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Release 5.0.1

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Returns `true` if the first argument is greater than but not equal to the second argument. Equivalent to the `>` operator.

• Each argument can be a literal Integer or Decimal number, a function returning a number, or a reference to a column that contains numbers.

Since the function returns a Boolean value, it can be used as a function or a conditional.

NOTE: Within an expression, you might choose to use the corresponding operator, instead of this function. For more information, see Comparison Operators.

## Basic Usage

`delete row: GREATERTHAN(Errors, 10)`

Output: Deletes all rows in which the value in the `Errors` column is greater than 10.

## Syntax and Arguments

`derive type:single value:GREATERTHAN(value1, value2)`

ArgumentRequired?Data TypeDescription
value1YstringThe first value. This can be a number, a function returning a number, or a column containing numbers.
value2YstringThe second value. This can be a number, a function returning a number, or a column containing numbers.

### value1, value2

Names of the column, expressions, or literals to compare.

• Missing values generate missing string results.

Usage Notes:

Required?Data TypeExample Value
YesColumn reference, function, or numeric or String value`myColumn`

## Examples

### Example - Basic Comparison Functions

This simple example demonstrate available comparison functions:

Source:

colAcolB
111
210
39
48
57
66
75
84
93
102
111

Transform:

Add the following transforms to your recipe, one for each comparison function:

`derive type:single value:LESSTHAN(colA, colB) as:'lt'`

`derive type:single value:LESSTHANEQUAL(colA, colB) as:'lte'`

`derive type:single value:EQUAL(colA, colB) as:'eq'`

`derive type:single value:NOTEQUAL(colA, colB) as:'neq'`

`derive type:single value:GREATERTHAN(colA, colB) as:'gt'`

`derive type:single value:GREATERTHANEQUAL(colA, colB) as:'gte'`

Results:

colAcolBgtegtneqeqltelt
111falsefalsetruefalsetruetrue
210falsefalsetruefalsetruetrue
39falsefalsetruefalsetruetrue
48falsefalsetruefalsetruetrue
57falsefalsetruefalsetruetrue
66truefalsefalsetruetruefalse
75truetruetruefalsefalsefalse
84truetruetruefalsefalsefalse
93truetruetruefalsefalsefalse
102truetruetruefalsefalsefalse
111truetruetruefalsefalsefalse

### Example - Using Comparisons to Test Ranges

In the town of Circleville, citizens are allowed to maintain a single crop circle in their backyard, as long as it confirms to the town regulations. Below is some data on the size of crop circles in town, with a separate entry for each home. Limits are displayed in the adjacent columns, with the `inclusive` columns indicating whether the minimum or maximum values are inclusive.

Tip: As part of this exercise, you can see how to you can extend your recipe to perform some simple financial analysis of the data.

Source:

House155.510Y25N
House21210Y25N
House314.2510Y25N
House43.510Y25N
House52710Y25N

Transform:

After the data is loaded into the Transformer page, you can begin comparing column values:

`derive type:single value: LESSTHANEQUAL(Radius_ft,minRadius_ft) as:'tooSmall'`

While accurate, the above transform does not account for the `minInclusive` value, which may be changed as part of your steps. Instead, you can delete the previous transform and use the following, which factors in the other column:

`derive type:single value: IF(minInclusive == 'Y',LESSTHANEQUAL(Radius_ft,minRadius_ft),LESSTHAN(Radius_ft,minRadius_ft)) as:'tooSmall'`

In this case, the `IF` function tests whether the minimum value is inclusive (values of `10` are allowed). If so, the `LESSTHANEQUAL` function is applied. Otherwise, the `LESSTHAN` function is applied. For the maximum limit, the following step applies:

`derive type:single value: IF(maxInclusive == 'Y',GREATERTHANEQUAL(Radius_ft,maxRadius_ft),GREATERTHAN(Radius_ft,maxRadius_ft)) as:'tooBig'`

Now, you can do some analysis of this data. First, you can insert a column containing the amount of the fine per foot above the maximum or below the minimum. Before the first `derive` command, insert the following, which is the fine (`\$15.00`) for each foot above or below the limits:

`derive type:single value: 15 as:'fineDollarsPerFt'`

At the end of the recipe, add the following new line, which calculates the fine for crop circles that are too small:

`derive type:single value: IF(tooSmall == 'true', (minRadius_ft - Radius_ft) * fineDollarsPerFt, 0.0) as: 'fine_Dollars'`

The above captures the too-small violations. To also capture the too-big violations, change the above to the following:

`derive type:single value: IF(tooSmall == 'true', (minRadius_ft - Radius_ft) * fineDollarsPerFt, if(tooBig == 'true', (Radius_ft - maxRadius_ft) * fineDollarsPerFt, '0.0')) as: 'fine_Dollars'`

In place of the original "false" expression (`0.0`), the above adds the test for the too-big values, so that all fines are included in a single column. You can reformat the `fine_Dollars` column to be in dollar format:

`set col: fine_Dollars value: NUMFORMAT(fine_Dollars, '\$###.00')`

Results:

After you drop the columns used in the calculation and move the remaining ones, you should end up with a dataset similar to the following:

House11555.510Y25N\$457.50
House2151210Y25N\$0.00
House31514.2510Y25N\$0.00
House4153.510Y25N\$97.50
House5152710Y25N\$30.00

Now that you have created all of the computations for generating these values, you can change values for `minRadius_ft`, `maxRadius_ft`, and `fineDollarsPerFt` to analyze the resulting fine revenue. Before or after the transform where you set the value for `fineDollarsPerFt`, you can insert something like the following:

`set col: minRadius_ft value:'12.5'`

After the step is added, select the last line in the recipe. Then, you can see how the values in the `fineDollars` column have been updated.

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