R/tokens_trim.R
tokens_trim.Rd
Returns a tokens object reduced in size based on document and term frequency, usually in terms of a minimum frequency, but may also be in terms of maximum frequencies. Setting a combination of minimum and maximum frequencies will select features based on a range.
tokens_trim(
x,
min_termfreq = NULL,
max_termfreq = NULL,
termfreq_type = c("count", "prop", "rank", "quantile"),
min_docfreq = NULL,
max_docfreq = NULL,
docfreq_type = c("count", "prop", "rank", "quantile"),
padding = FALSE,
verbose = quanteda_options("verbose")
)
a dfm object
minimum/maximum values of feature frequencies across all documents, below/above which features will be removed
how min_termfreq
and max_termfreq
are
interpreted. "count"
sums the frequencies; "prop"
divides the
term frequencies by the total sum; "rank"
is matched against the
inverted ranking of features in terms of overall frequency, so that 1, 2,
... are the highest and second highest frequency features, and so on;
"quantile"
sets the cutoffs according to the quantiles (see
quantile()
) of term frequencies.
minimum/maximum values of a feature's document frequency, below/above which features will be removed
specify how min_docfreq
and max_docfreq
are
interpreted. "count"
is the same as [docfreq](x, scheme = "count")
; "prop"
divides the document frequencies by the total
sum; "rank"
is matched against the inverted ranking of document
frequency, so that 1, 2, ... are the features with the highest and second
highest document frequencies, and so on; "quantile"
sets the cutoffs
according to the quantiles (see quantile()
) of document
frequencies.
if TRUE
, leave an empty string where the removed tokens
previously existed.
if TRUE
print the number of tokens and documents before and
after the function is applied. The number of tokens does not include paddings.
A tokens object with reduced size.
toks <- tokens(data_corpus_inaugural)
# keep only words occurring >= 10 times and in >= 2 documents
tokens_trim(toks, min_termfreq = 10, min_docfreq = 2, padding = TRUE)
#> Tokens consisting of 60 documents and 4 docvars.
#> 1789-Washington :
#> [1] "" "of" "the" "Senate" "and" "of" "the" ""
#> [9] "of" "" ":" ""
#> [ ... and 1,525 more ]
#>
#> 1793-Washington :
#> [1] "Fellow" "citizens" "," "I" "am" "again"
#> [7] "called" "upon" "by" "the" "voice" "of"
#> [ ... and 135 more ]
#>
#> 1797-Adams :
#> [1] "When" "it" "was" "first" "" "," "in" "early" "times"
#> [10] "," "that" "no"
#> [ ... and 2,565 more ]
#>
#> 1801-Jefferson :
#> [1] "" "and" "Fellow" "" ":" "" "upon" "to"
#> [9] "" "the" "duties" "of"
#> [ ... and 1,911 more ]
#>
#> 1805-Jefferson :
#> [1] "" "," "fellow" "citizens" ","
#> [6] "to" "that" "" "which" "the"
#> [11] "Constitution" "requires"
#> [ ... and 2,368 more ]
#>
#> 1809-Madison :
#> [1] "" "to" "" "from" "examples" "of"
#> [7] "the" "most" "" "authority" "," "I"
#> [ ... and 1,249 more ]
#>
#> [ reached max_ndoc ... 54 more documents ]
# keep only words occurring >= 10 times and no more than 90% of the documents
tokens_trim(toks, min_termfreq = 10, max_docfreq = 0.9, docfreq_type = "prop",
padding = TRUE)
#> Tokens consisting of 60 documents and 4 docvars.
#> 1789-Washington :
#> [1] "" "" "" "Senate" "" "" "" ""
#> [9] "" "" ":" ""
#> [ ... and 1,525 more ]
#>
#> 1793-Washington :
#> [1] "Fellow" "citizens" "" "" "am" "again"
#> [7] "called" "upon" "" "" "voice" ""
#> [ ... and 135 more ]
#>
#> 1797-Adams :
#> [1] "When" "" "was" "first" "" "" "" "early" "times"
#> [10] "" "" ""
#> [ ... and 2,565 more ]
#>
#> 1801-Jefferson :
#> [1] "" "" "Fellow" "" ":" "" "upon" ""
#> [9] "" "" "duties" ""
#> [ ... and 1,911 more ]
#>
#> 1805-Jefferson :
#> [1] "" "" "fellow" "citizens" ""
#> [6] "" "" "" "" ""
#> [11] "Constitution" "requires"
#> [ ... and 2,368 more ]
#>
#> 1809-Madison :
#> [1] "" "" "" "" "examples" ""
#> [7] "" "most" "" "authority" "" ""
#> [ ... and 1,249 more ]
#>
#> [ reached max_ndoc ... 54 more documents ]