In the pursuit of visualizing lunar migration, after generating a comprehensive dataset with Mockaroo, I faced the critical task of refining and structuring this data to reflect real-world constraints. This blog shows how I leveraged Alteryx to merge datasets and logically assign crew and travelers to trips, ensuring the integrity and realism of the story.

Overcoming Mockaroo’s Limitations: Unifying Traveler Data

Mockaroo’s free version limits exports to 1,000 rows, a challenge given the project’s scale. To simulate a diverse and extensive pool of lunar migrants, I created 23 separate datasets for a total of 23,000 travellers . Alteryx became the bridge, allowing me to union these datasets into a single, comprehensive file. This step was crucial, ensuring no detail was left behind as we moved forward.

Strategically Assigning Crew to Spaceship

The assignment of crew members to their respective trips required a thoughtful approach, especially considering the varying capacities of the spaceships. The process described in Alteryx about several meticulously planned steps:

Dataset Integration: By joining the SpaceShips dataset with Trips data on the ShipID, I ensured each trip was linked to its corresponding spaceship.
Respecting Capacity Constraints: The transformation of Capacity and Crew fields into integers allowed for precise, logical operations.
Row Generation for Crew Assignment: A RowCount field was introduced, incrementally matching the crew size dictated by the spaceship capacity, a crucial step in aligning crew assignments with realistic limits.
Finalizing Assignments: Selecting essential fields and generating a RecordID for each entry enabled a systematic alignment of crew members to trips, adhering strictly to capacity constraints.

Assigning Travelers to Trips with Precision

Employing a similar methodology, travelers were assigned to trips, mirroring the meticulous process used for crew assignments. This approach not only ensured that every traveler found their place but also respected the logistical realities of space travel, such as the limited capacity of spaceship.

Reflections on the Alteryx Journey

This phase of the visualization project highlighted Alteryx’s power in handling complex data challenges. The tool’s ability to perform advanced data manipulation and logical assignments was instrumental in moving from a broad dataset to a structured, analysis-ready format.

Next Steps: Visualizing The Prepared Data

With the data now meticulously prepared and structured, the stage is set for the final phase of the project: visualization. The upcoming blog will explore into how we bring this rich dataset to life through dynamic and insightful visualizations in Tableau.

 

 

Rodrigo Diaz
Author: Rodrigo Diaz

I'm Rodrigo from Mexico, and I am passionate about learning and career growth. I hold a law degree, a master's in business management, and a diploma in civil construction design. My expertise is in starting businesses and product design. I've worked in agriculture, IT, and the public sector, gaining insights into business operations. Outside work, I'm an avid golfer and enjoy spending time with my family, especially sharing experiences with my daughter.