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Hi Maveryx,
A solution to last week’s challenge can be found here.
Ellen Wiegand, a Senior Sales Engineer at Alteryx, brought us this brilliant challenge idea. We are truly grateful for your contribution, Ellen!
Considering the importance of renewable energy and to celebrate Earth Day, let's work on a challenge regarding sustainable energy! We have a dataset that provides detailed information about wind towers in the United States and its territories. The text input file contains the latitude and longitude coordinates for Alteryx headquarters in Irvine, California.
Looking only at wind towers with an Attribute Confidence of 3 and Projects with more than one wind tower, complete the following tasks. (Note: The Column Descriptors tool container in the workflow file contains the definitions of the values in the input dataset.)
What is the name of the project closest to the Irvine office (CA)?
How far away from the office is it?
Hint: The provided dataset is a flat file. To facilitate the data extraction, use the JSON Parse tool in the Developer tab of Designer.
Need a refresher? Review these lessons in Academy to gear up:
Parsing JSON
Creating Spatial Objects
Changing Data Layouts
Sources:
https://evwhs.digitalglobe.com
http://www.google.com/earth/download/ge/
http://datagateway.nrcs.usda.gov/
Good luck!
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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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We're on to Lap 2! At this point, our drivers have finished Lap 1 and had a chance to shake off the nerves and prepare for the next lap.
The first lap had our drivers prep and profile a data set that we will use in Lap 2. This lap features the predictive tools and their use for aiding in employee retention.
Like with Lap 1, if you would like to follow the format of the Grand Prix, give yourself 2 minutes to read the prompt without touching the mouse or keyboard. Our drivers had 12 minutes to complete this exercise. Good luck!
Pictured below are our finalists @BenMoss, @mattagee, and @NicoleJohnson trying on the Grand Prix helmet.
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Here at Alteryx we know the importance of growing our experience at solving data and business problems. Almost weekly (~40+ times a year) we share an Alteryx challenge with our internal Alteryx users and then we all develop our own solutions. Later we review our solutions in small groups.
Since many Alteryx users have expressed an interest in having us share these exercises, we have created this new section of the Community. The intent is to share and exercise about once a week on Monday. Some of the exercises will be easy and some you will find more challenging. The goal is to expose everyone to more of the things Alteryx can do and hopefully everyone will take away an idea or two on how to approach different challenges. The following week we will post a new challenge as well as an example solution to the previous week's challenge.
We hope you enjoy the exercises.
Exercise #1 Join to Range:
A company in Australia has source data which is made up of a series of postal codes (eg. 2000, 2001, 2002 etc.) amongst some other data fields. They have a separate reference table which contains postcode ranges (eg. 2000 to 2002) which they would like to use to match/filter their main data.
Each Customer Record needs to be joined to the Lookup table based on a Postal Area Ranged region. Then finally summarize the customer data by Region, Sales Rep, and Responder, then a count of customers.
Check and see what the result should look like by looking at the data labeled 'Output'. Your mission is to take the input files and blend them so your result matches the output shown. Good luck!
UPDATE 11/16/2015:
I have posted a solution to challenge #1 from last week. It is a good example to show the usefulness of the generate rows tool. Please keep in mind that this is just one solution using Alteryx to solve the problem, there can be many other solutions and approaches to the problem. Hopefully you had fun and learned something new in the process.
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The link to the solution for last challenge #37 is HERE.
One of the powers of Alteryx is to be able to batch processes without the need to write scripts of use complicated code. A single output tool can be configured to generate many output files.
Use Case: A company needs to blend data from three sources and generate an output file for each product - region combination, a total of 15 output files.
Objective: Create a cross join between the Product Group, Region Reference and Data tables to produce 15 unique CSV Data files. Please note that only 1 output tool should be leveraged in your solution.
Thanks to all that are playing along!
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