Teradata R Package Function Reference | 17.00 - 17.00 - MovingAverage - Teradata R Package

Description

The MovingAverage function calculates the moving average of the target columns based on the moving average types argument ("mvgtype").
Possible moving average types:

• 'C' - Cumulative moving average.

• 'E' - Exponential moving average.

• 'M' - Modified moving average.

• 'S' - Simple moving average.

• 'T' - Triangular moving average.

• 'W' - Weighted moving average.

Note: This function is only available when tdplyr is connected to Vantage 1.1 or later versions.

Usage

  td_moving_average_sqle (
      data = NULL,
      target.columns = NULL,
      alpha = 0.1,
      start.rows = 2,
      window.size = 10,
      include.first = FALSE,
      mavgtype = "C",
      data.partition.column = NULL,
      data.order.column = NULL
  )

Arguments

Value

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

Examples

    # Get the current context/connection
    con <- td_get_context()$connection
    
    # Load example data.
    loadExampleData("exponentialmovavg_example", "ibm_stock")
    loadExampleData("weightedmovavg_example", "stock_vol")
    
    # Create object(s) of class "tbl_teradata".
    ibm_stock <- tbl(con, "ibm_stock")
    
    # Example 1: Compute the exponential moving average
    td_exponential_mov_avg_out <- td_moving_average_sqle(data = ibm_stock,
                                                         data.partition.column = c("name"),
                                                         data.order.column = c("period"),
                                                         target.columns = c("stockprice"),
                                                         start.rows = 10,
                                                         include.first = TRUE,
                                                         mavgtype = "E"
                                                         )
                                                         
    # Example 2: Compute the cumulative moving average for "stockprice".
    td_cumulative_mov_avg_out <- td_moving_average_sqle(data = ibm_stock,
                                                       data.partition.column = c("name"),
                                                       data.order.column = c("period"),
                                                       target.columns = c("stockprice"),
                                                       mavgtype = "C"
                                                       ) 
     
    # Example 3: Compute the simple moving average for "stockprice".
    td_simple_mov_avg_out <- td_moving_average_sqle(data = ibm_stock,
                                           data.partition.column = "name",
                                           data.order.column = "period",
                                           target.columns = "stockprice",
                                           include.first = TRUE,
                                           window.size = 10,
                                           mavgtype = "S"
                                           )
                                           
    # The input table, stock_vol, contains hypothetical stock price and volume data of three
    # companies between 17 May 1961 and 21 June 1961.
    stock_vol <- tbl(con, "stock_vol")
      
    # Example 4: This example computes the weighted moving average for stockprice and volume
    # for three companies.
    td_weighted_mov_avg_out <- td_moving_average_sqle(data = stock_vol,
                                                   data.partition.column = c("id"),
                                                   data.order.column = c("name"),
                                                   target.columns = c("stockprice","volume"),
                                                   include.first = TRUE,
                                                   window.size = 5,
                                                   mavgtype = "W"
                                                   )  
                                                   
    # Example 5: Triangular Moving Average
    td_triangular_mov_avg_out <- td_moving_average_sqle(data = stock_vol,
                                                   data.partition.column = "name",
                                                   data.order.column = "period",
                                                   target.columns = c("stockprice"),
                                                   include.first = TRUE,
                                                   window.size = 3,
                                                   mavgtype = "T"
                                                   )
                                                   
     # Example 6: Modified Moving Average.
     td_modified_mov_avg_out <- td_moving_average_sqle(data = stock_vol,
                                                   data.partition.column = "name",
                                                   data.order.column = "period",
                                                   target.columns = c("stockprice"),
                                                   include.first = TRUE,
                                                   window.size = 3,
                                                   mavgtype = "M"
                                                   )