sdf_project {sparklyr} | R Documentation

## Project features onto principal components

### Description

Project features onto principal components

### Usage

    sdf_project(object, newdata, features = dimnames(object$components)[[1]],
      feature.prefix = "PC", ...)

### Arguments

`object` | A Spark PCA model object
`newdata` | An object coercible to a Spark DataFrame
`features` | A vector of names of columns to be projected
`feature.prefix` | The prefix used in naming the output features
`...` | Optional arguments; currently unused.

### Transforming Spark DataFrames

The family of functions prefixed with `sdf_` generally access the Scala Spark DataFrame API directly, as opposed to the `dplyr` interface which uses Spark SQL. These functions will 'force' any pending SQL in a `dplyr` pipeline, such that the resulting `tbl_spark` object returned will no longer have the attached 'lazy' SQL operations. Note that the underlying Spark DataFrame does execute its operations lazily, so that even though the pending set of operations (currently) are not exposed at the R level, these operations will only be executed when you explicitly `collect()` the table.

[Package sparklyr version 0.6.3 Index]