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 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 ....
ColTypeR(...)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()
Returns an object of class col_spec.
See readr::as.col_spec() for additional context and explication.
# You can simply return the below for default values
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
BeeBDC::ColTypeR()
#> cols_only(
#> database_id = col_character(),
#> scientificName = col_character(),
#> family = col_character(),
#> subfamily = col_character(),
#> genus = col_character(),
#> subgenus = col_character(),
#> subspecies = col_character(),
#> species = col_character(),
#> specificEpithet = col_character(),
#> infraspecificEpithet = col_character(),
#> acceptedNameUsage = col_character(),
#> taxonRank = col_character(),
#> scientificNameAuthorship = col_character(),
#> identificationQualifier = col_character(),
#> higherClassification = col_character(),
#> identificationReferences = col_character(),
#> typeStatus = col_character(),
#> previousIdentifications = col_character(),
#> verbatimIdentification = col_character(),
#> identifiedBy = col_character(),
#> dateIdentified = col_character(),
#> decimalLatitude = col_double(),
#> decimalLongitude = col_double(),
#> verbatimLatitude = col_character(),
#> verbatimLongitude = col_character(),
#> verbatimElevation = col_character(),
#> stateProvince = col_character(),
#> country = col_character(),
#> continent = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> locality = col_character(),
#> island = col_character(),
#> county = col_character(),
#> municipality = col_character(),
#> countryCode = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> level0Gid = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> level0Name = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> level1Gid = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> level1Name = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> license = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> issue = col_character(),
#> eventDate = col_character(),
#> eventTime = col_character(),
#> startDayOfYear = col_integer(),
#> endDayOfYear = col_integer(),
#> day = col_integer(),
#> month = col_integer(),
#> year = col_integer(),
#> basisOfRecord = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> type = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> occurrenceStatus = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> recordNumber = col_character(),
#> recordedBy = col_character(),
#> eventID = col_character(),
#> Location = col_character(),
#> samplingProtocol = col_character(),
#> samplingEffort = col_character(),
#> individualCount = col_double(),
#> organismQuantity = col_double(),
#> coordinatePrecision = col_double(),
#> coordinateUncertaintyInMeters = col_double(),
#> spatiallyValid = col_logical(),
#> catalogNumber = col_character(),
#> gbifID = col_character(),
#> datasetID = col_character(),
#> institutionCode = col_character(),
#> datasetName = col_character(),
#> otherCatalogNumbers = col_character(),
#> occurrenceID = col_character(),
#> taxonKey = col_character(),
#> coreid = col_character(),
#> recordId = col_character(),
#> collectionID = col_character(),
#> associatedSequences = col_character(),
#> verbatimScientificName = col_character(),
#> verbatimEventDate = col_character(),
#> associatedTaxa = col_character(),
#> associatedOrganisms = col_character(),
#> fieldNotes = col_character(),
#> sex = col_character(),
#> rights = col_character(),
#> rightsHolder = col_character(),
#> accessRights = col_character(),
#> dctermsLicense = col_character(),
#> dctermsType = col_character(),
#> dctermsAccessRights = col_character(),
#> associatedReferences = col_character(),
#> bibliographicCitation = col_character(),
#> dctermsBibliographicCitation = col_character(),
#> references = col_character(),
#> flags = col_character(),
#> informationWithheld = col_character(),
#> isDuplicateOf = col_character(),
#> hasCoordinate = col_logical(),
#> hasGeospatialIssues = col_logical(),
#> assertions = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> occurrenceYear = col_datetime(format = ""),
#> id = col_character(),
#> duplicateStatus = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> associatedOccurrences = col_character(),
#> locationRemarks = col_character(),
#> dataSource = col_character(),
#> dataBase_scientificName = col_character(),
#> .rou = col_logical(),
#> .val = col_logical(),
#> .equ = col_logical(),
#> .zer = col_logical(),
#> .cap = col_logical(),
#> .cen = col_logical(),
#> .sea = col_logical(),
#> .otl = col_logical(),
#> .gbf = col_logical(),
#> .inst = col_logical(),
#> .dpl = col_logical(),
#> .summary = col_logical(),
