Contents:
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)
Argument  Required?  Data Type  Description 

value1  Y  string  The first value. This can be a number, a function returning a number, or a column containing numbers. 
value2  Y  string  The second value. This can be a number, a function returning a number, or a column containing numbers. 
For more information on syntax standards, see Language Documentation Syntax Notes.
value1, value2
Names of the column, expressions, or literals to compare.
 Missing values generate missing string results.
Usage Notes:
Required?  Data Type  Example Value 

Yes  Column reference, function, or numeric or String value  myColumn 
Examples
Tip: For additional examples, see Common Tasks.
Example  Basic Comparison Functions

LESSTHAN
 See LESSTHAN Function.

LESSTHANEQUAL
 See LESSTHANEQUAL Function.

EQUAL
 See EQUAL Function. 
NOTEQUAL
 See NOTEQUAL Function. GREATERTHAN
 See GREATERTHAN Function.
GREATERTHANEQUAL
 See GREATERTHANEQUAL Function.
Source:
colA  colB 

1  11 
2  10 
3  9 
4  8 
5  7 
6  6 
7  5 
8  4 
9  3 
10  2 
11  1 
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:
colA  colB  gte  gt  neq  eq  lte  lt 

1  11  false  false  true  false  true  true 
2  10  false  false  true  false  true  true 
3  9  false  false  true  false  true  true 
4  8  false  false  true  false  true  true 
5  7  false  false  true  false  true  true 
6  6  true  false  false  true  true  false 
7  5  true  true  true  false  false  false 
8  4  true  true  true  false  false  false 
9  3  true  true  true  false  false  false 
10  2  true  true  true  false  false  false 
11  1  true  true  true  false  false  false 
Example  Using Comparisons to Test Ranges
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:
Location  Radius_ft  minRadius_ft  minInclusive  maxRadius_ft  maxInclusive 

House1  55.5  10  Y  25  N 
House2  12  10  Y  25  N 
House3  14.25  10  Y  25  N 
House4  3.5  10  Y  25  N 
House5  27  10  Y  25  N 
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 ($15.00
) for each foot above or below the limits:
derive type:single value: 15 as:'fineDollarsPerFt'
At the end of the recipe, add the following new line, which calculates the fine for crop circles that are too small:
derive type:single value: IF(tooSmall == 'true', (minRadius_ft  Radius_ft) * fineDollarsPerFt, 0.0) as: 'fine_Dollars'
The above captures the toosmall violations. To also capture the toobig violations, change the above to the following:
derive type:single value: IF(tooSmall == 'true', (minRadius_ft  Radius_ft) * fineDollarsPerFt, if(tooBig == 'true', (Radius_ft  maxRadius_ft) * fineDollarsPerFt, '0.0')) as: 'fine_Dollars'
In place of the original "false" expression (0.0
), the above adds the test for the toobig values, so that all fines are included in a single column. You can reformat the fine_Dollars
column to be in dollar format:
set col: fine_Dollars value: NUMFORMAT(fine_Dollars, '$###.00')
Results:
After you delete the columns used in the calculation and move the remaining ones, you should end up with a dataset similar to the following:
Location  fineDollarsPerFt  Radius_ft  minRadius_ft  minInclusive  maxRadius_ft  maxInclusive  fineDollars 

House1  15  55.5  10  Y  25  N  $457.50 
House2  15  12  10  Y  25  N  $0.00 
House3  15  14.25  10  Y  25  N  $0.00 
House4  15  3.5  10  Y  25  N  $97.50 
House5  15  27  10  Y  25  N  $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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