R/dfm_compress.R, R/fcm-methods.R
dfm_compress.Rd"Compresses" or groups a dfm or fcm whose dimension names are
the same, for either documents or features. This may happen, for instance,
if features are made equivalent through application of a thesaurus. It could also be needed after a
cbind.dfm() or rbind.dfm() operation. In most cases, you will not
need to call dfm_compress, since it is called automatically by functions that change the
dimensions of the dfm, e.g. dfm_tolower().
dfm_compress(x, margin = c("both", "documents", "features"))
fcm_compress(x)dfm_compress returns a dfm whose dimensions have been
recombined by summing the cells across identical dimension names
(docnames or featnames). The docvars will be
preserved for combining by features but not when documents are combined.
fcm_compress returns an fcm whose features have been
recombined by combining counts of identical features, summing their counts.
fcm_compress works only when the fcm was created with a
document context.
# dfm_compress examples
dfmat <- rbind(dfm(tokens(c("b A A", "C C a b B")), tolower = FALSE),
dfm(tokens("A C C C C C"), tolower = FALSE))
colnames(dfmat) <- char_tolower(featnames(dfmat))
dfmat
#> Document-feature matrix of: 3 documents, 5 features (46.67% sparse) and 0 docvars.
#> features
#> docs b a c a b
#> text1.1 1 2 0 0 0
#> text2.1 1 0 2 1 1
#> text1.2 0 1 5 0 0
dfm_compress(dfmat, margin = "documents")
#> Document-feature matrix of: 2 documents, 5 features (30.00% sparse) and 0 docvars.
#> features
#> docs b a c a b
#> text1 1 3 5 0 0
#> text2 1 0 2 1 1
dfm_compress(dfmat, margin = "features")
#> Document-feature matrix of: 3 documents, 3 features (22.22% sparse) and 0 docvars.
#> features
#> docs b a c
#> text1.1 1 2 0
#> text2.1 2 1 2
#> text1.2 0 1 5
dfm_compress(dfmat)
#> Document-feature matrix of: 2 documents, 3 features (0.00% sparse) and 0 docvars.
#> features
#> docs b a c
#> text1 1 3 5
#> text2 2 1 2
# no effect if no compression needed
dfmatsubset <- dfm(tokens(data_corpus_inaugural[1:5]))
dim(dfmatsubset)
#> [1] 5 1948
dim(dfm_compress(dfmatsubset))
#> [1] 5 1948
# compress an fcm
fcmat1 <- fcm(tokens("A D a C E a d F e B A C E D"),
context = "window", window = 3)
## this will produce an error:
# fcm_compress(fcmat1)
txt <- c("The fox JUMPED over the dog.",
"The dog jumped over the fox.")
toks <- tokens(txt, remove_punct = TRUE)
fcmat2 <- fcm(toks, context = "document")
colnames(fcmat2) <- rownames(fcmat2) <- tolower(colnames(fcmat2))
colnames(fcmat2)[5] <- rownames(fcmat2)[5] <- "fox"
fcmat2
#> Feature co-occurrence matrix of: 7 by 7 features.
#> features
#> features the fox jumped over fox dog jumped
#> the 0 2 1 2 2 2 1
#> fox 0 0 1 2 2 2 1
#> jumped 0 0 0 1 1 1 0
#> over 0 0 0 0 2 2 1
#> fox 0 0 0 0 0 2 1
#> dog 0 0 0 0 0 0 1
#> jumped 0 0 0 0 0 0 0
fcm_compress(fcmat2)
#> Feature co-occurrence matrix of: 5 by 5 features.
#> features
#> features the fox jumped over dog
#> the 0 4 2 2 2
#> fox 0 2 3 2 4
#> jumped 0 1 0 1 1
#> over 0 2 1 0 2
#> dog 0 0 1 0 0