Widget Calculations

Calculations define how your widget aggregates and displays data. In the widget editor, you’ll configure these using the Function dropdown.

Understanding calculations

Each data layer performs a calculation that transforms raw data into displayed values.

Calculation flow

Dataset → Filter → Group → Aggregate → Display

Dataset: Select your data source
Filter: Optionally limit which records to include
Group: Optionally categorize by a column (X-Axis)
Aggregate: Summarize into values using a function
Display: Show in the widget

Standard function types

These are available for most data visualization widgets. Select them from the Function dropdown in the data layer settings.

Count aggregations

Type	Description	Example
Row Count	Number of records in dataset	Total tickets, Customer count

Numeric aggregations

Type	Description	Example
Sum	Total of all values	Total revenue, Hours worked
Average	Mean value	Average ticket time, Avg order value
Median	Middle value (50th percentile)	Median salary, Median response time
Mode	Most common value	Most frequent status, Common category
Max	Largest value	Maximum order, Highest price
Min	Smallest value	Minimum price, Lowest inventory
Range	Difference between max and min	Price spread, Time range

Advanced functions

Type	Description	Use case
Custom	Create your own formula	Complex metrics, weighted averages
Percentage	Percentage matching a filter	Conversion rate, Success rate

Function requirements

Column requirements

Some functions require a specific column (set via Computation Column):

Function	Requires column	Column type
Row Count	No	N/A
Sum	Yes	Numeric
Average	Yes	Numeric
Median	Yes	Numeric
Max	Yes	Numeric, Date, Text
Min	Yes	Numeric, Date, Text
Mode	Yes	Any
Range	Yes	Numeric
Custom	No	N/A
Percentage	No	N/A

Data type compatibility

Numeric columns support all calculations.

Date columns support: Row Count, Max (latest), Min (earliest)

Text columns support: Row Count, Max, Min, Mode

Creating calculations

Step 1: Add a data layer

In the widget editor, make sure you’re on a data visualization widget
Data layers appear in the left panel under “Data Layers”
Click + Add Data Layer for additional calculations
Each layer can have its own dataset and function

Step 2: Select dataset

In the data layer settings on the right, find the Dataset field
Choose from available datasets or modified datasets
Columns load automatically

Step 3: Configure function

Enter a Layer Name for the data layer
Select the Function type (Row Count, Sum, Average, etc.)
If required, select the Computation Column
Configure additional options like X-Axis

Filtering data

Apply filters to limit which records are included.

Adding filters

Use the Filter Builder for precise logic:

In the data layer config, click Filter
A filter dialog opens
Define conditions using columns, operators, and values
Group conditions with AND/OR logic
Click Done to apply the updated filter

For complete details, see Filtering Data.

Filter logic

Groups: Nest conditions for complex logic (e.g., (A OR B) AND C)
Dynamic Values: Compare against other columns or Dashboard Filter Variables
Date Logic: Use relative ranges like “Last 30 Days” or “Start of This Month”

Date filters

Special options for date filtering:

Filter	Description
Today	Current date
This Week	Current week
This Month	Current month
This Quarter	Current quarter
This Year	Current year
Last N Days	Rolling window
Date Range	Specific start/end dates
Filter Variable	Use dashboard filter

Grouping data

Groupings break down calculations by category.

Setting the X-Axis (Group By)

For chart widgets, the X-Axis determines how data is grouped:

Select a column for the X Axis field
The calculation runs for each unique value
Results display as separate bars, lines, or pie slices

Splitting X-axis values with delimiters

Bar and line charts can split a single text value into multiple X-axis categories.

Select an X Axis column in the data layer
Enter a character in X Axis Delimiter (optional), such as , or |
If the column contains multiple values in one field, the widget splits them into separate categories

If your chart uses Secondary X Axis, you can also set Secondary X Axis Delimiter (optional) to split that field the same way.

Blank, empty, and literal null values on category-based X-axes display as Blank in the chart and drilldown instead of being skipped.

Grouping examples

X-Axis selection	Result
None (no X-axis)	Single total value
Product column	Value per product
Date column	Value over time
Status column	Value per status

Date groupings

When using date columns as X-axis, choose time increments:

Increment	Example output
Minute	2024-01-15 14:30
Hour	2024-01-15 14:00
Day	2024-01-15
Week	2024-W03
Month	January 2024
Quarter	Q1 2024
Year	2024

Custom calculations

Create custom metrics using the Custom function type.

Building custom formulas

Select Custom as the function type
Open the Custom Calculation Editor
Build formulas using columns and operations

Available operations

Arithmetic: Add, subtract, multiply, divide
Functions: ABS, ROUND, CEIL, FLOOR
Conditionals: IF statements
String: Concatenate, substring, length

Example custom calculations

[Revenue] - [Cost]                    // Profit
[Revenue] / [Quantity]                // Average price
[Price] * 1.1                         // Price with 10% markup
IF([Status] = "Closed", 1, 0)         // Closed count
[Amount] / [Total] * 100              // Percentage of total

Percentage calculations

Calculate what percentage of records meet specific conditions.

How it works

Select Percentage as the function type
Configure the Primary Filter (denominator)
Configure the Percentage Filter (numerator)
Result: (Matching records / Total records) x 100

Example

Conversion rate calculation:

Primary Filter: All leads (status is not null)
Secondary Filter: Converted leads (status = “Won”)
Result: Percentage of leads that converted

Multiple data layers

Widgets can contain multiple calculations for richer visualizations.

Widgets supporting multiple layers

Widget	Multiple layers support
Bar Chart	Yes - compare multiple series
Line Chart	Yes - overlay multiple trends
Pie Chart	Yes - compare datasets
Matrix	Yes - show multiple metrics
Funnel	Yes - compare funnels
Number	No - single value only
Gauge	Yes - up to 2 layers (current value + dynamic max)
Table	No - single dataset only

Adding multiple layers

Configure your first data layer
Click Add Data Layer
Select a different dataset or apply different filters
Configure the second calculation
Repeat as needed

Use cases

Bar Chart: Compare revenue vs. target per product
Line Chart: Show this year vs. last year trends
Pie Chart: Compare market share across regions
Matrix: Display multiple KPIs per employee

Standard vs. snapshot widgets

Standard widgets

Display current state of your data
Update in real-time as data changes
Good for: Live dashboards, current metrics

Snapshot widgets (Professional+)

Record values over time at scheduled intervals
Enable historical trend analysis
More resource-intensive but tracks changes
Good for: Trend reports, historical comparisons

When to use each

Use Standard when:

You need live, current data
Your source system tracks history
Performance is critical

Use Snapshot when:

Your source doesn’t store historical data
You need to track changes over time
You’re building trend reports

Troubleshooting

Unexpected results

Verify the correct column is selected
Check filter conditions are correct
Confirm aggregation type matches your intent
Review grouping (X-axis) settings

Null values

Null values are excluded from most aggregations
Use “Row Count” for total rows including nulls
Use “Min/Max” to check for null date ranges

Duplicate counts

Check for duplicate records in source data
Use appropriate grouping
Verify filters aren’t creating overlaps