Adding calculated fields - Amazon QuickSight

Adding calculated fields

Create calculated fields to transform your data by using one or more of the following:

  • Operators

  • Functions

  • Aggregate functions (you can only add these to an analysis)

  • Fields that contain data

  • Other calculated fields

You can add calculated fields to a dataset during data preparation or from the analysis page. When you add a calculated field to a dataset during data preparation, it's available to all analyses that use that dataset. When you add a calculated field to a dataset in an analysis, it's available only in that analysis.

Analyses support both single-row operations and aggregate operations. Single-row operations are those that supply a (potentially) different result for every row. Aggregate operations supply results that are always the same for entire sets of rows. For example, if you use a simple string function with no conditions, it changes every row. If you use an aggregate function, it applies to all the rows in a group. If you ask for the total sales amount for the US, the same number applies to the entire set. If you ask for data on a particular state, the total sales amount changes to reflect your new grouping. It still provides one result for the entire set.

By creating the aggregated calculated field within the analysis, you can then drill down into the data. The value of that aggregated field is recalculated appropriately for each level. This type of aggregation isn't possible during dataset preparation.

For example, let's say that you want to figure out the percentage of profit for each country, region, and state. You can add a calculated field to your analysis, (sum(salesAmount - cost)) / sum(salesAmount). This field is then calculated for each country, region, and state, at the time your analyst drills down into the geography.

Adding calculated fields to an analysis

To add a calculated field to an analysis
  1. In your analysis, choose Add at top left, and then choose Add calculated field.

    1. In the calculations editor that opens, do the following:

    2. Enter a name for the calculated field.

    3. Enter a formula using fields from your dataset, functions, and operators.

  2. When finished, choose Save.

For more information about how to create formulas using the available functions in QuickSight, see Calculated field function and operator reference for Amazon QuickSight .

Adding calculated fields to a dataset

You can add calculated fields directly to a dataset. The fields that you add become available to anyone who uses the dataset. When you use the dataset in an analysis, you can add additional calculated fields. The fields that you add to an analysis are available only in that analysis.

To add or edit a calculated field for a dataset
  1. Open the dataset that you want to work with. For more information, see Editing datasets.

  2. On the data prep page, do one of the following:

    • To create a new field, choose Add calculated field at left.

    • To edit an existing calculated field, choose it from Calculated fields at left, then choose Edit from the context (right-click) menu.

  3. In the calculation editor, enter a descriptive name for Add title to name the new calculated field. This name appears in the field list in the dataset, so it should look similar to the other fields. For this example, we name the field Total Sales This Year.

  4. (Optional) Add a comment, for example to explain what the expression does, by enclosing text in slashes and asterisks.

    /* Calculates sales per year for this year*/
  5. Identify the metrics, functions, and other items to use. For this example, we need to identify the following:

    • The metric to use

    • Functions: ifelse and datediff

    We want to build a statement like "If the sale happened during this year, show the total sales, and otherwise show 0."

    To add the ifelse function, open the Functions list. Choose All to close the list of all functions. Now you should see the function groups: Aggregate, Conditional, Date, and so on.

    Choose Conditional, and then double-click on ifelse to add it to the workspace.

  6. Place your cursor inside the parenthesis in the workspace, and add three blank lines.

    ifelse( )
  7. With your cursor on the first blank line, find the dateDiff function. It's listed for Functions under Dates. You can also find it by entering date for Search functions. The dateDiff function returns all functions that have date as part of their name. It doesn't return all functions listed under Dates; for example, the now function is missing from the search results.

    Double-click on dateDiff to add it to the first blank line of the ifelse statement.

    ifelse( dateDiff() )

    Add the parameters that dateDiff uses. Place your cursor inside the dateDiff parentheses to begin to add date1, date2, and period:

    1. For date1: The first parameter is the field that has the date in it. Find it under Fields, and add it to the workspace by double-clicking it or entering its name.

    2. For date2, add a comma, then choose truncDate() for Functions. Inside its parenthesis, add period and date, like this: truncDate( "YYYY", now() )

    3. For period: Add a comma after date2 and enter YYYY. This is the period for the year. To see a list of all the supported periods, find dateDiff in the Functions list, and open the documentation by choosing Learn more. If you're already viewing the documentation, as you are now, see dateDiff.

