2022: Week 39 - Solution


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


This week we revisit a favourite challenge of ours... a fill down! We are looking to help the HR department fill in the missing information around key employee dates.

Step 1 - Employee Field

First we want to focus on the employee field. This will allow us to 'copy' the employee name down until there is another employee. 

The first step is to create a table with each of our employees and their ID. To do this we can remove the Null values from the Employee field, then remove the other fields so we are left with Employee ID (renamed from Record ID) and Employee:


From here we want to join this table back to our original workflow using an inner join where the Record ID >= Employee ID:


This allows us to join all of the rows where the record id is less than the employee id. This creates an expanded table, but we can then use some filters to ensure that the names are correctly labelled.

To ensure we are filtering correctly, first we want to find the Max number for each Employee ID using an FIXED LOD: 

Employee ID Match: 


We can then use this field to filter to keep the rows where [Employee ID Match]=[Employee ID], then we can remove the Employee ID Match, Employee ID, & Employee fields so the table looks like this: 


Step 2 - Work Level Field

Next we want to use the same technique to fill in the Work Level field. To do this we again want to create a table that includes all of the different levels and the IDs.

To do this we want to remove the Employee-1, Stage, and Date fields then filter the Work Level field to remove the null values. 


We can then do a join back onto the previous step. This time it's an inner join where Record ID >= Work Level ID 


Again, as a result of the join, we need to filter some of the results. First, we need to find the maximum ID for each Work Level: 

Work Level ID Match


Then we can filter to keep only values where [Work Level ID Match]=[Work Level ID], then remove any additional fields so that the table looks like this:


Step 3 - Reorder Data

The final task is to reorder the data so that it is in the same order as the input. For this we want to sort alphabetically by employee, and then by earliest date. We can do this using a Rank, note you will need to remove the existing Record ID: 

Record ID


After ordering in the correct way we can output our data that 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

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

2023: Week 2 - International Bank Account Numbers

Image
Challenge By: Jenny Martin For week 2 of our beginner month, Data Source Bank has a requirement to construct International Bank Account Numbers (IBANs), even for Transactions taking place in the UK. We have all the information in separate fields, we just need to put it altogether in the following order: Inputs A list of the transactions, with information about the receiving bank account  A lookup table for the SWIFT Bank Codes  Requirements Input the data In the Transactions table, there is a Sort Code field which contains dashes. We need to remove these so just have a 6 digit string ( hint ) Use the SWIFT Bank Code lookup table to bring in additional information about the SWIFT code and Check Digits of the receiving bank account ( hint ) Add a field for the Country Code ( hint ) Hint: all these transactions take place in the UK so the Country Code should be GB Create the IBAN as above ( hint ) Hint: watch out for trying to combine sting fields with numeric fields - check data types Re
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