2020: Week 33 - Solution

 


This solution is by Tom Prowse and you can download our full solution here. 

After all the discussions around last week's challenge, this week we took our learning's and applied this to a real-life scenario. This week we want to calculate how many products we are wasting, and how much that's costing us as a result!

Step 1 - Calculate Units Sold

The first step is to join the Daily Sales and Scent tables together. We can do this with an inner join on Scent Code: 


We can do calculate the Units Sold, as we have both Daily Sales and Price within the same table.

Units Sold
[Daily Sales]/[Price]

Step 2 - Calculate Weekly Sales & Units Sold

Now we have our units sold, we can use the Orders table to calculate the weekly groupings. 

First we need to join the Orders table, we can do this with an inner join on Date: 



We then want to remove the Date-1 field, as this will make it easier in the next few steps! After removing the field, we want to do a self-join with our original workflow.


Then this time, we will use the following join conditions:


Using a similar logic to last week, we now want to identify the maximum date for each Date and Scent combination. We can use the following Fixed LOD to do this: 

Week


Now we have identified the Maximum date, we can use a filter to keep only the values that match the date field. Our filter calculation is: 
[Week]=[Date-1]
Now we are working at a weekly level, we can rename our Daily Sales to Weekly Sales. Our data table now looks like this: 


The final step at this stage is to use an aggregation tool to calculate the totals for the week. The aggregation setup looks like this: 


Step 3 - Calculate Waste & Profit

The final step this week is to calculate how much is wasted, and how much that is costing. We can use the following calculations to calculate this: 

Waste
[Units Ordered]-[Units Sold]
Waste Cost
round([Cost]*[Waste],2)

Profit

round([Weekly Sales]-[Waste Cost],2)

Our table now looks like this: 


Now we have calculated the profit metric, all we need to do is use another aggregation to calculate the total profit by Scent.



The final task this week is to rank and tidy the scents according to their profits. First we can use the following calculation to round the Total Profit to 2 decimal places: 

Total Profit
round([Total Profit],2)
Then finally we need to rank the scents using this calculation: 

Profitability Rank



Then we are ready to output the data:



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, @JonathanAllenby & @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