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R/ColTypeR.R
Defines functions ColTypeR
Documented in ColTypeR
# This function was written by James Dorey on the 20th of May 2022 in ored to define the columns
# used during occurrence record cleaning
# For help you may email James at jbdorey[at]me.com
#' Sets up column names and types
#'
#' This function uses [readr::cols_only()] to assign a column name and the type of data
#' (e.g., [readr::col_character()],
#' and [readr::col_integer()]). To see the default columns simply run [BeeBDC::ColTypeR()].
#' This is intended for use with [readr::read_csv()]. Columns that are not present will NOT be included
#' in the resulting tibble unless they are specified using [...].
#'
#' @param ... Additional arguments. These can be specified in addition to the ones default to the
#' function. For example:
#' * newCharacterColumn = [readr::col_character()],
#' * newNumericColumn = [readr::col_integer()],
#' * newLogicalColumn = [readr::col_logical()]
#'
#' @importFrom readr col_character col_double col_factor col_integer col_logical col_datetime
#' @importFrom dplyr %>%
#'
#' @return Returns an object of class col_spec.
#' See [readr::as.col_spec()] for additional context and explication.
#' @export
#'
#' @examples
#' # You can simply return the below for default values
#' library(dplyr)
#' BeeBDC::ColTypeR()
#'
#' # To add new columns you can write
#' ColTypeR(newCharacterColumn = readr::col_character(),
#' newNumericColumn = readr::col_integer(),
#' newLogicalColumn = readr::col_logical())
#'
#' # Try reading in one of the test datasets as an example:
#' beesFlagged %>% dplyr::as_tibble(col_types = BeeBDC::ColTypeR())
#' # OR
#' beesRaw %>% dplyr::as_tibble(col_types = BeeBDC::ColTypeR())
#'
#'
ColTypeR <- function(...){
ColTypes <- readr::cols_only(
# Character Strings
# CHR - taxonomy
database_id = readr::col_character(), scientificName = readr::col_character(),
family = readr::col_character(), subfamily = readr::col_character(), genus = readr::col_character(),
subgenus = readr::col_character(), subspecies = readr::col_character(), species = readr::col_character(),
specificEpithet = readr::col_character(), infraspecificEpithet = readr::col_character(),
acceptedNameUsage = readr::col_character(), taxonRank = readr::col_character(),
scientificNameAuthorship = readr::col_character(),
identificationQualifier = readr::col_character(), higherClassification = readr::col_character(),
identificationReferences = readr::col_character(), typeStatus = readr::col_character(),
previousIdentifications = readr::col_character(), verbatimIdentification = readr::col_character(),
identifiedBy = readr::col_character(), dateIdentified = readr::col_character(),
# DBL - Locality info
decimalLatitude = readr::col_double(), decimalLongitude = readr::col_double(),
verbatimLatitude = readr::col_character(), verbatimLongitude = readr::col_character(),
verbatimElevation = readr::col_character(),
# CHR/Factor - Locality info
stateProvince = readr::col_character(), country = readr::col_character(), continent = readr::col_factor(),
locality = readr::col_character(), island = readr::col_character(),
county = readr::col_character(), municipality = readr::col_character(),
# CHR/Factor - Country codes
countryCode = readr::col_factor(), level0Gid = readr::col_factor(), level0Name = readr::col_factor(),
level1Gid = readr::col_factor(), level1Name = readr::col_factor(), license = readr::col_factor(),
issue = readr::col_character(),
# Date/Time - Collection time
eventDate = readr::col_character(),
eventTime = readr::col_character(),
startDayOfYear = readr::col_integer(),
endDayOfYear = readr::col_integer(),
# Int - Collection time
day = readr::col_integer(), month = readr::col_integer(), year = readr::col_integer(),
# Factor - Collection info
basisOfRecord = readr::col_factor(), type = readr::col_factor(), occurrenceStatus = readr::col_factor(),
# CHR - Collection info
recordNumber = readr::col_character(), recordedBy = readr::col_character(), eventID = readr::col_character(),
