This document introduces you to the swfscDAS package, and
specifically its functionality and workflow. This package is intended to
standardize and streamline processing of DAS data. It was designed for
shipboard DAS data generated using the WinCruz software from the
Southwest Fisheries Science Center. However, the DAS files and the
swfscDAS package are and can be used by others, e.g. to
process decades of NOAA Fisheries survey data.
In DAS data, an event is only recorded when something changes or happens, which can complicate processing. Thus, the main theme of this package is enabling analyses and downstream processing by 1) determining associated state and condition information for each event and 2) pulling out event-specific information from the Data (Field) columns.
Data
This package includes a sample DAS file, which we will use in this document
y <- system.file("das_sample.das", package = "swfscDAS")
head(readLines(y))
#> [1] "1 B.062739 011313 N39:19.22 W137:36.26 1000 c 5 Y "
#> [2] "2 R.062739 011313 N39:19.22 W137:36.26 S "
#> [3] "3 P.062739 011313 N39:19.22 W137:36.26 280 001 126 "
#> [4] "4 V.062739 011313 N39:19.22 W137:36.26 3 03 230 10.0 "
#> [5] "5 N.062739 011313 N39:19.22 W137:36.26 023 09.8 "
#> [6] "6 W.062739 011313 N39:19.22 W137:36.26 1 250 6.0 "Check data format
The first step in processing DAS data is to ensure that the DAS file
has expected formatting and values. This package contains the
das_check function, which performs some basic checks. This
function is a precursor to a more comprehensive DASCHECK program, which
is currently in development. The checks performed by this function are
detailed in the function documentation, which can be accessed by running
?das_check. You can find the PDF with the expected DAS data
format at https://swfsc.github.io/swfscDAS/, or see
?das_format_pdf for how to access a local copy. To check
for valid species codes using this function, you can pass the function
an SpCodes.dat file
# Code not run
y.check <- das_check(y, skip = 0, print.cruise.nums = TRUE)Read and process data
Once QA/QC is complete and you have fixed any data entry errors, you can begin to process the DAS data. The backbone of this package is the reading and processing steps: 1) the data from the DAS file are read into the columns of a data frame and 2) state and condition information are extracted for each event. This means that after processing, you can simply look at any event (row) and determine the Beaufort, viewing conditions, etc., at the time of the event. All other functions in the package depend on the DAS data being in this processed state.
One other processing note is that the ‘DateTime’ column in the
das_read output has a time zone of “UTC” - this default
time zone value is meaningless and only present because POSIXct vectors
must have exactly one time zone. If you need to do conversions based on
time zone, then use the ‘OffsetGMT’ column and force_tz and
with_tz from the lubridate package. One reason
to do such conversions would be if data was collected in a non-local
time zone that causes the daily recorded effort to span more than one
day, since then ‘OnEffort’ values will be incorrect because
das_process resets the event sequence to off effort at the
beginning of a new day.
# Read
y.read <- das_read(y, skip = 0)
glimpse(y.read)
#> Rows: 259
#> Columns: 20
#> $ Event <chr> "B", "R", "P", "V", "N", "W", "V", "W", "W", "*", "P", "V", …
#> $ EffortDot <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, …
#> $ DateTime <dttm> 2013-01-13 06:27:39, 2013-01-13 06:27:39, 2013-01-13 06:27:…
#> $ Lat <dbl> 39.32033, 39.32033, 39.32033, 39.32033, 39.32033, 39.32033, …
#> $ Lon <dbl> -137.6043, -137.6043, -137.6043, -137.6043, -137.6043, -137.…
#> $ Data1 <chr> "1000", "S", "280", "3", "023", "1", "3", "1", "1", NA, "208…
#> $ Data2 <chr> "c", NA, "001", "03", "09.8", NA, "03", "02", NA, NA, "280",…
#> $ Data3 <chr> "5", NA, "126", "230", NA, NA, "230", "03", NA, NA, "001", "…
#> $ Data4 <chr> "Y", NA, NA, NA, NA, "250", NA, "257", "257", NA, NA, NA, NA…
#> $ Data5 <chr> NA, NA, NA, "10.0", NA, "6.0", "10.0", "6.0", "6.0", NA, NA,…
#> $ Data6 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "2.8…
#> $ Data7 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "1.0…
#> $ Data8 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "013…
#> $ Data9 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ Data10 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ Data11 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ Data12 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ EventNum <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12…
#> $ file_das <chr> "das_sample.das", "das_sample.das", "das_sample.das", "das_s…
#> $ line_num <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1…
# Process
y.proc <- das_process(y)
glimpse(y.proc)
#> Rows: 256
#> Columns: 40
#> $ Event <chr> "B", "R", "P", "V", "N", "W", "V", "W", "W", "*", "P", "V", …
#> $ DateTime <dttm> 2013-01-13 06:27:39, 2013-01-13 06:27:39, 2013-01-13 06:27:…
#> $ Lat <dbl> 39.32033, 39.32033, 39.32033, 39.32033, 39.32033, 39.32033, …
#> $ Lon <dbl> -137.6043, -137.6043, -137.6043, -137.6043, -137.6043, -137.…
