2024: Week 16 - Budget vs Actuals

Challenge by: Michał Mioduchowski

We're continuing with DS43's challenges so over to Michał to explain the next challenge. 

_____________________________________

For this week, Superbytes has requested us to look at their budget sheet for 2022. The company believes that their rough budget estimates were very close to the final budget allocations for that year. Examining historical budget data allows the company to identify trends in spending patterns over time. This can provide valuable insights into how expenses have evolved and help predict future expenditure trends. CEO Phil Down would like us to find the exact areas of mismatch between predicted and actual spending.

Inputs

There are 2 inputs for this challenge:
  • Forecasted Spending
 
  • Actual Spending

Requirements

  • Input the Excel file
  • Match Sheet 1 and Sheet 2 in formatting. Both should include:
    • Category field [String]
    • Budget/Actual field [Number (Decimal)]
  • Join both sheets based on Category field to create a single table with 3 columns:
    • Category [String]
    • Budget [Number]
    • Actual [Number]
  • Rename the fields to:
    • Category
    • Forecasted Spending
    • Actual Spending
  • Create a new calculated column with the differences between forecasted and actual values.
    • Values in the new column should be rounded to whole numbers [ROUND(...)]
  • Output the data

Output


  • 4 fields
    • Category
    • Forecasted Spending 
    • Actual Spending
    • Difference
  • 9 rows
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

2024: Week 2 - Average Price Analysis

Image
Created by: Carl Allchin It's the second week of our introductory challenges. This week the challenge will involve unions, aggregation and reshaping data. Input The input data set for this week is the output from week one. You can download them here . Requirements Input the two csv files Union the files together Convert the Date field to a Quarter Number instead Name this field Quarter Aggregate the data in the following ways: Median price per Quarter, Flow Card? and Class Minimum price per Quarter, Flow Card? and Class Maximum price per Quarter, Flow Card? and Class Create three separate flows where you have only one of the aggregated measures in each.  One for the minimum price One for the median price One for the maximum price Now pivot the data to have a column per class for each quarter and whether the passenger had a flow card or not Union these flows back together What's this you see??? Economy is the most expensive seats and first class is the cheapest? When you go and ...
Read more