Include vs fixed tableau

Include vs fixed tableau DEFAULT

Continuing on my post on FIXED LODs, I wanted to shed some light on INCLUDE and EXCLUDE Level of Detail expressions. It’s an advanced topic and if you haven’t read my blog introducing LODs, make sure to do so before continuing with this post.

I’ll be using Sample-Superstore dataset available in Tableau for the purpose of this blog.

INCLUDE Level of Detail

The syntax for INCLUDE LOD is very similar compared to FIXED. The only difference is changing the keyword to ‘include’. The meaning, however, is quite different. You may recall that I used Coach Andy’s approach to translate FIXED LODs into plain English. I decided to apply the idea to INCLUDE LODs as well.

You can translate INCLUDE LOD as ‘For every dimension in the view AND every listed dimension, calculate the aggregate expression’:

For example, if we already have Sub-Category and Category in the view, the calculation will get the sum of Sales for each Sub-Category, Category and Product:

Hold on, but what happens now? We don’t have Product in the view, so what are we actually seeing?

Whenever you use INCLUDE LOD, you’re using more granular data than what you have in the view, so then this calculation needs to be aggregated to get back to the original (view) level of detail:

To make sure you use the correct aggregation for INCLUDE calculation, you can wrap the whole expression in the function of choice or right-click on the field pill -> Default Properties -> Aggregation -> [choose the aggregation here].

Use Case

Let’s say you want to know what are the average Sales by product within each Sub-Category. One way is to have Sub-Category, Product ID and Sales in the view creating a bar chart and then finding the average with a reference line:

It doesn’t look very nice, slightly too busy especially that we only want to see the averages, we don’t really care about particular product sales.

Another way is to use an INCLUDE calculation:

Apart from cleaning up the view, you can easily sort the data based on the average Sales.

You could argue that this can be also achieved with a FIXED LOD, which is true:

INCLUDE, however, adds more flexibility: you can add more dimensions in the view and values will change accordingly:

FIXED LOD would result in wrong values, unless you build a separate calculation for each of the options.

EXCLUDE Level of Detail

EXCLUDE LOD is pretty much the opposite of INCLUDE. Instead of adding more dimensions, you’re getting rid of them.

You can translate EXCLUDE LOD as ‘For every dimension in the view EXCEPT the listed dimension(s), calculate the aggregate expression’:

If we have Sub-Category and Category in the view, the sum of Sales will be calculated for each Category, disregarding the Sub-Category field:

This time the calculation is performed at a less granular level than what you have in the view and so the resulting values will be duplicated in the view:

Use Case

One thing you can use exclude for is to present detailed data together with less granular values for reference. For example, you can calculate the difference between average sales for Sub-Categories and average sales for the corresponding Category.

You’ll need one EXCLUDE LOD for this:

And a basic calculation using the above:

If you add Category, Sub-Category, Avg(Sales) and the two calculations above, this is what you get in your view:

Again, you could argue that instead of EXCLUDE you could use FIXED. This is true (again). With EXCLUDE (just like with INCLUDE), you can add more dimensions into the view and the values will be recalculated:

FIXED calculation

would have duplicated values across the segments:

To get exactly the same values, you’d need to create separate caluclations for each option.

Recap

To recap, the type of LOD (and whether you need an LOD calculation in the first place) will depend on what you have in the view. Another factor is whether you want your calculation to be flexible or not.

Apart from exams and exercises I haven’t used many INCLUDE or EXCLUDE LODs but it is definitely beneficial to understand what is going on.

I imagine the calculations are based on temporary tables that are changed whenever you use an LOD expression:

Whenever the calculation is performed for more granular data than the view, the values will be aggregated. When the calculation is performed for less granular data, the values will be duplicated:

This is quite a lot to handle, but I hope this post was helpful. There is still more to LODs. For example, FIXED is calculated before INCLUDE or EXCLUDE and so it will work differently with filters. To learn more on LODs and filters, check out Tableau help page. The recording of my webinar on LODs is up on YouTube if you want to check it out and the workbook I used for that webinar is on Tableau Public.

Sours: https://www.thedataschool.co.uk/hanna-nykowska/tableau-lods-include-and-exclude

Super Easy Guide to Tableau LOD Calculations

Level of Details (LOD’s) Expressions in Tableau

Using LOD expressions, a user can compute different levels of aggregations that are not at the level of detail of the visualization. If you want to improve your Tableau skills check out: Best Tableau Online Courses.

LOD’s Expressions:

In Tableau, we have 3 types of Level of Details expressions. These are “INCLUDE”, “EXCLUDE” and “FIXED”.