#> $ OnEffort <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, …
#> $ Cruise <dbl> 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, …
#> $ Mode <chr> "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", …
#> $ OffsetGMT <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, …
#> $ EffType <chr> NA, "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "…
#> $ ESWsides <dbl> NA, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…
#> $ Course <dbl> NA, NA, NA, NA, 23, 23, 23, 23, 23, 23, 23, 23, 25, 25, 25, …
#> $ SpdKt <dbl> NA, NA, NA, NA, 9.8, 9.8, 9.8, 9.8, 9.8, 9.8, 9.8, 9.8, 10.2…
#> $ Bft <dbl> NA, NA, NA, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, …
#> $ SwellHght <dbl> NA, NA, NA, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, …
#> $ WindSpdKt <dbl> NA, NA, NA, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, …
#> $ RainFog <dbl> NA, NA, NA, NA, NA, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
#> $ HorizSun <dbl> NA, NA, NA, NA, NA, NA, NA, 2, NA, NA, NA, NA, NA, NA, NA, N…
#> $ VertSun <dbl> NA, NA, NA, NA, NA, NA, NA, 3, NA, NA, NA, NA, NA, NA, NA, N…
#> $ Glare <lgl> NA, NA, NA, NA, NA, NA, NA, FALSE, NA, NA, NA, NA, NA, NA, N…
#> $ Vis <dbl> NA, NA, NA, NA, NA, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6…
#> $ ObsL <chr> NA, NA, "280", "280", "280", "280", "280", "280", "280", "28…
#> $ Rec <chr> NA, NA, "001", "001", "001", "001", "001", "001", "001", "00…
#> $ ObsR <chr> NA, NA, "126", "126", "126", "126", "126", "126", "126", "12…
#> $ ObsInd <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ Data1 <chr> "1000", "S", "280", "3", "023", "1", "3", "1", "1", NA, "208…
#> $ Data2 <chr> "c", NA, "001", "03", "09.8", NA, "03", "02", NA, NA, "280",…
#> $ Data3 <chr> "5", NA, "126", "230", NA, NA, "230", "03", NA, NA, "001", "…
#> $ Data4 <chr> "Y", NA, NA, NA, NA, "250", NA, "257", "257", NA, NA, NA, NA…
#> $ Data5 <chr> NA, NA, NA, "10.0", NA, "6.0", "10.0", "6.0", "6.0", NA, NA,…
#> $ Data6 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "2.8…
#> $ Data7 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "1.0…
#> $ Data8 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "013…
#> $ Data9 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ Data10 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ Data11 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ Data12 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ EffortDot <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, …
#> $ EventNum <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12…
#> $ file_das <chr> "das_sample.das", "das_sample.das", "das_sample.das", "das_s…
#> $ line_num <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1…
# Note that das_read can read multiple files at once
y2.read <- das_read(c(y, y))Once you have processed the DAS data, you can easily access a variety of information. For instance, you can look at the different events or Beaufort values that occurred in the data, or filter for specific events to get the beginning and ending points of each effort section.
# The number of each event
table(y.proc$Event)
#>
#> ? * 1 2 3 4 A B C E F N P R s S t V W
#> 1 90 8 7 6 2 8 2 5 10 1 19 18 10 7 8 6 22 26
# The number of events per Beaufort value
table(y.proc$Bft)
#>
#> 2 3
#> 94 120
# Filter for R and E events to extract lat/lon points
y.proc %>%
filter(Event %in% c("R", "E")) %>%
select(Event, Lat, Lon, Cruise, Mode, EffType) %>%
head()
#> Event Lat Lon Cruise Mode EffType
#> 1 R 39.32033 -137.6043 1000 C S
#> 2 E 39.36717 -137.5817 1000 C S
#> 3 R 39.37617 -137.5978 1000 C S
#> 4 E 39.51933 -137.5277 1000 C S
#> 5 R 39.56800 -137.4530 1000 C S
#> 6 E 39.75433 -137.4107 1000 C SSightings
The swfscDAS package does contain specific functions for
extracting and/or summarizing particular information from the processed
data. First is das_sight, a function that returns a data
frame with pertinent sighting data pulled out to their own columns. Due
to the different data collected for each type of sighting and the
different needs of end-users, this function offers several different
return formats. For the “default” format there is one row for each
species of each sighting, for the “wide” format, there is one row for
each sighting event, and for the “complete” format there is one row for
every group size estimate for each sighting. See ?das_sight
for more details. The “complete” format is intended for users looking to
do observer corrections, or to combine estimates using a different
method than the arithmetic mean (e.g. the geometric mean)
y.sight <- das_sight(y.proc, return.format = "default")
y.sight %>%
select(Event, SightNo:PerpDistKm) %>%
glimpse()
#> Rows: 25
#> Columns: 32
#> $ Event <chr> "S", "S", "s", "s", "s", "s", "s", "s", "t", "t", "t", "S…
#> $ SightNo <chr> "1406", "1407", "1407", "1407", "1407", "1407", "1407", "…
#> $ Subgroup <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SightNoDaily <chr> "20130113_1", "20130113_2", NA, NA, NA, NA, NA, NA, NA, N…
#> $ Obs <chr> "208", "125", NA, NA, NA, NA, NA, NA, "280", "149", "228"…
#> $ ObsStd <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
