Defining the aggregation scope
There are usually two factors that together determine which records are used to define the value of aggregation in an expression. When working in visualizations, these factors are:
 Dimensional value (of the aggregation in a chart expression)
 Selections
Together, these factors define the scope of the aggregation. You may come across situations where you want your calculation to disregard the selection, the dimension or both. In chart functions, you can achieve this by using the
Method  Description 


Using the total qualifier inside your aggregation function disregards the dimensional value. The aggregation will be performed on all possible field values. The TOTAL qualifier may be followed by a list of one or more field names within angle brackets. These field names should be a subset of the chart dimension variables. In this case, the calculation is made disregarding all chart dimension variables except those listed, that is, one value is returned for each combination of field values in the listed dimension fields. Also, fields that are not currently a dimension in a chart may be included in the list. This may be useful in the case of group dimensions, where the dimension fields are not fixed. Listing all of the variables in the group causes the function to work when the drilldown level changes. 
Set analysis  Using set analysis inside your aggregation overrides the selection. The aggregation will be performed on all values split across the dimensions. 

Using the TOTAL qualifier and set analysis inside your aggregation overrides the selection and disregards the dimensions. 

Using the ALL qualifier inside your aggregation disregards the selection and the dimensions. The equivalent can be achieved with the =sum(All Sales) =sum({1} Total Sales) 
Example: TOTAL qualifier
The following example shows how
Year  Quarter  Sum(Amount)  Sum(TOTAL Amount)  Sum(Amount)/Sum(TOTAL Amount) 

3000  3000  100%  
2012  Q2  1700  3000  56,7% 
2013  Q2  1300  3000  43,3% 
Example: Set analysis
The following example shows how set analysis can be used to make a comparison between data sets before any selection was made. Assuming that
Year  Quarter  Sum(Amount)  Sum({1} Amount)  Sum(Amount)/Sum({1} Amount) 

3000  10800  27,8%  
2012  Q1  0  1100  0% 
2012  Q3  0  1400  0% 
2012  Q4  0  1800  0% 
2012  Q2  1700  1700  100% 
2013  Q1  0  1000  0% 
2013  Q3  0  1100  0% 
2013  Q4  0  1400  0% 
2013  Q2  1300  1300  100% 
Example: TOTAL qualifier and set analysis
The following example shows how set analysis and the
Year  Quarter  Sum(Amount)  Sum({1} TOTAL Amount)  Sum(Amount)/Sum({1} TOTAL Amount) 

3000  10800  27,8%  
2012  Q2  1700  10800  15,7% 
2013  Q2  1300  10800  12% 
Data used in examples: