2022: Week 42 - Missing Training Data

Challenge by: Jenny Martin

Recently, my colleague Joe Stokes brought me an interesting challenge that I knew needed to be turned into a Preppin' Data! 

He was working with sports player training data. Since the athletes wouldn't take part in the same training sessions every single day, this was leading to a lot of gaps in the data. As there was a metric which calculated the average across all sessions for each day, this could lead to some misleading conclusions, with a lot of variation over time.

Instead, what we want is to take the value from the previous session, even if the player didn't partake in the session on that day. This helps to keep the average a bit more stable. It also means we have data for every day, even if the player were off sick that day.


Input

Our input is very simple, with a row for each training session the player participated in and their resulting score.

Requirements

  • Input the data
  • For each player and each session, we want to know what the date is of the next session
    • E.g. for Player 1 who had an Agility session on 4th Jan, their next Agility session was 6th Jan - so we have missing data on 5th Jan
  •  For the most recent training session in the dataset, assign the next session as the maximum date in the dataset
  • Scaffold the data so we have a row for each player and each session
    • Careful of duplicates!
  • Create a flag to indicate whether the score comes from an actual session, or is carried over from the previous session
  • Exclude all weekends (Saturdays & Sundays) from the dataset
    • Players need time off too!
  • Output the data

Output

  • 5 fields
    • Player
    • Session
    • Session Date
    • Score
    • Flag
  • 6,750 rows (6,751 including headers)
You can download the full 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