Aggregation may be a crucial function to be utilized in any Business Intelligence tool because it covers the info being evaluated as a whole/aggregate and returns result accordingly. this is often of great use because generally enterprises use data in bulk and applying functions on individual fields and value might be a next to impossible task. most ordinarily used aggregate functions are Sum, Average, Min, Max etc.

i. Defining the Scope of Aggregation

Every time aggregation is applied on a knowledge set, the scope of aggregation must be defined. By scope it’s meant that which data values or records are relevant. Aggregation is merely applied over the relevant data hence defining scope is important. Scope is defined supported two factors, one is Selections and second is Dimensional values. Once these two factors are considered and applied, then after restricting them what remains is merely the relevant data values for aggregation.
During defining the scope of Qlik Sense aggregation i.e. setting the limit in order that the system takes up only the relevant data is chosen by the means of two methods; Total Qualifier and/or Set Analysis. It also can be said that by these methods the scope of aggregation are often re-defined. These methods are wont to disregard irrelevant data records in order that aggregation function can only be applied to the relevant data values. the tactic keywords are written within the mixture function keyword.
The points given below describes the methods intimately.
• TOTAL Qualifier– Using the entire qualifier inside your aggregation expression or function disregards the dimensional value. The aggregation are going to be performed on all possible field values and therefore the calculation is formed disregarding all visualization dimension variables except those listed. Such fields that aren’t currently a dimension during a visualization could also be included within the list also . it’s known to be useful within the case of group dimensions, where the dimension fields aren’t fixed.
• Set Analysis – Using set analysis inside your aggregation expression overrides the choice and therefore the aggregation are going to be performed on all values split across the size .
• TOTAL qualifier and set analysis – Using the entire qualifier and set analysis inside your aggregation expression overrides or disregards the choice and disregards the size .
• ALL qualifier – Using the ALL qualifier inside your aggregation expression disregards the choice and therefore the dimensions. an equivalent are often done using the {1} set analysis statement and therefore the TOTAL qualifier:

1. =sum(A11 Sales)
2. =sum({1} Total Sales)
Let us understand these methods through example. A sample data has been used for the instance.
1. AggregationScope:
2. LOAD * inline
3. Year Quarter Amount
4. 2017 Q1 1100
5. 2017 Q2 1700
6. 2017 Q3 1400
7. 2017 Q4 1800
8. 2018 Q1 1000
9. 2018 Q2 1300
10. 2018 Q3 1100
11. 2018 Q4 1400] (delimiter is ‘ ‘);

We will see how the entire Qualifier and Set analysis methods add restricting irrelevant data values for aggregation. the 2 methods are often used individually or together .

• Total Qualifier is employed within the aggregation function once you want the system to disregard the dimensional value i.e. the info values specific to the sector ‘Quarter’. The fourth column or field within the table given below uses TOTAL qualifier within a Sum aggregate function ‘Sum(TOTAL Amount)‘ which displays the sum of all the values within the field ‘Sum(Amount)’. Values from the sector during which total qualifier was applied is then utilized in the expression ‘Sum(Amount)/Sum(TOTAL Amount)’ which provides the share ratio of individual dimension values to the entire amount.

Year	Quarter	Sum(Amount)	Sum(TOTAL Amount)	Sum(Amount)/Sum(TOTAL Amount)
		3000	3000	100%
2017	Q2	1700	3000	56.7%
2018	Q2	1300	3000	43.3%

• Set Analysis method is employed when some selections made within the visualization are to be disregarded. it’s different from the entire qualifier because it’s used once we want to disregard the dimensional values. Whereas within the case of disregarding selections, the system disregards selections made by the user and takes altogether the info values from a field.
As wiped out the sample visualization below. The set analysis is included with Sum aggregation function ‘Sum({1} Amount)’. during this field, all of the values which aren’t present within the ‘Sum(Amount) column (because Q2 is selected) are present as a results of disregarding the choice made on Q2. The {1} during this expression denotes set definition and instructs the machine to ignore the selections and consider all of the values within the record.
The final expression for calculating the share is ‘Sum(Amount)/Sum({1} Amount)’ gives as a result the ratio of the values in Sum(Amount) field that has selections to the values of the sector Sum({1} Amount) where set analysis is applied.

Year	Quarter	Sum(Amount)	Sum({1} Amount)	Sum(Amount)/Sum({1}Amount)
		3000	10800	27.8%
2017	Q1	0	1100	0%
2017	Q3	0	1400	0%
2017	Q4	0	1800	0%
2017	Q2	1700	1700	100%
2018	Q1	0	1000	0%
2018	Q3	0	1100	0%
2018	Q4	0	1400	0%
2018	Q2	1300	1300	100%

• You also can use TOTAL qualifier and set analysis together. As you’ll see we’ve applied the 2 methods together within the column ‘Sum({1}TOTAL Amount)’. this may disregard both the dimensional value and active selections to only present the relevant data.
You must read Qlik Sense Inter Record Functions

Year	Quarter	Sum(Amount)	Sum({1}TOTAL Amount)	Sum(Amount)/Sum({1}TOTAL Amount)
		3000	10800	27.8%
2017	Q2	1700	10800	15.7%
2018	Q2	1300	10800	12%

General Aggregation syntax
The general syntax used when defining an aggregate function/expression is,
1. Aggrexpression ::= ( fieldref | operator1 aggrexpression | aggrexpression operator2 aggrexpression | functioninaggr |(aggrexpression) )
Where fieldref may be a field name and functionaggr stands for functionname (parameters2).

So, this brings us to the end of blog. This Tecklearn ‘Aggregate Functions in Qlik Sense’ blog helps you with commonly asked questions if you are looking out for a job in Qlik Sense BI. If you wish to learn Qlik Sense and build a career in Business Intelligence domain, then check out our interactive, Qlik Sense Certification Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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Introduction and Installation of Qlik Sense
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Qlik Sense Features
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• Types of Data Loading
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Data Modelling
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• Understanding QVD layer
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Data Visualization
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Set Analysis
• Set analysis in Qlik Sense
• Use set expression like identifiers, operators, modifiers and comparative analysis
Advance Set Analysis
• Deploy comparison sets and perform point-in-time analysis
Qlik Sense Storytelling
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Qlik Sense Visualization
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