Level of Detail Expressions are one of the fundamental Tableau tools that every Tableau user should have in their toolbox. However, many people struggle to conceptualize how LODs work, or in what instances to use them.
All tagged Level of Detail Calculations
One of the biggest gripes about Tableau Prep is that it isn't as fast as people want. However, most people aren't leveraging the best way to improve performance; input filters!
Input filters allow you to filter the data at input ensuring that unneeded data is never loaded into and processed in Tableau Prep. This can lead to massive performance benefits.
Ready to learn how to apply input filters and numerous ways they can be configured? Check out this video!
FIXED LODs are wonderful because they allow us to aggregate our measures at a chosen Dimension level, regardless of the level of detail of the worksheet. However, they aren't affected by Dimension Filters, because they're calculated before Dimension Filters are processed.
There are various scenarios where we need to work around that. We'll use Context Filters to help us where we need our LODs to be filtered, and we'll take a look at an example where you might not want your LOD to be filtered by your Dimension Filters.
FIXED Level of Detail Functions (LODs) let you specify the level of detail you want to aggregate a particular measure at. This allows us to work around the natural limitations of the level of detail in the worksheet we're working in.
FIXED LODs are a great place to start as they are the definitive, essential LOD. The two others (EXCLUDE and INCLUDE) are less common, but functional in their own unique ways. If you want to simplify and just learn one LOD type, FIXED is the one for you!
Do you want to keep all data associated with a value which has multiple records in a data set? Maybe it's how many students attended multiple schools, how many patients visited a hospital multiple times or how many customers made multiple purchases.
I was recently working on a project where we wanted to compare weekly performance, but it only made sense to compare weeks once they were complete. As a result, we routinely came across situations where we wanted to compare the last full week to the previous full week.
Level of Detail Expressions are one of the most powerful and least understood features in Tableau's calculation toolkit. Watch the below webinar recording to learn how they can be used to get more out of your Tableau workbooks.
I was recently asked by a student how they could compare several years of monthly values to the monthly average over that time span. They had something like this in mind…
Totals in Tableau are notoriously rigid. You can’t add two totals lines; one for summarized values and one for averaged values, in the same worksheet. You can have one, the other, or allow Tableau to use a field’s default aggregation for totals.
Here's an interesting challenges; how do you compare this year's values to the average of the previous 3 years in Tableau? The complex solution requires the use of the FIXED level of detail function. Watch the above video to follow along and learn how to perform this calculation.
As you likely know from using Table Calculations in Tableau, they only compute against the marks displayed in a worksheet. Check out this webinar in you need a refresher.
That means when a filter is applied to the worksheet, a table calculation will update to reflect only the data present in the worksheet.
I recently got an email from a former student explaining that they were trying to recreate something similar to the image below in Tableau.
The trouble she was running into was recreating the total header called “Planned Cost by Perf”. If you try to use a measure as a discrete header, you end up with the value summed for each partition. Here is an example using Superstore data: