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Usage

Source: vignettes/survdat.Rmd
survdat.Rmd

Connect to the database

Once you are granted permission to access the database and have Oracle’s instant Client installed you can make a connection as follows:

channel <- dbutils::connect_to_database(server="servername",uid="yourUsername")

channel is an object inherited from the DBIConnection-class. This object is passed as an argument to functions in survdat

Pull Data

Pulling the data can take up to 15 minutes to complete. see get_survdat_data() for details. To pull the survey data with conversion corrections applied use the following:

data <- get_survdat_data(channel)

data is a list of 5 elements.

  • The data
  • The set of sql statements made to pull the data
  • The function call statement
  • The date of the pull
  • The version of the package used to create the data pull

For detailed information regarding the different types of data pulls please review vignette("pullingData")

Plot the shapefiles

Two shape files are included in the package, strata.shp and EPU.shp

To read and plot the EPU.shp file:

areaPolygon <- sf::st_read(dsn = system.file("extdata","EPU.shp",package="survdat"), quiet=T)
plot_shapefile(areaPolygon)

EPU footprint

Stratified Mean

Under development.

Swept area biomass

Swept area biomass is estimated for particular regions of interest. These regions are specified via shape files.

All shapefiles are required to be an sf (simple features) object.

  • To calculate swept area biomass for any one of these regions, say Georges Bank in the Spring:
calc_swept_area(data$survdat,
                areaPolygon=areaPolygon,
                areaDescription="EPU",
                filterByArea="GB",
                filterBySeason = "SPRING")

The filterByArea value should be one of the values found in the areaDescription field of the shapefile

  • To return the values in TIDY format add the tidy=T argument:
calc_swept_area(data$survdat,
                areaPolygon=areaPolygon,
                areaDescription="EPU",
                filterByArea="GB",
                filterBySeason = "SPRING",
                tidy=T)

To visualize the overlap of the survey data and the regions, first define a coordinate reference system (crs) in which to project both the points and the shapefile. Convert the LAT and LON coordinates to the WGS84 reference ellipsoid (crs = 4326)

crs <- 4326
points <- sf::st_as_sf(data$survdat,
                       coords=("LON","LAT"),
                       crs=crs)
# plot data with regions
plot_data_area(points=data$survdat,
               polygons=area,
               crs)