The following is a detailed explanation of each of the LOD expression with examples:

I. INCLUDE

As the name suggests, “INCLUDE” level of detail expression compute aggregations considering dimensions which are specified in the calculation and also take into consideration those dimensions which are present in the view. Check out this tutorial for a Deep Dive into INCLUDE LOD calculation.

“INCLUDE” level of detail expression is used when a user wants to calculate values at the lower level of granularity and then aggregate and show the values at the higher level of granularity.

Example:
Let’s consider the requirement is to compare the average customer sales across different product segments.

In this case, although in the view we are going to show only “Segment” in calculations we need to include “Customer Name” as well so that average customer sales can be calculated.

Formula/Expression:AVG({INCLUDE [Customer Name] : SUM([Sales])})

Explanation: In the above formula, we are including the lower level of dimension i.e. Customer Name to get the aggregate (sum) values of Sales at that level and then we are showing the average at higher level i.e. Segment

Tableau View:

Use Include to use the Level of Detail Calculations in Tableau

II. EXCLUDE

“EXCLUDE” level of detail expression is used to omit specified dimensions from the aggregations. Using “EXCLUDE”, the user can omit the lower level granularity dimension which is present in the view and can directly calculate the value at higher granularity level. If you need more detail check out,  EXCLUDE Deep Dive.

“EXCLUDE” level of detail expression is majorly used to calculate ‘difference from overall average’ or ‘percent of total’.

Example:
Let’s consider the requirement is to compare total sales with the monthly sales of the East region.

To get the monthly sales we need to have “Region”, “Month” and “Sum of Sales” in the view. But, now to get the total sales of Region, we need to exclude “Month” in the calculation so that we can have total sales value for the region.

Formula/Expression:SUM({EXCLUDE [Month] : SUM([Sales])})

Explanation: In the above formula, we are excluding the lower level of dimension i.e. Month to get the aggregate (sum) values of Sales at a higher level i.e. Region.

Tableau View:

Use exclude to use the level of detail aggregation in Tableau.

III. FIXED

“FIXED” level of detail expression aggregates the value only at the dimensions which are specified by the user in the calculation. “FIXED” expression does not take into consideration those dimensions in the view. If you need more detail, check out a LOD FIXED Deep Dive.

Example:
Let’s consider the following expression which calculates the sales across regions.

Formula/Expression:SUM({FIXED [Region] : SUM([Sales])})

Explanation: The above calculation will show the sales value across the region. The objective of using FIXED is that even if a user puts another field say “State” in the view, the above calculation still calculates the value at region level only. It will disregard the other dimensions which are in the view i.e. State

Tableau View:

Use Fixed for a level of detail calculation in Tableau

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Sours: https://www.absentdata.com/level-of-detail-calculations/
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How To Use Level Of Detail (LOD) in Tableau: FIXED Function

This blog is part of the How To Use Level of Detail blog series. This blog will cover the FIXED Level of Detail expression and function.

Simply put, FIXED expressions aggregate the value ONLY at the dimensions specified in the calculation. Unlike the INCLUDE/EXCLUDE expression, the FIXED expression doesn’t take into consideration the dimensions in the view. However, here’s the plot twist. FIXED expressions are like the shapeshifters of LOD in that they can have a finer, coarser, or even the same level of detail than the view. Therefore, how you use it is dependent on the dimensions existing in the view already.

A common use case for FIXED is to find a specific value for a category E.g. a cohort. Let’s say we wanted to build a view that showed us each city’s performance and also highlighted which state had the top performing city. We would start by building a calculation to find the total sales in each state. For FIXED LOD calculations, we would read it from left to right with a “For each… Find the…” sentence. This calculation would return the total sum of sales for each state as requested.

Building on top of this example, we could extend the calculation to bind the max State by Sales with a nested calculation ( a calculation within a calculation). The second part of this calculation would allow us to find the top-performing state in the city. If the city’s sum of sales is the same as the top performing state in the country, then label it ‘Max’. I could then use this calculation to spotlight the state in my chart and allow two levels of details to concurrently occur in my view.

 

 

What’s the difference between INCLUDE and FIXED then?

As I mentioned in the EXCLUDE blog, there are cases where an INCLUDE/EXCLUDE and FIXED calculation may return a similar result. E.g. An Include City: Max Sales can have the same result as a FIXED City: Max Sales if the viz has only State as a dimension in view. The image below is an example of similar looking values for INCLUDE and FIXED.

