Teradata Package for R Function Reference | 17.20 - RowNormalizeTransform - 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

RowNormalizeTransform

Description

td_row_normalize_transform_sqle() function normalizes input columns row-wise, using td_row_normalize_fit_sqle() function output.

Usage

  td_row_normalize_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 generated by
td_row_normalize_fit_sqle() function or the instance of td_row_normalize_fit_sqle.
Types: tbl_teradata or td_row_normalize_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_row_normalize_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):

  1. output.data

  2. result

Examples

  
    
    # Get the current context/connection.
    con <- td_get_context()$connection
    
    # Load the example data.
    loadExampleData("tdplyr_example", "numerics")
    
    # Create tbl_teradata.
    numerics <- tbl(con, "numerics")
    
    # Check the list of available analytic functions.
    display_analytic_functions()
    
    # Example 1: Normalize "smallint_col" and "integer_col" columns using "INDEX"
    #            approach, "integer_col" as base column and base value as 100.
    fit_obj <- td_row_normalize_fit_sqle(data=numerics,
                                         target.columns=c("integer_col", "smallint_col"),
                                         approach="INDEX",
                                         base.column="integer_col",
                                         base.value=100.0)
    
    # Print the result.
    print(fit_obj$result)
    print(fit_obj$output.data)
    
    # Normalize the columns and accumulate the result by "id_col" column.
    # Note that tbl_teradata representing the model is passed as
    # input to "object".
    obj <- td_row_normalize_transform_sqle(data=numerics,
                                           object=fit_obj$result,
                                           accumulate="id_col")
    
    # Print the result.
    print(obj$result)
    
    # Example 2: Function to normalize the columns and accumulate the result
    #            by "id_col" column. Note that model is passed as instance of
    #            td_row_normalize_fit_sqle to "object".
    obj1 <- td_row_normalize_transform_sqle(data=numerics,
                                            object=fit_obj,
                                            accumulate="id_col")
    
    # Print the result.
    print(obj1$result)
    
    # Alternatively use S3 transform function to run transform on the output of
    # td_row_normalize_fit_sqle() function.
    
    obj1 <- transform(fit_obj,
                      data=numerics,
                      accumulate="id_col")
    
    # Print the result.
    print(obj1$result)