#> $ Bearing <dbl> 309, 326, 11, 5, 50, 71, 104, 2, 120, 270, 300, 270, 45, …
#> $ Reticle <dbl> 2.8, 0.4, 2.0, 3.5, NA, 4.5, 4.5, 2.2, NA, NA, NA, 14.0, …
#> $ DistNm <dbl> 1.06, 2.97, 1.30, 0.90, 0.50, 0.70, 0.70, 1.30, 0.03, 0.0…
#> $ Cue <dbl> 3, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3, NA, 3, NA, 3…
#> $ Method <dbl> 4, 4, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4, NA, 4, NA, 4…
#> $ Photos <chr> "N", "Y", NA, NA, NA, NA, NA, NA, NA, NA, NA, "N", NA, "Y…
#> $ Birds <chr> "N", "N", NA, NA, NA, NA, NA, NA, NA, NA, NA, "N", NA, "Y…
#> $ CalibSchool <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ PhotosAerial <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ Biopsy <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ Prob <lgl> FALSE, FALSE, NA, NA, NA, NA, NA, NA, NA, NA, NA, FALSE, …
#> $ nSp <int> 1, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, 1, NA, 2…
#> $ Mixed <lgl> FALSE, FALSE, NA, NA, NA, NA, NA, NA, NA, NA, NA, FALSE, …
#> $ SpCode <chr> "018", "076", NA, NA, NA, NA, NA, NA, "LV", "DC", "DC", "…
#> $ SpCodeProb <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ GsSchoolBest <dbl> NA, 8.00000, NA, NA, NA, NA, NA, NA, 1.00000, 2.00000, 1.…
#> $ GsSchoolHigh <dbl> NA, 14.00, NA, NA, NA, NA, NA, NA, NA, NA, NA, 20.00, NA,…
#> $ GsSchoolLow <dbl> 42.333333, 5.666667, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ GsSpBest <dbl> NA, 8.00000, NA, NA, NA, NA, NA, NA, 1.00000, 2.00000, 1.…
#> $ GsSpHigh <dbl> NA, 14.0000, NA, NA, NA, NA, NA, NA, NA, NA, NA, 20.0000,…
#> $ GsSpLow <dbl> 42.333333, 5.666667, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ CourseSchool <dbl> NA, NA, NA, NA, NA, 100, 100, NA, NA, NA, NA, NA, NA, NA,…
#> $ TurtleJFR <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ TurtleAge <chr> NA, NA, NA, NA, NA, NA, NA, NA, "A", "A", "J", NA, "A", N…
#> $ TurtleCapt <chr> NA, NA, NA, NA, NA, NA, NA, NA, "N", "N", "N", NA, NA, NA…
#> $ PerpDistKm <dbl> 1.525631e+00, 3.075807e+00, 4.593917e-01, 1.452712e-01, 7…
y.sight.wide <- das_sight(y.proc, return.format = "wide")
y.sight.wide %>%
select(Event, SightNo:PerpDistKm) %>%
glimpse()
#> Rows: 22
#> Columns: 58
#> $ Event <chr> "S", "S", "s", "s", "s", "s", "s", "s", "t", "t", "t", "S…
#> $ SightNo <chr> "1406", "1407", "1407", "1407", "1407", "1407", "1407", "…
#> $ Subgroup <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SightNoDaily <chr> "20130113_1", "20130113_2", NA, NA, NA, NA, NA, NA, NA, N…
#> $ Obs <chr> "208", "125", NA, NA, NA, NA, NA, NA, "280", "149", "228"…
#> $ ObsStd <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…
#> $ Bearing <dbl> 309, 326, 11, 5, 50, 71, 104, 2, 120, 270, 300, 270, 45, …
#> $ Reticle <dbl> 2.8, 0.4, 2.0, 3.5, NA, 4.5, 4.5, 2.2, NA, NA, NA, 14.0, …
#> $ DistNm <dbl> 1.06, 2.97, 1.30, 0.90, 0.50, 0.70, 0.70, 1.30, 0.03, 0.0…
#> $ Cue <dbl> 3, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3, NA, 3, NA, 3…
#> $ Method <dbl> 4, 4, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4, NA, 4, NA, 4…
#> $ Photos <chr> "N", "Y", NA, NA, NA, NA, NA, NA, NA, NA, NA, "N", NA, "Y…
#> $ Birds <chr> "N", "N", NA, NA, NA, NA, NA, NA, NA, NA, NA, "N", NA, "Y…
#> $ CalibSchool <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ PhotosAerial <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ Biopsy <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ Prob <lgl> FALSE, FALSE, NA, NA, NA, NA, NA, NA, NA, NA, NA, FALSE, …
#> $ nSp <int> 1, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, NA, 1, NA, 2…
#> $ Mixed <lgl> FALSE, FALSE, NA, NA, NA, NA, NA, NA, NA, NA, NA, FALSE, …
#> $ ObsEstimate <list> <"280", "001", "208">, <"280", "001", "125">, <NULL>, <N…
#> $ SpCode1 <chr> "018", "076", NA, NA, NA, NA, NA, NA, NA, NA, NA, "037", …
#> $ SpCode2 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SpCode3 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SpCode4 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SpCodeProb1 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SpCodeProb2 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SpCodeProb3 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SpCodeProb4 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SpPerc1 <dbl> 100, 100, NA, NA, NA, NA, NA, NA, NA, NA, NA, 100, NA, 10…
#> $ SpPerc2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SpPerc3 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SpPerc4 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ GsSchoolBest <dbl> NA, 8.00000, NA, NA, NA, NA, NA, NA, NA, NA, NA, 10.66667…
#> $ GsSchoolHigh <dbl> NA, 14.00, NA, NA, NA, NA, NA, NA, NA, NA, NA, 20.00, NA,…
#> $ GsSchoolLow <dbl> 42.333333, 5.666667, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ GsSpBest1 <dbl> NA, 8.00000, NA, NA, NA, NA, NA, NA, NA, NA, NA, 10.66667…
#> $ GsSpBest2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ GsSpBest3 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ GsSpBest4 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ GsSpHigh1 <dbl> NA, 14.0000, NA, NA, NA, NA, NA, NA, NA, NA, NA, 20.0000,…
