assessEngineSetUp: Assesses engine (R, Spark, Python, Spark Structured...

In analysisPipelines: Compose Interoperable Analysis Pipelines & Put Them in Production

Description

Assesses engine (R, Spark, Python, Spark Structured Streaming) set up

Usage

assessEngineSetUp(object)

## S4 method for signature 'BaseAnalysisPipeline'
assessEngineSetUp(object)

Arguments

object

A Pipeline object

Details

Assesses whether engines required for executing functions in an AnalysisPipeline or StreamingAnalysisPipeline object have been set up

This method is implemented on the base class as it is a shared functionality across Pipeline objects

Value

Tibble containing the details of available engines, whether they are required for a pipeline, a logical value reporting whether the engine has been set up, and comments.

See Also

Other Package core functions: BaseAnalysisPipeline-class, MetaAnalysisPipeline-class, checkSchemaMatch, createPipelineInstance, exportAsMetaPipeline, generateOutput, genericPipelineException, getInput, getLoggerDetails, getOutputById, getPipelinePrototype, getPipeline, getRegistry, initDfBasedOnType, initialize,BaseAnalysisPipeline-method, loadMetaPipeline, loadPipeline, loadPredefinedFunctionRegistry, loadRegistry, prepExecution, registerFunction, savePipeline, saveRegistry, setInput, setLoggerDetails, updateObject, visualizePipeline

Examples

## Not run: 
library(analysisPipelines)
pipelineObj <- AnalysisPipeline(input = iris)
pipelineObj %>>% univarCatDistPlots(uniCol = "Species", priColor = "blue",
 optionalPlots = 0) %>>% assessEngineSetUp

## End(Not run)

analysisPipelines documentation built on July 1, 2020, 7:09 p.m.