rgbif
now has the ability to clean data retrieved from GBIF based on GBIF issues. These issues are returned in data retrieved from GBIF, e.g., through the occ_search()
function. Inspired by magrittr
, we've setup a workflow for cleaning data based on using the operator %>%
. You don't have to use it, but as we show below, it can make the process quite easy.
Note that you can also query based on issues, e.g., occ_search(taxonKey=1, issue='DEPTH_UNLIKELY')
. However, we imagine it's more likely that you want to search for occurrences based on a taxonomic name, or geographic area, not based on issues, so it makes sense to pull data down, then clean as needed using the below workflow with occ_issues()
.
Note that occ_issues()
only affects the data element in the gbif class that is returned from a call to occ_search()
. Maybe in a future version we will remove the associated records from the hierarchy and media elements as they are remove from the data element.
You also get issues data back with occ_get()
, but occ_issues()
doesn't yet support working with data from occ_get()
.
Install from CRAN
install.packages("rgbif")
Or install the development version from GitHub
devtools::install_github("ropensci/rgbif")
Load rgbif
library('rgbif')
Get taxon key for Helianthus annuus
(key <- name_suggest(q='Helianthus annuus', rank='species')$key[1])
#> [1] 3119195
Then pass to occ_search()
(res <- occ_search(taxonKey=key, limit=100))
#> Records found [31357]
#> Records returned [100]
#> No. unique hierarchies [1]
#> No. media records [42]
#> Args [taxonKey=3119195, limit=100, offset=0, fields=all]
#> Source: local data frame [100 x 98]
#>
#> name key decimalLatitude decimalLongitude
#> <chr> <int> <dbl> <dbl>
#> 1 Helianthus annuus 1249279611 34.04810 -117.79884
#> 2 Helianthus annuus 1248872560 37.81227 -8.82959
#> 3 Helianthus annuus 1248887127 38.53339 -8.94263
#> 4 Helianthus annuus 1248873088 38.53339 -8.94263
#> 5 Helianthus annuus 1253308332 29.67463 -95.44804
#> 6 Helianthus annuus 1249286909 32.58747 -97.10081
#> 7 Helianthus annuus 1265544678 32.58747 -97.10081
#> 8 Helianthus annuus 1262385911 32.78328 -96.70352
#> 9 Helianthus annuus 1262375813 29.82586 -95.45604
#> 10 Helianthus annuus 1262379231 34.04911 -117.80066
#> .. ... ... ... ...
#> Variables not shown: issues <chr>, datasetKey <chr>, publishingOrgKey
#> <chr>, publishingCountry <chr>, protocol <chr>, lastCrawled <chr>,
#> lastParsed <chr>, extensions <chr>, basisOfRecord <chr>, taxonKey <int>,
#> kingdomKey <int>, phylumKey <int>, classKey <int>, orderKey <int>,
#> familyKey <int>, genusKey <int>, speciesKey <int>, scientificName <chr>,
#> kingdom <chr>, phylum <chr>, order <chr>, family <chr>, genus <chr>,
#> species <chr>, genericName <chr>, specificEpithet <chr>, taxonRank
#> <chr>, dateIdentified <chr>, year <int>, month <int>, day <int>,
#> eventDate <chr>, modified <chr>, lastInterpreted <chr>, references
#> <chr>, identifiers <chr>, facts <chr>, relations <chr>, geodeticDatum
#> <chr>, class <chr>, countryCode <chr>, country <chr>, rightsHolder
#> <chr>, identifier <chr>, verbatimEventDate <chr>, datasetName <chr>,
#> gbifID <chr>, verbatimLocality <chr>, collectionCode <chr>, occurrenceID
#> <chr>, taxonID <chr>, license <chr>, catalogNumber <chr>, recordedBy
#> <chr>, http...unknown.org.occurrenceDetails <chr>, institutionCode
#> <chr>, rights <chr>, eventTime <chr>, identificationID <chr>,
#> infraspecificEpithet <chr>, institutionID <chr>, nomenclaturalCode
#> <chr>, dataGeneralizations <chr>, footprintWKT <chr>, county <chr>,
#> municipality <chr>, language <chr>, occurrenceStatus <chr>, footprintSRS
#> <chr>, ownerInstitutionCode <chr>, higherClassification <chr>,
#> reproductiveCondition <chr>, identifiedBy <chr>, collectionID <chr>,
#> occurrenceRemarks <chr>, coordinateUncertaintyInMeters <dbl>,
#> informationWithheld <chr>, individualCount <int>, coordinatePrecision
#> <dbl>, elevation <dbl>, elevationAccuracy <dbl>, stateProvince <chr>,
#> locality <chr>, habitat <chr>, recordNumber <chr>, type <chr>,
#> preparations <chr>, continent <chr>, verbatimCoordinateSystem <chr>,
#> datasetID <chr>, accessRights <chr>, bibliographicCitation <chr>, depth
#> <dbl>, depthAccuracy <dbl>.