#> $ GsSpHigh2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ GsSpHigh3 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ GsSpHigh4 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ GsSpLow1 <dbl> 42.333333, 5.666667, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ GsSpLow2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ GsSpLow3 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ GsSpLow4 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ CourseSchool <dbl> NA, NA, NA, NA, NA, 100, 100, NA, NA, NA, NA, NA, NA, NA,…
#> $ TurtleSp <chr> NA, NA, NA, NA, NA, NA, NA, NA, "LV", "DC", "DC", NA, "DC…
#> $ TurtleGs <dbl> NA, NA, NA, NA, NA, NA, NA, NA, 1, 2, 1, NA, 1, NA, NA, N…
#> $ TurtleJFR <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ TurtleAge <chr> NA, NA, NA, NA, NA, NA, NA, NA, "A", "A", "J", NA, "A", N…
#> $ TurtleCapt <chr> NA, NA, NA, NA, NA, NA, NA, NA, "N", "N", "N", NA, NA, NA…
#> $ PinnipedSp <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ PinnipedGs <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ BoatType <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ BoatGs <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ PerpDistKm <dbl> 1.525631e+00, 3.075807e+00, 4.593917e-01, 1.452712e-01, 7…
y.sight.complete <- das_sight(y.proc, return.format = "complete")
y.sight.complete %>%
select(Event, SightNo:PerpDistKm) %>%
glimpse()
#> Rows: 37
#> Columns: 46
#> $ Event <chr> "S", "S", "S", "S", "S", "S", "s", "s", "s", "s", "s", "s…
#> $ SightNo <chr> "1406", "1406", "1406", "1407", "1407", "1407", "1407", "…
#> $ Subgroup <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SightNoDaily <chr> "20130113_1", "20130113_1", "20130113_1", "20130113_2", "…
#> $ Obs <chr> "208", "208", "208", "125", "125", "125", NA, NA, NA, NA,…
#> $ ObsStd <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, …
#> $ Bearing <dbl> 309, 309, 309, 326, 326, 326, 11, 5, 50, 71, 104, 2, 120,…
#> $ Reticle <dbl> 2.8, 2.8, 2.8, 0.4, 0.4, 0.4, 2.0, 3.5, NA, 4.5, 4.5, 2.2…
#> $ DistNm <dbl> 1.06, 1.06, 1.06, 2.97, 2.97, 2.97, 1.30, 0.90, 0.50, 0.7…
#> $ Cue <dbl> 3, 3, 3, 3, 3, 3, NA, NA, NA, NA, NA, NA, NA, NA, NA, 3, …
#> $ Method <dbl> 4, 4, 4, 4, 4, 4, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4, …
#> $ Photos <chr> "N", "N", "N", "Y", "Y", "Y", NA, NA, NA, NA, NA, NA, NA,…
#> $ Birds <chr> "N", "N", "N", "N", "N", "N", NA, NA, NA, NA, NA, NA, NA,…
#> $ CalibSchool <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ PhotosAerial <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ Biopsy <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ Prob <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, NA, NA, NA, NA,…
#> $ nSp <int> 1, 1, 1, 1, 1, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, …
#> $ Mixed <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, NA, NA, NA, NA,…
#> $ ObsEstimate <chr> "280", "001", "208", "280", "001", "125", NA, NA, NA, NA,…
#> $ SpCode1 <chr> "018", "018", "018", "076", "076", "076", NA, NA, NA, NA,…
#> $ SpCode2 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SpCode3 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SpCode4 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SpCodeProb1 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SpCodeProb2 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SpCodeProb3 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SpCodeProb4 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SpPerc1 <dbl> 100, 100, 100, 100, 100, 100, NA, NA, NA, NA, NA, NA, NA,…
#> $ SpPerc2 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SpPerc3 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ SpPerc4 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ GsSchoolBest <dbl> NA, NA, NA, 6, 9, 9, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ GsSchoolHigh <dbl> NA, NA, NA, 10, 10, 22, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ GsSchoolLow <dbl> 43, 36, 48, 6, 2, 9, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
#> $ CourseSchool <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, 100, 100, NA, NA, NA,…
#> $ TurtleSp <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "LV", "DC…
#> $ TurtleGs <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1, 2, 1, …
#> $ TurtleJFR <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ TurtleAge <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "A", "A",…
#> $ TurtleCapt <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "N", "N",…
#> $ PinnipedSp <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ PinnipedGs <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ BoatType <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ BoatGs <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ PerpDistKm <dbl> 1.52563078, 1.52563078, 1.52563078, 3.07580701, 3.0758070…You can also easily filter or subset the sighting data for the desired event code(s)
Effort
In addition, you can chop the effort data into segments, and
summarize the conditions and sightings on those segments using
das_effort and das_effort_sight. These effort
segments can be used for line transect estimates using the Distance
software, species distribution modeling, or summarizing the number of
sightings of certain species on each segment, among other uses.
