Description Usage Arguments Details Value See Also Examples
A ml.data.frame object is an abstraction layer of data stored in a MarkLogic Server database. It is created based on the provided query, collection, directory and/or fiedlFilter parameters. For query and fieldFilter parameters see details section. It present data in MarkLogic Server in a tabular format. The ml.data.frame object enables many of the operations that can be used with a data.frame object.
1 2 3 |
conn |
A ml.conn-class object created by ml.connect |
query |
The query string used to define the result, see details for more information about syntax. |
fieldFilter |
Field level filtering. Multiple field filters are separated by , See details for limitations. |
ns |
A character with the namespace URI to be used with fieldFilter, default is none |
collection |
A list of collection URI:s to filter on. |
directory |
A list of directory URI:s to filter on. |
relevanceScores |
TRUE/FALSE. If the result attributes score, confidence and fitness should be included. Default is FALSE |
docUri |
TRUE/FALSE. If the uri of the documents in the results should be included. Default is FALSE. |
The query parameter are using the string search grammar for searching for data, all of the syntax is supported except contstraints. This enables searches such as "dog AND cat" or "dog NEAR cat". The search is always done on all fields in the data, for a more precise search use the fieldFilter.
fieldFilter enables filtering on a specific field using comparison operators can be used. For the ">" "<" "!=" "<=" ">=" operators there muset exist a element range index on the source field or a error will be raised, element range index can be created using the ml.add.index function. "==" operator will always work since it does not depend of range indexes.
A ml.data.frame object.
as.data.frame
for pulling data, as.ml.data.frame
for uploading data, rm.ml.data.frame
for delete uploaded data
1 2 3 4 5 6 7 8 9 10 11 | ## Not run:
library(rfml)
localConn <- ml.connect()
# create a ml.data.frame based on a search
mlIris <- ml.data.frame(localConn, "setosa")
# using search and collection filtering
mlIris <- ml.data.frame(localConn, "setosa", collection = "iris")
# using field filter
mlIris <- ml.data.frame(localConn, fieldFilter = "Species == setosa")
## End(Not run)
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