Select or remove tokens from a tokens object — tokens

These function select or discard tokens from a tokens objects. For convenience, the functions tokens_remove and tokens_keep are defined as shortcuts for tokens_select(x, pattern, selection = "remove") and tokens_select(x, pattern, selection = "keep"), respectively. The most common usage for tokens_remove will be to eliminate stop words from a text or text-based object, while the most common use of tokens_select will be to select tokens with only positive pattern matches from a list of regular expressions, including a dictionary.

tokens_select(
  x,
  pattern,
  selection = c("keep", "remove"),
  valuetype = c("glob", "regex", "fixed"),
  case_insensitive = TRUE,
  padding = FALSE,
  window = 0,
  min_nchar = NULL,
  max_nchar = NULL,
  startpos = 1L,
  endpos = -1L,
  verbose = quanteda_options("verbose")
)

tokens_remove(x, ...)

tokens_keep(x, ...)

Arguments

x	tokens object whose token elements will be removed or kept
pattern	a character vector, list of character vectors, dictionary, or collocations object. See pattern for details.
selection	whether to `"keep"` or `"remove"` the tokens matching `pattern`
valuetype	the type of pattern matching: `"glob"` for "glob"-style wildcard expressions; `"regex"` for regular expressions; or `"fixed"` for exact matching. See valuetype for details.
case_insensitive	logical; if `TRUE`, ignore case when matching a `pattern` or dictionary values
padding	if `TRUE`, leave an empty string where the removed tokens previously existed. This is useful if a positional match is needed between the pre- and post-selected tokens, for instance if a window of adjacency needs to be computed.
window	integer of length 1 or 2; the size of the window of tokens adjacent to `pattern` that will be selected. The window is symmetric unless a vector of two elements is supplied, in which case the first element will be the token length of the window before `pattern`, and the second will be the token length of the window after `pattern`. The default is `0`, meaning that only the pattern matched token(s) are selected, with no adjacent terms. Terms from overlapping windows are never double-counted, but simply returned in the pattern match. This is because `tokens_select` never redefines the document units; for this, see `kwic()`.
min_nchar, max_nchar	optional numerics specifying the minimum and maximum length in characters for tokens to be removed or kept; defaults are `NULL` for no limits. These are applied after (and hence, in addition to) any selection based on pattern matches.
startpos, endpos	integer; position of tokens in documents where pattern matching starts and ends, where 1 is the first token in a document. For negative indexes, counting starts at the ending token of the document, so that -1 denotes the last token in the document, -2 the second to last, etc.
verbose	if `TRUE` print messages about how many tokens were selected or removed
...	additional arguments passed by `tokens_remove` and `tokens_keep` to `tokens_select`. Cannot include `selection`.

Value

a tokens object with tokens selected or removed based on their match to pattern

Examples

## tokens_select with simple examples
toks <- tokens(c("This is a sentence.", "This is a second sentence."),
                 remove_punct = TRUE)
tokens_select(toks, c("is", "a", "this"), selection = "keep", padding = FALSE)
#> Tokens consisting of 2 documents.
#> text1 :
#> [1] "This" "is"   "a"   
#> 
#> text2 :
#> [1] "This" "is"   "a"   
#> 
tokens_select(toks, c("is", "a", "this"), selection = "keep", padding = TRUE)
#> Tokens consisting of 2 documents.
#> text1 :
#> [1] "This" "is"   "a"    ""    
#> 
#> text2 :
#> [1] "This" "is"   "a"    ""     ""    
#> 
tokens_select(toks, c("is", "a", "this"), selection = "remove", padding = FALSE)
#> Tokens consisting of 2 documents.
#> text1 :
#> [1] "sentence"
#> 
#> text2 :
#> [1] "second"   "sentence"
#> 
tokens_select(toks, c("is", "a", "this"), selection = "remove", padding = TRUE)
#> Tokens consisting of 2 documents.
#> text1 :
#> [1] ""         ""         ""         "sentence"
#> 
#> text2 :
#> [1] ""         ""         ""         "second"   "sentence"
#> 

# how case_insensitive works
tokens_select(toks, c("is", "a", "this"), selection = "remove", case_insensitive = TRUE)
#> Tokens consisting of 2 documents.
#> text1 :
#> [1] "sentence"
#> 
#> text2 :
#> [1] "second"   "sentence"
#> 
tokens_select(toks, c("is", "a", "this"), selection = "remove", case_insensitive = FALSE)
#> Tokens consisting of 2 documents.
#> text1 :
#> [1] "This"     "sentence"
#> 
#> text2 :
#> [1] "This"     "second"   "sentence"
#> 

# use window
tokens_select(toks, "second", selection = "keep", window = 1)
#> Tokens consisting of 2 documents.
#> text1 :
#> character(0)
#> 
#> text2 :
#> [1] "a"        "second"   "sentence"
#> 
tokens_select(toks, "second", selection = "remove", window = 1)
#> Tokens consisting of 2 documents.
#> text1 :
#> [1] "This"     "is"       "a"        "sentence"
#> 
#> text2 :
#> [1] "This" "is"  
#> 
tokens_remove(toks, "is", window = c(0, 1))
#> Tokens consisting of 2 documents.
#> text1 :
#> [1] "This"     "sentence"
#> 
#> text2 :
#> [1] "This"     "second"   "sentence"
#> 
# tokens_remove example: remove stopwords
txt <- c(wash1 <- "Fellow citizens, I am again called upon by the voice of my country to
                   execute the functions of its Chief Magistrate.",
         wash2 <- "When the occasion proper for it shall arrive, I shall endeavor to express
                   the high sense I entertain of this distinguished honor.")
tokens_remove(tokens(txt, remove_punct = TRUE), stopwords("english"))
#> Tokens consisting of 2 documents.
#> text1 :
#>  [1] "Fellow"     "citizens"   "called"     "upon"       "voice"     
#>  [6] "country"    "execute"    "functions"  "Chief"      "Magistrate"
#> 
#> text2 :
#>  [1] "occasion"      "proper"        "shall"         "arrive"       
#>  [5] "shall"         "endeavor"      "express"       "high"         
#>  [9] "sense"         "entertain"     "distinguished" "honor"        
#> 

# token_keep example: keep two-letter words
tokens_keep(tokens(txt, remove_punct = TRUE), "??")
#> Tokens consisting of 2 documents.
#> text1 :
#> [1] "am" "by" "of" "my" "to" "of"
#> 
#> text2 :
#> [1] "it" "to" "of"
#>