countryOutlieRs {BeeBDC} | R Documentation |
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
|
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.
Examples
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")