Summarize number of sightings and animals for selected species by segment
Arguments
- x.list
output of
das_effort; a list of three data frames named 'segdata', 'sightinfo', and 'randpicks', respectively- sp.codes
character; species code(s) to include in segdata output. These must exactly match the species codes in the data, such as including leading zeros
- sp.events
character; event code(s) to include in the sightinfo output. This argument supersedes the 'included' value when determining whether a sighting is included in the segment summaries. Must be one or more of: "S", "K", "M", "G", "t", "p" (case-sensitive). The default is that all of these event codes are kept
- gs.columns
character; the column(s) to use to get the group size values that will be summarized in the segdata output. Must be one or more of 'GsSpBest', 'GsSpLow', and 'GsSpBest' (case-sensitive). See Details section for more information
Value
A list, identical to x.list except for
1) the nSI and ANI columns added to x.list$segdata,
one each for each element of sp.codes, and
2) the 'included' column of x.list$sightinfo, which has been set as
FALSE for sightings of species not listed in sp.codes.
Thus, the 'included' column in the output accurately reflects
the sightings that were included in the effort segment summaries
Details
This function takes the output of das_effort and
adds columns for the number of sightings (nSI) and number of animals (ANI)
for selected species (selected via sp.codes) for each segment
to the segdata element of x.list.
However, only sightings with an included value of TRUE
(included is a column in sightinfo) are included in the summaries.
Having this step separate from das_effort allows users to
personalize the included values as desired for their analysis.
The ANI columns are the sum of the 'GsSp...' column(s) from
das_sight specified using gs.columns.
If gs.columns specifies more than one column,
then the secondary columns will only be used if
the values for the previous columns are NA.
For instance, if gs.columns = c('GsSpBest', 'GsSpLow'),
then for each row in sightinfo, the value from GsSpLow
will be used only if the value from GsSpBest is NA
Examples
y <- system.file("das_sample.das", package = "swfscDAS")
y.proc <- das_process(y)
y.eff.cond <- das_effort(
y.proc, method = "condition", conditions = "Bft", seg.min.km = 0.05,
num.cores = 1
)
das_effort_sight(y.eff.cond, sp.codes = c("013", "076", "DC"), sp.events = c("S", "t"))
#> $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 70 39.56800
#> 4 4 3 2 das_sample.das 70 90 39.66133
#> 5 5 4 1 das_sample.das 99 121 39.94517
#> 6 6 5 1 das_sample.das 127 147 40.15217
#> 7 7 6 1 das_sample.das 150 160 40.26867
#> 8 8 6 2 das_sample.das 160 164 40.32033
#> 9 9 7 1 das_sample.das 167 174 40.38250
#> 10 10 7 2 das_sample.das 174 181 40.42965
#> 11 11 8 1 das_sample.das 188 199 40.52200
#> 12 12 9 1 das_sample.das 232 240 40.98717
#> 13 13 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 39.66133 -137.4130 2013-01-13 09:59:50
#> 4 -137.4130 2013-01-13 09:59:50 39.75433 -137.4107 2013-01-13 10:36:27
#> 5 -137.3692 2013-01-13 11:51:51 40.12745 -137.2488 2013-01-13 13:16:38
#> 6 -137.1737 2013-01-13 13:50:07 40.26617 -137.1348 2013-01-13 14:38:13
#> 7 -137.1268 2013-01-13 14:59:19 40.32033 -137.1108 2013-01-13 15:20:26
#> 8 -137.1108 2013-01-13 15:20:26 40.37596 -137.0915 2013-01-13 15:43:08
#> 9 -137.0977 2013-01-13 15:58:41 40.42965 -137.0745 2013-01-13 16:20:02
#> 10 -137.0745 2013-01-13 16:20:02 40.45133 -137.0628 2013-01-13 16:29:50
#> 11 -137.0533 2013-01-13 16:59:54 40.52533 -137.0515 2013-01-13 17:01:21
