is_named() checks that x has names attributes, and that none of
the names are missing or empty (NA or ""). is_dictionaryish()
checks that an object is a dictionary: that it has actual names and
in addition that there are no duplicated names. have_name()
is a vectorised version of is_named().
is_named(x) is_dictionaryish(x) have_name(x)
| x | An object to test. |
|---|
is_named() and is_dictionaryish() are scalar predicates
and return TRUE or FALSE. have_name() is vectorised and
returns a logical vector as long as the input.
# A data frame usually has valid, unique names is_named(mtcars) #> [1] TRUE have_name(mtcars) #> [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE is_dictionaryish(mtcars) #> [1] TRUE # But data frames can also have duplicated columns: dups <- cbind(mtcars, cyl = seq_len(nrow(mtcars))) is_dictionaryish(dups) #> [1] FALSE # The names are still valid: is_named(dups) #> [1] TRUE have_name(dups) #> [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE # For empty objects the semantics are slightly different. # is_dictionaryish() returns TRUE for empty objects: is_dictionaryish(list()) #> [1] TRUE # But is_named() will only return TRUE if there is a names # attribute (a zero-length character vector in this case): x <- set_names(list(), character(0)) is_named(x) #> [1] TRUE # Empty and missing names are invalid: invalid <- dups names(invalid)[2] <- "" names(invalid)[5] <- NA # is_named() performs a global check while have_name() can show you # where the problem is: is_named(invalid) #> [1] FALSE have_name(invalid) #> [1] TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE # have_name() will work even with vectors that don't have a names # attribute: have_name(letters) #> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [25] FALSE FALSE