Computes the correlation coefficient between two columns. Source values can be of Integer or Decimal type.
The correlation coefficient measures the relationship between two sets of values. You can use it as a measurement for how changes in one value affect changes in the other.
- Values range between -1 (negative correlation) and +1 (positive correlation).
- Negative correlation means that the second number tends to decrease when the first number increases.
- Positive correlation means that the second number tends to increase when the first number increases.
- A correlation coefficient that is close to 0 indicates a weak or non-existent correlation.
Wrangle vs. SQL: This function is part of Wrangle, a proprietary data transformation language. Wrangle is not SQL. For more information, see Wrangle Language.
Basic Usage
correl(initialInvestment,ROI)
Output: Returns the correlation coefficient between the values in the initialInvestment
column and the ROI
column.
Syntax and Arguments
correl(function_col_ref1,<span>function_col_ref2</span>) [group:group_col_ref] [limit:limit_count]
Argument | Required? | Data Type | Description |
---|---|---|---|
function_col_ref1 | Y | string | Name of column that is the first input to the function |
function_col_ref2 | Y | string | Name of column that is the second input to 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_ref1, function_col_ref2
Name of the column the values of which you want to calculate the correlation. 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) | myInputs |
Tip: For additional examples, see Common Tasks.
Examples
Functions: Source: The following table contains height in inches and weight in pounds for a set of students. Transformation: You can use the following transformations to calculate the correlation co-efficient, the covariance, and the sampling method covariance between the two data columns: Results: Item Description
CORREL Function
Computes the correlation coefficient between two columns. Source values can be of Integer or Decimal type.
COVAR Function
Computes the covariance between two columns using the population method. Source values can be of Integer or Decimal type.
COVARSAMP Function
Computes the covariance between two columns using the sample method. Source values can be of Integer or Decimal type.
ROUND Function
Rounds input value to the nearest integer. Input can be an Integer, a Decimal, a column reference, or an expression. Optional second argument can be used to specify the number of digits to which to round.
Student heightIn weightLbs 1 70 134 2 67 135 3 67 147 4 67 160 5 72 136 6 73 146 7 71 135 8 63 145 9 67 138 10 66 138 11 71 161 12 70 131 13 74 131 14 67 157 15 73 161 16 70 133 17 63 132 18 64 153 19 64 156 20 72 154
Transformation Name
New formula
Parameter: Formula type
Single row formula
Parameter: Formula
round(correl(heightIn, weightLbs), 3)
Parameter: New column name
'corrHeightAndWeight'
Transformation Name
New formula
Parameter: Formula type
Single row formula
Parameter: Formula
round(covar(heightIn, weightLbs), 3)
Parameter: New column name
'covarHeightAndWeight'
Transformation Name
New formula
Parameter: Formula type
Single row formula
Parameter: Formula
round(covarsamp(heightIn, weightLbs), 3)
Parameter: New column name
'covarHeightAndWeight-Sample'
Student heightIn weightLbs covarHeightAndWeight-Sample covarHeightAndWeight corrHeightAndWeight 1 70 134 -2.876 -2.732 -0.074 2 67 135 -2.876 -2.732 -0.074 3 67 147 -2.876 -2.732 -0.074 4 67 160 -2.876 -2.732 -0.074 5 72 136 -2.876 -2.732 -0.074 6 73 146 -2.876 -2.732 -0.074 7 71 135 -2.876 -2.732 -0.074 8 63 145 -2.876 -2.732 -0.074 9 67 138 -2.876 -2.732 -0.074 10 66 138 -2.876 -2.732 -0.074 11 71 161 -2.876 -2.732 -0.074 12 70 131 -2.876 -2.732 -0.074 13 74 131 -2.876 -2.732 -0.074 14 67 157 -2.876 -2.732 -0.074 15 73 161 -2.876 -2.732 -0.074 16 70 133 -2.876 -2.732 -0.074 17 63 132 -2.876 -2.732 -0.074 18 64 153 -2.876 -2.732 -0.074 19 64 156 -2.876 -2.732 -0.074 20 72 154 -2.876 -2.732 -0.074
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