das_effort chops continuous effort sections (the event
sequence from R to E events) into effort segments using one of several
different chopping methods: condition (a new effort segment every time a
condition changes), equal length (effort segments of equal length), or
section (each segment is a full continuous effort section, i.e. it runs
from an R event to an E event). das_effort_sight takes the
output of das_effort and returns the number of included
sightings and animals per segment for specified species codes.
Both functions return a list of three data frames: segdata,
sightinfo, and randpicks. These data frames and the different chopping
methodologies are described in depth in the function documentation
(?das_effort and ?das_effort_sight), but
briefly segdata contains information about each effort segment,
sightinfo contains information about the sightings such as their
corresponding segment, and randpicks contains information specific to
the ‘equal length’ chopping method. das_effort and
das_effort_sight are separate functions to allow the user
more control over which sightings should be included in the effort
segment summaries (see ?das_effort). See below for how to
chop/split effort lines by strata.
# Chop the effort into 10km segments
y.eff.eq <- das_effort(
y.proc, method = "equallength", seg.km = 10, dist.method = "greatcircle",
num.cores = 1
)
#> No argument was passed via randpicks.load, and thus new randpicks values will be generated
# Chop the effort every time a condition changes
y.eff <- das_effort(
y.proc, method = "condition", seg.min.km = 0,
dist.method = "greatcircle", conditions = c("Bft", "SwellHght", "Vis"),
num.cores = 1
)
y.eff.sight <- das_effort_sight(y.eff, sp.codes = c("018", "076"))
glimpse(y.eff.sight$segdata)
#> Rows: 20
#> Columns: 31
#> $ segnum <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16…
#> $ section_id <int> 1, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8,…
#> $ section_sub_id <dbl> 1, 1, 2, 1, 2, 3, 4, 5, 6, 7, 1, 2, 1, 1, 2, 1, 2, 1,…
#> $ file <chr> "das_sample.das", "das_sample.das", "das_sample.das",…
#> $ stlin <int> 2, 23, 33, 59, 69, 70, 75, 78, 84, 85, 99, 108, 127, …
#> $ endlin <int> 20, 33, 43, 69, 70, 75, 78, 84, 85, 90, 108, 121, 147…
#> $ lat1 <dbl> 39.32033, 39.37617, 39.42950, 39.56800, 39.66082, 39.…
#> $ lon1 <dbl> -137.6043, -137.5978, -137.5715, -137.4530, -137.4132…
#> $ DateTime1 <dttm> 2013-01-13 06:27:39, 2013-01-13 06:58:04, 2013-01-13…
#> $ lat2 <dbl> 39.36716, 39.42950, 39.51933, 39.66082, 39.66133, 39.…
#> $ lon2 <dbl> -137.5817, -137.5715, -137.5277, -137.4132, -137.4130…
#> $ DateTime2 <dttm> 2013-01-13 06:46:25, 2013-01-13 07:20:02, 2013-01-13…
#> $ mlat <dbl> 39.34377, 39.40288, 39.47435, 39.61433, 39.66108, 39.…
#> $ mlon <dbl> -137.5930, -137.5848, -137.5493, -137.4327, -137.4131…
#> $ mDateTime <dttm> 2013-01-13 06:37:02, 2013-01-13 07:09:03, 2013-01-13…
#> $ dist <dbl> 5.5577, 6.3431, 10.6674, 10.8651, 0.0574, 1.4189, 1.9…
#> $ year <dbl> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013,…
#> $ month <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ day <int> 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 1…
#> $ mtime <chr> "06:37:02", "07:09:03", "07:38:33", "09:40:55", "09:5…
#> $ Cruise <dbl> 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,…
#> $ Mode <chr> "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C"…
#> $ EffType <chr> "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S"…
#> $ ESWsides <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…
#> $ maxdistBft <dbl> 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 3, 3, 3, 3, 2, 3, 2, 2,…
#> $ maxdistSwellHght <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,…
#> $ maxdistVis <dbl> 6.0, 6.0, 5.5, 5.5, 6.0, 6.0, 5.5, 4.5, 3.5, 2.5, 5.8…
#> $ nSI_018 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ ANI_018 <dbl> 42.33333, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000…
#> $ nSI_076 <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ ANI_076 <dbl> 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
glimpse(y.eff.sight$sightinfo)
#> Rows: 13
#> Columns: 64
#> $ segnum <int> 1, 3, 4, 13, 13, 15, 17, 18, 18, 20, 20, 20, 20
#> $ mlat <dbl> 39.34377, 39.47435, 39.61433, 40.20895, 40.20895, 40.3483…
#> $ mlon <dbl> -137.5930, -137.5493, -137.4327, -137.1531, -137.1531, -1…
#> $ Event <chr> "S", "S", "t", "t", "S", "t", "S", "S", "S", "S", "S", "S…
#> $ DateTime <dttm> 2013-01-13 06:46:02, 2013-01-13 07:56:22, 2013-01-13 09:3…
#> $ year <dbl> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 20…
#> $ Lat <dbl> 39.36617, 39.51767, 39.59733, 40.18283, 40.26567, 40.3605…
