Teradata Package for R Function Reference | 17.20 - ScaleTransform - Teradata Package for R - Look here for syntax, methods and examples for the functions included in the Teradata Package for R.

ScaleTransform

Description

td_scale_transform_sqle() function scales specified columns in input data, using td_scale_fit_sqle() function output.

Usage

  td_scale_transform_sqle (
      data = NULL,
      object = NULL,
      accumulate = NULL,
      ...
  )

Arguments

data	Required Argument. Specifies the input tbl_teradata. Types: tbl_teradata
object	Required Argument. Specifies the tbl_teradata containing the output of generated by td_scale_fit_sqle() function or the instance of td_scale_fit_sqle. Types: tbl_teradata or td_scale_fit_sqle
accumulate	Optional Argument. Specifies the names of input tbl_teradata columns to copy to the output. Types: character OR vector of Strings (character)
...	Specifies the generic keyword arguments SQLE functions accept. Below are the generic keyword arguments: persist: Optional Argument. Specifies whether to persist the results of the function in a table or not. When set to TRUE, results are persisted in a table; otherwise, results are garbage collected at the end of the session. Default Value: FALSE Types: logical volatile: Optional Argument. Specifies whether to put the results of the function in a volatile table or not. When set to TRUE, results are stored in a volatile table, otherwise not. Default Value: FALSE Types: logical Function allows the user to partition, hash, order or local order the input data. These generic arguments are available for each argument that accepts tbl_teradata as input and can be accessed as: "<input.data.arg.name>.partition.column" accepts character OR vector of Strings (character) (Strings) "<input.data.arg.name>.hash.column" accepts character OR vector of Strings (character) (Strings) "<input.data.arg.name>.order.column" accepts character OR vector of Strings (character) (Strings) "local.order.<input.data.arg.name>" accepts logical Note: These generic arguments are supported by tdplyr if the underlying SQL Engine function supports, else an exception is raised.

Value

Function returns an object of class "td_scale_transform_sqle" which is a named list containing object of class "tbl_teradata".
Named list member(s) can be referenced directly with the "$" operator using the name(s):result

Examples

  
    
    # Get the current context/connection.
    con <- td_get_context()$connection
    
    # Load the example data.
    loadExampleData("scalemap_example", "scale_housing")
    
    # Create tbl_teradata.
    scaling_house <- tbl(con, "scale_housing")
    
    # Check the list of available analytic functions.
    display_analytic_functions()
    
    # Example 1: Scale "lotsize" with respect to mean value of the column.
    fit_obj <- td_scale_fit_sqle(data=scaling_house,
                                 target.columns="lotsize",
                                 scale.method="MEAN",
                                 miss.value="KEEP",
                                 global.scale=FALSE,
                                 multiplier="1",
                                 intercept="0")
    
    # Print the result.
    print(fit_obj$result)
    
    # Scale "lotsize" column.
    # Note that tbl_teradata representing the model is passed as
    # input to "object".
    obj <- td_scale_transform_sqle(data=scaling_house,
                                   object=fit_obj$result,
                                   accumulate="price")
    
    # Print the result.
    print(obj$result)
    
    # Example 2: Scale "lotsize" column. Note that model is passed as instance of
    #            td_scale_fit_sqle to "object".
    obj1 <- td_scale_transform_sqle(data=scaling_house,
                                    object=fit_obj,
                                    accumulate="price")
    
    # Print the result.
    print(obj1$result)
    
    # Alternatively use S3 transform function to run transform on the output of
    # td_scale_fit_sqle() function.
    
    obj1 <- transform(fit_obj,
                      data=scaling_house,
                      accumulate="price")
    
    # Print the result.
    print(obj1$result)