32254 -137.1102 t 2013-01-13 15:36:47 2013 40.36050 -137.0978
#> 7 7 40.41714 -137.0810 S 2013-01-13 16:29:50 2013 40.45133 -137.0628
#> 8 8 40.52365 -137.0524 S 2013-01-13 17:00:45 2013 40.52400 -137.0522
#> 9 8 40.52365 -137.0524 S 2013-01-13 17:00:45 2013 40.52400 -137.0522
#> 10 9 41.00151 -135.5881 F 2013-01-14 11:25:32 2013 40.98950 -135.5965
#> 11 10 41.03500 -135.5671 S 2013-01-14 11:47:51 2013 41.04017 -135.5635
#> 12 10 41.03500 -135.5671 S 2013-01-14 11:47:51 2013 41.04017 -135.5635
#> 13 10 41.03500 -135.5671 S 2013-01-14 11:49:14 2013 41.04333 -135.5615
#> 14 10 41.03500 -135.5671 S 2013-01-14 11:49:14 2013 41.04333 -135.5615
#> OnEffort Cruise Mode OffsetGMT EffType ESWsides Course SpdKt Bft SwellHght
#> 1 TRUE 1000 C 5 S 2 25 10.2 3 3
#> 2 TRUE 1000 C 5 S 2 26 9.7 3 3
#> 3 TRUE 1000 C 5 S 2 27 9.0 3 3
#> 4 TRUE 1000 C 5 S 2 20 9.3 3 3
#> 5 TRUE 1000 C 5 S 2 20 9.3 3 3
#> 6 TRUE 1000 C 5 S 2 16 8.9 2 3
#> 7 TRUE 1000 C 5 S 2 25 8.9 2 3
#> 8 TRUE 1000 C 5 S 2 30 9.5 2 3
#> 9 TRUE 1000 C 5 S 2 30 9.5 2 3
#> 10 TRUE 1000 C 5 S 2 35 9.5 2 1
#> 11 TRUE 1000 C 5 S 2 23 9.6 2 1
#> 12 TRUE 1000 C 5 S 2 23 9.6 2 1
#> 13 TRUE 1000 C 5 S 2 23 9.6 2 1
#> 14 TRUE 1000 C 5 S 2 23 9.6 2 1
#> WindSpdKt RainFog HorizSun VertSun Glare Vis ObsL Rec ObsR ObsInd EffortDot
#> 1 10 1 NA NA NA 6.0 208 280 001 <NA> TRUE
#> 2 10 3 2 2 FALSE 5.5 125 208 280 <NA> TRUE
#> 3 10 1 2 2 FALSE 5.5 001 126 149 <NA> TRUE
#> 4 6 1 8 1 FALSE 6.0 280 001 126 <NA> TRUE
#> 5 6 1 8 1 FALSE 6.0 280 001 126 <NA> TRUE
#> 6 6 1 9 1 FALSE 6.0 125 208 280 <NA> TRUE
#> 7 6 1 8 2 FALSE 6.0 149 125 208 <NA> TRUE
#> 8 6 1 8 2 FALSE 6.0 126 149 125 <NA> TRUE
#> 9 6 1 8 2 FALSE 6.0 126 149 125 <NA> TRUE
#> 10 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> 11 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> 12 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> 13 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> 14 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> EventNum file_das line_num SightNo Subgroup SightNoDaily Obs ObsStd
#> 1 15 das_sample.das 15 1406 <NA> 20130113_1 208 TRUE
#> 2 35 das_sample.das 38 1407 <NA> 20130113_2 125 TRUE
#> 3 59 das_sample.das 65 <NA> <NA> <NA> 280 FALSE
#> 4 131 das_sample.das 137 <NA> <NA> <NA> 228 FALSE
#> 5 136 das_sample.das 142 1408 <NA> 20130113_3 280 TRUE
#> 6 153 das_sample.das 162 <NA> <NA> <NA> 231 FALSE
#> 7 167 das_sample.das 176 1409 <NA> 20130113_4 149 TRUE
#> 8 181 das_sample.das 193 1410 <NA> 20130113_5 125 TRUE
#> 9 181 das_sample.das 193 1410 <NA> 20130113_5 125 TRUE
#> 10 8 das_sample.das 238 <NA> <NA> <NA> 149 TRUE
#> 11 18 das_sample.das 248 1412 <NA> 20130114_1 149 TRUE
#> 12 18 das_sample.das 248 1412 <NA> 20130114_1 149 TRUE
#> 13 20 das_sample.das 252 1413 <NA> 20130114_2 208 TRUE
#> 14 20 das_sample.das 252 1413 <NA> 20130114_2 208 TRUE
#> Bearing Reticle DistNm Cue Method Photos Birds CalibSchool PhotosAerial
#> 1 309 2.8 1.06 3 4 N N <NA> <NA>
#> 2 326 0.4 2.97 3 4 Y N <NA> <NA>
#> 3 120 NA 0.03 NA NA <NA> <NA> <NA> <NA>
