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Return the first or last part of a dfm

Source: R/dfm-subsetting.R
head.dfm.Rd

For a dfm object, return the dfm with only the first or last n documents.

Usage

# S3 method for class 'dfm'
head(x, n = 6L, ...)

# S3 method for class 'dfm'
tail(x, n = 6L, ...)

Arguments

x

a dfm object

n

an integer vector of length up to dim(x) (or 1, for non-dimensioned objects). A logical is silently coerced to integer. Values specify the indices to be selected in the corresponding dimension (or along the length) of the object. A positive value of n[i] includes the first/last n[i] indices in that dimension, while a negative value excludes the last/first abs(n[i]), including all remaining indices. NA or non-specified values (when length(n) < length(dim(x))) select all indices in that dimension. Must contain at least one non-missing value.

...

arguments to be passed to or from other methods.

Value

A dfm class object corresponding to the subset of documents determined by by n.

Examples

head(data_dfm_lbgexample, 3)
#> Document-feature matrix of: 3 documents, 37 features (54.05% sparse) and 0
#> docvars.
#>     features
#> docs A B  C  D  E  F   G   H   I   J
#>   R1 2 3 10 22 45 78 115 146 158 146
#>   R2 0 0  0  0  0  2   3  10  22  45
#>   R3 0 0  0  0  0  0   0   0   0   0
#> [ reached max_nfeat ... 27 more features ]
head(data_dfm_lbgexample, -4)
#> Document-feature matrix of: 2 documents, 37 features (54.05% sparse) and 0
#> docvars.
#>     features
#> docs A B  C  D  E  F   G   H   I   J
#>   R1 2 3 10 22 45 78 115 146 158 146
#>   R2 0 0  0  0  0  2   3  10  22  45
#> [ reached max_nfeat ... 27 more features ]

tail(data_dfm_lbgexample)
#> Document-feature matrix of: 6 documents, 37 features (54.05% sparse) and 0
#> docvars.
#>     features
#> docs A B  C  D  E  F   G   H   I   J
#>   R1 2 3 10 22 45 78 115 146 158 146
#>   R2 0 0  0  0  0  2   3  10  22  45
#>   R3 0 0  0  0  0  0   0   0   0   0
#>   R4 0 0  0  0  0  0   0   0   0   0
#>   R5 0 0  0  0  0  0   0   0   0   0
#>   V1 0 0  0  0  0  0   0   2   3  10
#> [ reached max_nfeat ... 27 more features ]
tail(data_dfm_lbgexample, n = 3)
#> Document-feature matrix of: 3 documents, 37 features (54.05% sparse) and 0
#> docvars.
#>     features
#> docs A B C D E F G H I  J
#>   R4 0 0 0 0 0 0 0 0 0  0
#>   R5 0 0 0 0 0 0 0 0 0  0
#>   V1 0 0 0 0 0 0 0 2 3 10
#> [ reached max_nfeat ... 27 more features ]