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 [21970]
#> Records returned [100]
#> No. unique hierarchies [1]
#> No. media records [63]
#> No. facets [0]
#> Args [taxonKey=3119195, limit=100, offset=0, fields=all]
#> # A tibble: 100 × 77
#> name key decimalLatitude decimalLongitude
#> <chr> <int> <dbl> <dbl>
#> 1 Helianthus annuus 1249279611 34.04810 -117.79884
#> 2 Helianthus annuus 1315048347 34.04377 -116.94136
#> 3 Helianthus annuus 1305118889 18.40386 -66.04487
#> 4 Helianthus annuus 1249286909 32.58747 -97.10081
#> 5 Helianthus annuus 1253308332 29.67463 -95.44804
#> 6 Helianthus annuus 1262375813 29.82586 -95.45604
#> 7 Helianthus annuus 1262385911 32.78328 -96.70352
#> 8 Helianthus annuus 1265544678 32.58747 -97.10081
#> 9 Helianthus annuus 1262379231 34.04911 -117.80066
#> 10 Helianthus annuus 1265560496 34.12861 -118.20700
#> # ... with 90 more rows, and 73 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>, dateIdentified <chr>,
#> # year <int>, month <int>, day <int>, eventDate <chr>, modified <chr>,
#> # lastInterpreted <chr>, references <chr>, license <chr>,
#> # identifiers <chr>, facts <chr>, relations <chr>, geodeticDatum <chr>,
#> # class <chr>, countryCode <chr>, country <chr>, rightsHolder <chr>,
#> # identifier <chr>, verbatimEventDate <chr>, datasetName <chr>,
#> # verbatimLocality <chr>, gbifID <chr>, collectionCode <chr>,
#> # occurrenceID <chr>, taxonID <chr>, recordedBy <chr>,
#> # catalogNumber <chr>, http...unknown.org.occurrenceDetails <chr>,
#> # institutionCode <chr>, rights <chr>, eventTime <chr>,
#> # identificationID <chr>, coordinateUncertaintyInMeters <dbl>,
#> # occurrenceRemarks <chr>, informationWithheld <chr>,
#> # individualCount <int>, elevation <dbl>, elevationAccuracy <dbl>,
#> # stateProvince <chr>, county <chr>, municipality <chr>, locality <chr>,
#> # coordinatePrecision <dbl>, habitat <chr>, identifiedBy <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 [21970]
#> Records returned [99]
#> No. unique hierarchies [1]
#> No. media records [63]
#> No. facets [0]
#> Args [taxonKey=3119195, limit=100, offset=0, fields=all]
#> # A tibble: 99 × 77
#> name key decimalLatitude decimalLongitude
#> <chr> <int> <dbl> <dbl>
#> 1 Helianthus annuus 1249279611 34.04810 -117.79884
#> 2 Helianthus annuus 1315048347 34.04377 -116.94136
#> 3 Helianthus annuus 1305118889 18.40386 -66.04487
#> 4 Helianthus annuus 1249286909 32.58747 -97.10081
#> 5 Helianthus annuus 1253308332 29.67463 -95.44804
#> 6 Helianthus annuus 1262375813 29.82586 -95.45604
#> 7 Helianthus annuus 1262385911 32.78328 -96.70352
#> 8 Helianthus annuus 1265544678 32.58747 -97.10081
#> 9 Helianthus annuus 1262379231 34.04911 -117.80066
#> 10 Helianthus annuus 1265560496 34.12861 -118.20700
#> # ... with 89 more rows, and 73 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>, dateIdentified <chr>,
#> # year <int>, month <int>, day <int>, eventDate <chr>, modified <chr>,
#> # lastInterpreted <chr>, references <chr>, license <chr>,
#> # identifiers <chr>, facts <chr>, relations <chr>, geodeticDatum <chr>,
#> # class <chr>, countryCode <chr>, country <chr>, rightsHolder <chr>,
#> # identifier <chr>, verbatimEventDate <chr>, datasetName <chr>,
#> # verbatimLocality <chr>, gbifID <chr>, collectionCode <chr>,
#> # occurrenceID <chr>, taxonID <chr>, recordedBy <chr>,
#> # catalogNumber <chr>, http...unknown.org.occurrenceDetails <chr>,
#> # institutionCode <chr>, rights <chr>, eventTime <chr>,
#> # identificationID <chr>, coordinateUncertaintyInMeters <dbl>,
#> # occurrenceRemarks <chr>, informationWithheld <chr>,
#> # individualCount <int>, elevation <dbl>, elevationAccuracy <dbl>,
#> # stateProvince <chr>, county <chr>, municipality <chr>, locality <chr>,