#> 4 300 NA 0.02 NA NA <NA> <NA> <NA> <NA>
#> 5 270 14.0 0.28 3 4 N N <NA> <NA>
#> 6 45 NA 0.05 NA NA <NA> <NA> <NA> <NA>
#> 7 344 0.2 3.68 3 4 Y Y <NA> <NA>
#> 8 70 1.4 1.66 3 4 Y N <NA> <NA>
#> 9 70 1.4 1.66 3 4 Y N <NA> <NA>
#> 10 309 1.7 1.47 NA NA <NA> <NA> <NA> <NA>
#> 11 359 0.3 3.28 2 4 Y N <NA> <NA>
#> 12 359 0.3 3.28 2 4 Y N <NA> <NA>
#> 13 38 0.8 2.23 3 4 Y N <NA> <NA>
#> 14 38 0.8 2.23 3 4 Y N <NA> <NA>
#> Biopsy Prob nSp Mixed SpCode SpCodeProb GsSchoolBest GsSchoolHigh
#> 1 <NA> FALSE 1 FALSE 018 <NA> NA NA
#> 2 <NA> FALSE 1 FALSE 076 <NA> 8.00000 14.00
#> 3 <NA> NA NA NA LV <NA> 1.00000 NA
#> 4 <NA> NA NA NA DC <NA> 1.00000 NA
#> 5 <NA> FALSE 1 FALSE 037 <NA> 10.66667 20.00
#> 6 <NA> NA NA NA DC <NA> 1.00000 NA
#> 7 <NA> FALSE 1 FALSE 016 <NA> 46.66667 79.00
#> 8 <NA> FALSE 2 TRUE 013 <NA> 41.75000 72.75
#> 9 <NA> FALSE 2 TRUE 016 <NA> 41.75000 72.75
#> 10 <NA> NA NA NA <NA> <NA> NA NA
#> 11 <NA> FALSE 2 TRUE 018 <NA> 151.50000 249.00
#> 12 <NA> FALSE 2 TRUE 277 <NA> 151.50000 249.00
#> 13 <NA> TRUE 2 TRUE 016 016 21.25000 37.75
#> 14 <NA> TRUE 2 TRUE 277 016 21.25000 37.75
#> GsSchoolLow GsSpBest GsSpHigh GsSpLow CourseSchool TurtleJFR TurtleAge
#> 1 42.333333 NA NA 42.333333 NA <NA> <NA>
#> 2 5.666667 8.00000 14.0000 5.666667 NA <NA> <NA>
#> 3 NA 1.00000 NA NA NA <NA> A
#> 4 NA 1.00000 NA NA NA <NA> J
#> 5 10.666667 10.66667 20.0000 10.666667 NA <NA> <NA>
#> 6 NA 1.00000 NA NA NA <NA> A
#> 7 46.666667 46.66667 79.0000 46.666667 NA <NA> <NA>
#> 8 41.750000 30.06000 52.3800 30.060000 NA <NA> <NA>
#> 9 41.750000 11.69000 20.3700 11.690000 NA <NA> <NA>
#> 10 NA NA NA NA NA <NA> <NA>
#> 11 151.500000 128.77500 211.6500 128.775000 NA <NA> <NA>
#> 12 151.500000 22.72500 37.3500 22.725000 NA <NA> <NA>
#> 13 21.250000 15.08750 26.8025 15.087500 NA <NA> <NA>
#> 14 21.250000 6.16250 10.9475 6.162500 NA <NA> <NA>
#> TurtleCapt PerpDistKm included
#> 1 <NA> 1.52563078 TRUE
#> 2 <NA> 3.07580701 TRUE
#> 3 N 0.04811637 FALSE
#> 4 N 0.03207758 FALSE
#> 5 <NA> 0.51856000 TRUE
#> 6 <NA> 0.06547809 FALSE
#> 7 <NA> 1.87856781 TRUE
#> 8 <NA> 2.88891582 TRUE
#> 9 <NA> 2.88891582 TRUE
#> 10 <NA> 2.11573325 TRUE
#> 11 <NA> 0.10601569 TRUE
#> 12 <NA> 0.10601569 TRUE
#> 13 <NA> 2.54265727 TRUE
#> 14 <NA> 2.54265727 TRUE
#>
#> $randpicks
#> effort_section randpicks
#> 1 1 NA
#> 2 2 NA
#> 3 3 NA
#> 4 4 NA
#> 5 5 NA
#> 6 6 NA
#> 7 7 NA
#> 8 8 NA
#> 9 9 NA
#> 10 10 NA
#>
# \donttest{
# 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
)
#> $segdata
#> segnum section_id section_sub_id file stlin endlin lat1
#> lat 1 1 1 das_sample.das 2 20 39.32033
#> lat2 2 2 1 das_sample.das 23 34 39.37617
#> lat1 3 2 2 das_sample.das 34 43 39.43510
#> lat3 4 3 1 das_sample.das 59 68 39.56800
#> lat11 5 3 2 das_sample.das 68 90 39.65336
#> lat4 6 4 1 das_sample.das 99 113 39.94517
#> lat12 7 4 2 das_sample.das 113 121 40.02582
#> lat5 8 5 1 das_sample.das 127 147 40.15217
#> lat6 9 6 1 das_sample.das 150 164 40.26867
#> lat7 10 7 1 das_sample.das 167 181 40.38250
