Combine a dfm with another dfm, or numeric, or matrix object, returning a dfm with the combined documents or features, respectively.
dfm, numeric, or matrix objects to be joined column-wise
(cbind) or row-wise (rbind) to the first. Numeric objects
not confirming to the row or column dimension will be recycled as normal.
cbind(x, y, ...) combines dfm objects by columns, returning a
dfm object with combined features from input dfm objects. Note that this
should be used with extreme caution, as joining dfms with different
documents will result in a new row with the docname(s) of the first dfm,
merging in those from the second. Furthermore, if features are shared
between the dfms being cbinded, then duplicate feature labels will result.
In both instances, warning messages will result.
rbind(x, y, ...) combines dfm objects by rows, returning a
dfm object with combined features from input dfm objects. Features are
matched between the two dfm objects, so that the order and names of the
features do not need to match. The order of the features in the resulting
dfm is not guaranteed. The attributes and settings of this new dfm are not
currently preserved.
# cbind() for dfm objects
(dfmat1 <- dfm(tokens(c("a b c d", "c d e f"))))
#> Document-feature matrix of: 2 documents, 6 features (33.33% sparse) and 0 docvars.
#> features
#> docs a b c d e f
#> text1 1 1 1 1 0 0
#> text2 0 0 1 1 1 1
(dfmat2 <- dfm(tokens(c("a b", "x y z"))))
#> Document-feature matrix of: 2 documents, 5 features (50.00% sparse) and 0 docvars.
#> features
#> docs a b x y z
#> text1 1 1 0 0 0
#> text2 0 0 1 1 1
cbind(dfmat1, dfmat2)
#> Warning: cbinding dfms with overlapping features
#> Document-feature matrix of: 2 documents, 11 features (40.91% sparse) and 0 docvars.
#> features
#> docs a b c d e f a b x y
#> text1 1 1 1 1 0 0 1 1 0 0
#> text2 0 0 1 1 1 1 0 0 1 1
#> [ reached max_nfeat ... 1 more feature ]
cbind(dfmat1, 100)
#> Document-feature matrix of: 2 documents, 7 features (28.57% sparse) and 0 docvars.
#> features
#> docs a b c d e f feat1
#> text1 1 1 1 1 0 0 100
#> text2 0 0 1 1 1 1 100
cbind(100, dfmat1)
#> Document-feature matrix of: 2 documents, 7 features (28.57% sparse) and 0 docvars.
#> features
#> docs feat1 a b c d e f
#> text1 100 1 1 1 1 0 0
#> text2 100 0 0 1 1 1 1
cbind(dfmat1, matrix(c(101, 102), ncol = 1))
#> Document-feature matrix of: 2 documents, 7 features (28.57% sparse) and 0 docvars.
#> features
#> docs a b c d e f feat1
#> text1 1 1 1 1 0 0 101
#> text2 0 0 1 1 1 1 102
cbind(matrix(c(101, 102), ncol = 1), dfmat1)
#> Document-feature matrix of: 2 documents, 7 features (28.57% sparse) and 0 docvars.
#> features
#> docs feat1 a b c d e f
#> text1 101 1 1 1 1 0 0
#> text2 102 0 0 1 1 1 1
# rbind() for dfm objects
(dfmat1 <- dfm(tokens(c(doc1 = "This is one sample text sample."))))
#> Document-feature matrix of: 1 document, 6 features (0.00% sparse) and 0 docvars.
#> features
#> docs this is one sample text .
#> doc1 1 1 1 2 1 1
(dfmat2 <- dfm(tokens(c(doc2 = "One two three text text."))))
#> Document-feature matrix of: 1 document, 5 features (0.00% sparse) and 0 docvars.
#> features
#> docs one two three text .
#> doc2 1 1 1 2 1
(dfmat3 <- dfm(tokens(c(doc3 = "This is the fourth sample text."))))
#> Document-feature matrix of: 1 document, 7 features (0.00% sparse) and 0 docvars.
#> features
#> docs this is the fourth sample text .
#> doc3 1 1 1 1 1 1 1
rbind(dfmat1, dfmat2)
#> Document-feature matrix of: 2 documents, 8 features (31.25% sparse) and 0 docvars.
#> features
#> docs this is one sample text . two three
#> doc1 1 1 1 2 1 1 0 0
#> doc2 0 0 1 0 2 1 1 1
rbind(dfmat1, dfmat2, dfmat3)
#> Document-feature matrix of: 3 documents, 10 features (40.00% sparse) and 0 docvars.
#> features
#> docs this is one sample text . two three the fourth
#> doc1 1 1 1 2 1 1 0 0 0 0
#> doc2 0 0 1 0 2 1 1 1 0 0
#> doc3 1 1 0 1 1 1 0 0 1 1