Preppin' Data

Challenge by:  Will Sutton It's time for another collaboration with  #GamesNightViz ! Over the last few months, the #GamesNightViz team have been playing Elden Ring which has inspired this challenge. We’re looking for the highest damaging weapons we can use throughout our playthrough. Using data tables from https://eldenring.wiki.fextralife.com/Weapons we have the base stats for all the melee weapons however, it’s a little messy!  The goal is to create an output of the highest damaging weapon by its required level. Input A rather wide dataset! Don't worry, it'll be explained in the requirements which fields we need and what the values mean. Requirements Load the dataset  - updated 14/09 Split the dataset into two tables Damage Stats: 1 containing: Name, Category, Phy, Mag, Fire, Ligh, Holy Level Requirements: 1 containing: Name, Str, Dex, Int, Fai, Arc For Damage Stats, if you look at the data in Phy, Mag, Fire, Ligh, Holy: the first value shows the attack damage...

Challenge By: Algirdas Grajauskas It's always great when a Data Schooler in training comes up with an idea for a Preppin' Data Challenge! Al is part of DS31 and has been in training for 3 months now. For this challenge we're going to clean up and analyse his Spotify data.  Input Unclean Spotify data – from Spotify  Requirements Input the data Create a new field which would break down milliseconds into seconds and minutes e.g. 208,168 turned into minutes would be 3.47min Extract the year from the timestamp field Rank the artists by total minutes played overall For each year, find the ranking of the artists by total minutes played Reshape the data so we can compare how artist position changes year to year Filter to the overall top 100 artists Output the data Output 10 fields Overall Rank Artist Name 2015 2016 2017 2018 2019 2020 2021 2022 100 rows (101 including headers) You can download the  full output here .  After you finish the challenge make sure to fill in ...

Challenge By: Owen Barnes We have a guest contributor this week! Owen has just finished up his training at the Data School and is a regular Preppin' Data participator. So here's his challenge: This challenge will be useful for anyone trying to improve their knowledge of Tableau Prep functions, and their data parsing in general. There is an opportunity to use ReGex in this challenge, but there is a longer workaround with other steps available. There will also be the chance to use LODs in this workflow. We have been given a set of messy strings, which contain useful information that we need to connect to other datasets to eventually find out how much revenue we have generated by selling different products. This string provides us with information such as the quantity of items sold, the product ID code, the phone number of the buyer, and the area code which will let us find out where they are purchasing from. There will also be some small calculations needed to join certain datas...

Challenge By: Jenny Martin What's one of the benefits of preparing your own data? Being able to start your analysis sooner! Sometimes I can find opening Tableau Desktop to explore my data gets a little distracting by trying to visualise it before I've decided on the story. Starting my analysis of the dataset in Tableau Prep helps me, personally, to stay more focused! It's clear where the outliers are, what the distribution of the dataset is and therefore what the story should be. For this week's challenge we're looking at a dataset that was used in December 2020 for Sports Viz Sunday  (thanks to Kate Brown for sharing!) This dataset comes from the PGA and LPGA  2019 Golf tours and lists the total prize money for the top 100 players. For those of us who aren't too familiar with golf, the PGA is the men's tour, whilst the LPGA is the women's tour. Input We have one input this week: Official Money Requirements Input the data Answer these questions: What...

Challenge by: Jenny Martin Sometimes when researching one Preppin' Data idea, you encounter a rather hideous data structure that makes you wonder, "could Prep handle this?" Suddenly you're on a completely different tangent to the challenge you were originally planning and you've got a chunky Prep workflow that just begs to be turned into a challenge itself. So here we are, looking at the most popular baby names for boys and girls in England and Wales in 2019.  Inputs The data itself comes from the Office for National Statistics : Download inputs There is one input for boys names and one input for girls names. As you can see, each month is its own table and they are laid out next to each other in the Excel sheet. Not an ideal input for Tableau Desktop! Pay particular attention to May and August which have additional rows as there have been ties in the rankings.  Requirements Input the data I recommend starting with the boys names Remove totals Pivot to create a mon...

This week, things get a little more political. No Chin & Beard Suds Co don't have a political opinion in the US Elections but we do want to monitor what is going on. So let's look at how the 2020 Democratic Presidential Nomination is developing. Using data from RealClearPolitics.com , we are also making it easier for our Tableau Desktop users to follow the results by forming the dataset into a Bump Chart. Normally this would involve Table Calculations in Tableau Desktop but we want to prepare the Dataset so it makes it a lot easier for anyone to build this chart type and complete the ranking for them (this can be done in Prep 2020.1 or a longer work around in previous Prep versions - I built it pre-2020). Link to the viz Requirements Input data Remove the Average Record for the polls Clean up your Dates Remove any Null Poll Results Form a Rank (modified competition) of the candidates per Poll based on their results Determine the spread for each poll fro...