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] 9206251
Then pass to occ_search()
(res <- occ_search(taxonKey=key, limit=100))
#> Records found [14820]
#> Records returned [100]
#> No. unique hierarchies [1]
#> No. media records [2]
#> No. facets [0]
#> Args [limit=100, offset=0, taxonKey=9206251, fields=all]
#> # A tibble: 100 × 98
#> name key decimalLatitude decimalLongitude
#> <chr> <int> <dbl> <dbl>
#> 1 Helianthus annuus 1437798345 51.03513 4.518690
#> 2 Helianthus annuus 1437786250 50.91574 3.579770
#> 3 Helianthus annuus 1454554504 50.22000 9.630000
#> 4 Helianthus annuus 1273001624 59.32739 10.803912
#> 5 Helianthus annuus 1454554470 49.32000 12.000000
#> 6 Helianthus annuus 1272997264 58.66189 6.721671
#> 7 Helianthus annuus 1454553080 49.75000 9.300000
#> 8 Helianthus annuus 1272995969 59.83241 10.763219
#> 9 Helianthus annuus 1454553865 NA NA
#> 10 Helianthus annuus 1454555306 48.21000 12.080000
#> # ... with 90 more rows, and 94 more variables: issues <chr>,
#> # datasetKey <chr>, publishingOrgKey <chr>, publishingCountry <chr>,
#> # protocol <chr>, lastCrawled <chr>, lastParsed <chr>, crawlId <int>,
#> # 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>,
#> # coordinateUncertaintyInMeters <dbl>, continent <chr>, year <int>,
#> # month <int>, day <int>, eventDate <chr>, modified <chr>,
#> # lastInterpreted <chr>, license <chr>, identifiers <chr>, facts <chr>,
#> # relations <chr>, geodeticDatum <chr>, class <chr>, countryCode <chr>,
#> # country <chr>, identifier <chr>, verbatimEventDate <chr>,
#> # nomenclaturalCode <chr>, dataGeneralizations <chr>,
#> # verbatimCoordinateSystem <chr>, datasetName <chr>, language <chr>,
#> # gbifID <chr>, occurrenceID <chr>, type <chr>, catalogNumber <chr>,
#> # recordedBy <chr>, institutionCode <chr>, ownerInstitutionCode <chr>,
#> # datasetID <chr>, accessRights <chr>, bibliographicCitation <chr>,
#> # locality <chr>, collectionCode <chr>, individualCount <int>,
#> # elevation <dbl>, elevationAccuracy <dbl>, stateProvince <chr>,
#> # municipality <chr>, county <chr>, coordinatePrecision <dbl>,
#> # habitat <chr>, dateIdentified <chr>, identifiedBy <chr>,
#> # references <chr>, recordNumber <chr>, institutionID <chr>,
#> # dynamicProperties <chr>, associatedTaxa <chr>, startDayOfYear <chr>,
#> # verbatimElevation <chr>, collectionID <chr>, verbatimLocality <chr>,
#> # fieldNotes <chr>, higherGeography <chr>, endDayOfYear <chr>,
#> # higherClassification <chr>, rightsHolder <chr>, rights <chr>,
#> # occurrenceRemarks <chr>, footprintWKT <chr>, occurrenceStatus <chr>,
#> # footprintSRS <chr>, eventID <chr>, fieldNumber <chr>
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 [14820]
#> Records returned [36]
#> No. unique hierarchies [1]
#> No. media records [2]
#> No. facets [0]
#> Args [limit=100, offset=0, taxonKey=9206251, fields=all]
#> # A tibble: 36 × 98
#> name key decimalLatitude decimalLongitude
#> <chr> <int> <dbl> <dbl>
#> 1 Helianthus annuus 1273001624 59.32739 10.803912
#> 2 Helianthus annuus 1272997264 58.66189 6.721671
#> 3 Helianthus annuus 1272995969 59.83241 10.763219
#> 4 Helianthus annuus 1323229476 59.08521 11.036315
#> 5 Helianthus annuus 1323241585 59.57240 10.847597
#> 6 Helianthus annuus 1273024475 59.08521 11.036315
#> 7 Helianthus annuus 1323261789 58.66189 6.721671
#> 8 Helianthus annuus 1305561325 48.57490 7.759700
#> 9 Helianthus annuus 1425285026 59.21189 10.284695
#> 10 Helianthus annuus 1323319952 59.08830 10.060628
#> # ... with 26 more rows, and 94 more variables: issues <chr>,
#> # datasetKey <chr>, publishingOrgKey <chr>, publishingCountry <chr>,
#> # protocol <chr>, lastCrawled <chr>, lastParsed <chr>, crawlId <int>,
#> # 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>,
#> # coordinateUncertaintyInMeters <dbl>, continent <chr>, year <int>,
#> # month <int>, day <int>, eventDate <chr>, modified <chr>,
#> # lastInterpreted <chr>, license <chr>, identifiers <chr>, facts <chr>,
#> # relations <chr>, geodeticDatum <chr>, class <chr>, countryCode <chr>,
#> # country <chr>, identifier <chr>, verbatimEventDate <chr>,
#> # nomenclaturalCode <chr>, dataGeneralizations <chr>,
#> # verbatimCoordinateSystem <chr>, datasetName <chr>, language <chr>,
#> # gbifID <chr>, occurrenceID <chr>, type <chr>, catalogNumber <chr>,
#> # recordedBy <chr>, institutionCode <chr>, ownerInstitutionCode <chr>,
#> # datasetID <chr>, accessRights <chr>, bibliographicCitation <chr>,
#> # locality <chr>, collectionCode <chr>, individualCount <int>,
#> # elevation <dbl>, elevationAccuracy <dbl>, stateProvince <chr>,
