2021: Week 12 - Maldives Tourism

Challenge By: Jenny Martin

One of the best things about being a Dr Prepper is that people are always bringing interesting datasets to your attention. A little while ago, Tableau Zen Master Lorna Brown showed me a dataset with all kinds of information on tourism in the Maldives. This database has a lot of data on different Key Economic Indicators, but as you can imagine, it also has a bit of a quirky structure! For inspiration as to why we might want to clean this data up, check out Lorna's viz below:

Link to viz

Input

Our input is very wide this week, with 136 fields and only 28 rows. It covers tourism in the Maldives from 2010 to 2020. The source of this data is here but you can download it in the usual way from here.


Requirements

  • Input the data
  • Pivot all of the month fields into a single column (help)
  • Rename the fields and ensure that each field has the correct data type
  • Filter out the nulls (help)
  • Filter our dataset so our Values are referring to Number of Tourists
  • Our goal now is to remove all totals and subtotals from our dataset so that only the lowest level of granularity remains. Currently we have Total > Continents > Countries, but we don't have data for all countries in a continent, so it's not as simple as just filtering out the totals and subtotals. Plus in our Continents level of detail, we also have The Middle East and UN passport holders as categories. If you feel confident in your prep skills, this (plus the output) should be enough information to go on, but otherwise read on for a breakdown of the steps we need to take:
    • Filter out Total tourist arrivals
    • Split our workflow into 2 streams: Continents and Countries
      • Hint: the hierarchy field will be useful here
    • Split out the Continent and Country names from the relevant fields (help)
    • Aggregate our Country stream to the Continent level (help)
    • Join the two streams together and work out how many tourists arrivals there are that we don't know the country of (help)
    • Add in a Country field with the value "Unknown" (help)
    • Union this back to here we had our Country breakdown (help)
  • Output the data

Output


  • 4 fields
    • Month
    • Breakdown
    • Country
    • Number of Tourists
  • 1,826 rows (1,827 including headers)

The full output can be downloaded 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