countryOutlieRs {BeeBDC}

Flag country-level outliers with a provided checklist.

Description

This function flags country-level outliers using the checklist provided with this package. For additional context and column names, see beesChecklist().

Usage

countryOutlieRs(
  checklist = NULL,
  data = NULL,
  keepAdjacentCountry = TRUE,
  pointBuffer = NULL,
  scale = 50,
  stepSize = 1e+06,
  mc.cores = 1
)

Arguments

checklist

A data frame or tibble. The formatted checklist which was built based on the Discover Life website.

data

A data frame or tibble. The a Darwin Core occurrence dataset.

keepAdjacentCountry

Logical. If TRUE, occurrences in countries that are adjacent to checklist countries will be kept. If FALSE, they will be flagged.

pointBuffer

Numeric. A buffer around points to help them align with a country or coastline. This provides a good way to retain points that occur right along the coast or borders of the maps in rnaturalearth

scale

Numeric. The value fed into the map scale parameter for rnaturalearth::ne_countries()'s scale parameter: Scale of map to return, one of 110, 50, 10 or 'small', 'medium', 'large', where smaller numbers are higher resolution. WARNING: This function is tested on 110 and 50.

stepSize

Numeric. The number of occurrences to process in each chunk. Default = 1000000.

mc.cores

Numeric. If > 1, the function will run in parallel using mclapply using the number of cores specified. If = 1 then it will be run using a serial loop. NOTE: Windows machines must use a value of 1 (see ?parallel::mclapply). Additionally, be aware that each thread can use large chunks of memory. If the cores throw issues, consider setting mc.cores to 1. Default = 1.

Value

The input data with two new columns, .countryOutlier or .sea. There are three possible values for the new column: TRUE == passed, FALSE == failed (not in country or in the ocean), NA == did not overlap with rnaturalearth map.

See Also

continentOutlieRs() for implementation at the continent level. Implementation at the continent level may be lighter and more manageable on the data side of things where country-level checklists don't exist. Additionally, see beesChecklist() for input data. Note, not all columns are necessary if you are building your own dataset. At a minimum you will need validName, country, iso_a3_eh (to match rnaturalearth).

Examples

if(requireNamespace("rnaturalearthdata")){
library(magrittr)
  # Load in the test dataset
beesRaw <- BeeBDC::beesRaw
  # For the sake of this example, use the testChecklist
system.file("extdata", "testChecklist.rda", package="BeeBDC") |> load()
  # For real examples, you might download the beesChecklist from FigShare using 
  #  [BeeBDC::beesChecklist()]

beesRaw_out <- countryOutlieRs(checklist = testChecklist,
                               data = beesRaw %>%
                               dplyr::filter(dplyr::row_number() %in% 1:50),
                               keepAdjacentCountry = TRUE,
                               pointBuffer = 1,
                               scale = 50,
                               stepSize = 1000000,
                               mc.cores = 1)
table(beesRaw_out$.countryOutlier, useNA = "always")
} # END if require