The link to the solution for last challenge #12 is 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 some of the more complex functions like RegEx can be used, but it is not absolutely necessary.
The use case:
We have HTML data that is in a single field, the HTML contains an HTML Table.
The input contains a series of name/value pairs within the description field. The description field has a HTML table that contains 14 name/value contained within <td> tags. Each pairing can be found on a different row (designated by the <tr> tag).
The objective is to produce a table containing the 14 name/value pairs.
Good luck, I look forward to your feedback.
Update: As of 9/20/19, the start and solution files were updated. Your solution may not match those posted by Community members prior to this date.
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The link to last week’s challenge is HERE.
Wow, exercise #51 already. Can’t believe it has been nearly a year!
For this week I would like to investigate some of the geospatial tools available in Alteryx The spatial tools will allow you to calculate distances, create trade areas, find intersection of spatial objects, calculate sizes, etc.. Even if you don’t have the spatial data from Alteryx (the spatial data provides supporting data packages for geocoding, address standardization and calculations using the road network), most of the tools will still perform as long as you have the spatial references or objects already available in your input data.
Use Case: For regulatory purposes we have been asked to identify the all the counties within a 15-mile radius of each of our stores as well as identifying the percent of the county’s area that is overlapped (Coverage).
Objective:
Part 1) Calculate the percent overlap by individual store
Part 2) Calculate the percent of overlap by county for the entire store network
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We hope you enjoyed last week's challenge. The solution has been posted here. For the second challenge lets look at removing characters and splitting data into columns based on delimiters.
Many products will export textual data with delimiters such as quotes. This is done so that strings can contain delimiters or control characters within them. Having more than one type of delimiter can be hard for ETL programs to interpret. In the input text file, there are two different delimiters (double quotes, single quotes) and they surround different data types.
Use Alteryx to strip out the delimiters as superfluous and format the data as represented in the output.
You may notice that we have started classifying the exercises into beginner, Intermediate and advanced. This classification is used by Alteryx internally to sequence exercises as users advance.
Update 11/23/2015:
The solution has been uploaded.
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A solution to last week's challenge can be found here.
Using the values in the attached file "womens_world_cup_data.txt", determine which team won the most matches.
Go Team USA!
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A solution to last week’s challenge can be found here.
Because having a reliable electricity source is important to many people, we tend to notice when the power goes out! While we might expect service outages during severe weather events and other natural disasters, sometimes the culprit is... fluffier and cuter, or slithers.
Using the provided dataset, find out: • How many outage events can we attribute to animals? List the culprits with the count of events in descending order.
Hint Use the animals listed in the "Operative" field and information from the "Article Title" field to replace values of "Animal" with the actual animals.
Note
All animal types present in this dataset are included in the "Operative" column.
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