#> lat8 11 8 1 das_sample.das 188 199 40.52200
#> lat9 12 9 1 das_sample.das 232 240 40.98717
#> lat10 13 10 1 das_sample.das 242 259 41.02383
#> lon1 DateTime1 lat2 lon2 DateTime2
#> lat -137.6043 2013-01-13 06:27:39 39.36716 -137.5817 2013-01-13 06:46:25
#> lat2 -137.5978 2013-01-13 06:58:04 39.43510 -137.5687 2013-01-13 07:22:21
#> lat1 -137.5687 2013-01-13 07:22:21 39.51933 -137.5277 2013-01-13 07:57:05
#> lat3 -137.4530 2013-01-13 09:22:13 39.65336 -137.4163 2013-01-13 09:56:43
#> lat11 -137.4163 2013-01-13 09:56:43 39.75433 -137.4107 2013-01-13 10:36:27
#> lat4 -137.3692 2013-01-13 11:51:51 40.02582 -137.3171 2013-01-13 12:29:17
#> lat12 -137.3171 2013-01-13 12:29:17 40.12745 -137.2488 2013-01-13 13:16:38
#> lat5 -137.1737 2013-01-13 13:50:07 40.26617 -137.1348 2013-01-13 14:38:13
#> lat6 -137.1268 2013-01-13 14:59:19 40.37596 -137.0915 2013-01-13 15:43:08
#> lat7 -137.0977 2013-01-13 15:58:41 40.45133 -137.0628 2013-01-13 16:29:50
#> lat8 -137.0533 2013-01-13 16:59:54 40.52533 -137.0515 2013-01-13 17:01:21
#> lat9 -135.5980 2013-01-14 11:24:32 41.01582 -135.5782 2013-01-14 11:37:24
#> lat10 -135.5743 2013-01-14 11:40:38 41.04599 -135.5595 2013-01-14 11:50:29
#> mlat mlon mDateTime dist year month day mtime
#> lat 39.34377 -137.5930 2013-01-13 06:37:02 5.5577 2013 1 13 06:37:02
#> lat2 39.40568 -137.5834 2013-01-13 07:10:12 7.0106 2013 1 13 07:10:12
#> lat1 39.47716 -137.5480 2013-01-13 07:39:43 10.0000 2013 1 13 07:39:43
#> lat3 39.61063 -137.4343 2013-01-13 09:39:28 10.0000 2013 1 13 09:39:28
#> lat11 39.70406 -137.3954 2013-01-13 10:16:35 11.8086 2013 1 13 10:16:35
#> lat4 39.98555 -137.3433 2013-01-13 12:10:34 10.0000 2013 1 13 12:10:34
#> lat12 40.07696 -137.2836 2013-01-13 12:52:57 12.7090 2013 1 13 12:52:57
#> lat5 40.20895 -137.1531 2013-01-13 14:14:10 13.0922 2013 1 13 14:14:10
#> lat6 40.32254 -137.1102 2013-01-13 15:21:13 12.3011 2013 1 13 15:21:13
#> lat7 40.41714 -137.0810 2013-01-13 16:14:15 8.1984 2013 1 13 16:14:15
#> lat8 40.52365 -137.0524 2013-01-13 17:00:37 0.4016 2013 1 13 17:00:37
#> lat9 41.00151 -135.5881 2013-01-14 11:30:58 3.5940 2013 1 14 11:30:58
#> lat10 41.03500 -135.5671 2013-01-14 11:45:33 2.7600 2013 1 14 11:45:33
#> Cruise Mode EffType ESWsides avgBft avgSwellHght avgHorizSun avgVertSun
#> lat 1000 C S 2 3.000000 3 2.000000 3
#> lat2 1000 C S 2 3.000000 3 NA NA
#> lat1 1000 C S 2 3.000000 3 2.000000 2
#> lat3 1000 C S 2 3.000000 3 2.000000 2
#> lat11 1000 C S 2 2.078121 3 2.000000 2
#> lat4 1000 C S 2 3.000000 3 12.000000 12
#> lat12 1000 C S 2 3.000000 3 12.000000 12
#> lat5 1000 C S 2 3.000000 3 8.000000 1
#> lat6 1000 C S 2 2.479576 3 8.520424 1
#> lat7 1000 C S 2 2.682617 3 8.000000 2
#> lat8 1000 C S 2 2.000000 3 8.000000 2
#> lat9 1000 C S 2 2.000000 1 NA NA
#> lat10 1000 C S 2 2.000000 1 NA NA
#> avgGlare avgVis avgCourse avgSpdKt
#> lat 0 6.000000 23.56198 9.912395
#> lat2 NA 5.952398 28.71439 9.157122
#> lat1 0 5.500000 26.00000 9.700000
#> lat3 0 5.500000 27.00000 9.000000
#> lat11 0 4.744176 153.79707 9.531382
#> lat4 0 5.941191 34.11787 9.570596