#> $ Lon <dbl> -137.5820, -137.5285, -137.4400, -137.1622, -137.1350, -1…
#> $ OnEffort <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRU…
#> $ Cruise <dbl> 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 100…
#> $ Mode <chr> "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C…
#> $ OffsetGMT <int> 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5
#> $ EffType <chr> "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S…
#> $ ESWsides <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
#> $ Course <dbl> 25, 26, 27, 20, 20, 16, 25, 30, 30, 23, 23, 23, 23
#> $ SpdKt <dbl> 10.2, 9.7, 9.0, 9.3, 9.3, 8.9, 8.9, 9.5, 9.5, 9.6, 9.6, 9…
#> $ Bft <dbl> 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2
#> $ SwellHght <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 1, 1, 1, 1
#> $ WindSpdKt <dbl> 10, 10, 10, 6, 6, 6, 6, 6, 6, 5, 5, 5, 5
#> $ RainFog <dbl> 1, 3, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 3
#> $ HorizSun <dbl> NA, 2, 2, 8, 8, 9, 8, 8, 8, NA, NA, NA, NA
#> $ VertSun <dbl> NA, 2, 2, 1, 1, 1, 2, 2, 2, NA, NA, NA, NA
#> $ Glare <lgl> NA, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALS…
#> $ Vis <dbl> 6.0, 5.5, 5.5, 6.0, 6.0, 6.0, 6.0, 6.0, 6.0, 4.0, 4.0, 4.…
#> $ ObsL <chr> "208", "125", "001", "280", "280", "125", "149", "126", "…
#> $ Rec <chr> "280", "208", "126", "001", "001", "208", "125", "149", "…
#> $ ObsR <chr> "001", "280", "149", "126", "126", "280", "208", "125", "…
#> $ ObsInd <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ EffortDot <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRU…
#> $ EventNum <chr> "15", "35", "59", "131", "136", "153", "167", "181", "181…
#> $ file_das <chr> "das_sample.das", "das_sample.das", "das_sample.das", "da…
#> $ line_num <int> 15, 38, 65, 137, 142, 162, 176, 193, 193, 248, 248, 252, …
#> $ SightNo <chr> "1406", "1407", NA, NA, "1408", NA, "1409", "1410", "1410…
#> $ Subgroup <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ SightNoDaily <chr> "20130113_1", "20130113_2", NA, NA, "20130113_3", NA, "20…
#> $ Obs <chr> "208", "125", "280", "228", "280", "231", "149", "125", "…
#> $ ObsStd <lgl> TRUE, TRUE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, …
#> $ Bearing <dbl> 309, 326, 120, 300, 270, 45, 344, 70, 70, 359, 359, 38, 38
#> $ Reticle <dbl> 2.8, 0.4, NA, NA, 14.0, NA, 0.2, 1.4, 1.4, 0.3, 0.3, 0.8,…
#> $ DistNm <dbl> 1.06, 2.97, 0.03, 0.02, 0.28, 0.05, 3.68, 1.66, 1.66, 3.2…
#> $ Cue <dbl> 3, 3, NA, NA, 3, NA, 3, 3, 3, 2, 2, 3, 3
#> $ Method <dbl> 4, 4, NA, NA, 4, NA, 4, 4, 4, 4, 4, 4, 4
#> $ Photos <chr> "N", "Y", NA, NA, "N", NA, "Y", "Y", "Y", "Y", "Y", "Y", …
#> $ Birds <chr> "N", "N", NA, NA, "N", NA, "Y", "N", "N", "N", "N", "N", …
#> $ CalibSchool <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ PhotosAerial <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ Biopsy <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ Prob <lgl> FALSE, FALSE, NA, NA, FALSE, NA, FALSE, FALSE, FALSE, FAL…
#> $ nSp <int> 1, 1, NA, NA, 1, NA, 1, 2, 2, 2, 2, 2, 2
#> $ Mixed <lgl> FALSE, FALSE, NA, NA, FALSE, NA, FALSE, TRUE, TRUE, TRUE,…
#> $ SpCode <chr> "018", "076", "LV", "DC", "037", "DC", "016", "013", "016…
#> $ SpCodeProb <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "016", "016"
#> $ GsSchoolBest <dbl> NA, 8.00000, 1.00000, 1.00000, 10.66667, 1.00000, 46.6666…
#> $ GsSchoolHigh <dbl> NA, 14.00, NA, NA, 20.00, NA, 79.00, 72.75, 72.75, 249.00…
#> $ GsSchoolLow <dbl> 42.333333, 5.666667, NA, NA, 10.666667, NA, 46.666667, 41…
#> $ GsSpBest <dbl> NA, 8.00000, 1.00000, 1.00000, 10.66667, 1.00000, 46.6666…
#> $ GsSpHigh <dbl> NA, 14.0000, NA, NA, 20.0000, NA, 79.0000, 52.3800, 20.37…
#> $ GsSpLow <dbl> 42.333333, 5.666667, NA, NA, 10.666667, NA, 46.666667, 30…
#> $ CourseSchool <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ TurtleJFR <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ TurtleAge <chr> NA, NA, "A", "J", NA, "A", NA, NA, NA, NA, NA, NA, NA
#> $ TurtleCapt <chr> NA, NA, "N", "N", NA, NA, NA, NA, NA, NA, NA, NA, NA
#> $ PerpDistKm <dbl> 1.52563078, 3.07580701, 0.04811637, 0.03207758, 0.5185600…
#> $ included <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FAL…Strata
This package contains several methods to incorporate strata into your
DAS data processing: das_intersects_strata for assigning
points to strata and das_effort for chopping effort lines
by strata. These functions contain a ‘strata.files’ argument, which
expects a (named) list of paths to CSV files; names are automatically
generated if not provided. These files must have headers, longitude
values in column one and latitude values in column two, and be closed
polygons.
