rm_repeated_phrases(text.var, trim = !extract, clean = TRUE, pattern = "@rm_repeated_phrases", replacement = "", extract = FALSE, dictionary = getOption("regex.library"), ...)
TRUE removes leading and trailing white
spaces.TRUE extra white spaces and escaped
character will be removed.fixed = TRUE) to be matched in the given
character vector. Default, @rm_repeated_phrases uses the
rm_repeated_phrases regex from the regular expression dictionary from
the dictionary argument.pattern.TRUE the repeated phrases are extracted
into a list of vectors.pattern begins with "@rm_".gsub.Remove/replace/extract repeating phrases from a string.
x <- c( "this is a big is a Big deal", "I want want to see", "I want, want to see", "I want...want to see see see how", "I like it. It is cool", "this is a big is a Big deal for those of, those of you who are." ) rm_repeated_phrases(x)[1] "this deal" "I to see" "I to see" "I to how" [5] "I like it. It is cool" "this deal for you who are."rm_repeated_phrases(x, extract=TRUE)[[1]] [1] "is a big is a Big" [[2]] [1] "want want" [[3]] [1] "want, want" [[4]] [1] "want...want" "see see see" [[5]] [1] NA [[6]] [1] "is a big is a Big" "those of, those of"
gsub,
stri_extract_all_regex
Other rm_.functions: as_numeric,
as_numeric2, rm_number;
as_time, as_time2,
rm_time, rm_transcript_time;
rm_abbreviation; rm_angle,
rm_bracket,
rm_bracket_multiple,
rm_curly, rm_round,
rm_square; rm_between,
rm_between_multiple;
rm_caps_phrase; rm_caps;
rm_citation_tex; rm_citation;
rm_city_state_zip;
rm_city_state; rm_date;
rm_default; rm_dollar;
rm_email; rm_emoticon;
rm_endmark; rm_hash;
rm_nchar_words; rm_non_ascii;
rm_non_words; rm_percent;
rm_phone; rm_postal_code;
rm_repeated_characters;
rm_repeated_words; rm_tag;
rm_title_name;
rm_twitter_url, rm_url;
rm_white, rm_white_bracket,
rm_white_colon,
rm_white_comma,
rm_white_endmark,
rm_white_lead,
rm_white_lead_trail,
rm_white_multiple,
rm_white_punctuation,
rm_white_trail; rm_zip