Tableau

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. ...

In a previous post I walked through a LOD (level of detail) based alternative to the LOOKUP table calculation. In that example I was looking at sales last month, and in a recent Tableau Forums question someone was asking if it could be extended to determine average sales from the previous three months. The answer was “yes” and you can check the whole thread on the Forums, including other options. The main difference from my previous post is that we needed to extend the group numbers from two to four, so that any given month falls into 4 “higher level groups” that we can target with a LOD. We then chose which LOD to use based on the index (_i). ...

I recently helped with a Tableau community forums question where the user needed to: filter to the top N categories based on a measure, using a dense rank (so that categories with the same value had the same rank), but list no more than M categories within each rank (based on another criteria) This screenshot illustrates the requirement where we want to see the top 5 categories based on sales, where categories with the same sales have the same rank. But we only want to see the top 2 within each rank, based on lowest cost: ...

If you’ve ever built a filled map in Tableau where some of the areas are too small to stand out or label effectively, then you’ve probably come across hex maps as a solution. A hex map means that each area is the same size, regardless of the actual relative sizes. For example, note how the Nelson (NSN) region in this filled map of New Zealand is relatively small compared to other regions, and hence hard to see and label: ...

The LOOKUP table calculation in Tableau is really handy when you want to show or use a value from a previous row in the view. For example if you are showing sales per month and need to use the sales figure from the previous month to calculate month-on-month growth. In the example above our calculation for “Sales last period” is: ...

I don’t usually get to attend Tableau User Groups. We don’t (yet?) have one down in the depths of New Zealand’s south island, and it’s a long drive to the nearest one in Christchurch. But with New Zealand and much of the world in some form of lock down, Tableau has encouraged and supported virtual user group meetings. So I was excited to dial into this weeks virtual New Zealand Tableau User Group meeting jointly arranged by Alex, Thabata, Jeff and Paul from the Auckland, Christchurch and Wellington groups. The icing on the cake was being invited to speak about my experience with Makeover Monday! ...

Late last year I started to actively help out on the Tableau Forums. What a great decision! I’d forgotten how much fun it could be to (1) pick up a discrete challenge; (2) help others out; and (3) learn so much more in the process. One of the questions I recently chipped in on was about circular references in a sequential calculation. The background to the question is really interesting and I ended up spending a few hours digging into epidemiological models, but that’s a different story! Whilst trying to help I took a fresh look at the PREVIOUS_VALUE function in Tableau. I have to admit, prior to this I had thought that PREVIOUS_VALUE(x) was just the same as LOOKUP(x,-1) … turns out that isn’t the case! ...

An interesting and deceptively simple data set on alcohol consumption by country for 2019 week 26. I like the simplicity of the table of data and the factors affecting the top 25 that are discussed in the article. The chart itself would be better as bars not columns in my opinion, allowing the country names to be laid out for easier reading. As Eva noted in her submission showing liters of pure alcohol consumed per capita per year isn’t that easy to relate to. Digging into the definitions for standard drinks / units I was surprised to find that there is quite a range, and that some countries still don’t define a standard drink. I decided to focus on that aspect for my makeover. ...

Andy Kriebel selected a data set about US workers paid at/below the minimum wage for those choosing to participate in week 3, 2019. The original viz highlights some of the regional differences for 2017 by showing the data geographically. I like that I can see regional differences, but I found myself wanting to see the trend over time (as it’s available in the data set) to see if the geographical trends are part of an ongoing story. ...