Which functional types of emoji does each row use?

emoji_type() adds .emoji_type, the distinct functional types present in each row (see as_emoji_type()), separated by | when a row spans more than one. The face-versus-object contrast it exposes is the key variable in the consumer-behaviour literature on emoji in reviews and marketing copy.

Usage

emoji_type(data, text)

Arguments

data: A data frame or tibble containing a text column. Grouped data frames are accepted. The verbs that work a row at a time (adding columns, or keeping and expanding rows) carry the grouping through to their result, as dplyr::mutate() and dplyr::filter() do. The verbs that pool across rows – the counts, the co-occurrence edge lists, the time series – warn that they ignore the grouping and return one corpus-wide answer.
text: The text column to scan, supplied unquoted. Any atomic column is accepted and read as character, so a factor works and a numeric, Date or logical one simply contains no emoji. A list column – or a data-frame column – is refused rather than coerced, because coercing one deparses it and the emoji found would be in the code rather than in your data. What counts as an emoji is the same in every verb; see the Detection section of tidyEmoji for the one case that surprises people, code points that are emoji only when they carry U+FE0F.

Value

data, as a tibble, with an added .emoji_type column. Unlike emoji_categorize(), no rows are dropped: a row with no emoji gets NA, as does a row whose emoji cannot be typed – see Details.

Details

.emoji_type is NA for two different reasons, and this column cannot tell you which: a row with no emoji at all, and a row whose every emoji is one the recode cannot type. The second is rare but not impossible – the recode maps the ten Unicode groups the catalogue currently uses, so a glyph in a group added to Unicode after your emoji package was built has no type – and it is the same conflation emoji_categorize() describes for .emoji_category. emoji_faceness() separates them: .emoji_n_typed is NA when the row had no emoji and 0 when it had emoji that could not be typed. emoji_provenance() reports which catalogue you are matching against.

Examples

df <- data.frame(text = c("yum \U0001f355 \U0001f600", "\U0001f44d", "none"))
emoji_type(df, text)
#> # A tibble: 3 × 2
#>   text      .emoji_type
#>   <chr>     <chr>      
#> 1 yum 🍕 😀 face|food  
#> 2 👍        gesture    
#> 3 none      NA