#> lat12 0 6.000000 35.00000 9.316406
#> lat5 0 6.000000 20.43919 9.343919
#> lat6 0 6.000000 16.47958 9.091830
#> lat7 0 6.000000 25.00000 8.900000
#> lat8 0 6.000000 30.00000 9.500000
#> lat9 NA 4.000000 35.00000 9.500000
#> lat10 NA 4.000000 23.00000 9.600000
#>
#> $sightinfo
#> segnum mlat mlon Event DateTime year Lat Lon
#> 1 1 39.34377 -137.5930 S 2013-01-13 06:46:02 2013 39.36617 -137.5820
#> 2 3 39.47716 -137.5480 S 2013-01-13 07:56:22 2013 39.51767 -137.5285
#> 3 4 39.61063 -137.4343 t 2013-01-13 09:34:27 2013 39.59733 -137.4400
#> 4 8 40.20895 -137.1531 t 2013-01-13 14:02:55 2013 40.18283 -137.1622
#> 5 8 40.20895 -137.1531 S 2013-01-13 14:37:56 2013 40.26567 -137.1350
#> 6 9 40.32254 -137.1102 t 2013-01-13 15:36:47 2013 40.36050 -137.0978
#> 7 10 40.41714 -137.0810 S 2013-01-13 16:29:50 2013 40.45133 -137.0628
#> 8 11 40.52365 -137.0524 S 2013-01-13 17:00:45 2013 40.52400 -137.0522
#> 9 11 40.52365 -137.0524 S 2013-01-13 17:00:45 2013 40.52400 -137.0522
#> 10 12 41.00151 -135.5881 F 2013-01-14 11:25:32 2013 40.98950 -135.5965
#> 11 13 41.03500 -135.5671 S 2013-01-14 11:47:51 2013 41.04017 -135.5635
#> 12 13 41.03500 -135.5671 S 2013-01-14 11:47:51 2013 41.04017 -135.5635
#> 13 13 41.03500 -135.5671 S 2013-01-14 11:49:14 2013 41.04333 -135.5615
#> 14 13 41.03500 -135.5671 S 2013-01-14 11:49:14 2013 41.04333 -135.5615
#> OnEffort Cruise Mode OffsetGMT EffType ESWsides Course SpdKt Bft SwellHght
#> 1 TRUE 1000 C 5 S 2 25 10.2 3 3
#> 2 TRUE 1000 C 5 S 2 26 9.7 3 3
#> 3 TRUE 1000 C 5 S 2 27 9.0 3 3
#> 4 TRUE 1000 C 5 S 2 20 9.3 3 3
#> 5 TRUE 1000 C 5 S 2 20 9.3 3 3
#> 6 TRUE 1000 C 5 S 2 16 8.9 2 3
#> 7 TRUE 1000 C 5 S 2 25 8.9 2 3
#> 8 TRUE 1000 C 5 S 2 30 9.5 2 3
#> 9 TRUE 1000 C 5 S 2 30 9.5 2 3
#> 10 TRUE 1000 C 5 S 2 35 9.5 2 1
#> 11 TRUE 1000 C 5 S 2 23 9.6 2 1
#> 12 TRUE 1000 C 5 S 2 23 9.6 2 1
#> 13 TRUE 1000 C 5 S 2 23 9.6 2 1
#> 14 TRUE 1000 C 5 S 2 23 9.6 2 1
#> WindSpdKt RainFog HorizSun VertSun Glare Vis ObsL Rec ObsR ObsInd EffortDot
#> 1 10 1 NA NA NA 6.0 208 280 001 <NA> TRUE
#> 2 10 3 2 2 FALSE 5.5 125 208 280 <NA> TRUE
#> 3 10 1 2 2 FALSE 5.5 001 126 149 <NA> TRUE
#> 4 6 1 8 1 FALSE 6.0 280 001 126 <NA> TRUE
#> 5 6 1 8 1 FALSE 6.0 280 001 126 <NA> TRUE
#> 6 6 1 9 1 FALSE 6.0 125 208 280 <NA> TRUE
#> 7 6 1 8 2 FALSE 6.0 149 125 208 <NA> TRUE
#> 8 6 1 8 2 FALSE 6.0 126 149 125 <NA> TRUE
#> 9 6 1 8 2 FALSE 6.0 126 149 125 <NA> TRUE
#> 10 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> 11 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> 12 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> 13 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> 14 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> EventNum file_das line_num SightNo Subgroup SightNoDaily Obs ObsStd
#> 1 15 das_sample.das 15 1406 <NA> 20130113_1 208 TRUE
#> 2 35 das_sample.das 38 1407 <NA> 20130113_2 125 TRUE
#> 3 59 das_sample.das 65 <NA> <NA> <NA> 280 FALSE
#> 4 131 das_sample.das 137 <NA> <NA> <NA> 228 FALSE
