rm_caps_phrase(text.var, trim = !extract, clean = TRUE, pattern = "@rm_caps_phrase", 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_caps_phrae uses the
rm_caps_phrase regex from the regular expression dictionary from
the dictionary argument.pattern.TRUE the all caps strings are extracted
into a list of vectors.pattern begins with "@rm_".gsub.Remove/replace/extract 'all caps' phrases containing 1 or more consecutive upper case letters from a string. If one word phrase the word must be 3+ letters long.
x <- c("UGGG! When I use caps I am YELLING!", "Or it may mean this is VERY IMPORTANT!", "or trying to make a LITTLE SEEM like IT ISN'T LITTLE" ) rm_caps_phrase(x)[1] "! When I use caps I am !" "Or it may mean this is !" "or trying to make a like"rm_caps_phrase(x, extract=TRUE)[[1]] [1] "UGGG" "YELLING" [[2]] [1] "VERY IMPORTANT" [[3]] [1] "LITTLE SEEM" "IT ISN'T LITTLE"
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;
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_phrases;
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