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Hi Maveryx,
A solution to last week’s challenge can be found here.
In April, we celebrate Earth Month, a time dedicated to raising awareness and taking action for environmental conservation and sustainability.
This weekly challenge delves into temperatures, highlighting their crucial role in our planet's health. The dataset presents comprehensive information on global temperature records, covering various countries worldwide. It includes average temperature records in Celsius for major cities from 1743 to 2013.
To solve this challenge, we will be concentrating on the data from 1950 onwards.
Your tasks are as follows:
Determine which cities have average temperatures greater than or equal to 25 degrees.
Among the cities identified in the previous task, identify the country with the highest number of such cities.
Examining all countries within the dataset, pinpoint the year with the highest average temperature and the year with the lowest average temperature across the globe.
Need a refresher? Review these lessons in Academy to gear up:
Sorting Data
Separating Data into Columns and Rows
Summarizing Data
Source: https://www.kaggle.com/datasets/maso0dahmed/global-temperature-records-1850-2022
Good luck!
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A link to the last challenge is HERE.
The knapsack problem:
There are 5 boxes of varying weights and dollar amounts - which boxes should be chosen to maximize the amount of money while still keeping the overall weight under or equal to 15 kg?
From a challenge standpoint, which combination of box/boxes is most optimal if you were only allowed 1 box in the backpack? 2 boxes in the backpack? 3 boxes in the backpack? or 4 boxes in the backpack?
Output 1: details the number of boxes and total $ without going over 15kg.
Output 2: details the specific blocks per batch.
I have included spatial objects as part of the input should you want to use a location optimizer macro as part of the solution. The new prescriptive simulation tools may also be a good choice for a solution. We are looking forward to seeing the solutions you come up with. Please don't feel constrained by the example output, your solution may be better! I am looking forward to seeing your solutions in this new more interactive forum.
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Here is this week’s challenge, I would like to thank everyone for playing along and for your feedback. You can find the solution to the previous challenge here.
For this challenge let’s look at creating a multi-level hierarchy from employee-manager data. As always there are several ways to do this challenge, I have designated it as an advanced challenge because there is an elegant way to solve it using iterative macros. The advantage to the iterative macro solution is that it becomes very dynamic. Other hard coded solutions would get you to the answer with this data, but if the depth of the hierarchy were to change, you would have to modify the workflow to support the change. It is a great example to see how iterative macros can make a workflow dynamic.
The use case:
An HR department wants to use Alteryx to quickly understand the reporting structure for employees across their organization.
The Input source contains 5 employees and an identifier that uniquely identifies the individual and the manager they report to.
The goal is to create a hierarchy field identifying each relationship between employee and manager(s). For example, a Director reports directly to the Vice President which is 1 level up. The Director is then 2 levels away from the CEO (in this data set). As a result the hierarchy identifier represents how many levels removed the employee is from management team they report into.
Give it a try, I look forward to your feedback.
UPDATED 2/16/2016:
The Solution has been included.
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A solution to last week’s challenge can be found here.
This challenge comes to us from @hannah_malek . Thank you for your contribution, Hannah!
Every month, the Risk Team at XYZ Bank compiles a risk report in which each key risk indicator (KRI) is assigned a red, amber, green (RAG) status according to specific thresholds. These thresholds are unique for each KRI and may be modified periodically.
The dataset comprises a collection of KRIs, dates, relevant KRI details, as well as the corresponding thresholds for each KRI on a given date. These include threshold limits for the red category and upper and lower limits for the amber and green categories, along with their respective operators.
Your challenge this week is to calculate the RAG status for each KRI based on the RAG thresholds for that particular date.
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Strava is a popular app that serves a social network for all athletes. In particular, the app is popular with runner and cyclists. However, sometimes we want to see the data differently from what is served to us. Below is a GPX file containing a mountain bike ride I completed a few weeks ago. The challenge is to parse it and create report snippets like these:
A speed and elevation dual-axis chart
A map of the route
Feel free to try anything else on top of these! I will not be providing a starting workflow since the results are pictured above, and connecting to the .gpx file is part of the challenge!
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