2024: Week 15 - Solution



Solution by Tom Prowse and you can download the workflow here.



Step 1 - Days to Removal

First we want to combine both data sets by using an inner join on Product ID: 




This then allows us to calculate the number of days that it took to remove the products. We can calculate this using a datediff calculation: 

Days to Removal 
DATEDIFF('day', #2025-05-13#, [Date])

As a result our table should look like this: 



Step 2 - Recall Targets

Next we want to categories the products to determine whether or not they had met the target. Using an IF calculation we can give each one a category: 

Category
IF [Days to Removal]<=7
THEN 'On Target'
ELSEIF ISNULL([Days to Removal])
THEN 'Incomplete'
ELSE 'Overdue'
END

This allows us to give each of the products a category based on whether they have been removed on time or not.



Step 3 - Output 1 - Store Rank

We can now start to create our outputs. The first one is where we rank our stores based on how long it has taken them to remove the items. 

First, we need to calculate the average removal time per store. For this we can use an aggregation step where we group by Store and calculate the Avg of Days to Removal. 



After the aggregation we can rank the stores based on the avg days to removal across the whole table: 

Rank 


Then finally we can convert the time to days and hours by first duplicating the Days to Removal field, then changing the original Days to Removal to a whole number. This will give us the number of days, then we can use the duplicated field for the hours.

To calculate the hours, we can subtract the days from the days and hours, then multiply by 24: 

Hours to Removal
ROUND(([Hours to Removal]-[Days to Removal])*24)

We can then output our first table that looks like this: 



Step 4 - Output 2 - Overdue & Incomplete

For the second output we want to highlight how many products missed the deadline or are incomplete. For this we want to create a new branch then filter the table so that we exclude the 'On Target' values (or keep the 'Overdue' and 'Incomplete' ones).

Then we calculate the number of days that they are overdue: 

Days Overdue 
DATEDIFF('day', #2025-05-20#, [Date])

After this, we use an aggregation step to calculate the avg days overdue and the quantity for each store: 


Finally we just need to rename the field Days Overdue field to Avg  and round to a whole number.

Avg Days Overdue 
ROUND([Avg Days Overdue])

The final output should look like this: 



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