2024: Week 40 - Vrroom

Challenge by: Abhishek Minz 

Vrroom is an online platform for used cars where individuals and dealerships can advertise their vehicles. At present, five different dealerships are using the website. Vrroom's management team need to find out which dealership is taking the longest time (in days) to sell their vehicles through the platform. 

Input 

There are two csv data sets this week:

1. The Adverts (ads) data set:

2. The Users data set:

There are 365 users of the website - each vehicle listed is classed as a different registration number.

Requirements

  • Input the data sets
  • Break the Users data set into individual records (you should have 365 rows)
  • The User data is formed from:
    • 1st 7 characters is the User ID
    • The last letter signifies whether the user is a private individual ('P') or Dealership (D)
    • The 3 characters after the User ID for Dealerships is the Dealership ID
  • With the Ads data, remove any unsold vehicles
  • Join the data sets together
  • Find when an advert is first posted
  • Only keep the records where advert was first posted
  • Find the time between when a vehicle is first advertised on the site to when the vehicle was sold
  • Find the average days for each sale a dealership has listed (group up to the nearest whole day)
  • Output the data

Output



2 data fields:
  • Dealership ID
  • Dealership Avg Days to Sell
5 rows (6 rows incl. headers)

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