termvectors: Termvectors

Description

Termvectors

Usage

termvectors(
  conn,
  index,
  type = NULL,
  id = NULL,
  body = list(),
  pretty = TRUE,
  field_statistics = TRUE,
  fields = NULL,
  offsets = TRUE,
  parent = NULL,
  payloads = TRUE,
  positions = TRUE,
  realtime = TRUE,
  preference = "random",
  routing = NULL,
  term_statistics = FALSE,
  version = NULL,
  version_type = NULL,
  ...
)

Arguments

conn

an Elasticsearch connection object, see connect()

index

(character) The index in which the document resides.

type

(character) The type of the document. optional

(character) The id of the document, when not specified a doc param should be supplied.

body

(character) Define parameters and or supply a document to get termvectors for

pretty

(logical) pretty print. Default: TRUE

field_statistics

(character) Specifies if document count, sum of document frequencies and sum of total term frequencies should be returned. Default: TRUE

fields

(character) A comma-separated list of fields to return.

offsets

(character) Specifies if term offsets should be returned. Default: TRUE

parent

(character) Parent id of documents.

payloads

(character) Specifies if term payloads should be returned. Default: TRUE

positions

(character) Specifies if term positions should be returned. Default: TRUE

realtime

(character) Specifies if request is real-time as opposed to near-real-time (Default: TRUE).

preference

(character) Specify the node or shard the operation should be performed on (Default: random).

routing

(character) Specific routing value.

term_statistics

(character) Specifies if total term frequency and document frequency should be returned. Default: FALSE

version

(character) Explicit version number for concurrency control

version_type

(character) Specific version type, valid choices are: 'internal', 'external', 'external_gte', 'force'

...

Curl args passed on to crul::verb-POST

Details

Returns information and statistics on terms in the fields of a particular document. The document could be stored in the index or artificially provided by the user (Added in 1.4). Note that for documents stored in the index, this is a near realtime API as the term vectors are not available until the next refresh.

References

https://www.elastic.co/guide/en/elasticsearch/reference/current/docs-termvectors.html

Examples

Run this code

# NOT RUN {
x <- connect()

if (!index_exists(x, 'plos')) {
  plosdat <- system.file("examples", "plos_data.json",
    package = "elastic")
  plosdat <- type_remover(plosdat)
  invisible(docs_bulk(x, plosdat))
}
if (!index_exists(x, 'omdb')) {
  omdb <- system.file("examples", "omdb.json", package = "elastic")
  omdb <- type_remover(omdb)
  invisible(docs_bulk(x, omdb))
}

body <- '{
  "fields" : ["title"],
  "offsets" : true,
  "positions" : true,
  "term_statistics" : true,
  "field_statistics" : true
}'
termvectors(x, 'plos', id = 29, body = body)

body <- '{
  "fields" : ["Plot"],
  "offsets" : true,
  "positions" : true,
  "term_statistics" : true,
  "field_statistics" : true
}'
termvectors(x, 'omdb', id = Search(x, "omdb", size=1)$hits$hits[[1]]$`_id`,
body = body)
# }