First, das_intersects_strata allows you to add columns
to data frames indicating if a point intersects one or more strata
polygons. You can pass this function either a data frame or a list. If a
list then it must be the output of das_effort or
das_effort_sight, and the function will use the segment
midpoints to determine if a segment (and associated sightings)
intersected each stratum.
stratum.file <- system.file("das_sample_stratum.csv", package = "swfscDAS")
y.eff.sight.strata <- das_intersects_strata(y.eff.sight, list(InPoly = stratum.file))
glimpse(y.eff.sight.strata$segdata)
#> Rows: 20
#> Columns: 32
#> $ segnum <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16…
#> $ section_id <int> 1, 2, 2, 3, 3, 3, 3, 3, 3, 3, 4, 4, 5, 6, 6, 7, 7, 8,…
#> $ section_sub_id <dbl> 1, 1, 2, 1, 2, 3, 4, 5, 6, 7, 1, 2, 1, 1, 2, 1, 2, 1,…
#> $ file <chr> "das_sample.das", "das_sample.das", "das_sample.das",…
#> $ stlin <int> 2, 23, 33, 59, 69, 70, 75, 78, 84, 85, 99, 108, 127, …
#> $ endlin <int> 20, 33, 43, 69, 70, 75, 78, 84, 85, 90, 108, 121, 147…
#> $ lat1 <dbl> 39.32033, 39.37617, 39.42950, 39.56800, 39.66082, 39.…
#> $ lon1 <dbl> -137.6043, -137.5978, -137.5715, -137.4530, -137.4132…
#> $ DateTime1 <dttm> 2013-01-13 06:27:39, 2013-01-13 06:58:04, 2013-01-13…
#> $ lat2 <dbl> 39.36716, 39.42950, 39.51933, 39.66082, 39.66133, 39.…
#> $ lon2 <dbl> -137.5817, -137.5715, -137.5277, -137.4132, -137.4130…
#> $ DateTime2 <dttm> 2013-01-13 06:46:25, 2013-01-13 07:20:02, 2013-01-13…
#> $ mlat <dbl> 39.34377, 39.40288, 39.47435, 39.61433, 39.66108, 39.…
#> $ mlon <dbl> -137.5930, -137.5848, -137.5493, -137.4327, -137.4131…
#> $ mDateTime <dttm> 2013-01-13 06:37:02, 2013-01-13 07:09:03, 2013-01-13…
#> $ dist <dbl> 5.5577, 6.3431, 10.6674, 10.8651, 0.0574, 1.4189, 1.9…
#> $ year <dbl> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013,…
#> $ month <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ day <int> 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 1…
#> $ mtime <chr> "06:37:02", "07:09:03", "07:38:33", "09:40:55", "09:5…
#> $ Cruise <dbl> 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,…
#> $ Mode <chr> "C", "C", "C", "C", "C", "C", "C", "C", "C", "C", "C"…
#> $ EffType <chr> "S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "S"…
#> $ ESWsides <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,…
#> $ maxdistBft <dbl> 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 3, 3, 3, 3, 2, 3, 2, 2,…
#> $ maxdistSwellHght <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,…
#> $ maxdistVis <dbl> 6.0, 6.0, 5.5, 5.5, 6.0, 6.0, 5.5, 4.5, 3.5, 2.5, 5.8…
#> $ nSI_018 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ ANI_018 <dbl> 42.33333, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000…
#> $ nSI_076 <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ ANI_076 <dbl> 0, 0, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ InPoly <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,…In addition, you can chop/split effort lines by strata using the
‘strata.files’ argument of das_effort. Effort lines are
chopped/split by strata before then being processed using the specified
method (condition, equal length, etc.). The ‘segdata’ element of the
das_effort output will contain a ‘stratum’ column, which
contains the name of the stratum that the segment is in. See
?das_effort and ?das_effort_strata for more
details.
