compute_corrs: Compute correlations
In tonyelhabr/tetext: Tony's Personal Package for Text Analysis

Description Usage Arguments Details Value See Also

Compute correlations among pairs of words.

compute_corrs_at(data = NULL, token = NULL, feature = NULL,
  num_top_ngrams = 50, num_top_corrs = 50, adjust = FALSE,
  return_corrs = TRUE, return_words = FALSE, return_both = FALSE)

compute_corrs(..., token, feature)

`data`	data.frame.
`token`	bare for NSE; character for SE. Name of column in `data` to use as `item` in `widyr::pairwise_cor()`.
`feature`	bare for NSE; character for SE. Name of column in `data` to use as `feature` in `widyr::pairwise_cor()`.
`num_top_ngrams`	numeric. Useful primarily to prevent `widyr::pairwise_cor()` from hanging up. If between 0 and 1, then interpreted as a percentage.
`num_top_corrs`	numeric. Useful primarily to limit input to a network visualation. does not hang up. If between 0 and 1, then interpreted as a percentage.
`adjust`	logical. Indicates whether or not to scale `num_top_ngrams` and `num_top_corrs` (if they are between 0 and 1) to the count of unique words. Is is likely that the input percentage values are specified with the number of words in mind.
`return_corrs`	logical. Whether to return just the correlations and token pairs, but not the token counts. This is the default option.
`return_words`	logical. Whether to return the counts used to compute the correlations. Useful when creating a network visualization.
`return_both`	logical. Useful when creating a network visualization so that words can be used as nodes and correlations can be used weights.
`...`	dots. Parameters to pass directly to `visualize_time()`.