2023: Week 40 - Solution



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



Step 1 - Pivot Class

First we want to bring all of the classes into a single column so that we have a field for the Subject, Class or Dropped, and Student Name. 

To do this we can use a Columns to Rows pivot where we use a wildcard pivot to bring in all fields with the word 'Class':



Step 2 - Group & Active Flag

Next we want to correct the spelling mistakes within the Subject field by using the in-built grouping features within Tableau Prep. We want to group by 'Spelling' which will group together the subjects that are spelt similarly into the correct spellings. 



We can then remove any nulls from this list and this will give us all of our relevant subjects in the correct format.

After this we can create a flag to determine whether the class is active or dropped. To determine this we can remove the number from the Class or Dropped Class field, again using the in-built functionality within Tableau Prep.



We're now ready to use an aggregation to calculate the total per subject and active status. Within the aggregation we can group by Subject and Active Flag, then count the Name


At this stage our table should look like this: 


Step 3 - Drop Out Rate

The final part of this task is to calculate the drop out rate for each class. 

First we need to pivot the data so that we have the total count of active students and drop outs for each subject on a single row. We want to use a Rows to Columns pivot where we pivot by the Active Flag and Sum the Name field. 



We can then rename the fields and calculate the following: 

Total Enrolled
[Active] + [Drop Outs]

Then the drop out rate:

Drop Out Rate
[Drop Outs] / [Total Enrolled]


Our final output should look like this: 


You can download 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