#> 5 136 das_sample.das 142 1408 <NA> 20130113_3 280 TRUE
#> 6 153 das_sample.das 162 <NA> <NA> <NA> 231 FALSE
#> 7 167 das_sample.das 176 1409 <NA> 20130113_4 149 TRUE
#> 8 181 das_sample.das 193 1410 <NA> 20130113_5 125 TRUE
#> 9 181 das_sample.das 193 1410 <NA> 20130113_5 125 TRUE
#> 10 8 das_sample.das 238 <NA> <NA> <NA> 149 TRUE
#> 11 18 das_sample.das 248 1412 <NA> 20130114_1 149 TRUE
#> 12 18 das_sample.das 248 1412 <NA> 20130114_1 149 TRUE
#> 13 20 das_sample.das 252 1413 <NA> 20130114_2 208 TRUE
#> 14 20 das_sample.das 252 1413 <NA> 20130114_2 208 TRUE
#> Bearing Reticle DistNm Cue Method Photos Birds CalibSchool PhotosAerial
#> 1 309 2.8 1.06 3 4 N N <NA> <NA>
#> 2 326 0.4 2.97 3 4 Y N <NA> <NA>
#> 3 120 NA 0.03 NA NA <NA> <NA> <NA> <NA>
#> 4 300 NA 0.02 NA NA <NA> <NA> <NA> <NA>
#> 5 270 14.0 0.28 3 4 N N <NA> <NA>
#> 6 45 NA 0.05 NA NA <NA> <NA> <NA> <NA>
#> 7 344 0.2 3.68 3 4 Y Y <NA> <NA>
#> 8 70 1.4 1.66 3 4 Y N <NA> <NA>
#> 9 70 1.4 1.66 3 4 Y N <NA> <NA>
#> 10 309 1.7 1.47 NA NA <NA> <NA> <NA> <NA>
#> 11 359 0.3 3.28 2 4 Y N <NA> <NA>
#> 12 359 0.3 3.28 2 4 Y N <NA> <NA>
#> 13 38 0.8 2.23 3 4 Y N <NA> <NA>
#> 14 38 0.8 2.23 3 4 Y N <NA> <NA>
#> Biopsy Prob nSp Mixed SpCode SpCodeProb GsSchoolBest GsSchoolHigh
#> 1 <NA> FALSE 1 FALSE 018 <NA> NA NA
#> 2 <NA> FALSE 1 FALSE 076 <NA> 8.00000 14.00
#> 3 <NA> NA NA NA LV <NA> 1.00000 NA
#> 4 <NA> NA NA NA DC <NA> 1.00000 NA
#> 5 <NA> FALSE 1 FALSE 037 <NA> 10.66667 20.00
#> 6 <NA> NA NA NA DC <NA> 1.00000 NA
#> 7 <NA> FALSE 1 FALSE 016 <NA> 46.66667 79.00
#> 8 <NA> FALSE 2 TRUE 013 <NA> 41.75000 72.75
#> 9 <NA> FALSE 2 TRUE 016 <NA> 41.75000 72.75
#> 10 <NA> NA NA NA <NA> <NA> NA NA
#> 11 <NA> FALSE 2 TRUE 018 <NA> 151.50000 249.00
#> 12 <NA> FALSE 2 TRUE 277 <NA> 151.50000 249.00
#> 13 <NA> TRUE 2 TRUE 016 016 21.25000 37.75
#> 14 <NA> TRUE 2 TRUE 277 016 21.25000 37.75
#> GsSchoolLow GsSpBest GsSpHigh GsSpLow CourseSchool TurtleJFR TurtleAge
#> 1 42.333333 NA NA 42.333333 NA <NA> <NA>
#> 2 5.666667 8.00000 14.0000 5.666667 NA <NA> <NA>
#> 3 NA 1.00000 NA NA NA <NA> A
#> 4 NA 1.00000 NA NA NA <NA> J
#> 5 10.666667 10.66667 20.0000 10.666667 NA <NA> <NA>
#> 6 NA 1.00000 NA NA NA <NA> A
#> 7 46.666667 46.66667 79.0000 46.666667 NA <NA> <NA>
#> 8 41.750000 30.06000 52.3800 30.060000 NA <NA> <NA>
#> 9 41.750000 11.69000 20.3700 11.690000 NA <NA> <NA>
#> 10 NA NA NA NA NA <NA> <NA>
#> 11 151.500000 128.77500 211.6500 128.775000 NA <NA> <NA>
#> 12 151.500000 22.72500 37.3500 22.725000 NA <NA> <NA>
#> 13 21.250000 15.08750 26.8025 15.087500 NA <NA> <NA>
#> 14 21.250000 6.16250 10.9475 6.162500 NA <NA> <NA>
#> TurtleCapt PerpDistKm included
#> 1 <NA> 1.52563078 TRUE
#> 2 <NA> 3.07580701 TRUE
#> 3 N 0.04811637 FALSE
#> 4 N 0.03207758 FALSE
#> 5 <NA> 0.51856000 TRUE
#> 6 <NA> 0.06547809 FALSE
#> 7 <NA> 1.87856781 TRUE
#> 8 <NA> 2.88891582 TRUE
#> 9 <NA> 2.88891582 TRUE
#> 10 <NA> 2.11573325 TRUE
#> 11 <NA> 0.10601569 TRUE
#> 12 <NA> 0.10601569 TRUE
#> 13 <NA> 2.54265727 TRUE
#> 14 <NA> 2.54265727 TRUE
#>
#> $randpicks
#> effort_section randpicks