The dataset gbifissues
can be retrieved using the function gbif_issues()
. The dataset's first column code
is a code that is used by default in the results from occ_search()
, while the second column issue
is the full issue name given by GBIF. The third column is a full description of the issue.
head(gbif_issues())
#> code issue
#> 1 bri BASIS_OF_RECORD_INVALID
#> 2 ccm CONTINENT_COUNTRY_MISMATCH
#> 3 cdc CONTINENT_DERIVED_FROM_COORDINATES
#> 4 conti CONTINENT_INVALID
#> 5 cdiv COORDINATE_INVALID
#> 6 cdout COORDINATE_OUT_OF_RANGE
#> description
#> 1 The given basis of record is impossible to interpret or seriously different from the recommended vocabulary.
#> 2 The interpreted continent and country do not match up.
#> 3 The interpreted continent is based on the coordinates, not the verbatim string information.
#> 4 Uninterpretable continent values found.
#> 5 Coordinate value given in some form but GBIF is unable to interpret it.
#> 6 Coordinate has invalid lat/lon values out of their decimal max range.
You can query to get certain issues
gbif_issues()[ gbif_issues()$code %in% c('cdround','cudc','gass84','txmathi'), ]
#> code issue
#> 10 cdround COORDINATE_ROUNDED
#> 12 cudc COUNTRY_DERIVED_FROM_COORDINATES
#> 23 gass84 GEODETIC_DATUM_ASSUMED_WGS84
#> 39 txmathi TAXON_MATCH_HIGHERRANK
#> description
#> 10 Original coordinate modified by rounding to 5 decimals.
#> 12 The interpreted country is based on the coordinates, not the verbatim string information.
#> 23 Indicating that the interpreted coordinates assume they are based on WGS84 datum as the datum was either not indicated or interpretable.
#> 39 Matching to the taxonomic backbone can only be done on a higher rank and not the scientific name.
The code cdround
represents the GBIF issue COORDINATE_ROUNDED
, which means that
Original coordinate modified by rounding to 5 decimals.
The content for this information comes from http://gbif.github.io/gbif-api/apidocs/org/gbif/api/vocabulary/OccurrenceIssue.html.
Now that we know a bit about GBIF issues, you can parse your data based on issues. Using the data generated above, and using the function %>%
imported from magrittr
, we can get only data with the issue gass84
, or GEODETIC_DATUM_ASSUMED_WGS84
(Note how the records returned goes down to 98 instead of the initial 100).
res %>%
occ_issues(gass84)
#> Records found [31357]
#> Records returned [63]
#> No. unique hierarchies [1]
#> No. media records [42]
#> Args [taxonKey=3119195, limit=100, offset=0, fields=all]
#> Source: local data frame [63 x 98]
#>
#> name key decimalLatitude decimalLongitude
#> <chr> <int> <dbl> <dbl>
#> 1 Helianthus annuus 1249279611 34.04810 -117.79884
#> 2 Helianthus annuus 1253308332 29.67463 -95.44804
#> 3 Helianthus annuus 1249286909 32.58747 -97.10081
#> 4 Helianthus annuus 1265544678 32.58747 -97.10081
#> 5 Helianthus annuus 1262385911 32.78328 -96.70352
#> 6 Helianthus annuus 1262375813 29.82586 -95.45604
#> 7 Helianthus annuus 1262379231 34.04911 -117.80066
#> 8 Helianthus annuus 1270045172 33.92958 -117.37322
#> 9 Helianthus annuus 1265590198 25.76265 -100.25513
#> 10 Helianthus annuus 1265560496 34.12861 -118.20700
#> .. ... ... ... ...