#> # coordinatePrecision <dbl>, habitat <chr>, identifiedBy <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 [21970]
#> Records returned [100]
#> No. unique hierarchies [1]
#> No. media records [63]
#> No. facets [0]
#> Args [taxonKey=3119195, limit=100, offset=0, fields=all]
#> # A tibble: 100 × 77
#> name key decimalLatitude decimalLongitude
#> <chr> <int> <dbl> <dbl>
#> 1 Helianthus annuus 1249279611 34.04810 -117.79884
#> 2 Helianthus annuus 1315048347 34.04377 -116.94136
#> 3 Helianthus annuus 1305118889 18.40386 -66.04487
#> 4 Helianthus annuus 1249286909 32.58747 -97.10081
#> 5 Helianthus annuus 1253308332 29.67463 -95.44804
#> 6 Helianthus annuus 1262375813 29.82586 -95.45604
#> 7 Helianthus annuus 1262385911 32.78328 -96.70352
#> 8 Helianthus annuus 1265544678 32.58747 -97.10081
#> 9 Helianthus annuus 1262379231 34.04911 -117.80066
#> 10 Helianthus annuus 1265560496 34.12861 -118.20700
#> # ... with 90 more rows, and 73 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>, dateIdentified <chr>,
#> # year <int>, month <int>, day <int>, eventDate <chr>, modified <chr>,
#> # lastInterpreted <chr>, references <chr>, license <chr>,
#> # identifiers <chr>, facts <chr>, relations <chr>, geodeticDatum <chr>,
#> # class <chr>, countryCode <chr>, country <chr>, rightsHolder <chr>,
#> # identifier <chr>, verbatimEventDate <chr>, datasetName <chr>,
#> # verbatimLocality <chr>, gbifID <chr>, collectionCode <chr>,
#> # occurrenceID <chr>, taxonID <chr>, recordedBy <chr>,
#> # catalogNumber <chr>, http...unknown.org.occurrenceDetails <chr>,
#> # institutionCode <chr>, rights <chr>, eventTime <chr>,
#> # identificationID <chr>, coordinateUncertaintyInMeters <dbl>,
#> # occurrenceRemarks <chr>, informationWithheld <chr>,
#> # individualCount <int>, elevation <dbl>, elevationAccuracy <dbl>,
#> # stateProvince <chr>, county <chr>, municipality <chr>, locality <chr>,
#> # coordinatePrecision <dbl>, habitat <chr>, identifiedBy <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$data[,c(1,5)])
#> # A tibble: 6 × 2
#> name issues
#> <chr> <chr>
#> 1 Helianthus annuus COORDINATE_ROUNDED,GEODETIC_DATUM_ASSUMED_WGS84
#> 2 Helianthus annuus COORDINATE_ROUNDED,GEODETIC_DATUM_ASSUMED_WGS84
#> 3 Helianthus annuus COORDINATE_ROUNDED,GEODETIC_DATUM_ASSUMED_WGS84
#> 4 Helianthus annuus COORDINATE_ROUNDED,GEODETIC_DATUM_ASSUMED_WGS84
#> 5 Helianthus annuus COORDINATE_ROUNDED,GEODETIC_DATUM_ASSUMED_WGS84
#> 6 Helianthus annuus COORDINATE_ROUNDED,GEODETIC_DATUM_ASSUMED_WGS84
Sometimes you may want to have each type of issue as a separate column.
Split out each issue type into a separate column, with number of columns equal to number of issue types
out <- res %>% occ_issues(mutate = "split")
head(out$data[,c(1,5:10)])
#> # A tibble: 6 × 7
#> name cdround gass84 datasetKey
#> <chr> <chr> <chr> <chr>
#> 1 Helianthus annuus y y 50c9509d-22c7-4a22-a47d-8c48425ef4a7
#> 2 Helianthus annuus y y 50c9509d-22c7-4a22-a47d-8c48425ef4a7
#> 3 Helianthus annuus y y 50c9509d-22c7-4a22-a47d-8c48425ef4a7
#> 4 Helianthus annuus y y 50c9509d-22c7-4a22-a47d-8c48425ef4a7
#> 5 Helianthus annuus y y 50c9509d-22c7-4a22-a47d-8c48425ef4a7
#> 6 Helianthus annuus y y 50c9509d-22c7-4a22-a47d-8c48425ef4a7
#> # ... with 3 more variables: publishingOrgKey <chr>,
#> # publishingCountry <chr>, protocol <chr>
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 COORDINATE_ROUNDED GEODETIC_DATUM_ASSUMED_WGS84
#> <chr> <chr> <chr>
#> 1 Helianthus annuus y y
#> 2 Helianthus annuus y y
#> 3 Helianthus annuus y y
#> 4 Helianthus annuus y y
#> 5 Helianthus annuus y y
#> 6 Helianthus annuus y y
#> # ... with 4 more variables: datasetKey <chr>, publishingOrgKey <chr>,
#> # publishingCountry <chr>, protocol <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.