Typical Scenarios of LOD Expressions in Tableau

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Advanced Tableau - Level of Detail Expressions / LOD

Take Your Tableau Data Visualization and Data Analytics Skills to the Next Level and Design Custom Solutions with Ease

06:12:03 of on-demand video • Updated September 2019

  • Understanding LOD expressions and using them confidently
  • Performing calculations in Tableau that are at a different level of detail than the view
  • Analyzing and solving complex analytical challenges
  • Understanding the different levels of details of multivariate datasets
  • Cohort analysis
  • Market basket analysis
  • User retention analysis
  • Binning aggregates by dimensions
  • Proportional brushing
  • Relative comparison of values/ categories
  • Nesting LOD expressions
English [Auto] After completing all those projects and exercises I think it would be useful to summarize the material and name a couple of scenarios were Elodie expressions come in handy in the first part of discourse to learn about what the few Elodie and the calculation level of detail are in the tableau environment. And then we went through the three types of Elodie expressions include exclude and fixed here we discussed what the difference co-oping keywords do and also how they compare to each other. At this stage you learned about the first and very basic case of using in Elodie expression namely when you need the data to be aggregated at a de-friend level of detail as to a few Elodie. In this case we're mainly compared a member against a whole category or an average against a totem. We also compared Elodie calculations against the reference lines. How to get the same results and also what kind of advantages the analysis has. When choosing an elegy expression over a reference line. So this was the first scenario comparison of data aggregated at different levels of detail. Of course the intro lectures well not the only case where we used Elodie expressions for comparison. We actually did that throughout the whole course but in combination with other methods after those central parts we went on with intermediate and advanced examples. The first one of them was the topic of burning aggregates. Here you learnt that a fixed expression is a deal to create bins of measure by using categories those categories can be defined by a dimension or some conditional statement. What makes the fixed expression ideal for that task is that this one can be used both as a measure and a dimension. After converting the cast from calculation to a dimension it can be used for histogram black charts along side with a measure. Basically the second scenario where you want to consider using an Elodie expression is when you want to been aggregates by a given condition or a category using the same principles in a slightly different way allows the user to perform even cohort analysis into blue. After that we took advantage of another quality of the fixed expression namely that it bypasses the mentioned filters particularly in the lectures on Proportional refereeing. We use that one extensively this way we could build visualisations were some measures regathered the filters while other ones ignore them. So you want to consider using an Elodie expression in cases where you want to apply a filter to only some fields in your view or if you want a calculation to include data that has been filtered out from the few it is basically two sides of the same quality. The last Chandler a scenario I want to mention here is to use an LCD expression to resolve the error message can not mix aggregate and non aggregate values the fixed expression is an ideal tool for that because that one evaluates the aggregation at zero level. Therefore it can be used alongside with role level expressions including X Kloed work like some kind of complement nation to the few Elodie. Therefore they work at the aggregate level. And this leaves the fixed expression the only useful tool for resolving that error message of course apart from restructuring the whole through so as you can see there are a bunch of different ways in how to utilize Elodie calculations in your daily work. As I said in the intro some analytical questions are easy to ask but can be surprisingly hard to answer with a chanterelle tool set of Tablo. Those are questions addressing different detail levels of the data. At the same time sometimes you can find some sort of a workaround but the final outcome can still contain too much churn which is rather to be avoided. In today's day to science you might find that discourse was rather heavy on the fixed expression and we talked less about the other two scoping keywords. This is because the include and exclude expressions are very limited compared to fixed the way in how you can utilize them is much more narrow. And basically the same process over and over again include adds to the few Elodie while exclude subtracts from the few Elodie sort of like a one trick pony. The fixed expression on the other hand is much more flexible. And with that one the analyst has more freedom to come up with custom solutions. As you probably noticed during the course the fix the expression is more universal. While the other two have their particular place in their workflow. All right I hope you found this summary helpful. If you have any questions concerning discourse feel free to ask them in the Q&A section of the course. Do also not forget to leave a rating and drop some lines on what you think about the course. Other than that I wish you best luck in your life in Korea and I hope that I see you again in one of my other courses.