Description Usage Arguments Details Value See Also Examples

Returns 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.

1 | ```
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 |

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.

dfm object, with a subset of documents (and docvars) selected according to arguments

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
testcorp <- 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)))
testdfm <- dfm(testcorp)
# selecting on a docvars condition
dfm_subset(testdfm, grp > 1)
# selecting on a supplied vector
dfm_subset(testdfm, c(TRUE, FALSE, TRUE, FALSE))
# selecting on a dfm
dfm1 <- dfm(c(d1 = "a b b c", d2 = "b b c d"))
dfm2 <- dfm(c(d1 = "x y z", d2 = "a b c c d", d3 = "x x x"))
dfm_subset(dfm1, subset = dfm2)
dfm_subset(dfm1, subset = dfm2[c(3,1,2), ])
``` |

```
quanteda version 0.99
Using 2 of 1 threads for parallel computing
Attaching package: 'quanteda'
The following object is masked from 'package:utils':
View
Document-feature matrix of: 2 documents, 6 features (41.7% sparse).
2 x 6 sparse Matrix of class "dfmSparse"
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 "dfmSparse"
features
docs a b c d e f
d1 1 1 1 1 0 0
d3 0 2 1 0 1 0
Document-feature matrix of: 3 documents, 4 features (50% sparse).
3 x 4 sparse Matrix of class "dfmSparse"
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% sparse).
3 x 4 sparse Matrix of class "dfmSparse"
features
docs a b c d
d3 0 0 0 0
d1 1 2 1 0
d2 0 2 1 1
```

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