    Add a few spaces for readability, if you like. Your expression should look like the following.

    ifelse( dateDiff( {Date}, truncDate( "YYYY", now() ) ,"YYYY" ) )
  8. Specify the return value. For our example, the first parameter in ifelse needs to return a value of TRUE or FALSE. Because we want the current year, and we're comparing it to this year, we specify that the dateDiff statement should return 0. The if part of the ifelse evaluates as true for rows where there is no difference between the year of the sale and the current year.

    dateDiff( {Date}, truncDate( "YYYY", now() ) ,"YYYY" ) = 0

    To create a field for TotalSales for last year, you can change 0 to 1.

    Another way to do the same thing is to use addDateTime instead of truncDate. Then for each previous year, you change the first parameter for addDateTime to represent each year. For this, you use -1 for last year, -2 for the year before that, and so on. If you use addDateTime, you leave the dateDiff function = 0 for each year.

    dateDiff( {Discharge Date}, addDateTime(-1, "YYYY", now() ) ,"YYYY" ) = 0 /* Last year */
  9. Move your cursor to the first blank line, just under dateDiff. Add a comma.

    For the then part of the ifelse statement, we need to choose the measure (metric) that contains the sales amount, TotalSales.

    To choose a field, open the Fields list and double-click a field to add it to the screen. Or you can enter the name. Add curly braces { } around names that contain spaces. It's likely that your metric has a different name. You can know which field is a metric by the number sign in front of it (#).

    Your expression should look like the following now.

    ifelse( dateDiff( {Date}, truncDate( "YYYY", now() ) ,"YYYY" ) = 0 ,{TotalSales} )
  10. Add an else clause. The ifelse function doesn't require one, but we want to add it. For reporting purposes, you usually don't want to have any null values, because sometimes rows with nulls are omitted.

    We set the else part of the ifelse to 0. The result is that this field is 0 for rows that contain sales from previous years.

    To do this, on the blank line add a comma and then a 0. If you added the comment at the beginning, your finished ifelse expression should look like the following.

    /* Calculates sales per year for this year*/ ifelse( dateDiff( {Date}, truncDate( "YYYY", now() ) ,"YYYY" ) = 0 ,{TotalSales} ,0 )
  11. Save your work by choosing Save at upper right.

    If there are errors in your expression, the editor displays an error message at the bottom. Check your expression for a red squiggly line, then hover your cursor over that line to see what the error message is. Common errors include missing punctuation, missing parameters, misspellings, and invalid data types.

    To avoid making any changes, choose Cancel.

To add a parameter value to a calculated field
  1. You can reference parameters in calculated fields. By adding the parameter to your expression, you add the current value of that parameter.

  2. To add a parameter, open the Parameters list, and select the parameter whose value you want to include.

  3. (Optional) To manually add a parameter to the expression, type the name of the parameter. Then enclosed it in curly braces {}, and prefix it with a $, for example ${parameterName}.

You can change the data type of any field in your dataset, including the types of calculated fields. You can only choose data types that match the data that's in the field.

To change the data type of a calculated field
  • For Calculated fields (at left), choose the field that you want to change, then choose Change data type from the context (right-click) menu.

Unlike the other fields in the dataset, calculated fields can't be disabled. Instead, delete them.

To delete a calculated field
  • For Calculated fields (at left), choose the field that you want to change, then choose Delete from the context (right-click) menu.

Handling decimal values in calculated fields

The decimal data type supports up to four decimal places to the right of the decimal point. During data preparation, calculated fields that use decimal data with more than four decimal places use the full value to perform the calculation. If the result is again decimal data that uses more than four decimal places, the result is then truncated when the dataset is imported into SPICE or displayed in an analysis.

As an example, take decimal field Field_A with a value of 0.00006, which is displayed in the user interface as 0.0. The full value 0.00006 is still used in all calculations. The following are some examples of how you can use this value in calculations:

  • Field_A > 0 = true. The calculated field value displayed in the analysis or imported into SPICE is true.

  • ceil(Field_A) = 1. The calculated field value displayed in the analysis or imported into SPICE is 1.

  • Field_A + 0.00009 = 0.00015. The calculated field value displayed in the analysis or imported into SPICE is 0.0001.

  • Field_A * 1.5 = 0.00009. The calculated field value displayed in the analysis or imported into SPICE is 0.0.