#> # municipality <chr>, county <chr>, coordinatePrecision <dbl>,
#> # habitat <chr>, dateIdentified <chr>, identifiedBy <chr>,
#> # references <chr>, recordNumber <chr>, institutionID <chr>,
#> # dynamicProperties <chr>, associatedTaxa <chr>, startDayOfYear <chr>,
#> # verbatimElevation <chr>, collectionID <chr>, verbatimLocality <chr>,
#> # fieldNotes <chr>, higherGeography <chr>, endDayOfYear <chr>,
#> # higherClassification <chr>, rightsHolder <chr>, rights <chr>,
#> # occurrenceRemarks <chr>, footprintWKT <chr>, occurrenceStatus <chr>,
#> # footprintSRS <chr>, eventID <chr>, fieldNumber <chr>
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 [14820]
#> Records returned [100]
#> No. unique hierarchies [1]
#> No. media records [2]
#> No. facets [0]
#> Args [limit=100, offset=0, taxonKey=9206251, fields=all]
#> # A tibble: 100 × 98
#> name key decimalLatitude decimalLongitude
#> <chr> <int> <dbl> <dbl>
#> 1 Helianthus annuus 1437798345 51.03513 4.518690
#> 2 Helianthus annuus 1437786250 50.91574 3.579770
#> 3 Helianthus annuus 1454554504 50.22000 9.630000
#> 4 Helianthus annuus 1273001624 59.32739 10.803912
#> 5 Helianthus annuus 1454554470 49.32000 12.000000
#> 6 Helianthus annuus 1272997264 58.66189 6.721671
#> 7 Helianthus annuus 1454553080 49.75000 9.300000
#> 8 Helianthus annuus 1272995969 59.83241 10.763219
#> 9 Helianthus annuus 1454553865 NA NA
#> 10 Helianthus annuus 1454555306 48.21000 12.080000
#> # ... with 90 more rows, and 94 more variables: issues <chr>,
#> # datasetKey <chr>, publishingOrgKey <chr>, publishingCountry <chr>,
#> # protocol <chr>, lastCrawled <chr>, lastParsed <chr>, crawlId <int>,
#> # 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>,
#> # coordinateUncertaintyInMeters <dbl>, continent <chr>, year <int>,
#> # month <int>, day <int>, eventDate <chr>, modified <chr>,
#> # lastInterpreted <chr>, license <chr>, identifiers <chr>, facts <chr>,
#> # relations <chr>, geodeticDatum <chr>, class <chr>, countryCode <chr>,
#> # country <chr>, identifier <chr>, verbatimEventDate <chr>,
#> # nomenclaturalCode <chr>, dataGeneralizations <chr>,
#> # verbatimCoordinateSystem <chr>, datasetName <chr>, language <chr>,
#> # gbifID <chr>, occurrenceID <chr>, type <chr>, catalogNumber <chr>,
#> # recordedBy <chr>, institutionCode <chr>, ownerInstitutionCode <chr>,
#> # datasetID <chr>, accessRights <chr>, bibliographicCitation <chr>,
#> # locality <chr>, collectionCode <chr>, individualCount <int>,
#> # elevation <dbl>, elevationAccuracy <dbl>, stateProvince <chr>,
#> # municipality <chr>, county <chr>, coordinatePrecision <dbl>,
#> # habitat <chr>, dateIdentified <chr>, identifiedBy <chr>,
#> # references <chr>, recordNumber <chr>, institutionID <chr>,
#> # dynamicProperties <chr>, associatedTaxa <chr>, startDayOfYear <chr>,
#> # verbatimElevation <chr>, collectionID <chr>, verbatimLocality <chr>,
#> # fieldNotes <chr>, higherGeography <chr>, endDayOfYear <chr>,
#> # higherClassification <chr>, rightsHolder <chr>, rights <chr>,
#> # occurrenceRemarks <chr>, footprintWKT <chr>, occurrenceStatus <chr>,
#> # footprintSRS <chr>, eventID <chr>, fieldNumber <chr>
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$data[,c(1,5)])
#> # A tibble: 6 × 2
#> name
#> <chr>
#> 1 Helianthus annuus
#> 2 Helianthus annuus
#> 3 Helianthus annuus
#> 4 Helianthus annuus
#> 5 Helianthus annuus
#> 6 Helianthus annuus
#> # ... with 1 more variables: issues <chr>
Sometimes you may want to have each type of issue as a separate column.
Split out each issue type into a separate column, with number of columns equal to number of issue types
out <- res %>% occ_issues(mutate = "split")
head(out$data[,c(1,5:10)])
#> # A tibble: 6 × 7
#> name refuriiv bri cudc cdround gass84 cucdmis
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Helianthus annuus y n n n n n
#> 2 Helianthus annuus y n n n n n
#> 3 Helianthus annuus n y y n n n
#> 4 Helianthus annuus n n n y y n
#> 5 Helianthus annuus n n y n n n
#> 6 Helianthus annuus n n n y y 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)])
#> # A tibble: 6 × 7
#> name REFERENCES_URI_INVALID BASIS_OF_RECORD_INVALID
#> <chr> <chr> <chr>
#> 1 Helianthus annuus y n
#> 2 Helianthus annuus y n
#> 3 Helianthus annuus n y
#> 4 Helianthus annuus n n
#> 5 Helianthus annuus n n
#> 6 Helianthus annuus n n
#> # ... with 4 more variables: COUNTRY_DERIVED_FROM_COORDINATES <chr>,
#> # COORDINATE_ROUNDED <chr>, GEODETIC_DATUM_ASSUMED_WGS84 <chr>,
#> # COUNTRY_COORDINATE_MISMATCH <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.