The full_clean() function performs automated cleaning steps, including options for: removing
duplicate data points, checking locality precision, removing points with skewed coordinates,
removing plain zero records, removing records based on basis of record, and spatially thinning collection points.
This function also provides the option to interactively inspect and remove types of basis of record.
Usage
full_clean(
df,
synonyms.list,
event.date = "eventDate",
year = "year",
month = "month",
day = "day",
occ.id = "occurrenceID",
remove.NA.occ.id = FALSE,
remove.NA.date = FALSE,
aggregator = "aggregator",
id = "ID",
taxa.filter = "fuzzy",
scientific.name = "scientificName",
accepted.name = NA,
remove.zero = TRUE,
precision = TRUE,
digits = 2,
remove.skewed = TRUE,
basis.list = NA,
basis.of.record = "basisOfRecord",
latitude = "latitude",
longitude = "longitude",
remove.flagged = TRUE,
thin.points = TRUE,
distance = 5,
reps = 100,
one.point.per.pixel = TRUE,
raster = NA,
resolution = 0.5,
remove.duplicates = TRUE
)Arguments
- df
Data frame of occurrence records.
- synonyms.list
A list of synonyms for a species.
- event.date
Default = "eventDate". The name of the event date column in the data frame.
- year
Default = "year". The name of the year column in the data frame.
- month
Default = "month". The name of the month column in the data frame.
- day
Default = "day". The name of the day column in the data frame.
- occ.id
Default = "occurrenceID". The name of the occurrenceID column in the data frame.
- remove.NA.occ.id
Default = FALSE. This will remove records with missing occurrence IDs when set to
TRUE.- remove.NA.date
Default = FALSE. This will remove records with missing event dates when set to
TRUE.- aggregator
Default = "aggregator". The name of the column in the data frame that identifies the aggregator that provided the record. This is equal to iDigBio or GBIF.
- id
Default = "ID". The name of the id column in the data frame, which contains unique IDs defined from GBIF (keys) or iDigBio (UUID).
- taxa.filter
The type of filter to be used--either "exact", "fuzzy", or "interactive".
- scientific.name
Default = "scientificName". The name of the scientificName column in the data frame.
- accepted.name
The accepted scientific name for the species. If provided, an additional column will be added to the data frame with the accepted name for further manual comparison.
- remove.zero
Default = TRUE. Indicates that points at (0.00, 0.00) should be removed.
- precision
Default = TRUE. Indicates that coordinates should be rounded to match the coordinate uncertainty.
- digits
Default = 2. Indicates digits to round coordinates to when
precision = TRUE.- remove.skewed
Default = TRUE. Utilizes the
remove_skewed()function to remove skewed coordinate values.- basis.list
A list of basis to keep. If a list is not supplied, this filter will not occur.
- basis.of.record
Default = "basisOfRecord". The name of the basis of record column in the data frame.
- latitude
Default = "latitude". The name of the latitude column in the data frame.
- longitude
Default = "longitude". The name of the longitude column in the data frame.
- remove.flagged
Default = TRUE. An option to remove points with problematic locality information.
- thin.points
Default = TRUE. An option to spatially thin occurrence records.
- distance
Default = 5. Distance in km to separate records.
- reps
Default = 100. Number of times to perform thinning algorithm.
- one.point.per.pixel
Default = TRUE. An option to only retain one point per pixel.
- raster
Raster object which will be used for ecological niche comparisons. A SpatRaster should be used.
- resolution
Default = 0.5. Options - 0.5, 2.5, 5, and 10 (in min of a degree). 0.5 min of a degree is equal to 30 arc sec.
- remove.duplicates
Default = TRUE. An option to remove duplicate points.
Value
df is a data frame with the cleaned data.
Information about the columns in the returned data frame can be found in the documentation for gators_download(). An additional column named "accepted_name" will be returned if an accepted.name was provided.
Details
This function is entirely automated and thus does not take advantage of the interactive options provided in the individual cleaning functions. Using this wrapper is recommended for data processing that does not require interactive/manual cleaning and inspection. All cleaning steps, except taxonomic harmonization, can be bypassed by setting their associated input variables to FALSE. This function requires packages dplyr, magrittr, and raster.