R: Compute the (weighted) document frequency of a feature

docfreq {quanteda} | R Documentation

## Compute the (weighted) document frequency of a feature

### Description

For a dfm object, returns a (weighted) document frequency for each term. The default is a simple count of the number of documents in which a feature occurs more than a given frequency threshold. (The default threshold is zero, meaning that any feature occurring at least once in a document will be counted.)

### Usage

    docfreq(
      x,
      scheme = c("count", "inverse", "inversemax", "inverseprob", "unary"),
      base = 10,
      smoothing = 0,
      k = 0,
      threshold = 0
    )

### Arguments

`x` | a dfm
`scheme` | type of document frequency weighting, computed as follows, where N is defined as the number of documents in the dfm and s is the smoothing constant: `count` df_j, the number of documents for which n_{ij} > threshold `inverse` \textrm{log}_{base}≤ft(s + \frac{N}{k + df_j}\right) `inversemax` \textrm{log}_{base}≤ft(s + \frac{\textrm{max}(df_j)}{k + df_j}\right) `inverseprob` \textrm{log}_{base}≤ft(\frac{N - df_j}{k + df_j}\right) `unary` 1 for each feature
`base` | the base with respect to which logarithms in the inverse document frequency weightings are computed; default is 10 (see Manning, Raghavan, and Schütze 2008, p123).
`smoothing` | added to the quotient before taking the logarithm
`k` | added to the denominator in the "inverse" weighting types, to prevent a zero document count for a term
`threshold` | numeric value of the threshold above which a feature will considered in the computation of document frequency. The default is 0, meaning that a feature's document frequency will be the number of documents in which it occurs greater than zero times.

### Value

a numeric vector of document frequencies for each feature

### References

Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge: Cambridge University Press. https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf

### Examples

    dfmat1 <- dfm(data_corpus_inaugural[1:2])
    docfreq(dfmat1[, 1:20])

    # replication of worked example from
    # https://en.wikipedia.org/wiki/Tf-idf#Example_of_tf.E2.80.93idf
    dfmat2 <-
        matrix(c(1,1,2,1,0,0, 1,1,0,0,2,3),
               byrow = TRUE, nrow = 2,
               dimnames = list(docs = c("document1", "document2"),
                               features = c("this", "is", "a", "sample",
                                            "another", "example"))) %>%
        as.dfm()
    dfmat2
    docfreq(dfmat2)
    docfreq(dfmat2, scheme = "inverse")
    docfreq(dfmat2, scheme = "inverse", k = 1, smoothing = 1)
    docfreq(dfmat2, scheme = "unary")
    docfreq(dfmat2, scheme = "inversemax")
    docfreq(dfmat2, scheme = "inverseprob")

[Package quanteda version 3.1.0 Index]