dfm_subset.RdReturns document subsets of a dfm that meet certain conditions,
including direct logical operations on docvars (document-level variables).
dfm_subset functions identically to subset.data.frame,
using non-standard evaluation to evaluate conditions based on the
docvars in the dfm.
dfm_subset(x, subset, select, ...)
| x | dfm object to be subsetted |
|---|---|
| subset | logical expression indicating the documents to keep: missing values are taken as false |
| select | expression, indicating the docvars to select from the dfm; or a dfm object, in which case the returned dfm will contain the same documents as the original dfm, even if these are empty. See Details. |
| ... | not used |
dfm object, with a subset of documents (and docvars) selected according to arguments
To select or subset features, see dfm_select instead.
When select is a dfm, then the returned dfm will be equal in
document dimension and order to the dfm used for selection. This is the
document-level version of using dfm_select where
pattern is a dfm: that function matches features, while
dfm_subset will match documents.
corp <- corpus(c(d1 = "a b c d", d2 = "a a b e", d3 = "b b c e", d4 = "e e f a b"), docvars = data.frame(grp = c(1, 1, 2, 3))) dfmat <- dfm(corp) # selecting on a docvars condition dfm_subset(dfmat, grp > 1)#> Document-feature matrix of: 2 documents, 6 features (41.7% sparse). #> 2 x 6 sparse Matrix of class "dfm" #> features #> docs a b c d e f #> d3 0 2 1 0 1 0 #> d4 1 1 0 0 2 1#> Document-feature matrix of: 2 documents, 6 features (41.7% sparse). #> 2 x 6 sparse Matrix of class "dfm" #> features #> docs a b c d e f #> d1 1 1 1 1 0 0 #> d3 0 2 1 0 1 0# selecting on a dfm dfmat1 <- dfm(c(d1 = "a b b c", d2 = "b b c d")) dfmat2 <- dfm(c(d1 = "x y z", d2 = "a b c c d", d3 = "x x x")) dfm_subset(dfmat1, subset = dfmat2)#> Document-feature matrix of: 3 documents, 4 features (50.0% sparse). #> 3 x 4 sparse Matrix of class "dfm" #> features #> docs a b c d #> d1 1 2 1 0 #> d2 0 2 1 1 #> d3 0 0 0 0#> Document-feature matrix of: 3 documents, 4 features (50.0% sparse). #> 3 x 4 sparse Matrix of class "dfm" #> features #> docs a b c d #> d3 0 0 0 0 #> d1 1 2 1 0 #> d2 0 2 1 1