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

Teradata® Package for R Function Reference

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
Product
Teradata Package for R
Release Number
17.20
Published
March 2024
Language
English (United States)
Last Update
2024-05-03
dita:id
TeradataR_FxRef_Enterprise_1720
Product Category
Teradata Vantage

Transform

Description

The td_transform_sqle() function applies numeric transformations to input columns, using td_fit_sqle() output.

Usage

  td_transform_sqle (
      data = NULL,
      object = NULL,
      id.columns = NULL,
      ...
  )

Arguments

data

Required Argument.
Specifies the input tbl_teradata.
Types: tbl_teradata

object

Required Argument.
Specifies the tbl_teradata which contains the model data generated by the td_fit_sqle() function or instance of td_fit_sqle.
Types: tbl_teradata or td_fit_sqle

id.columns

Optional Argument.
Specifies the input tbl_teradata numeric columns to exactly copy to the output. By default, all numeric columns will be converted to float.
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_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("tdplyr_example", "iris_input", "transformation_table")
    
    # Create tbl_teradata object.
    iris_input <- tbl(con, "iris_input")
    transformation_df <- tbl(con, "transformation_table")

    transformation_df <- transformation_df 
    
    # Check the list of available analytic functions.
    display_analytic_functions()
    
    # Example 1: Run td_fit_sqle() with all arguments
    # and pass the output to td_transform_sqle().
    fit_df <- td_fit_sqle(data=iris_input,
                          object=transformation_df,
                          object.order.column='targetcolumn')
    
    # Run td_transform_sqle() with persist as TRUE in order to save the result.
    # Note that tbl_teradata representing the model is passed as
    # input to "object".
    transform_result <- td_transform_sqle(data=iris_input,
                                          data.partition.column='sepal_length',
                                          data.order.column='sepal_length',
                                          object=fit_df$result,
                                          object.order.column='targetcolumn',
                                          id.columns=c('species', 'id'),
                                          persist=TRUE)
                                          
    
    
    # Print the result.
    print(transform_result$result)
    
    # Example 2: Transform the 'petal_length', 'sepal_length', 'petal_width',
    #            'sepal_width' according to transformation_df tbl_teradata.
    #            Note that model is passed as instance of td_fit to "object".
    transform_result1 <- td_transform_sqle(data=iris_input,
                                           data.partition.column='sepal_length',
                                           data.order.column='sepal_length',
                                           object=fit_df,
                                           object.order.column='targetcolumn',
                                           id.columns=c('species', 'id'),
                                           persist=TRUE)
    
    # Print the result.
    print(transform_result1$result)
    
    # Alternatively use S3 transform function to run transform on the output of
    # td_fit_sqle() function.
    
    transform_result1 <- transform(fit_df,
                                   data=iris_input,
                                   data.partition.column='sepal_length',
                                   data.order.column='sepal_length',
                                   object.order.column='targetcolumn',
                                   id.columns=c('species', 'id'))
    
    # Print the result.
    print(transform_result1$result)