2024: Week 25 - Solution


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


Step 1 - Split Complaint Description

First we want to work on the Complaints data source and then parse the Complaint Description into the Product ID, Issue Type, and the Description.

Product ID 
The product ID is the first set of numbers and letters before the '-' symbol. For this we can use a split to split only the first occurrence before the '-' and leave the remaining text in a separate field: 



This leaves us with the Product ID and the remaining text in separate columns, and we can then remove the original Complaint Description field: 


Issue Type 
Next from the remain text we can extract the issue type. For this we want to use the ':' as the separator: 



Then we can rename this field and remove the original split field: 



Complaint Description 
Finally we can extract the Description in a nice format by removing the ' " ' from either end. For this we can use two splits, and make sure that we are using the " as a separator



We can then tidy the table up by removing any additional fields and renaming the split field to Complaint Description: 



Step 2 - Category Code

In order to combine the two data sources we need to create a Category code for each table. On the existing Complaints workstream we need to extract the first two letters from the Product ID so we can use a Left function for this: 

Category Code 
LEFT([Product ID],2)


We can do the same with the Categories data source but we also need to ensure that it is all upper case: 

Category Code 
UPPER(LEFT([Product Category],2))

We can now join both tables using an inner join on the category code field: 




Step 3 - Filter Dates

The final step is to make sure we are only looking at dates where the Date Resolved happens after the Date Received. 

For this we need to ensure that the Date Resolved and the Date Received fields both have the data type of a Date. 

We can then use a calculated field within a filter to make sure we are filtered to only where [Date Received]<=[Date Resolved]

After removing any unneeded fields the output should look like this: 



You can view the output 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