#> 1 1 NA
#> 2 2 1
#> 3 3 2
#> 4 4 2
#> 5 5 1
#> 6 6 1
#> 7 7 NA
#> 8 8 NA
#> 9 9 NA
#> 10 10 NA
#>
# 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
)
#> although coordinates are longitude/latitude, st_intersection assumes that they
#> are planar
#> $segdata
#> segnum section_id section_sub_id file stlin endlin lat1
#> 1 1 1 1 das_sample.das 2 20 39.32033
#> 2 2 2 1 das_sample.das 23 43 39.37617
#> 3 3 3 1 das_sample.das 59 90 39.56800
#> 4 4 4 1 das_sample.das 99 121 39.94517
#> 5 5 5 1 das_sample.das 127 147 40.15217
#> 6 6 6 1 das_sample.das 150 164 40.26867
#> 7 7 7 1 das_sample.das 167 181 40.38250
#> 8 8 8 1 das_sample.das 188 199 40.52200
#> 9 9 9 1 das_sample.das 232 240 40.98717
#> 10 10 10 1 das_sample.das 242 259 41.02383
#> lon1 DateTime1 lat2 lon2 DateTime2
#> 1 -137.6043 2013-01-13 06:27:39 39.36716 -137.5817 2013-01-13 06:46:25
#> 2 -137.5978 2013-01-13 06:58:04 39.51933 -137.5277 2013-01-13 07:57:05
#> 3 -137.4530 2013-01-13 09:22:13 39.75433 -137.4107 2013-01-13 10:36:27
#> 4 -137.3692 2013-01-13 11:51:51 40.12745 -137.2488 2013-01-13 13:16:38
#> 5 -137.1737 2013-01-13 13:50:07 40.26617 -137.1348 2013-01-13 14:38:13
#> 6 -137.1268 2013-01-13 14:59:19 40.37596 -137.0915 2013-01-13 15:43:08
#> 7 -137.0977 2013-01-13 15:58:41 40.45133 -137.0628 2013-01-13 16:29:50
#> 8 -137.0533 2013-01-13 16:59:54 40.52533 -137.0515 2013-01-13 17:01:21
#> 9 -135.5980 2013-01-14 11:24:32 41.01582 -135.5782 2013-01-14 11:37:24
#> 10 -135.5743 2013-01-14 11:40:38 41.04599 -135.5595 2013-01-14 11:50:29
#> mlat mlon mDateTime dist year month day mtime
#> 1 39.34377 -137.5930 2013-01-13 06:37:02 5.5577 2013 1 13 06:37:02
#> 2 39.44767 -137.5625 2013-01-13 07:27:34 17.0106 2013 1 13 07:27:34
#> 3 39.66117 -137.4131 2013-01-13 09:59:20 21.8086 2013 1 13 09:59:20
#> 4 40.03679 -137.3101 2013-01-13 12:34:14 22.7090 2013 1 13 12:34:14
#> 5 40.20895 -137.1531 2013-01-13 14:14:10 13.0922 2013 1 13 14:14:10
#> 6 40.32254 -137.1102 2013-01-13 15:21:13 12.3011 2013 1 13 15:21:13
#> 7 40.41714 -137.0810 2013-01-13 16:14:15 8.1984 2013 1 13 16:14:15
#> 8 40.52365 -137.0524 2013-01-13 17:00:37 0.4016 2013 1 13 17:00:37
#> 9 41.00151 -135.5881 2013-01-14 11:30:58 3.5940 2013 1 14 11:30:58
#> 10 41.03500 -135.5671 2013-01-14 11:45:33 2.7600 2013 1 14 11:45:33
#> Cruise Mode EffType ESWsides avgBft avgSwellHght avgHorizSun avgVertSun
#> 1 1000 C S 2 3.000000 3 2.000000 3
#> 2 1000 C S 2 3.000000 3 2.000000 2
#> 3 1000 C S 2 2.500835 3 2.000000 2
#> 4 1000 C S 2 3.000000 3 12.000000 12
#> 5 1000 C S 2 3.000000 3 8.000000 1
#> 6 1000 C S 2 2.479576 3 8.520424 1
#> 7 1000 C S 2 2.682617 3 8.000000 2
#> 8 1000 C S 2 2.000000 3 8.000000 2
#> 9 1000 C S 2 2.000000 1 NA NA
#> 10 1000 C S 2 2.000000 1 NA NA
#> avgGlare avgVis avgCourse avgSpdKt stratum
#> 1 0 6.000000 23.56198 9.912395 <NA>
#> 2 0 5.686447 27.11868 9.476263 <NA>
#> 3 0 5.090748 95.65614 9.287725 <NA>
#> 4 0 5.974103 34.61155 9.428340 <NA>
#> 5 0 6.000000 20.43919 9.343919 Stratum1
#> 6 0 6.000000 16.47958 9.091830 Stratum1
