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

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



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


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


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


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


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.


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


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.


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).


  # 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
#>  - Extracting country data from points...
#>  - Buffering failed points by pointBuffer...
#>  - Prepare the neighbouring country dataset...
#> although coordinates are longitude/latitude, st_intersects assumes that they
#> are planar
#>  - Compare points with the checklist...
#>  - Combining data...
#>  - Sorting and removing potentially duplicated buffered points...
#>  - Finished. 
#> We have matched 24 records to their exact country and 2 to an adjacent country
#> We failed to match 1 occurrences to any 'exact' or 'neighbouring' country.
#> There are 23 'NA' occurrences for the .countryOutlier column.
#> countryOutlieRs:
#> Flagged 1  for country outlier and flagged  0  for in the .sea records.
#> Three columns were added to the database:
#>  1.  The '.countryOutlier' column was added which is a filtering column. 
#>  2.  The 'countryMatch' columns indicates exact, neighbour, or noMatch. 
#>  3. The '.sea' column was added as a filtering column for points in the ocean.  The '.sea' column includes the user input buffer in its calculation.
#>  - Completed in 1.41 secs
#>     1    26    23