Documentation Index
Fetch the complete documentation index at: https://docs.dualentry.com/llms.txt
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How to Use the Custom Report Builder
The custom report builder lets you create ad-hoc and reusable reports beyond the standard financial statements. You choose the data dimensions, measures, filters, and layout, then save the report for repeated use or scheduled delivery.Create a New Report
Navigate to Reporting → Custom Reports → New Report. You choose a report type to start:- Tabular: a flat table of rows and columns, useful for transaction-level detail and export to Excel.
- Summary: grouped rows with subtotals, useful for management-level views and variance analysis.
Select Dimensions and Measures
Dimensions define the rows of your report. You select one or more from: account, company, classification, customer, vendor, or period. When you choose multiple dimensions, the report nests them in the order you specify. Reorder dimensions by dragging them in the dimension list to change the nesting hierarchy. Measures define the calculated columns. Available measures include debit, credit, net, balance, count, budget amount, and variance (actual minus budget). You add as many measure columns as you need. Each measure applies to the full dataset after filters, so every row in the report includes a value for every selected measure. You reorder measure columns by dragging them in the column list.Apply Filters
Filters narrow the dataset before DualEntry calculates the report. You filter by company, date range, account range, classification, or record status (posted, draft, voided). Filters are inclusive - only data matching every active filter appears in the output. You combine multiple filters to isolate specific slices of data. For example, filter by a single entity and a date range to see only that entity’s activity for the period, then add an account range filter to focus on operating expenses. Filters persist when you save the report, so the report runs with the same criteria each time unless you override them at runtime.Grouping and Ordering
You control how rows are grouped and sorted. Group by one or more dimensions to create collapsible sections with subtotals. Within each group, set the sort order (ascending or descending) by any measure column. Grouping and ordering persist when you save the report. When you group by multiple dimensions, DualEntry nests groups in the order you specify. For example, grouping by company and then by account produces a section for each company, with account-level rows within each section. Each group level displays a subtotal row. You expand and collapse groups interactively when viewing the report, and the export includes the full expanded detail regardless of the collapsed state on screen.Add Comparison Columns
Comparison columns place a prior period or budget alongside your current data. You add period-over-period comparisons - prior month, prior year, or vs. budget - and DualEntry appends the comparison value plus the dollar and percentage variance for each row. You add multiple comparison columns to a single report. For example, include both prior month and prior year comparisons to see short-term and long-term trends side by side. Each comparison column calculates independently, so the prior-month variance and prior-year variance reflect the correct base values. Comparison columns respect the same filters applied to the primary data, ensuring consistent date ranges and entity scope across all columns.Add Calculated Columns
For derived metrics, you add calculated columns using formulas. Common examples include percentage of revenue, year-over-year growth, and gross margin. Formulas reference other columns in the report by name and support standard arithmetic operators. You create calculated columns that reference both primary measures and comparison columns. For example, define a gross margin column as(Revenue - COGS) / Revenue by referencing those measure columns by name. Calculated columns appear alongside standard measures in the report output and are included in exports. You edit or remove calculated columns at any time without affecting the underlying data.
Calculated columns evaluate after all filters and grouping are applied, so they always reflect the visible dataset.