#> Variables not shown: issues <chr>, datasetKey <chr>, publishingOrgKey
#> <chr>, publishingCountry <chr>, protocol <chr>, lastCrawled <chr>,
#> lastParsed <chr>, extensions <chr>, basisOfRecord <chr>, taxonKey <int>,
#> kingdomKey <int>, phylumKey <int>, classKey <int>, orderKey <int>,
#> familyKey <int>, genusKey <int>, speciesKey <int>, scientificName <chr>,
#> kingdom <chr>, phylum <chr>, order <chr>, family <chr>, genus <chr>,
#> species <chr>, genericName <chr>, specificEpithet <chr>, taxonRank
#> <chr>, dateIdentified <chr>, year <int>, month <int>, day <int>,
#> eventDate <chr>, modified <chr>, lastInterpreted <chr>, references
#> <chr>, identifiers <chr>, facts <chr>, relations <chr>, geodeticDatum
#> <chr>, class <chr>, countryCode <chr>, country <chr>, rightsHolder
#> <chr>, identifier <chr>, verbatimEventDate <chr>, datasetName <chr>,
#> gbifID <chr>, verbatimLocality <chr>, collectionCode <chr>, occurrenceID
#> <chr>, taxonID <chr>, license <chr>, catalogNumber <chr>, recordedBy
#> <chr>, http...unknown.org.occurrenceDetails <chr>, institutionCode
#> <chr>, rights <chr>, eventTime <chr>, identificationID <chr>,
#> infraspecificEpithet <chr>, institutionID <chr>, nomenclaturalCode
#> <chr>, dataGeneralizations <chr>, footprintWKT <chr>, county <chr>,
#> municipality <chr>, language <chr>, occurrenceStatus <chr>, footprintSRS
#> <chr>, ownerInstitutionCode <chr>, higherClassification <chr>,
#> reproductiveCondition <chr>, identifiedBy <chr>, collectionID <chr>,
#> occurrenceRemarks <chr>, coordinateUncertaintyInMeters <dbl>,
#> informationWithheld <chr>, individualCount <int>, coordinatePrecision
#> <dbl>, elevation <dbl>, elevationAccuracy <dbl>, stateProvince <chr>,
#> locality <chr>, habitat <chr>, recordNumber <chr>, type <chr>,
#> preparations <chr>, continent <chr>, verbatimCoordinateSystem <chr>,
#> datasetID <chr>, accessRights <chr>, bibliographicCitation <chr>, depth
#> <dbl>, depthAccuracy <dbl>.
Note also that we've set up occ_issues()
so that you can pass in issue names without having to quote them, thereby speeding up data cleaning.
Next, we can remove data with certain issues just as easily by using a -
sign in front of the variable, like this, removing data with issues depunl
and mdatunl
.
res %>%
occ_issues(-depunl, -mdatunl)
#> Records found [31357]
#> Records returned [94]
#> No. unique hierarchies [1]
#> No. media records [42]
#> Args [taxonKey=3119195, limit=100, offset=0, fields=all]
#> Source: local data frame [94 x 98]
#>
#> name key decimalLatitude decimalLongitude
#> <chr> <int> <dbl> <dbl>
#> 1 Helianthus annuus 1249279611 34.04810 -117.79884
#> 2 Helianthus annuus 1248872560 37.81227 -8.82959
#> 3 Helianthus annuus 1248887127 38.53339 -8.94263
#> 4 Helianthus annuus 1248873088 38.53339 -8.94263
#> 5 Helianthus annuus 1253308332 29.67463 -95.44804
#> 6 Helianthus annuus 1249286909 32.58747 -97.10081
#> 7 Helianthus annuus 1265544678 32.58747 -97.10081
#> 8 Helianthus annuus 1262385911 32.78328 -96.70352
#> 9 Helianthus annuus 1262375813 29.82586 -95.45604
#> 10 Helianthus annuus 1262379231 34.04911 -117.80066
#> .. ... ... ... ...
