dfm_trim: Trim a dfm using frequency threshold-based feature selection
In koheiw/quanteda.core: Quantitative Analysis of Textual Data

Description Usage Arguments Value Note See Also Examples

Returns a document by feature matrix 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.

Feature selection is implemented by considering features across all documents, by summing them for term frequency, or counting the documents in which they occur for document frequency. Rank and quantile versions of these are also implemented, for taking the first n features in terms of descending order of overall global counts or document frequencies, or as a quantile of all frequencies.

dfm_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"),
  sparsity = NULL,
  verbose = quanteda_options("verbose"),
  ...
)

`x`	a dfm object
`min_termfreq, max_termfreq`	minimum/maximum values of feature frequencies across all documents, below/above which features will be removed
`termfreq_type`	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.
`min_docfreq, max_docfreq`	minimum/maximum values of a feature's document frequency, below/above which features will be removed
`docfreq_type`	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.
`sparsity`	equivalent to `1 - min_docfreq`, included for comparison with tm
`verbose`	print messages
`...`	not used

A dfm reduced in features (with the same number of documents)

Trimming a dfm object is an operation based on the values in the document-feature matrix. To select subsets of a dfm based on the features themselves (meaning the feature labels from featnames()) – such as those matching a regular expression, or removing features matching a stopword list, use dfm_select().

dfm_select(), dfm_sample()

(dfmat <- dfm(data_corpus_inaugural[1:5]))

# keep only words occurring >= 10 times and in >= 2 documents
dfm_trim(dfmat, min_termfreq = 10, min_docfreq = 2)

# keep only words occurring >= 10 times and in at least 0.4 of the documents
dfm_trim(dfmat, min_termfreq = 10, min_docfreq = 0.4)

# keep only words occurring <= 10 times and in <=2 documents
dfm_trim(dfmat, max_termfreq = 10, max_docfreq = 2)

# keep only words occurring <= 10 times and in at most 3/4 of the documents
dfm_trim(dfmat, max_termfreq = 10, max_docfreq = 0.75)

# keep only words occurring 5 times in 1000, and in 2 of 5 of documents
dfm_trim(dfmat, min_docfreq = 0.4, min_termfreq = 0.005, termfreq_type = "prop")

# keep only words occurring frequently (top 20%) and in <=2 documents
dfm_trim(dfmat, min_termfreq = 0.2, max_docfreq = 2, termfreq_type = "quantile")

## Not run: 
# compare to removeSparseTerms from the tm package
(dfmattm <- convert(dfmat, "tm"))
tm::removeSparseTerms(dfmattm, 0.7)
dfm_trim(dfmat, min_docfreq = 0.3)
dfm_trim(dfmat, sparsity = 0.7)

## End(Not run)