Preppin' Data

Challenge by: Jenny Martin As I've mentioned before in a previous challenge, I'm a big fan of a quiz show called Richard Osman's House of Games. However, I've often found the way that they decide the overall winner of the week a little troubling. Each day the player who has scored the most, will receive 4 points, 2nd place will receive 3 points, 3rd place will receive 2 points and last place will receive one point. These points will be added up across the week to determine the overall winner, but with a twist! Each Friday double points are awarded so 1st place receives 8 points and so on. This leads me to wondering: Would there be a different winner if there was no double points Friday? What about if participants weren't ranked at the end of each day and they had a running total score across the week instead, would that lead to a different winner? What about doubling the scores on the Friday, instead of the points awarded? Input Luckily I didn't have to collect ...

 Challenge By: Jenny Martin Recently, I've been playing with data about the best movie trilogies of all time, according to IMDb. So I thought I'd create a Preppin' Data challenge to allow you all to do the same! View on Tableau Public Inputs There are 2 inputs for this challenge: Top 30 Trilogies  Films  Requirements Input the data Split out the Number in Series  field into Film Order  and Total Films in Series Work out the average rating for each trilogy Work out the highest ranking for each trilogy Rank the trilogies based on the average rating and use the highest ranking metric to break ties (make sure you haven't rounded the numeric fields yet!) We have noticed a slight error in the way that Tableau Prep is calculating this rank, so don't worry if your output is different to ours, we are investigating! Remove the word trilogy from the Trilogy field Bring the 2 datasets together by the ranking fields Output the data Output 7 fields Trilogy Ranking Trilogy Tr...

Week 27 by Tom Prowse For this week's challenge we are planning a staycation here in the UK, and what better to do than go on a surfing trip! We have identified the area of South Devon, UK as a great surfing area and used the Surf-Forecast website to help us gather some information about each of the surfing sites.   The four of us here at Preppin Data all have a range of skills and requirements for our trip, so can you select the best place for each of us to go? Input 1 Input, 3 Sheets: Information Data from the Surf-Forecast website (and some of our own made-up data!) Location Data about each location, including the best season for surfing Preppers Data about each of our requirements and skill levels Requirements Input Data Find a way to join all three tables together Identify which locations would be suitable for each person Watch out for any duplicated person & site combinations Rank the top sites in terms of rating for each person. If there is a tie, then use the reli...

You can find our full solution workflow below and download it here ! The full solution workflow. Learning Objectives: Practice wildcard unions to manage & speed up inputs. Practice using filter calculations for more precise and versatile filtering. How to workaround the lack of table-calcs in Prep to: Add ranks to your data. Create a top N% filter. Unioning all the relevant data In last week’s solution post we mentioned how using the Wildcard Union could be used to not only quickly import and combine your data but also ensure you only import the data you actually want to import. The Wildcard Union. Add one of sales files to the canvas. Switch to the Multiple Files tab. Select Wildcard Union . Define a “ Matching Pattern ” of “Sales_*”.   This means only files that start with “Sales_” will be including in the Wildcard Union. Click Apply . Obtaining data from just the last 6 months Here we’re also reinforcing lessons from last week by in...

A few weeks back we mentioned that we were cleaning up our mailing lists. Well, now our marketing department is looking to generate further revenue and believes a great way of doing that is rewarding our highest spending customers by emailing them “15 per scent discount” codes! They’ve decided the optimal cut-off for who receives these codes is the top 8% of customers by total sales from orders placed within the last 6 months. Why top 8%? For valid reasons, not just to stop people from mentally working out where the 10% cut off is I'm sure. To help you, they’ve zipped up a bunch of data and sent it over to you (though it seems like in their haste they may have sent some unnecessary files over too). The data contains sales for the last 12 months (as of 24/05/2019). From this they want a list of all the email addresses for the top 8% of customers (by total sales over the last 6 months) along with their rank (by total sales over… you get the idea) and the total sales value. The...