2023: Week 29 - Moving Averages

Challenge by: Jenny Martin

Since the 2023.2 version of Prep has been out for a while now, we thought it was high time we played around with a few features! These will be: 

We'll be using them to help AllChains perform a monthly analysis across their Stores. 

One of the key things to watch out for when using a Moving Average is that you have a complete dataset i.e. when you're averaging across 3 months, those 3 months should be consecutive. If not, you're going to want to make sure to scaffold your data, so you have a row for each month, even if there were no sales in that month.

Input

One input this week containing AllChains sales data:

Requirements

  • Input the data
  • We only want to consider complete months so filter values up to June 2023
    • The values after July 2023 are forecasted values
  • Aggregated the data to a monthly level for each Store and Bike Type
  • Notice how not every Bike Type has a sale in every Store each Month (Road bikes in Brixton, for example, didn't have any sales in February 2020). We need to scaffold the data:
    • Use the Lookup function to find the Next Sale Month for each Store and Bike Type
    • You will have nulls for the most recent Month. Replace these nulls with the most recent date in the dataset (June 2023)
    • Use the New Rows step to fill in the missing Months - watch out for duplicate rows!
    • For the months which have been filled in, make sure the value for the Sales and Profit is 0
  • Calculate the 3 month Moving Average for the Profit for each Store and Bike Type
  • Output the data, naming the file dynamically with the Month and Year in which the file was fun
    • e.g. July 2023 Analysis

Output


  • 6 fields
    • Month
    • Store
    • Bike Type
    • Sales
    • Profit
    • 3 Month Moving Average Profit
  • 2,089 rows (2,090 including headers)
You can download the output from here.

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