It’s key, therefore, to remember that the difference is that:

  1. FIXED calculations are not relative to the dimensions in the view
  2. FIXED calculations are higher in the Tableau Order of Operations. A FIXED expression will trump an INCLUDE/EXCLUDE LOD Calc, meaning that if they were both

When we add City into the Row shelf, the view is now a finer grain than the FIXED calculation we made. You can see that in the view there are 2 dimensions (State and City) where else our FIXED Calculation has only 1 dimension. Now because our FIXED calculation is coarser than the view, our values duplicated down the pane. In such situations, it becomes more obvious the use for FIXED vs INCLUDE or EXCLUDE.

 

Read more from the How To Use Level of Detail in Tableau series:

How To Use Level of Details – General Level of Detail

How To Use Level of Details – Include Function

How To Use Level of Details – Exclude Function

Sours: https://www.thedataschool.com.au/pris-lam/how-to-use-level-of-detail-in-tableau-fixed-function/

How Level of Detail Expressions Work in Tableau

This article explains how level of detail expressions are computed and how they function in Tableau. For more information about LOD expressions and how they work, see the Understanding Level of Detail (LOD) Expressions(Link opens in a new window) whitepaper on the Tableau website.

Row Level Expressions and View Level Expressions

In Tableau, expressions referencing unaggregated datasource columns are computed for each row in the underlying table. In this case, the dimensionality of the expression is row level. An example of a row-level expression is:

This calculation will be evaluated in each row of the database. For each row, the Sales value in that row will be divided by the Profit value in that row, producing a new column with the result of the multiplication (a profit ratio).

If you create a calculation with this definition, save it with the name [ProfitRatio], and then drag it from the Data pane to a shelf, Tableau typically aggregates the calculated field for the view:

By contrast, expressions referencing aggregated data source columns are computed at the dimensionality defined by the dimensions in the view. In this case, the dimensionality of the expression is view level. An example of a view-level expression is:

If you drag this calculation to a shelf (or type it directly on a shelf as an ad-hoc calculation), Tableau encloses it in an AGG function:

AGG(SUM(Sales) / SUM(Profit))

This is what is known as an aggregate calculation. For details, see Aggregate Functions in Tableau(Link opens in a new window).

Dimension and set fields placed on any of the locations highlighted in the following image contribute to the view level of detail:

Before level of detail expressions were supported in Tableau, it was not possible to create calculations at a level of detail other than the view level. For example, if you attempt to save the following expression, Tableau displays the error message: “Cannot mix aggregate and non-aggregate arguments with this function”:

The user’s intent in this case was to compare store sales for each individual store to the average of sales for all stores. This can now be accomplished with a level of detail expression:

This is what is known as a table-scoped level of detail expression. See Table-Scoped

Limitations for Level of Detail Expressions

The following limitations and constraints apply for level of detail expressions. Also see Data Source Constraints for Level of Detail Expressions.

  • Level of detail expressions that reference floating-point measures can behave unreliably when used in a view that requires comparison of the values in the expression. For details, see Understanding data types in calculations(Link opens in a new window).

  • Level of detail expressions are not shown on the Data Source page. See Data Source Page.

  • When referencing a parameter in a dimensionality declaration, always use the parameter name, and not the parameter value.

  • With data blending, the linking field from the primary data source must be in the view before you can use a level of detail expression from the secondary data source. See Troubleshoot Data Blending.

In addition, some data sources have complexity limits. Tableau will not disable calculations for these databases, but query errors are a possibility if calculations become too complex.

Level of Detail Expressions Can Be Dimensions or Measures

When you save a level of detail expression, Tableau adds it to either the Dimensions or the Measures area in the Data pane.

FIXED level of detail expressions can result in measures or dimensions, depending on the underlying field in the aggregate expression. So MIN([Date])} will be a dimension because [Date] is a dimension, and {fixed Store : SUM([Sales])} will be a measure because [Sales] is a measure. When a FIXED level of detail expression is saved as a measure you have the option of moving it to dimensions.

INCLUDE and EXCLUDE level of detail expressions are always measures.

Filters and Level of Detail Expressions

There are several different kinds of filters in Tableau and they get executed in the following order from top to bottom.

The text on the right shows where level of detail expressions are evaluated in this sequence.

Extract Filters (in orange) are only relevant if you’re creating a Tableau Extract from a data source. Table calculations filters (dark blue) are applied after calculations are executed and therefore hide marks without filtering out the underlying data used in the calculations.

If you’re familiar with SQL, you can think of measure filters as equivalent to the HAVING clause in a query, and dimension filters as equivalent to the WHERE clause.

