Parse and examine further GBIF name issues on a dataset.

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) { # 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") }