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Parse and examine further GBIF name issues on a dataset.

Source: R/name_issues.R
name_issues.Rd

Parse and examine further GBIF name issues on a dataset.

Usage

name_issues(.data, ..., mutate = NULL)

Arguments

.data

Output from a call to name_usage()

...

Named parameters to only get back (e.g. bbmn), or to remove (e.g. -bbmn).

mutate

(character) One of:

  • split Split issues into new columns.

  • expand Expand issue abbreviated codes into descriptive names. for downloads datasets, this is not super useful since the issues come to you as expanded already.

  • split_expand Split into new columns, and expand issue names.

For split and split_expand, values in cells become y ("yes") or n ("no")

References

https://gbif.github.io/gbif-api/apidocs/org/gbif/api/vocabulary/NameUsageIssue.html

Examples

if (FALSE) { # \dontrun{
# what do issues mean, can print whole table
head(gbif_issues())
# or just name related issues
gbif_issues()[which(gbif_issues()$type %in% c("name")),]
# or search for matches
gbif_issues()[gbif_issues()$code %in% c('bbmn','clasna','scina'),]
# compare out data to after name_issues use
(aa <- name_usage(name = "Lupus"))
aa %>% name_issues("clasna")

## or parse issues in various ways
### remove data rows with certain issue classes
aa %>% name_issues(-clasna, -scina)

### expand issues to more descriptive names
aa %>% name_issues(mutate = "expand")

### split and expand
aa %>% name_issues(mutate = "split_expand")

### split, expand, and remove an issue class
aa %>% name_issues(-bbmn, mutate = "split_expand")

## Or you can use name_issues without %>%
name_issues(aa, -bbmn, mutate = "split_expand")
} # }