Location = readr::col_character(), samplingProtocol = readr::col_character(),
samplingEffort = readr::col_character(),
# Int - Collection info
individualCount = readr::col_double(), organismQuantity = readr::col_double(),
# mixed - Information uncertainty
coordinatePrecision = readr::col_double(), coordinateUncertaintyInMeters = readr::col_double(),
spatiallyValid = readr::col_logical(),
# CHR - Database information
catalogNumber = readr::col_character(), gbifID = readr::col_character(), datasetID = readr::col_character(),
institutionCode = readr::col_character(), datasetName = readr::col_character(),
otherCatalogNumbers = readr::col_character(), occurrenceID = readr::col_character(),
taxonKey = readr::col_character(), coreid = readr::col_character(),
recordId = readr::col_character(), collectionID = readr::col_character(),
associatedSequences = readr::col_character(),
# CHR - Verbatim information
verbatimScientificName = readr::col_character(), verbatimEventDate = readr::col_character(),
# CHR/Factor - Aux info
associatedTaxa = readr::col_character(), associatedOrganisms = readr::col_character(),
fieldNotes = readr::col_character(), sex = readr::col_character(),
# CHR - Rights info
rights = readr::col_character(), rightsHolder = readr::col_character(), accessRights = readr::col_character(),
dctermsLicense = readr::col_character(), dctermsType = readr::col_character(),
dctermsAccessRights = readr::col_character(), associatedReferences = readr::col_character(),
bibliographicCitation = readr::col_character(), dctermsBibliographicCitation = readr::col_character(),
references = readr::col_character(),
# Record notes
# CHR
flags = readr::col_character(), informationWithheld = readr::col_character(),
isDuplicateOf = readr::col_character(),
# Logical
hasCoordinate = readr::col_logical(), hasGeospatialIssues = readr::col_logical(),
# Factor
assertions = readr::col_factor(),
# mix - ALA columns
occurrenceYear = readr::col_datetime(), id = readr::col_character(), duplicateStatus = readr::col_factor(),
associatedOccurrences = readr::col_character(),
# CHR - SCAN column
locationRemarks = readr::col_character(),
# CHR - dataset origin column
dataSource = readr::col_character(),
# bdc columns
dataBase_scientificName = readr::col_character(), .rou = readr::col_logical(),
.val = readr::col_logical(), .equ = readr::col_logical(), .zer = readr::col_logical(), .cap = readr::col_logical(),
.cen = readr::col_logical(), .sea = readr::col_logical(), .otl = readr::col_logical(), .gbf = readr::col_logical(),
.inst = readr::col_logical(), .dpl = readr::col_logical(), .summary = readr::col_logical(),
names_clean = readr::col_character(), verbatim_scientificName = readr::col_character(),
.uncer_terms = readr::col_logical(), .eventDate_empty = readr::col_logical(),
.year_outOfRange = readr::col_logical(),
.duplicates = readr::col_logical(), .lonFlag = readr::col_logical(), .latFlag = readr::col_logical(),
.gridSummary = readr::col_logical(), .basisOfRecords_notStandard = readr::col_logical(),
.scientificName_empty = readr::col_logical(), .coordinates_empty = readr::col_logical(),
.coordinates_outOfRange = readr::col_logical(), coordinates_transposed = readr::col_logical(),
country_suggested = readr::col_character(), .countryOutlier = readr::col_logical(),
countryMatch = readr::col_character(), .expertOutlier = readr::col_logical(),
# jbd flags
.occurrenceAbsent = readr::col_logical(), .coordinates_country_inconsistent = readr::col_logical(),
.unLicensed = readr::col_logical(), .invalidName = readr::col_logical(),
.sequential = readr::col_logical(), idContinuity = readr::col_logical(),
.uncertaintyThreshold = readr::col_logical(),
.GBIFflags = readr::col_logical(),
# Paige columns
finalLatitude = readr::col_double(), finalLongitude = readr::col_double(),
Source = readr::col_character(),
# Dynamic dots for extra columns specified by the user
...
) # END ColTypes
return(ColTypes)
} # END ColTypeR
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