#> 7 0 6.000000 25.00000 8.900000 Stratum1
#> 8 0 6.000000 30.00000 9.500000 Stratum1
#> 9 NA 4.000000 35.00000 9.500000 Stratum1
#> 10 NA 4.000000 23.00000 9.600000 Stratum1
#>
#> $sightinfo
#> segnum mlat mlon Event DateTime year Lat Lon
#> 1 1 39.34377 -137.5930 S 2013-01-13 06:46:02 2013 39.36617 -137.5820
#> 2 2 39.44767 -137.5625 S 2013-01-13 07:56:22 2013 39.51767 -137.5285
#> 3 3 39.66117 -137.4131 t 2013-01-13 09:34:27 2013 39.59733 -137.4400
#> 4 5 40.20895 -137.1531 t 2013-01-13 14:02:55 2013 40.18283 -137.1622
#> 5 5 40.20895 -137.1531 S 2013-01-13 14:37:56 2013 40.26567 -137.1350
#> 6 6 40.32254 -137.1102 t 2013-01-13 15:36:47 2013 40.36050 -137.0978
#> 7 7 40.41714 -137.0810 S 2013-01-13 16:29:50 2013 40.45133 -137.0628
#> 8 8 40.52365 -137.0524 S 2013-01-13 17:00:45 2013 40.52400 -137.0522
#> 9 8 40.52365 -137.0524 S 2013-01-13 17:00:45 2013 40.52400 -137.0522
#> 10 9 41.00151 -135.5881 F 2013-01-14 11:25:32 2013 40.98950 -135.5965
#> 11 10 41.03500 -135.5671 S 2013-01-14 11:47:51 2013 41.04017 -135.5635
#> 12 10 41.03500 -135.5671 S 2013-01-14 11:47:51 2013 41.04017 -135.5635
#> 13 10 41.03500 -135.5671 S 2013-01-14 11:49:14 2013 41.04333 -135.5615
#> 14 10 41.03500 -135.5671 S 2013-01-14 11:49:14 2013 41.04333 -135.5615
#> OnEffort Cruise Mode OffsetGMT EffType ESWsides Course SpdKt Bft SwellHght
#> 1 TRUE 1000 C 5 S 2 25 10.2 3 3
#> 2 TRUE 1000 C 5 S 2 26 9.7 3 3
#> 3 TRUE 1000 C 5 S 2 27 9.0 3 3
#> 4 TRUE 1000 C 5 S 2 20 9.3 3 3
#> 5 TRUE 1000 C 5 S 2 20 9.3 3 3
#> 6 TRUE 1000 C 5 S 2 16 8.9 2 3
#> 7 TRUE 1000 C 5 S 2 25 8.9 2 3
#> 8 TRUE 1000 C 5 S 2 30 9.5 2 3
#> 9 TRUE 1000 C 5 S 2 30 9.5 2 3
#> 10 TRUE 1000 C 5 S 2 35 9.5 2 1
#> 11 TRUE 1000 C 5 S 2 23 9.6 2 1
#> 12 TRUE 1000 C 5 S 2 23 9.6 2 1
#> 13 TRUE 1000 C 5 S 2 23 9.6 2 1
#> 14 TRUE 1000 C 5 S 2 23 9.6 2 1
#> WindSpdKt RainFog HorizSun VertSun Glare Vis ObsL Rec ObsR ObsInd EffortDot
#> 1 10 1 NA NA NA 6.0 208 280 001 <NA> TRUE
#> 2 10 3 2 2 FALSE 5.5 125 208 280 <NA> TRUE
#> 3 10 1 2 2 FALSE 5.5 001 126 149 <NA> TRUE
#> 4 6 1 8 1 FALSE 6.0 280 001 126 <NA> TRUE
#> 5 6 1 8 1 FALSE 6.0 280 001 126 <NA> TRUE
#> 6 6 1 9 1 FALSE 6.0 125 208 280 <NA> TRUE
#> 7 6 1 8 2 FALSE 6.0 149 125 208 <NA> TRUE
#> 8 6 1 8 2 FALSE 6.0 126 149 125 <NA> TRUE
#> 9 6 1 8 2 FALSE 6.0 126 149 125 <NA> TRUE
#> 10 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> 11 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> 12 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> 13 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> 14 5 3 NA NA NA 4.0 149 125 208 <NA> TRUE
#> EventNum file_das line_num SightNo Subgroup SightNoDaily Obs ObsStd
#> 1 15 das_sample.das 15 1406 <NA> 20130113_1 208 TRUE
#> 2 35 das_sample.das 38 1407 <NA> 20130113_2 125 TRUE
#> 3 59 das_sample.das 65 <NA> <NA> <NA> 280 FALSE
#> 4 131 das_sample.das 137 <NA> <NA> <NA> 228 FALSE
#> 5 136 das_sample.das 142 1408 <NA> 20130113_3 280 TRUE
#> 6 153 das_sample.das 162 <NA> <NA> <NA> 231 FALSE
#> 7 167 das_sample.das 176 1409 <NA> 20130113_4 149 TRUE