#> Variables not shown: issues <chr>, datasetKey <chr>, publishingOrgKey
#> <chr>, publishingCountry <chr>, protocol <chr>, lastCrawled <chr>,
#> lastParsed <chr>, extensions <chr>, basisOfRecord <chr>, taxonKey <int>,
#> kingdomKey <int>, phylumKey <int>, classKey <int>, orderKey <int>,
#> familyKey <int>, genusKey <int>, speciesKey <int>, scientificName <chr>,
#> kingdom <chr>, phylum <chr>, order <chr>, family <chr>, genus <chr>,
#> species <chr>, genericName <chr>, specificEpithet <chr>, taxonRank
#> <chr>, dateIdentified <chr>, year <int>, month <int>, day <int>,
#> eventDate <chr>, modified <chr>, lastInterpreted <chr>, references
#> <chr>, identifiers <chr>, facts <chr>, relations <chr>, geodeticDatum
#> <chr>, class <chr>, countryCode <chr>, country <chr>, rightsHolder
#> <chr>, identifier <chr>, verbatimEventDate <chr>, datasetName <chr>,
#> gbifID <chr>, verbatimLocality <chr>, collectionCode <chr>, occurrenceID
#> <chr>, taxonID <chr>, license <chr>, catalogNumber <chr>, recordedBy
#> <chr>, http...unknown.org.occurrenceDetails <chr>, institutionCode
#> <chr>, rights <chr>, eventTime <chr>, identificationID <chr>,
#> infraspecificEpithet <chr>, institutionID <chr>, nomenclaturalCode
#> <chr>, dataGeneralizations <chr>, footprintWKT <chr>, county <chr>,
#> municipality <chr>, language <chr>, occurrenceStatus <chr>, footprintSRS
#> <chr>, ownerInstitutionCode <chr>, higherClassification <chr>,
#> reproductiveCondition <chr>, identifiedBy <chr>, collectionID <chr>,
#> occurrenceRemarks <chr>, coordinateUncertaintyInMeters <dbl>,
#> informationWithheld <chr>, individualCount <int>, coordinatePrecision
#> <dbl>, elevation <dbl>, elevationAccuracy <dbl>, stateProvince <chr>,
#> locality <chr>, habitat <chr>, recordNumber <chr>, type <chr>,
#> preparations <chr>, continent <chr>, verbatimCoordinateSystem <chr>,
#> datasetID <chr>, accessRights <chr>, bibliographicCitation <chr>, depth
#> <dbl>, depthAccuracy <dbl>.
Another thing we can do with occ_issues()
is go from issue codes to full issue names in case you want those in your dataset (here, showing only a few columns to see the data better for this demo):
out <- res %>% occ_issues(mutate = "expand")
head(out$data[,c(1,5)])
#> Source: local data frame [6 x 2]
#>
#> name issues
#> <chr> <chr>
#> 1 Helianthus annuus COORDINATE_ROUNDED,GEODETIC_DATUM_ASSUMED_WGS84
#> 2 Helianthus annuus
#> 3 Helianthus annuus
#> 4 Helianthus annuus
#> 5 Helianthus annuus COORDINATE_ROUNDED,GEODETIC_DATUM_ASSUMED_WGS84
#> 6 Helianthus annuus COORDINATE_ROUNDED,GEODETIC_DATUM_ASSUMED_WGS84
Sometimes you may want to have each type of issue as a separate column.
Split out each issue type into a separate column, with number of columns equal to number of issue types
out <- res %>% occ_issues(mutate = "split")
head(out$data[,c(1,5:10)])
#> Source: local data frame [6 x 7]
#>
#> name cdround gass84 cucdmis zerocd refuriiv cudc
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Helianthus annuus y y n n n n
#> 2 Helianthus annuus n n n n n n
#> 3 Helianthus annuus n n n n n n
#> 4 Helianthus annuus n n n n n n
#> 5 Helianthus annuus y y n n n n
#> 6 Helianthus annuus y y n n n n
Or you can expand each issue type into its full name, and split each issue into a separate column.
out <- res %>% occ_issues(mutate = "split_expand")
head(out$data[,c(1,5:10)])
#> Source: local data frame [6 x 7]
#>
#> name COORDINATE_ROUNDED GEODETIC_DATUM_ASSUMED_WGS84
#> <chr> <chr> <chr>
#> 1 Helianthus annuus y y
#> 2 Helianthus annuus n n
#> 3 Helianthus annuus n n
#> 4 Helianthus annuus n n
#> 5 Helianthus annuus y y
#> 6 Helianthus annuus y y
#> Variables not shown: COUNTRY_COORDINATE_MISMATCH <chr>, ZERO_COORDINATE
#> <chr>, REFERENCES_URI_INVALID <chr>, COUNTRY_DERIVED_FROM_COORDINATES
#> <chr>.
We hope this helps users get just the data they want, and nothing more. Let us know if you have feedback on data cleaning functionality in rgbif
at info@ropensci.org or at https://github.com/ropensci/rgbif/issues.