COUNTDISTINCTIF Function

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Generates the count of distinct non-null values for rows in each group that meet a specific condition.

NOTE: When added to a transform, this function is applied to the current sample. If you change your sample or run the job, the computed values for this function are updated. Transforms that change the number of rows in subsequent recipe steps do not affect the values computed for this step.

To perform a simple counting of distinct non-nulls without conditionals, use the COUNTDISTINCT function. See COUNTDISTINCT Function.

Basic Usage

pivot value: COUNTDISTINCTIF(entries, entryValidation == 'Ok') group:City limit:1

Output: Generates a two-column table containing the unique values for City and the count of distinct non-null values in the entries column for that City value when the entryValidation value is 'Ok'. The limit parameter defines the maximum number of output columns.

Syntax and Arguments

pivot value:COUNTDISTINCTIF(col_ref, test_expression) [group:group_col_ref] [limit:limit_count]

Argument	Required?	Data Type	Description
col_ref	Y	string	Reference to the column you wish to evaluate.
test_expression	Y	string	Expression that is evaluated. Must resolve to `true` or `false`

For more information on syntax standards, see Language Documentation Syntax Notes.

For more information on the group and limit parameter, see Pivot Transform.

col_ref

Name of the column whose values you wish to use in the calculation. Column must be a numeric (Integer or Decimal) type.

Usage Notes:

Required?	Data Type	Example Value
Yes	String that corresponds to the name of the column	`myValues`

test_expression

This parameter contains the expression to evaluate. This expression must resolve to a Boolean (true or false) value.

Usage Notes:

Required?	Data Type	Example Value
Yes	String expression that evaluates to `true` or `false`	`(LastName == 'Mouse' && FirstName == 'Mickey')`

Examples

Tip: For additional examples, see Common Tasks.

Example - Summarize Voter Registrations

This example illustrates how you can use the following conditional calculation functions to analyze polling data:

SUMIF - Sum of a set of values by group that meet a specified condition. See SUMIF Function.
COUNTDISTINCTIF - Sum of a set of values by group that meet a specified condition. See COUNTDISTINCTIF Function.

Source:

Here is some example polling data across 16 precincts in 8 cities across 4 counties, where registrations have been invalidated at the polling station, preventing voters from voting. Precincts where this issue has occurred previously have been added to a watch list (precinctWatchList).

totalReg	invalidReg	precinctWatchList	precinctId	cityId	countyId
731	24	y	1	1	1
743	29	y	2	1	1
874	0		3	2	1
983	0		4	2	1
622	29		5	3	2
693	0		6	3	2
775	37	y	7	4	2
1025	49	y	8	4	2
787	13		9	5	3
342	0		10	5	3
342	39	y	11	6	3
387	28	y	12	6	3
582	59		13	7	4
244	0		14	7	4
940	6	y	15	8	4
901	4	y	16	8	4

Transform:

First, you want to sum up the invalid registrations (invalidReg) by city:

derive type:single value:SUMIF(invalidReg, precinctWatchList == "y") group:cityId as:'invalidRegbyCityId'

The invalidRegbyCityId column contains invalid registrations across the entire city. Now, at the county level, you want to identify the number of precincts that were on the watch list and were part of a city-wide registration problem. This step performs an aggregation:

pivot value:COUNTDISTINCTIF(precinctId, invalidRegbyCityId > 60) group:countyId limit:1

Results:

countyId	countdistinctif_precinctId
1	0
2	2
3	2
4	0

The voting officials in counties 2 and 3 should investigate their precinct registration issues.

See Also for COUNTDISTINCTIF Function:

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