#> names_clean = col_character(),
#> verbatim_scientificName = col_character(),
#> .uncer_terms = col_logical(),
#> .eventDate_empty = col_logical(),
#> .year_outOfRange = col_logical(),
#> .duplicates = col_logical(),
#> .lonFlag = col_logical(),
#> .latFlag = col_logical(),
#> .gridSummary = col_logical(),
#> .basisOfRecords_notStandard = col_logical(),
#> .scientificName_empty = col_logical(),
#> .coordinates_empty = col_logical(),
#> .coordinates_outOfRange = col_logical(),
#> coordinates_transposed = col_logical(),
#> country_suggested = col_character(),
#> .countryOutlier = col_logical(),
#> countryMatch = col_character(),
#> .expertOutlier = col_logical(),
#> .occurrenceAbsent = col_logical(),
#> .coordinates_country_inconsistent = col_logical(),
#> .unLicensed = col_logical(),
#> .invalidName = col_logical(),
#> .sequential = col_logical(),
#> idContinuity = col_logical(),
#> .uncertaintyThreshold = col_logical(),
#> .GBIFflags = col_logical(),
#> finalLatitude = col_double(),
#> finalLongitude = col_double(),
#> Source = col_character()
#> )
# To add new columns you can write
ColTypeR(newCharacterColumn = readr::col_character(),
newNumericColumn = readr::col_integer(),
newLogicalColumn = readr::col_logical())
#> cols_only(
#> database_id = col_character(),
#> scientificName = col_character(),
#> family = col_character(),
#> subfamily = col_character(),
#> genus = col_character(),
#> subgenus = col_character(),
#> subspecies = col_character(),
#> species = col_character(),
#> specificEpithet = col_character(),
#> infraspecificEpithet = col_character(),
#> acceptedNameUsage = col_character(),
#> taxonRank = col_character(),
#> scientificNameAuthorship = col_character(),
#> identificationQualifier = col_character(),
#> higherClassification = col_character(),
#> identificationReferences = col_character(),
#> typeStatus = col_character(),
#> previousIdentifications = col_character(),
#> verbatimIdentification = col_character(),
#> identifiedBy = col_character(),
#> dateIdentified = col_character(),
#> decimalLatitude = col_double(),
#> decimalLongitude = col_double(),
#> verbatimLatitude = col_character(),
#> verbatimLongitude = col_character(),
#> verbatimElevation = col_character(),
#> stateProvince = col_character(),
#> country = col_character(),
#> continent = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> locality = col_character(),
#> island = col_character(),
#> county = col_character(),
#> municipality = col_character(),
#> countryCode = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> level0Gid = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> level0Name = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> level1Gid = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> level1Name = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> license = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> issue = col_character(),
#> eventDate = col_character(),
#> eventTime = col_character(),
#> startDayOfYear = col_integer(),
#> endDayOfYear = col_integer(),
#> day = col_integer(),
#> month = col_integer(),
#> year = col_integer(),
#> basisOfRecord = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> type = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> occurrenceStatus = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> recordNumber = col_character(),
#> recordedBy = col_character(),
#> eventID = col_character(),
#> Location = col_character(),
#> samplingProtocol = col_character(),
#> samplingEffort = col_character(),
#> individualCount = col_double(),
#> organismQuantity = col_double(),
#> coordinatePrecision = col_double(),
#> coordinateUncertaintyInMeters = col_double(),
#> spatiallyValid = col_logical(),
#> catalogNumber = col_character(),
#> gbifID = col_character(),
#> datasetID = col_character(),
#> institutionCode = col_character(),
#> datasetName = col_character(),
#> otherCatalogNumbers = col_character(),
#> occurrenceID = col_character(),
#> taxonKey = col_character(),
#> coreid = col_character(),
#> recordId = col_character(),
#> collectionID = col_character(),
#> associatedSequences = col_character(),
#> verbatimScientificName = col_character(),
#> verbatimEventDate = col_character(),
#> associatedTaxa = col_character(),
#> associatedOrganisms = col_character(),
#> fieldNotes = col_character(),
#> sex = col_character(),
#> rights = col_character(),
#> rightsHolder = col_character(),
#> accessRights = col_character(),
#> dctermsLicense = col_character(),
#> dctermsType = col_character(),
#> dctermsAccessRights = col_character(),
#> associatedReferences = col_character(),
#> bibliographicCitation = col_character(),
#> dctermsBibliographicCitation = col_character(),
#> references = col_character(),
#> flags = col_character(),
