Compute the (weighted) document frequency of a feature

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.)

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 $$\textrm{log}_{base}\left(s + \frac{N}{k + df_j}\right)$$ inversemax $$\textrm{log}_{base}\left(s + \frac{\textrm{max}(df_j)}{k + df_j}\right)$$ inverseprob $$\textrm{log}_{base}\left(\frac{N - df_j}{k + df_j}\right)$$ 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])
#> fellow-citizens              of             the          senate             and 
#>               1               2               2               1               2 
#>           house representatives               :           among    vicissitudes 
#>               1               1               2               1               1 
#>        incident              to            life              no           event 
#>               1               2               1               1               1 
#>           could            have          filled              me            with 
#>               1               2               1               2               1 

# 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
#> Document-feature matrix of: 2 documents, 6 features (33.3% sparse).
#>            features
#> docs        this is a sample another example
#>   document1    1  1 2      1       0       0
#>   document2    1  1 0      0       2       3
docfreq(dfmat2)
#>    this      is       a  sample another example 
#>       2       2       1       1       1       1 
docfreq(dfmat2, scheme = "inverse")
#>    this      is       a  sample another example 
#> 0.00000 0.00000 0.30103 0.30103 0.30103 0.30103 
docfreq(dfmat2, scheme = "inverse", k = 1, smoothing = 1)
#>      this        is         a    sample   another   example 
#> 0.2218487 0.2218487 0.3010300 0.3010300 0.3010300 0.3010300 
docfreq(dfmat2, scheme = "unary")
#>    this      is       a  sample another example 
#>       1       1       1       1       1       1 
docfreq(dfmat2, scheme = "inversemax")
#>    this      is       a  sample another example 
#> 0.00000 0.00000 0.30103 0.30103 0.30103 0.30103 
docfreq(dfmat2, scheme = "inverseprob")
#>    this      is       a  sample another example 
#>       0       0       0       0       0       0