docfreq.Rd
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"), smoothing = 0, k = 0, base = 10, threshold = 0, use.names = TRUE)
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:

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 
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). 
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. 
use.names  logical; if 
...  not used 
a numeric vector of document frequencies for each feature
Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge: Cambridge University Press. https://nlp.stanford.edu/IRbook/pdf/irbookonlinereading.pdf
#> fellowcitizens 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/Tfidf#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#> Documentfeature matrix of: 2 documents, 6 features (33.3% sparse). #> 2 x 6 sparse Matrix of class "dfm" #> features #> docs this is a sample another example #> document1 1 1 2 1 0 0 #> document2 1 1 0 0 2 3docfreq(dfmat2)#> this is a sample another example #> 2 2 1 1 1 1docfreq(dfmat2, scheme = "inverse")#> this is a sample another example #> 0.00000 0.00000 0.30103 0.30103 0.30103 0.30103docfreq(dfmat2, scheme = "inverse", k = 1, smoothing = 1)#> this is a sample another example #> 0.2218487 0.2218487 0.3010300 0.3010300 0.3010300 0.3010300docfreq(dfmat2, scheme = "unary")#> this is a sample another example #> 1 1 1 1 1 1docfreq(dfmat2, scheme = "inversemax")#> this is a sample another example #> 0.00000 0.00000 0.30103 0.30103 0.30103 0.30103docfreq(dfmat2, scheme = "inverseprob")#> this is a sample another example #> 0 0 0 0 0 0