#> informationWithheld = col_character(),
#> isDuplicateOf = col_character(),
#> hasCoordinate = col_logical(),
#> hasGeospatialIssues = col_logical(),
#> assertions = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> occurrenceYear = col_datetime(format = ""),
#> id = col_character(),
#> duplicateStatus = col_factor(levels = NULL, ordered = FALSE, include_na = FALSE),
#> associatedOccurrences = col_character(),
#> locationRemarks = col_character(),
#> dataSource = col_character(),
#> dataBase_scientificName = col_character(),
#> .rou = col_logical(),
#> .val = col_logical(),
#> .equ = col_logical(),
#> .zer = col_logical(),
#> .cap = col_logical(),
#> .cen = col_logical(),
#> .sea = col_logical(),
#> .otl = col_logical(),
#> .gbf = col_logical(),
#> .inst = col_logical(),
#> .dpl = col_logical(),
#> .summary = col_logical(),
#> names_clean = col_character(),
#> verbatim_scientificName = col_character(),
#> .uncer_terms = col_logical(),
#> .eventDate_empty = col_logical(),
#> .year_outOfRange = col_logical(),
#> .duplicates = col_logical(),
#> .lonFlag = col_logical(),
#> .latFlag = col_logical(),
#> .gridSummary = col_logical(),
#> .basisOfRecords_notStandard = col_logical(),
#> .scientificName_empty = col_logical(),
#> .coordinates_empty = col_logical(),
#> .coordinates_outOfRange = col_logical(),
#> coordinates_transposed = col_logical(),
#> country_suggested = col_character(),
#> .countryOutlier = col_logical(),
#> countryMatch = col_character(),
#> .expertOutlier = col_logical(),
#> .occurrenceAbsent = col_logical(),
#> .coordinates_country_inconsistent = col_logical(),
#> .unLicensed = col_logical(),
#> .invalidName = col_logical(),
#> .sequential = col_logical(),
#> idContinuity = col_logical(),
#> .uncertaintyThreshold = col_logical(),
#> .GBIFflags = col_logical(),
#> finalLatitude = col_double(),
#> finalLongitude = col_double(),
#> Source = col_character(),
#> newCharacterColumn = col_character(),
#> newNumericColumn = col_integer(),
#> newLogicalColumn = col_logical()
#> )
# Try reading in one of the test datasets as an example:
beesFlagged %>% dplyr::as_tibble(col_types = BeeBDC::ColTypeR())
#> # A tibble: 100 × 124
#> database_id scientificName family subfamily genus subgenus subspecies species
#> <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <chr>
#> 1 Dorey_data… Pseudoanthidi… Megac… Megachil… Pseu… NA NA Pseudo…
#> 2 Dorey_data… Macrotera arc… Andre… Panurgin… Macr… NA NA Macrot…
#> 3 Dorey_data… Xanthesma fur… Colle… Euryglos… Xant… NA NA Xanthe…
#> 4 Dorey_data… Exomalopsis s… Apidae Apinae Exom… NA NA Exomal…
#> 5 Dorey_data… Osmia bicolor… Megac… Megachil… Osmia NA NA Osmia …
#> 6 Paige_data… Augochlorella… Halic… Halictin… Augo… NA NA Augoch…
#> 7 Dorey_data… Megachile api… Megac… Megachil… Mega… NA NA Megach…
#> 8 Dorey_data… Trigona dalla… Apidae Apinae Trig… NA NA Trigon…
#> 9 Dorey_data… Habropoda mis… Apidae Apinae Habr… NA NA Habrop…
#> 10 Dorey_data… Lasioglossum … Halic… Halictin… Lasi… NA NA Lasiog…
#> # ℹ 90 more rows
#> # ℹ 116 more variables: specificEpithet <chr>, infraspecificEpithet <chr>,
#> # acceptedNameUsage <lgl>, taxonRank <chr>, scientificNameAuthorship <chr>,
#> # identificationQualifier <lgl>, higherClassification <chr>,
#> # identificationReferences <lgl>, typeStatus <chr>,
#> # previousIdentifications <chr>, verbatimIdentification <chr>,
#> # identifiedBy <chr>, dateIdentified <chr>, decimalLatitude <dbl>, …
# OR
beesRaw %>% dplyr::as_tibble(col_types = BeeBDC::ColTypeR())
#> # A tibble: 100 × 90
#> database_id scientificName family subfamily genus subgenus subspecies species
#> <chr> <chr> <chr> <chr> <chr> <chr> <lgl> <chr>
#> 1 Dorey_data… Pseudoanthidi… Megac… Megachil… Pseu… NA NA Pseudo…
#> 2 Dorey_data… Macrotera arc… Andre… Panurgin… Macr… NA NA Macrot…
#> 3 Dorey_data… Xanthesma fur… Colle… Euryglos… Xant… NA NA Xanthe…
#> 4 Dorey_data… Exomalopsis s… Apidae Apinae Exom… NA NA Exomal…
#> 5 Dorey_data… Osmia bicolor… Megac… Megachil… Osmia NA NA Osmia …
#> 6 Paige_data… Augochlorella… Halic… Halictin… Augo… NA NA Augoch…
#> 7 Dorey_data… Megachile api… Megac… Megachil… Mega… NA NA Megach…
#> 8 Dorey_data… Trigona dalla… Apidae Apinae Trig… NA NA Trigon…
#> 9 Dorey_data… Habropoda mis… Apidae Apinae Habr… NA NA Habrop…
#> 10 Dorey_data… Lasioglossum … Halic… Halictin… Lasi… NA NA Lasiog…
#> # ℹ 90 more rows
#> # ℹ 82 more variables: specificEpithet <chr>, infraspecificEpithet <chr>,
#> # acceptedNameUsage <lgl>, taxonRank <chr>, scientificNameAuthorship <chr>,
#> # identificationQualifier <lgl>, higherClassification <chr>,
#> # identificationReferences <lgl>, typeStatus <chr>,
#> # previousIdentifications <chr>, verbatimIdentification <chr>,
#> # identifiedBy <chr>, dateIdentified <chr>, decimalLatitude <dbl>, …