The most frequent emoji in a text column

top_n_emojis() returns the n most frequent emoji. By default each emoji (unicode) appears on a single row; set duplicated = TRUE to list every name an emoji is known by, so glyphs that share several names occupy several rows.

Usage

top_n_emojis(
  data,
  text,
  n = 20,
  duplicated = FALSE,
  duplicated_unicode = lifecycle::deprecated()
)

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.

n

Number of emoji to return. Default 20.

duplicated

If TRUE, emoji with several names occupy several rows. Default FALSE.

duplicated_unicode

Use duplicated instead. Accepts only the values it ever meant – TRUE, FALSE, "yes" or "no" – and errors on anything else rather than reading it as FALSE, which is what "TRUE" and 1 used to get.

Value

A tibble with columns emoji_name, unicode, emoji_category and n, sorted by descending n with ties broken by the glyph so the order is deterministic – the same rule emoji_frequency() uses. When a tie straddles position n the glyph order decides which side of the cut each emoji falls on, and a corpus with fewer than n distinct emoji returns every one of them rather than padding to n.

duplicated = TRUE leaves several rows sharing both n and unicode, which that rule does not settle. Within one glyph the rows come out in emoji_unicode_crosswalk's order for the glyph's codepoint key, and the first of them carries the same emoji_name that duplicated = FALSE reports. Because the join is on the key rather than on the spelling, a glyph collects the aliases of every spelling of itself: the unqualified U+26F9 U+200D U+2640 is listed under woman_bouncing_ball and under the two aliases the fully-qualified spelling carries. That is what "every name an emoji is known by" means here.

See also

emoji_frequency() for the full distribution.

Examples

df <- data.frame(text = c("\U0001f600\U0001f600\U0001f3c1", "\U0001f621"))
top_n_emojis(df, text, n = 2)
#> # A tibble: 2 × 4
#>   emoji_name     unicode emoji_category        n
#>   <chr>          <chr>   <chr>             <int>
#> 1 grinning       😀      Smileys & Emotion     2
#> 2 checkered_flag 🏁      Flags                 1