FIXED calculations are applied before dimension filters, so unless you promote the fields on your Filter shelf to Improve View Performance with Context Filters, they will be ignored. For example, consider if you have the following calculation on one shelf in a view, along with [State] on a different shelf:

This calculation will give you the ratio of a state’s sales to total sales.

If you then put [State] on the Filters shelf to hide some of the states, the filter will affect only the numerator in the calculation. Since the denominator is a FIXED level of detail expression, it will still divide the sales for the states still in the view against the total sales for all states—including the ones that have been filtered out of the view.

INCLUDE and EXCLUDE level of detail expressions are considered after Dimension filters. So if you want filters to apply to your FIXED level of detail expression but don’t want to use Context Filters, consider rewriting them as INCLUDE or EXCLUDE expressions.

Aggregation and Level of Detail Expressions

The level of detail of the view determines the number of marks in your view. When you add a level of detail expression to the view, Tableau must reconcile two levels of detail—the one in the view, and the one in your expression.

The behavior of a level of detail expression in the view varies depending on whether the expression's level of detail is coarser, finer, or the same as the level of detail in the view. What do we mean by “coarser” or “finer” in this case?

Level of Detail Expression is Coarser Than View Level of Detail

An expression has a coarser level of detail than the view when it references a subset of the dimensions in the view. For example, for a view that contained the dimensions [Category] and [Segment], you could create a level of detail expression that uses only one of these dimensions:

In this case, the expression has a coarser level of detail than the view. It bases its values on one dimension ([Segment]), whereas the view is basing its view on two dimensions ([Segment] and [Category]).

The result is that using the level of detail expression in the view causes certain values to be replicated—that is, to appear multiple times.

Replicated values are useful for comparing specific values against average values within a category. For example the following calculation subtracts average sales for a customer from the average sales overall:

When values are being replicated, changing the aggregation for the relevant field in the view (for example, from AVG to SUM) will not change the result of the aggregation.

Level of Detail Expression is Finer Than View Level of Detail

An expression has a finer level of detail than the view when it references a superset of the dimensions in the view. When you use such an expression in the view, Tableau will aggregate results up to the view level. For example, the following level of detail expression references two dimensions:

When this expression is used in a view that has only [Segment] as its level of detail, the values must be aggregated. Here’s what you would see if you dragged that expression to a shelf:

An aggregation—in this case, average—is automatically assigned by Tableau. You can change the aggregation as needed.

Adding a Level of Detail Expression to the View

Whether a level of detail expression is aggregated or replicated in the view is determined by the expression type (FIXED, INCLUDE, or EXCLUDE) and whether the expression’s granularity is coarser or finer than the view’s.

  • INCLUDE level of detail expressions will have either the same level of detail as the view or a finer level of detail than the view. Therefore, values will never be replicated.

  • FIXED level of detail expressions can have a finer level of detail than the view, a coarser level of detail, or the same level of detail. The need to aggregate the results of a FIXED level of detail depends on what dimensions are in the view.

  • EXCLUDE level of detail expressions always cause replicated values to appear in the view. When calculations including EXCLUDE level of detail expressions are placed on a shelf, Tableau defaults to the ATTR aggregation (as opposed to SUM or AVG) to indicate that the expression is not actually being aggregated and that changing the aggregation will have no effect on the view.

Level of detail expressions are always automatically wrapped in an aggregate when they are added to a shelf in the view unless they’re used as dimensions. So if you double-click on a shelf and type

and then press Enter to commit the expression, what you now see on the shelf is

But if you double-click into the shelf to edit the expression, what you see in edit mode is the original expression.

If you wrap a level of detail expression in an aggregation when you create it, Tableau will use the aggregation you specified rather than assigning one when any calculation including that expression is placed on a shelf. When no aggregation is needed (because the expression’s level of detail is coarser than the view’s), the aggregation you specified is still shown when the expression is on a shelf, but it is ignored.

Data Source Constraints for Level of Detail Expressions

For some data sources, only more recent versions support level of detail expressions. Some data sources do not support level of detail expressions at all.

In addition, some data sources have complexity limits. Tableau will not disable calculations for these databases, but query errors are a possibility if calculations become too complex.