#> 8 181 das_sample.das 193 1410 <NA> 20130113_5 125 TRUE
#> 9 181 das_sample.das 193 1410 <NA> 20130113_5 125 TRUE
#> 10 8 das_sample.das 238 <NA> <NA> <NA> 149 TRUE
#> 11 18 das_sample.das 248 1412 <NA> 20130114_1 149 TRUE
#> 12 18 das_sample.das 248 1412 <NA> 20130114_1 149 TRUE
#> 13 20 das_sample.das 252 1413 <NA> 20130114_2 208 TRUE
#> 14 20 das_sample.das 252 1413 <NA> 20130114_2 208 TRUE
#> Bearing Reticle DistNm Cue Method Photos Birds CalibSchool PhotosAerial
#> 1 309 2.8 1.06 3 4 N N <NA> <NA>
#> 2 326 0.4 2.97 3 4 Y N <NA> <NA>
#> 3 120 NA 0.03 NA NA <NA> <NA> <NA> <NA>
#> 4 300 NA 0.02 NA NA <NA> <NA> <NA> <NA>
#> 5 270 14.0 0.28 3 4 N N <NA> <NA>
#> 6 45 NA 0.05 NA NA <NA> <NA> <NA> <NA>
#> 7 344 0.2 3.68 3 4 Y Y <NA> <NA>
#> 8 70 1.4 1.66 3 4 Y N <NA> <NA>
#> 9 70 1.4 1.66 3 4 Y N <NA> <NA>
#> 10 309 1.7 1.47 NA NA <NA> <NA> <NA> <NA>
#> 11 359 0.3 3.28 2 4 Y N <NA> <NA>
#> 12 359 0.3 3.28 2 4 Y N <NA> <NA>
#> 13 38 0.8 2.23 3 4 Y N <NA> <NA>
#> 14 38 0.8 2.23 3 4 Y N <NA> <NA>
#> Biopsy Prob nSp Mixed SpCode SpCodeProb GsSchoolBest GsSchoolHigh
#> 1 <NA> FALSE 1 FALSE 018 <NA> NA NA
#> 2 <NA> FALSE 1 FALSE 076 <NA> 8.00000 14.00
#> 3 <NA> NA NA NA LV <NA> 1.00000 NA
#> 4 <NA> NA NA NA DC <NA> 1.00000 NA
#> 5 <NA> FALSE 1 FALSE 037 <NA> 10.66667 20.00
#> 6 <NA> NA NA NA DC <NA> 1.00000 NA
#> 7 <NA> FALSE 1 FALSE 016 <NA> 46.66667 79.00
#> 8 <NA> FALSE 2 TRUE 013 <NA> 41.75000 72.75
#> 9 <NA> FALSE 2 TRUE 016 <NA> 41.75000 72.75
#> 10 <NA> NA NA NA <NA> <NA> NA NA
#> 11 <NA> FALSE 2 TRUE 018 <NA> 151.50000 249.00
#> 12 <NA> FALSE 2 TRUE 277 <NA> 151.50000 249.00
#> 13 <NA> TRUE 2 TRUE 016 016 21.25000 37.75
#> 14 <NA> TRUE 2 TRUE 277 016 21.25000 37.75
#> GsSchoolLow GsSpBest GsSpHigh GsSpLow CourseSchool TurtleJFR TurtleAge
#> 1 42.333333 NA NA 42.333333 NA <NA> <NA>
#> 2 5.666667 8.00000 14.0000 5.666667 NA <NA> <NA>
#> 3 NA 1.00000 NA NA NA <NA> A
#> 4 NA 1.00000 NA NA NA <NA> J
#> 5 10.666667 10.66667 20.0000 10.666667 NA <NA> <NA>
#> 6 NA 1.00000 NA NA NA <NA> A
#> 7 46.666667 46.66667 79.0000 46.666667 NA <NA> <NA>
#> 8 41.750000 30.06000 52.3800 30.060000 NA <NA> <NA>
#> 9 41.750000 11.69000 20.3700 11.690000 NA <NA> <NA>
#> 10 NA NA NA NA NA <NA> <NA>
#> 11 151.500000 128.77500 211.6500 128.775000 NA <NA> <NA>
#> 12 151.500000 22.72500 37.3500 22.725000 NA <NA> <NA>
#> 13 21.250000 15.08750 26.8025 15.087500 NA <NA> <NA>
#> 14 21.250000 6.16250 10.9475 6.162500 NA <NA> <NA>
#> TurtleCapt PerpDistKm included
#> 1 <NA> 1.52563078 TRUE
#> 2 <NA> 3.07580701 TRUE
#> 3 N 0.04811637 FALSE
#> 4 N 0.03207758 FALSE
#> 5 <NA> 0.51856000 TRUE
#> 6 <NA> 0.06547809 FALSE
#> 7 <NA> 1.87856781 TRUE
#> 8 <NA> 2.88891582 TRUE
#> 9 <NA> 2.88891582 TRUE
#> 10 <NA> 2.11573325 TRUE
#> 11 <NA> 0.10601569 TRUE
#> 12 <NA> 0.10601569 TRUE
#> 13 <NA> 2.54265727 TRUE
#> 14 <NA> 2.54265727 TRUE
#>
#> $randpicks
#> effort_section randpicks
#> 1 1 NA
#> 2 2 NA
#> 3 3 NA
#> 4 4 NA
#> 5 5 NA
#> 6 6 NA
#> 7 7 NA
#> 8 8 NA
#> 9 9 NA
#> 10 10 NA
#>
# }