percentileCont
The percentileCont function calculates percentile based on a
continuous distribution of the numbers in the measure. It uses the grouping and
sorting that are applied in the field wells. It answers questions like: What
values are representative of this percentile? To return an exact percentile
value that might not be present in your dataset, use
percentileCont. To return the nearest percentile value that is
present in your dataset, use percentileDisc instead.
Syntax
percentileCont(expression,percentile, [group-by level])
Arguments
- measure
-
Specifies a numeric value to use to compute the percentile. The argument must be a measure or metric. Nulls are ignored in the calculation.
- percentile
-
The percentile value can be any numeric constant 0–100. A percentile value of 50 computes the median value of the measure.
- group-by level
-
(Optional) Specifies the level to group the aggregation by. The level added can be any dimension or dimensions independent of the dimensions added to the visual.
The argument must be a dimension field. The group-by level must be enclosed in square brackets
[ ]. For more information, see LAC-A functions.
Returns
The result of the function is a number.
Usage notes
The percentileCont function calculates a result based on a
continuous distribution of the values from a specified measure. The result
is computed by linear interpolation between the values after ordering them
based on settings in the visual. It's different from
percentileDisc, which simply returns a value from the set
of values that are aggregated over. The result from
percentileCont might or might not exist in the values from
the specified measure.
Examples of percentileCont
The following examples help explain how percentileCont works.
Example Comparing median, percentileCont, and
percentileDisc
The following example shows the median for a dimension (category) by
using the median, percentileCont, and
percentileDisc functions. The median value is the same
as the percentileCont value. percentileCont interpolates a
value, which might or might not be in the data set. However, because
percentileDisc always displays a value that exists in
the dataset, the two results might not match. The last column in this
example shows the difference between the two values. The code for each
calculated field is as follows:
-
50%Cont = percentileCont(example, 50 ) -
median = median(example) -
50%Disc = percentileDisc(example, 50 ) -
Cont-Disc = percentileCont(example, 50 ) − percentileDisc(example, 50 ) -
example = left((To make a simpler example, we used this expression to shorten the names of categories down to their first letter.)category, 1 )
example median 50%Cont 50%Disc Cont-Disc -------- ----------- ------------ -------------- ------------ A 22.48 22.48 22.24 0.24 B 20.96 20.96 20.95 0.01 C 24.92 24.92 24.92 0 D 24.935 24.935 24.92 0.015 E 14.48 14.48 13.99 0.49
Example 100th percentile as maximum
The following example shows a variety of percentileCont
values for the example field. The calculated fields
n%Cont are
defined as percentileCont( {. The interpolated
values in each column represent the numbers that fall into that
percentile bucket. In some cases, the actual data values match the
interpolated values. For example, the column example}
,n)100%Cont shows
the same value for every row because 6783.02 is the highest
number.
example 50%Cont 75%Cont 99%Cont 100%Cont --------- ----------- ----------- ------------ ----------- A 20.97 84.307 699.99 6783.02 B 20.99 88.84 880.98 6783.02 C 20.99 90.48 842.925 6783.02 D 21.38 85.99 808.49 6783.02
You can also specify at what level to group the computation using one or more dimensions in the view or in your dataset. This is called a LAC-A function. For more information about LAC-A functions, see LAC-A functions. The following example calculates the 30th percentile based on a continuous distribution of the numbers at the Country level, but not across other dimensions (Region) in the visual.
percentileCont({Sales}, 30, [Country])
