Package 'tm.plugin.dc'

Distributed Corpus

Description

Data structures and operators for distributed corpora.

Usage

DCorpus( x,
         readerControl = list(reader   = reader(x),
                              language = "en"),
         storage = NULL, keep = TRUE, ... )
## S3 method for class 'DCorpus'
as.VCorpus(x)
as.DCorpus( x, storage = NULL, ... )

Arguments

x	for DCorpus, a Source object. At the moment only DirSource is supported. For as.VCorpus() and as.DCorpus(), an object to be coerced to a VCorpus/DCorpus. Currently coercion from/to classic tm corpora (VCorpus) is implemented.
readerControl	A list with the named components reader representing a reading function capable of handling the file format found in x, and language giving the text's language (preferably as IETF language tags, see language in package NLP).
storage	The storage subsystem to use with the DCorpus. Currently two types of storages are supported: local disk storage using the Local File System (LFS) and the Hadoop Distributed File System (HDFS). Default: 'LFS'.
keep	Should revisions be used when operating on the DCorpus? Default: TRUE
...	Optional arguments for the reader.

Details

When constructing a distributed corpus the input source is extracted via the supplied reader and stored on the given file system (argument storage). While the data set resides on the corresponding storage (e.g., HDFS), only a symbolic representation is held in R (a so-called DList) which allows to access the corpus via corresponding (DList) methods. Since the available memory for the distributed corpus is only restricted by available disk space in the given storage (and not main memory like in a standard tm corpus) by default we also store a set of so-called revisions, i.e., stages of the (processed) corpus. Revisions can be turned off later on using the keepRevisions() replacement function.\

The constructed corpus object inherits from a tm Corpus and has several slots containing meta information:

meta

Corpus Meta Data contains corpus specific meta data in form of tag-value pairs.

dmeta

Document Meta Data of class data.frame contains document specific meta data for the corpus. This is mainly available to be compatible with standard tm corpus definitions but not yet actually used in the distributed scenario.

keep

A logical indicating whether revisions representing stages e.g., in a preprocessing chain should be kept or not.

Value

An object inheriting from DCorpus and Corpus.

Author(s)

Ingo Feinerer and Stefan Theussl

See Also

Corpus for basic information on the corpus infrastructure employed by package tm.

Examples

## Similar to example in package 'tm'
reut21578 <- system.file("texts", "crude", package = "tm")
dc <- DistributedCorpus(DirSource(reut21578),
readerControl = list(reader = readReut21578XMLasPlain) )
dc

## Coercion
data("crude")
as.DistributedCorpus(crude)
as.VCorpus(dc)

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Revisions of a Distributed Corpus

Description

Each modification of the documents in the corpus results in a new stage, i.e., revision of the corpus. To allow fast switching between multiple revisions all modifications may be kept on the file system. The function setRevision() allows to go back to any stage in the history of the corpus. The function keepRevisions() shows if revisions are turned on or off; the corresponding replacement function is used to set the desired behavior.

Usage

getRevisions( corpus )
removeRevision( corpus, revision )
setRevision( corpus, revision )
keepRevisions( corpus )
`keepRevisions<-`( corpus, value )

Arguments

corpus	A distributed corpus of class DCorpus.
revision	The revision which is to be set as active or removed.
value	A logical indicating whether revisions should be kept or not.

Value

Whereas getRevisions() returns a list of character strings naming all available revisions, setRevision() returns the distributed corpus with the given revision marked as active. The function keepRevisions() returns a logical indicating whether revisions are used or not.

Examples

## provide data on storage
data("crude")
dc <- as.DCorpus(crude)
## do some preprocessing
dc <- tm_map(dc, content_transformer(tolower))
## retrieve available revisions
revs <- getRevisions(dc)
revs
## go back to original revision
setRevision(dc, revs[2])
keepRevisions(dc)
keepRevisions(dc) <- FALSE

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Term-Document Matrix from Distributed Corpora

Description

Constructs a term-document matrix given a distributed corpus.

Usage

## S3 method for class 'DCorpus'
TermDocumentMatrix(x, control = list())

Arguments

x	A distributed corpus.
control	A named list of control options. The component weighting must be a weighting function capable of handling a TermDocumentMatrix. It defaults to weightTf for term frequency weighting. All other options are delegated internally to a termFreq call.

Value

An object of class TermDocumentMatrix containing a sparse term-document matrix. The attribute Weighting contains the weighting applied to the matrix.

See Also

The documentation of termFreq gives an extensive list of possible options.

TermDocumentMatrix

Examples

data("crude")
tdm <- TermDocumentMatrix(as.DCorpus(crude),
                          list(stopwords = TRUE, weighting = weightTfIdf))
inspect(tdm[149:152,1:5])

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Transformations on Distributed Corpora

Description

Interface to apply transformation functions to distributed corpora. See tm_map in tm for more information.

Usage

## S3 method for class 'DCorpus'
tm_map(x, FUN, ...)

Arguments

x	A distributed corpus of class DCorpus.
FUN	a transformation function taking a text document as input and returning a text document. The function content_transformer can be used to create a wrapper to get and set the content of text documents.
...	arguments to FUN.

Value

A DCorpus with FUN applied to each document in x. If revisions are enabled, the original documents contained in x can be retrieved via getting back to the corresponding revision using the function setRevision().

See Also

getTransformations for available transformations in package tm.

Examples

data("crude")
tm_map(as.DCorpus(crude), content_transformer(tolower))

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