Project features onto principal components
R/sdf_ml.R
sdf_project
Description
Project features onto principal components
Usage
sdf_project(
object,
newdata,
features = dimnames(object$pc)[[1]],
feature_prefix = NULL,
...
) Arguments
| Arguments | Description |
|---|---|
| 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. |
Section
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.