#> 12 -135.5980 2013-01-14 11:24:32 41.01582 -135.5782 2013-01-14 11:37:24
#> 13 -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 39.61458 -137.4326 2013-01-13 09:41:01 10.9225 2013 1 13 09:41:01
#> 4 39.70804 -137.3939 2013-01-13 10:18:08 10.8861 2013 1 13 10:18:08
#> 5 40.03679 -137.3101 2013-01-13 12:34:14 22.7090 2013 1 13 12:34:14
#> 6 40.20895 -137.1531 2013-01-13 14:14:10 13.0922 2013 1 13 14:14:10
#> 7 40.29448 -137.1187 2013-01-13 15:09:52 5.8993 2013 1 13 15:09:52
#> 8 40.34833 -137.1019 2013-01-13 15:31:47 6.4018 2013 1 13 15:31:47
#> 9 40.40618 -137.0864 2013-01-13 16:09:21 5.5964 2013 1 13 16:09:21
#> 10 40.44053 -137.0687 2013-01-13 16:24:56 2.6020 2013 1 13 16:24:56
#> 11 40.52365 -137.0524 2013-01-13 17:00:37 0.4016 2013 1 13 17:00:37
#> 12 41.00151 -135.5881 2013-01-14 11:30:58 3.5940 2013 1 14 11:30:58
#> 13 41.03500 -135.5671 2013-01-14 11:45:33 2.7600 2013 1 14 11:45:33
#> Cruise Mode EffType ESWsides maxdistBft nSI_013 ANI_013 nSI_076 ANI_076
#> 1 1000 C S 2 3 0 0.00 0 0
#> 2 1000 C S 2 3 0 0.00 1 8
#> 3 1000 C S 2 3 0 0.00 0 0
#> 4 1000 C S 2 2 0 0.00 0 0
#> 5 1000 C S 2 3 0 0.00 0 0
#> 6 1000 C S 2 3 0 0.00 0 0
#> 7 1000 C S 2 3 0 0.00 0 0
#> 8 1000 C S 2 2 0 0.00 0 0
#> 9 1000 C S 2 3 0 0.00 0 0
#> 10 1000 C S 2 2 0 0.00 0 0
#> 11 1000 C S 2 2 1 30.06 0 0
#> 12 1000 C S 2 2 0 0.00 0 0
#> 13 1000 C S 2 2 0 0.00 0 0
#> nSI_DC ANI_DC
#> 1 0 0
#> 2 0 0
#> 3 0 0
#> 4 0 0
#> 5 0 0
#> 6 0 0
#> 7 0 0
#> 8 0 0
#> 9 0 0
#> 10 0 0
#> 11 0 0
#> 12 0 0
#> 13 0 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 2 39.44767 -137.5625 S 2013-01-13 07:56:22 2013 39.51767 -137.5285
#> 3 3 39.61458 -137.4326 t 2013-01-13 09:34:27 2013 39.59733 -137.4400
#> 4 6 40.20895 -137.1531 t 2013-01-13 14:02:55 2013 40.18283 -137.1622
#> 5 6 40.20895 -137.1531 S 2013-01-13 14:37:56 2013 40.26567 -137.1350
#> 6 8 40.34833 -137.1019 t 2013-01-13 15:36:47 2013 40.36050 -137.0978
#> 7 10 40.44053 -137.0687 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 13 41.03500 -135.5671 S 2013-01-14 11:47:51 2013 41.04017 -135.5635
#> 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 11:49:14 2013 41.04333 -135.5615
#> 13 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 23 9.6 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
#> 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
#> 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 18 das_sample.das 248 1412 <NA> 20130114_1 149 TRUE
#> 11 18 das_sample.das 248 1412 <NA> 20130114_1 149 TRUE
#> 12 20 das_sample.das 252 1413 <NA> 20130114_2 208 TRUE
#> 13 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>
#> 10 359 0.3 3.28 2 4 Y N <NA> <NA>
#> 11 359 0.3 3.28 2 4 Y N <NA> <NA>
#> 12 38 0.8 2.23 3 4 Y N <NA> <NA>
#> 13 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> FALSE 2 TRUE 018 <NA> 151.50000 249.00
#> 11 <NA> FALSE 2 TRUE 277 <NA> 151.50000 249.00
#> 12 <NA> TRUE 2 TRUE 016 016 21.25000 37.75
#> 13 <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>
#> 10 151.500000 128.77500 211.6500 128.775000 NA <NA> <NA>
#> 11 151.500000 22.72500 37.3500 22.725000 NA <NA> <NA>
#> 12 21.250000 15.08750 26.8025 15.087500 NA <NA> <NA>
#> 13 21.250000 6.16250 10.9475 6.162500 NA <NA> <NA>
#> TurtleCapt PerpDistKm included
#> 1 <NA> 1.52563078 FALSE
#> 2 <NA> 3.07580701 TRUE
#> 3 N 0.04811637 FALSE
#> 4 N 0.03207758 FALSE
#> 5 <NA> 0.51856000 FALSE
#> 6 <NA> 0.06547809 FALSE
#> 7 <NA> 1.87856781 FALSE
#> 8 <NA> 2.88891582 TRUE
#> 9 <NA> 2.88891582 FALSE
#> 10 <NA> 0.10601569 FALSE
#> 11 <NA> 0.10601569 FALSE
#> 12 <NA> 2.54265727 FALSE
#> 13 <NA> 2.54265727 FALSE
#>
#> $randpicks
#> NULL
#>