Cloud Quest #4: FIFA™ Women's World Cup Analysis
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Hi Maveryx,
We are launching an updated process to submit Cloud Quest solutions!
Starting today and moving forward we will provide a JSON workflow start file for each quest. The workflow contains starting data preloaded into a Text Input tool (or tools).
Upload this JSON file into your Alteryx Analytics Cloud Library, then you can open the file and start solving the quest in Designer Cloud!
Similarly, once you are finished, you can export your JSON solution file to attach to your reply on Community.
- Remember to attach your solution file to your reply on Community to get credit towards your Cloud Quest badge count.
- Please continue to upload a screenshot of your solution workflow so other Maveryx can check out your creativity!
Review the following instructions to learn how to import and export Alteryx Designer Cloud workflows.
Import the Cloud Quest Start File
- Download the provided JSON start file and upload it into your Analytics Cloud library.
- Open the workflow in Designer Cloud.
- Start solving!
Export Your Solution
- Once you have completed your quest, go back to your Analytics Cloud library.
- Download your workflow solution file.
- Upload your JSON file as an attachment to your comment.
Now, with that out of the way...
Let’s dive into this week's quest!
You are a data analyst at a sports statistics firm. Your department is focused on analyzing historical data from major international sporting events, and your current project is the FIFA Women’s World Cup™.
You have been provided with a dataset named Womens World Cup dataset. This file contains detailed Women's World Cup match records, including teams, match outcomes, and dates from 2016 through 2019.
Your goal is to use Alteryx Designer Cloud to process this dataset and determine which team won the most matches in years 2016-2018. The outcome should consist of two columns: one for the team and the other for the number of wins.
Hint: Use the IF/THEN expression in the Formula tool to identify the winners. Also, ensure to exclude any matches where there was a tie.
If you find yourself struggling with any of the tasks, feel free to explore these interactive lessons in the Maveryx Academy for guidance:
- Getting Started with Designer Cloud
- Building Connections in Designer Cloud
- Building Your Workflow in Designer Cloud
Here’s to a successful quest!
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My solution.
Result
The result did not match with the expected output if I filter with the year.
My result | netherlands, 15 |
Expected result | england, 16 |
Formula Tool
Year = ToNumber(Right([date], 2))
Winner =
IF [score_i] > [score_j] THEN [Team_i]
ELSEIF [score_i] < [score_j] THEN [Team_j]
ELSE Null() ENDIF
Filter Tool
[Year] >= 16 AND [Year] <= 18 AND !IsNull([Winner])
Sample Tool
I like the Sample Tool has the built-in Sort function named "Sample Based on Order".
It's really a nice idea and I want the same feature on Designer Desktop.
But I see the below note when I click the (i) button;
The row position of incoming data isn't preserved.
Sort by column to return an accurate range.
It makes me worried. What does it mean?
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@Yoshiro_Fujimori Your result is correct. After filtering out all dates from 2019, the Netherlands wins with a count of 15.
Your question about the Sample tool has a nuanced answer. The behavior of tools differs between Cloud Native Mode and Standard Mode. When this quest was created, Standard Mode did not yet exist. The sorting functionality you’re referring to is only available in the Cloud Native version of the Sample tool. In Standard Mode, tools behave as they do in Designer Desktop, so they will function in the way you’re accustomed to.
The reason for this difference lies in how Cloud Native Mode is designed. It is built to connect to massive cloud databases, and the Cloud Native version of tools operates similarly to In-DB tools in Designer Desktop. Specifically, it takes a 10MB random sample of the input data to display in the results window. The entire dataset is processed only when you select Run Job, using pushdown processing.
As a result, tools like the Multi-Row Formula or Generate Rows cannot be used in Cloud Native Mode when the results depend on exact row order. In your small example, you wouldn’t notice a difference in the number of rows between Cloud Native Mode and Standard Mode because the dataset is under 10MB. For small datasets like this, Standard Mode is generally recommended.
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