y.eff.strata.section <- das_effort(
y.proc, method = "section", strata.files = list(stratum.file),
num.cores = 1
)
#> although coordinates are longitude/latitude, st_intersection assumes that they
#> are planar
y.eff.strata.condition <- das_effort(
y.proc, method = "condition", seg.min.km = 0,
strata.files = list(Poly1 = stratum.file),
num.cores = 1
)
#> although coordinates are longitude/latitude, st_intersection assumes that they
#> are planar
glimpse(y.eff.strata.section$segdata)
#> Rows: 10
#> Columns: 33
#> $ segnum <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
#> $ section_id <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
#> $ section_sub_id <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
#> $ file <chr> "das_sample.das", "das_sample.das", "das_sample.das", "…
#> $ stlin <int> 2, 23, 59, 99, 127, 150, 167, 188, 232, 242
#> $ endlin <int> 20, 43, 90, 121, 147, 164, 181, 199, 240, 259
#> $ lat1 <dbl> 39.32033, 39.37617, 39.56800, 39.94517, 40.15217, 40.26…
#> $ lon1 <dbl> -137.6043, -137.5978, -137.4530, -137.3692, -137.1737, …
#> $ DateTime1 <dttm> 2013-01-13 06:27:39, 2013-01-13 06:58:04, 2013-01-13 09…
#> $ lat2 <dbl> 39.36716, 39.51933, 39.75433, 40.12745, 40.26617, 40.37…
#> $ lon2 <dbl> -137.5817, -137.5277, -137.4107, -137.2488, -137.1348, …
#> $ DateTime2 <dttm> 2013-01-13 06:46:25, 2013-01-13 07:57:05, 2013-01-13 10…
#> $ mlat <dbl> 39.34377, 39.44767, 39.66117, 40.03679, 40.20895, 40.32…
#> $ mlon <dbl> -137.5930, -137.5625, -137.4131, -137.3101, -137.1531,…
#> $ mDateTime <dttm> 2013-01-13 06:37:02, 2013-01-13 07:27:34, 2013-01-13 09…
#> $ dist <dbl> 5.5577, 17.0106, 21.8086, 22.7090, 13.0922, 12.3011, 8.…
#> $ year <dbl> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, …
#> $ month <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
#> $ day <int> 13, 13, 13, 13, 13, 13, 13, 13, 14, 14
#> $ mtime <chr> "06:37:02", "07:27:34", "09:59:20", "12:34:14", "14:14:…
#> $ Cruise <dbl> 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1…
#> $ Mode <chr> "C", "C", "C", "C", "C", "C", "C", "C", "C", "C"
#> $ EffType <chr> "S", "S", "S", "S", "S", "S", "S", "S", "S", "S"
#> $ ESWsides <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
#> $ avgBft <dbl> 3.000000, 3.000000, 2.500835, 3.000000, 3.000000, 2.47…
#> $ avgSwellHght <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 1, 1
#> $ avgHorizSun <dbl> 2.000000, 2.000000, 2.000000, 12.000000, 8.000000, 8.52…
#> $ avgVertSun <dbl> 3, 2, 2, 12, 1, 1, 2, 2, NA, NA
#> $ avgGlare <dbl> 0, 0, 0, 0, 0, 0, 0, 0, NA, NA
#> $ avgVis <dbl> 6.000000, 5.686447, 5.090748, 5.974103, 6.000000, 6.000…
#> $ avgCourse <dbl> 23.56198, 27.11868, 95.65614, 34.61155, 20.43919, 16.47…
#> $ avgSpdKt <dbl> 9.912395, 9.476263, 9.287725, 9.428340, 9.343919, 9.091…
#> $ stratum <chr> NA, NA, NA, NA, "Stratum1", "Stratum1", "Stratum1", "St…Comments
In addition, you can use das_comments to generate
comment strings. This is particularly useful if looking for comments
with keywords
y.comm <- das_comments(y.proc)
glimpse(select(y.comm, Event, line_num, comment_str))
#> Rows: 5
#> Columns: 3
#> $ Event <chr> "C", "C", "C", "C", "C"
#> $ line_num <int> 71, 81, 186, 218, 241
#> $ comment_str <chr> " Well? ", " going left to avoid ", …
y.comm[str_detect(y.comm$comment_str, "gear"), ] #Could also use grepl() here
#> Event DateTime Lat Lon OnEffort Cruise Mode OffsetGMT
#> 238 C 2013-01-14 11:37:27 41.016 -135.578 FALSE 1000 C 5
#> EffType ESWsides Course SpdKt Bft SwellHght WindSpdKt RainFog HorizSun
#> 238 S 2 35 9.5 2 1 5 3 NA
#> VertSun Glare Vis ObsL Rec ObsR ObsInd Data1 Data2 Data3 Data4 Data5 Data6
#> 238 NA NA 4 149 125 208 <NA> cros sing fishi ng ge ar <NA>
#> Data7 Data8 Data9 Data10 Data11 Data12 EffortDot EventNum
#> 238 <NA> <NA> <NA> <NA> <NA> FALSE 11
#> file_das line_num comment_str
#> 238 das_sample.das 241 crossing fishing gear