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.

After you finish the challenge make sure to fill in the participation tracker, then share your solution on Twitter using #PreppinData and tagging @Datajedininja, @JennyMartinDS14 & @TomProwse1

You can also post your solution on the Tableau Forum where we have a Preppin' Data community page. Post your solutions and ask questions if you need any help! 

Popular posts from this blog

2024: Week 1 - Prep Air's Flow Card

Image
Created by: Carl Allchin Welcome to a New Year of Preppin' Data challenges. For anyone new to the challenges then let us give you an overview how the weekly challenge works.  Each Wednesday the Preppin' crew (Jenny, myself or a guest contributor) drop a data set(s) that requires some reshaping and/or cleaning to get it ready for analysis.  You can use any tool or language you want to do the reshaping (we build the challenges in Tableau Prep but love seeing different tools being learnt / tried). Share your solution on LinkedIn, Twitter/X, GitHub or the Tableau Forums Fill out our tracker so you can monitor your progress and involvement The following Tuesday we will post a written solution in Tableau Prep (thanks Tom) and a video walkthrough too (thanks Jenny) As with each January for the last few years, we'll set a number of challenges aimed at beginners. This is a great way to learn a number of fundamental data preparation skills or a chance to learn a new tool — New Year&
Read more

2023: Week 1 The Data Source Bank

Image
 Created by: Carl Allchin Welcome to a New Year of Preppin' Data. These are weekly exercises to help you learn and develop data preparation skills. We publish the challenges on a Wednesday and share a solution the following Tuesday. You can take the challenges whenever you want and we love to see your solutions. With data preparation, there is never just one way to complete the tasks so sharing your solutions will help others learn too. Share on Twitter, LinkedIn, the Tableau Forums or wherever you want to too. Tag Jenny Martin, Tom Prowse or myself or just use the #PreppinData to share your solutions.  The challenges are designed for learning Tableau Prep but we have a broad community who complete the challenges in R, Python, SQL, DBT, EasyMorph and many other tools. We love seeing people learn new tools so feel free to use whatever tools you want to complete the challenges.  A New Year means we start afresh so January's challenges will be focused on beginners. We will use dif
Read more

How to...Handle Free Text

Image
Free isn't always a good thing. In data, Free text is the example to state when proving that statements correct. However, lots of benefit can be gained from understanding data that has been entered in Free Text fields. What do we mean by Free Text? Free Text is the string based data that comes from allowing people to type answers in to systems and forms. The resulting data is normally stored within one column, with one answer per cell. As Free Text means the answer could be anything, this is what you get - absolutely anything. From expletives to slang, the words you will find in the data may be a challenge to interpret but the text is the closest way to collect the voice of your customer / employee. The Free Text field is likely to contain long, rambling sentences that can simply be analysed. If you count these fields, you are likely to have one of each entry each. Therefore, simply counting the entries will not provide anything meaningful to your analysis. The value is in
Read more