2022: Week 12 - Gender Pay Gap

 Challenge by: Jenny Martin

I spent my International Women's Day being really fascinated by the Gender Pay Gap Bot on Twitter. I decided to look at the publicly available data that it was based on and found it a little confusing. It made me realise that the thing I enjoyed most about the Pay Gap Bot, was that it made it the insight from the data clear in a succinct manner. So let's do that for the historical data too! 

Inputs

We're using the data currently available on the Gender Pay Gap Service from 2017 to 2022:

 There are 5 input files.

Requirements

  • Input the data
  • Combine the files
  • Keep only relevant fields
  • Extract the Report years from the file paths
  • Create a Year field based on the the first year in the Report name
  • Some companies have changed names over the years. For each EmployerId, find the most recent report they submitted and apply this EmployerName across all reports they've submitted
  • Create a Pay Gap field to explain the pay gap in plain English
    • You may encounter floating point inaccuracies. Find out more about how to resolve them here
    • In this dataset, a positive DiffMedianHourlyPercent means the women's pay is lower than the men's pay, whilst a negative value indicates the other way around
    • The phrasing should be as follows:
      • In this organisation, women's median hourly pay is X% higher/lower than men's.
      • In this organisation, men's and women's median hourly pay is equal.
  • Output the data

Output


  • 7 fields
    • Year
    • Report
    • EmployerName
    • EmployerId
    • EmployerSize
    • DiffMedianHourlyPercent
    • Pay Gap
  • 41,288 rows (41,289 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 use dif
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