loadPredefinedFunctionRegistry: Loading the registry of predefined functions
In analysisPipelines: Compose Interoperable Analysis Pipelines & Put Them in Production

Description Usage Details See Also Examples

View source: R/core-functions.R

Loading the registry of predefined functions

1	loadPredefinedFunctionRegistry()

Loads the registry of predefined functions

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

## Not run: 
  library(analysisPipelines)
  loadPredefinedFunctionRegistry()

## End(Not run)

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

analysisPipelines index

README.md Analysis pipelines - Core functionality and working with R data frames and functions Analysis pipelines for working with Python functions Analysis pipelines for working with Spark DataFrames for one-time/ batch analyses An introduction to meta-pipelines Interoperable analysis pipelines Streaming Analysis Pipelines for working with Apache Spark Structured Streaming Using pipelines inside Shiny widgets or apps

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