MODE Function

Computes the mode (most frequent value) from all row values in a column, according to their grouping. Input column can be of Integer or Decimal type.

• If a row contains a missing or null value, it is not factored into the calculation. If the entire column contains no values, the function returns a null v alue.
• If there is a tie in which the most occurrences of a value is shared between values, then no value is returned from the function.
• When used in a pivot transform, the function is computed for each instance of the value specified in the group parameter. See Pivot Transform.

For a version of this function computed over a rolling window of rows, see ROLLINGMODE Function.

Basic Usage

pivot value:MODE(count_visits) group:postal_code limit:1

Output: Generates a two-column table containing the unique values from the postal_code column and the mode of the values in the count_visits column for the postal_code value. The limit parameter defines the maximum number of output columns.

Syntax

pivot value:MODE(function_col_ref) [group:group_col_ref] [limit:limit_count]

Argument	Required?	Data Type	Description
function_col_ref	Y	string	Name of column to which to apply the function

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

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

function_col_ref

Name of the column the values of which you want to calculate the function. Column must contain Integer or Decimal values.

• Literal values are not supported as inputs.
• Multiple columns and wildcards are not supported.

Usage Notes:

Required?	Data Type	Example Value
Yes	String (column reference)	myValues

Examples

Example - Statistics on Test Scores

This example illustrates how you can apply statistical functions to your dataset. Calculations include average (mean), max, min, standard deviation, and variance.

Source:

Students took a test and recorded the following scores. You want to perform some statistical analysis on them:

Student	Score
Anna	84
Ben	71
Caleb	76
Danielle	87
Evan	85
Faith	92
Gabe	85
Hannah	99
Ian	73
Jane	68

Transform:

You can use the following transforms to calculate the average (mean), minimum, and maximum scores:

derive type:single value:AVERAGE(Score) as:'avgScore'

derive type:single value:MIN(Score) as:'minScore'

derive type:single value:MAX(Score) as:'maxScore'

To apply statistical functions to your data, you can use the VAR and STDEV functions, which can be used as the basis for other statistical calculations.

derive type:single value:VAR(Score)

derive type:single value:STDEV(Score)

For each score, you can now calculate the variation of each one from the average, using the following:

derive type:single value:((Score - avg_Score) / stdev_Score) as:'stDevs'

Now, you want to apply grades based on a formula:

Grade	standard deviations from avg (stDevs)
A	stDevs > 1
B	stDevs > 0.5
C	-1 <= stDevs <= 0.5
D	stDevs < -1
F	stDevs < -2

You can build the following transform using the IF function to calculate grades.

derive type:single value:IF((stDevs > 1),'A',IF((stDevs < -2),'F',IF((stDevs < -1),'D',IF((stDevs > 0.5),'B','C'))))

For more information, see IF Function.

To clean up the content, you might want to apply some formatting to the score columns. The following reformats the stdev_Score and stDevs columns to display two decimal places:

set col:stdev_Score value:NUMFORMAT(stdev_Score, '##.00')

set col:stDevs value:NUMFORMAT(stDevs, '##.00')

derive type:single value:MODE(Score) as:'modeScore'

Results:

Student	Score	modeScore	avgScore	minScore	maxScore	var_Score	stdev_Score	stDevs	Grade
Anna	84	85	82	68	99	87.00000000000001	9.33	0.21	C
Ben	71	85	82	68	99	87.00000000000001	9.33	-1.18	D
Caleb	76	85	82	68	99	87.00000000000001	9.33	-0.64	C
Danielle	87	85	82	68	99	87.00000000000001	9.33	0.54	B
Evan	85	85	82	68	99	87.00000000000001	9.33	0.32	C
Faith	92	85	82	68	99	87.00000000000001	9.33	1.07	A
Gabe	85	85	82	68	99	87.00000000000001	9.33	0.32	C
Hannah	99	85	82	68	99	87.00000000000001	9.33	1.82	A
Ian	73	85	82	68	99	87.00000000000001	9.33	-0.96	C
Jane	68	85	82	68	99	87.00000000000001	9.33	-1.50	D