The #PreppinData 2024 week 6 challenge was to find the latest salary per staff member and summarise their tax position given UK income tax bands. We’re now into intermediary level challenges and so there are less prescriptive steps, and more options to solve the problem your way. For me the problem had two key parts: (1) get the latest row per staff member; and (2) the various calculations for salary and tax paid based on tax bands. ...
The final week of beginner month for #PreppinData involved a bit more complexity around joins, calculations and outputs. On top of Tableau’s getting started tutorial #PreppinData has been a great way to get into Tableau Prep. I’ve invested about 12-15 hours of time and feel like I’ve got a good initial grasp of the product. The challenge walk through provided less info on the “how”, which in some ways was quite nice as I felt more license to solve the problem my way. On the other hand I wonder if I should have made my flow less complex instead of aiming for one data set that could then be filtered down to the different outputs. ...
#PreppinData 2024 week covered using join types, in this case to understand which seats aren’t chosen given a seating plan and booking data. I had a preconceived idea of the solution here, as I’m used to using LEFT OUTER JOIN in SQL and then having a WHERE clause that returns rows where the result from the right hand table IS NULL. So I was expecting to have a join and then a filter in my flow. However, Tableau Prep has some additional join types that let you return entries where there are only values in the left table, only values in the right table, or even a “not inner” join for entries only in the left or right but not in both. I gave the left only option a go and it did the job nicely! Great how you can click on the segment of the venn diagram representation of the join to select the type too. ...
Two lots of Tableau Prep practice this week. A forum question (see end of post) and #PreppinData 2024 week 3. The challenge for #PreppinData was to join targets from a spreadsheet, with a sheet per quarter, to our previous sales figures. And then to calculate difference from target. Similar union and clean up steps to previous challenges to get to the point where there are two data sets to join, and where we have consistent fields in both (first letter of class, and a month number). Then the join is pretty straightforward: ...
Week two of getting to grips with Tableau Prep and I decided to countinue with #PreppingData. The team of Carl Allchin, Jenny Martin and Tom Prowse do a great job of picking challenges that gradually introduce you to functionality. This week covered unions, aggregation and reshaping data using pivots. I was particularly interested in pivots, as that’s a frequent challenge people have on the Tableau forums where we talk about data prep being a good option. ...
I’ve been meaning to explore Tableau Prep for a while and finally took it for a test drive. Many data professionals have experienced the need to prepare and clean data prior to analysis in tools like Tableau Desktop. Classic examples are: splitting data out of a single combined field; un-pivoting when each year of a measure is in a separate column; or maybe combining sales data from multiple differently formatted sources. ...