Preppin' Data

Challenge by: Jenny Martin We're getting spooky this week, not only with Halloween data, but also with another click-only challenge! That's right, typed calculations are absolutely forbidden. We've got sales data from a company selling Halloween costumes around the world, but it certainly needs a little tidying up. With their fiscal year coming to an end on Halloween itself, they want to see how their sales are comparing to last year's. Let's hope the results aren't too scary! Input There is one input this week, containing Halloween costume sales from different countries:  Requirements Remember this is a click-only challenge, no typed calculations allowed! (Although you may rename fields, we're not that harsh)  Input the data. Each costume is in the language of the country the sales occur in. Group these costumes together by their English translation. You should have 9 costumes once you've finished grouping There have been some errors when inputting cou

  Solution by Tom Prowse and our full solution can be downloaded here . This week's challenge was based around cleaning a formatted spreadsheet that focused on the most popular baby names in England & Wales from 2019. This sort of problem occurs often when a spreadsheet is formatted nicely in Excel, but not so nicely when trying to use in Prep! Step 1 - Input Files The first task is to input all of the tables for each gender. At first we are only given the option to use a single table from the input, and when bringing this into our workflow, a lot of the months are combined within the same fields. This isn't ideal for Prep! Therefore, the first step is to use the Data Interpreter. This identifies the different tables within the spreadsheet and breaks these down so that each can be a separate input.  Now we have separate inputs for each section we can identify which ones have the same structure, these include: Boys Table 5 A6:O18 = Jan - Apr Table 5 A20:O32 = May-Aug Table 5

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

  Solution by Tom Prowse and you can download the full solution here .  This week's challenge was all around calculating the Year/Month/Week to Date calculations which are used throughout various different companies to give an indication about how they are performing. Instead of just basing these calculations on a single date, we wanted to take this a step further and compare the Week and Day number so that we are comparing the same periods throughout the year. This may not be the most simple way to solve it, but I wanted to try and make it as future proofed as possible! For some alternative solutions take a look at the Preppin Data Tableau Forums to see some other community members workflows. Step 1 - Create Daily Targets Table Our targets table is currently only a weekly breakdown so we want to use a daily scaffold to help build this out so we have a target for each day.  The first step is to bring in the Transactions table, which we will use for the daily scaffold. Within this

 Challenge by Tom Prowse This week we are back in the (home) office after a week at Tableau Conference-ish and want to know how we are performing so far this year. As for most businesses, it's been a tough year so we want to find some comparisons between our sales this year, last year and our targets.  We have two inputs:  1, Transactions This is a list of our daily sales for each product. It contains the Price, Quantity and Income.  2, Targets This is a list of targets that have been provided by the finance team. It is a weekly breakdown for this year by product. Requirements  Input the Data Create a daily Targets table. Assume there are 7 days in the week and the daily demand is split evenly throughout the week. Eg, if the weekly target is 700, then 100 per day.  Categorise whether a row/transaction happened this year, last year or is a target. Combine the Transactions & Targets tables.  Only keep the Year to Date for each period. As the 9th October can fall on a different da