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Emoji co-occurrence counts, with an optional diagonal

Source: R/emoji-relate.R
emoji_cooccurrence.Rd

emoji_cooccurrence() is emoji_pairs() under the name the matrix form goes by, with one argument added and one taken away.

Usage

emoji_cooccurrence(data, text, doc_id = NULL, diagonal = FALSE, sort = TRUE)

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.

doc_id

Optional unquoted column identifying documents. Rows sharing a value are treated as one document. Default: each row is a document.

The result has a row per pair, so it grows with the square of the distinct emoji in a document: a day or a conversation is cheap, and pooling a whole corpus under one id is not. 800 distinct emoji in one document is 319,600 pairs and a few seconds; 3790 would be 7.2 million.

diagonal

If TRUE, include one item1 == item2 row per emoji with its document frequency. Default FALSE.

sort

If TRUE (default), sort by descending n (ties broken by item1, item2 so the order is deterministic). FALSE sorts by item1 then item2 instead – still a fixed order, computed in the C locale, not the order the pairs happened to be counted in.

Value

A tibble with columns item1, item2 and n.

Details

Added: diagonal = TRUE also returns the item1 == item2 rows, whose n is the number of documents containing that emoji (the diagonal of the co-occurrence matrix, i.e. its document frequency).

Taken away: there is no directed here. A co-occurrence matrix is symmetric, so an ordered pair has no meaning on it and the diagonal this verb exists to add would not either. Use emoji_pairs() when you want directed = TRUE; the off-diagonal rows the two verbs return are otherwise identical.

Glyphs are canonicalised through the package's codepoint key, so two spellings of one emoji make one row rather than two; see Which spelling comes back in tidyEmoji.

See also

emoji_pairs(), emoji_ngrams().

Examples

df <- data.frame(text = c("\U0001f602\U0001f60d", "\U0001f602"))
emoji_cooccurrence(df, text, diagonal = TRUE)
#> # A tibble: 3 × 3
#>   item1 item2     n
#>   <chr> <chr> <int>
#> 1 😂    😂        2
#> 2 😂    😍        1
#> 3 😍    😍        1