2023: Week 30 - % Difference From

Challenge by: Jenny Martin

For this challenge we're continuing to play around with the new features in the latest 2023.2 version of Tableau Prep. These will be:

  • % Difference From 
  • Lookup function

Both these calculations will help us work across multiple rows of data in slightly different ways. We'll be using them to help AllChains analyse the performance of their Stores over the last financial year.

Input

We have one Excel file that contains a sheet for each Month of Sales. 

Requirements

  • Input the data
  • Bring all the Months together into a single table
  • Merge together misaligned fields
  • Create an Order Date field
  • The financial year for Allchains starts in July. Convert the dates to Fiscal Quarters
  • Aggregate the data to a Quarterly level for each Store, ignoring the product details
  • Calculate the % Difference in Sales from the Previous Quarter
    • Multiply this value by 100 and round to 1 decimal place
  • Use the lookup function so that we can compare the % Difference for all Quarters in a single row
  • Evaluate each Store's performance by categorising them in the following way:
    • If a Store has seen consistent growth - "Going from strength to strength"
    • If a Store has seen consistent decline - "Going from bad to worse"
    • If a Store has 1 negative Quarter:
      • In Q2 - "Good growth in last half"
      • In Q3 - "Some good growth, but concerns in Q3"
      • In Q4 - "Good growth, until Q4"
    • If a Store has only 1 positive Quarter:
      • In Q2 - "Concerning performance in last half"
      • In Q3 - "Concerning performance, excluding Q3"
      • In Q4 - "Concerning performance, but improving in Q4"
  • Output the data

Output

  • 5 fields
    • Store
    • Quarter
    • Sales
    • % Difference Q on Q
    • Store Evaluation
  • 20 rows (21 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 u...
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