Data SourceSupport
Actian Vectorwise Not supported.
Amazon EMR Hadoop HiveSupported for Hive 0.13 and later.
Amazon RedshiftSupported.
Aster DatabaseSupported for version 4.5 and later.
Cloudera HadoopSupported for Hive 0.13 and later.
Cloudera ImpalaSupported for Impala 1.2.2 and later.
Cubes (multidimensional data sources)Not supported.
DataStax EnterpriseNot supported.
EXASOLSupported.
FirebirdSupported for version 2.0 and later.
Generic ODBCLimited. Depends on the specific data source.
Google Big QuerySupported for standard SQL, not supported for legacy SQL.
Hortonworks Hadoop Hive

Supported for Hive 0.13 and later.

On version 1.1 of HIVE level of detail expressions that produce cross joins are not reliable.

Cross join occur when there is no explicit field to join on. For example, for a level of detail expression when the view only contains one dimension [Ship Mode], Tableau creates a cross-join. A cross join produces rows which combine each row from the first table with each row from the second table.

IBM BigInsightsSupported.
IBM DB2Supported for version 8.1 and later.
MarkLogicSupported for version 7.0 and later.
Microsoft AccessNot supported.
Microsoft Jet-based connections (legacy connectors for Microsoft Excel, Microsoft Access, and text)Not supported.
Microsoft SQL ServerSQL Server 2005 and later.
MySQLSupported.
IBM PDA (Netezza)Supported version 7.0 and later.
OracleSupported version 9i and later.
Actian Matrix (ParAccel)Supported version 3.1 and later.
Pivotal GreenplumSupported for version 3.1 and later.
PostgreSQLSupported version 7 and later.
Progress OpenEdgeSupported.
SAP HANASupported.
SAP Sybase ASESupported.
SAP Sybase IQSupported version 15.1 and later.
Spark SQLSupported.
SplunkNot supported.
Tableau Data ExtractSupported.
TeradataSupported.
VerticaSupported for version 6.1 and later.

See Also

Create Level of Detail Expressions in Tableau

Understanding Level of Detail (LOD) Expressions(Link opens in a new window)

Sours: https://help.tableau.com/current/pro/desktop/en-us/calculations_calculatedfields_lod_overview.htm

Tableau fixed include vs

How do the INCLUDE function and FIXED function (LOD expression) work while adding a condition for both functions in Tableau? What is the difference between the INCLUDE function and the FIXED function in Tableau?

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INCLUDE –  is used when you want to calculate a measure at a lower level of detail than the viz’s detail level. Your calculation will look at the dimensions on your view plus any additional dimensions you put in your statement.

{INCLUDE [City]: SUM([SalesYTD])}

{INCLUDE [City] – This is where you specify the level of detail

SUM([SalesYTD])} – Here, we indicate the measure to be calculated at this level.

In the example: We have countries on the X-axis, and in the bottom graph, we show SalesYTD. In the upper graph, along the same X-axis, we show AVG by LOD function with City detail. Thus, we can compare total sales and average sales for each city by country.

FIXED –  function calculates your measure independent of what’s on your view. The Fixed function does not look at the view at all; it will calculate your measure depending on what dimensions you put in your equation and nothing else.

{FIXED [TerritoryGroup]: SUM([SalesYTD])}

{FIXED [TerritoryGroup]: – this is where you capture the level of detail of the data

SUM([SalesYTD])} – here you indicate the measure that will need to be calculated at a fixed level of data detail

You can also use both of these LOD functions in an IF statement. So, for example, you need to show a fixed value only to Europe, then you can use the following expression:

{FIXED [TerritoryGroup]:– this is where you fix the level of detail

SUM(– then we need to specify the type of data aggregation, in our case, this is the sum of the result of the IF expression

IF [TerritoryGroup]= “Europe” – the expression evaluates to TRUE or FALSE

THEN ([SalesYTD])– if the previous expression is TRUE, then we will need to output the SalesYTD field

ELSE 0 END)} – if the previous condition equals FALSE, then “0” will be included in the sum

{FIXED [City]:– this is where you fix the level of detail

SUM(– then we need to specify the type of data aggregation, in our case, this is the sum of the result of the IF expression

IF  [City]= “Berlin” – the expression evaluates to TRUE or FALSE

THEN ([SalesYTD])– if the previous expression is TRUE, then we will need to output the SalesYTD field

ELSE 0 END)} – if the previous condition equals FALSE, then “0” will be included in the sum

That is the difference between the INCLUDE and FIXED functions in Tableau.


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Sours: https://www.datameer.com/blog/tableau_how-do-the-include-function-and-fixed-function-lod-expression-work-while-adding-a-condition-for-both-functions-what-is-the-difference-between-the-include-function-and-the-fixed-function/
Tableau LOD Level of Detail Expressions Explained FIXED, INCLUDE, EXCLUDE Complete Tutorial With

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