as.tokens: Coercion, checking, and combining functions for tokens...

In quanteda: Quantitative Analysis of Textual Data

Coercion, checking, and combining functions for tokens objects

Description

Coercion functions to and from tokens objects, checks for whether an object is a tokens object, and functions to combine tokens objects.

Usage

## S3 method for class 'tokens'
as.list(x, ...)

## S3 method for class 'tokens'
as.character(x, use.names = FALSE, ...)

is.tokens(x)

as.tensor(x, ...)

## S3 method for class 'tokens'
as.tensor(x, length = NULL, ...)

as.tokens(x, concatenator = "_", ...)

## S3 method for class 'spacyr_parsed'
as.tokens(
  x,
  concatenator = "/",
  include_pos = c("none", "pos", "tag"),
  use_lemma = FALSE,
  ...
)

is.tokens(x)

Arguments

x	object to be coerced or checked
...	additional arguments used by specific methods. For c.tokens, these are the tokens objects to be concatenated.
use.names	logical; preserve names if TRUE. For as.character and unlist only.
length	optional integer specifying the maximum length (number of positions) for the sparse tensor. If NULL (default), the length is inferred from the maximum token position across all documents.
concatenator	character; the concatenation character that will connect the tokens making up a multi-token sequence.
include_pos	character; whether and which part-of-speech tag to use: "none" do not use any part of speech indicator, "pos" use the pos variable, "tag" use the tag variable. The POS will be added to the token after "concatenator".
use_lemma	logical; if TRUE, use the lemma rather than the raw token

Details

The concatenator is used to automatically generate dictionary values for multi-word expressions in tokens_lookup() and dfm_lookup(). The underscore character is commonly used to join elements of multi-word expressions (e.g. "piece_of_cake", "New_York"), but other characters (e.g. whitespace " " or a hyphen "-") can also be used. In those cases, users have to tell the system what is the concatenator in your tokens so that the conversion knows to treat this character as the inter-word delimiter, when reading in the elements that will become the tokens.

Value

as.list returns a simple list of characters from a tokens object.

as.character returns a character vector from a tokens object.

is.tokens returns TRUE if the object is of class tokens, FALSE otherwise.

as.tensor returns a sparse COO tensor from a tokens object, compatible with the torch package. Each document is represented as a row, and token positions as columns. Values are the integer token IDs.

as.tokens returns a quanteda tokens object.

is.tokens returns TRUE if the object is of class tokens, FALSE otherwise.

Examples

## Not run: 
library(torch)
toks <- tokens(c(doc1 = "a b c d e f g",
                 doc2 = "a b c g",
                 doc3 = ""))
as.tensor(toks)

## End(Not run)

# create tokens object from list of characters with custom concatenator
dict <- dictionary(list(country = "United States",
                   sea = c("Atlantic Ocean", "Pacific Ocean")))
lis <- list(c("The", "United-States", "has", "the", "Atlantic-Ocean",
              "and", "the", "Pacific-Ocean", "."))
toks <- as.tokens(lis, concatenator = "-")
tokens_lookup(toks, dict)

quanteda documentation built on April 7, 2026, 1:06 a.m.