Presence data cleaning routine

Data cleaning wrapper using CoordinateCleaner package.

Usage

data_clean(occ, pred = NULL,
           species = NA, lon = NA, lat = NA,
           capitals = TRUE,
           centroids = TRUE,
           duplicated = TRUE,
           identical = TRUE,
           institutions = TRUE,
           invalid = TRUE,
           terrestrial = TRUE,
           independent_test = TRUE)

Arguments

occ: A occurrences_sdm object or input_sdm.
pred: A sdm_area object. If occ is a input_sdm object with predictors data, than pred is obtained from it.
species: A character stating the name of the column with species names in occ (see details).
lon: A character stating the name of the column with longitude in occ (see details).
lat: A character stating the name of the column with latitude in occ (see details).
capitals: Boolean to turn on/off the exclusion from countries capitals coordinates (see ?cc_cap)
centroids: Boolean to turn on/off the exclusion from countries centroids coordinates (see ?cc_cen)
duplicated: Boolean to turn on/off the exclusion from duplicated records (see ?cc_dupl)
identical: Boolean to turn on/off the exclusion from records with identical lat/long values (see ?cc_equ)
institutions: Boolean to turn on/off the exclusion from biodiversity institutions coordinates (see ?cc_inst)
invalid: Boolean to turn on/off the exclusion from invalid coordinates (see ?cc_val)
terrestrial: Boolean to turn on/off the exclusion from coordinates falling on sea (see ?cc_sea)
independent_test: Boolean. If occ has independent test data, the data cleaning routine is also applied on it.

Value

A occurrences_sdm object or input_sdm with cleaned presence data.

Details

If the user does not used GBIF_data function to obtain species records, the function may have problems to find which column from the presences table has species, longitude and latitude information. In this regard, we implemented the parameters species, lon and lat so the use can explicitly inform which columns should be used. If they remain as NA (standard) the function will try to guess which columns are the correct one.

Author

Luíz Fernando Esser (luizesser@gmail.com) https://luizfesser.wordpress.com

Examples

# Create sdm_area object:
sa <- sdm_area(parana, cell_size = 50000, crs = 6933)
#> ! Making grid over study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.

# Include predictors:
sa <- add_predictors(sa, bioc) |> select_predictors(c("bio1", "bio12"))
#> ! Making grid over the study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.

# Create occurrences:
oc <- occurrences_sdm(occ, crs = 6933) |> join_area(sa)
#> Warning: Some records from `occ` do not fall in `pred`.
#> ℹ 3 elements from `occ` were excluded.
#> ℹ If this seems too much, check how `occ` and `pred` intersect.

# Create input_sdm:
i <- input_sdm(oc, sa)

# Clean coordinates (terrestrial is set to false to make the run quicker):
i <- data_clean(i, terrestrial = FALSE)
#> Cell_ids identified, removing duplicated cell_id.
#> Testing country capitals
#> Removed 0 records.
#> Testing country centroids
#> Removed 0 records.
#> Testing duplicates
#> Removed 0 records.
#> Testing equal lat/lon
#> Removed 0 records.
#> Testing biodiversity institutions
#> Removed 1 records.
#> Testing coordinate validity
#> Removed 0 records.
#> Predictors identified, procceding with grid filter (removing NA and duplicated data).