Advanced Filter

Filter & Sort

Filter rows using one or more conditions combined with AND/OR logic and parenthesised grouping, keeping only the rows that match your criteria. This is the primary tool for subsetting data before analysis -- for example, selecting only a specific treatment arm, age range, or experimental condition.

When to Use

You need to keep only rows matching specific criteria before running a statistical test (e.g., only female participants over age 30, or only samples from a particular batch).
You want to combine multiple conditions with AND logic: for example, Age > 30 AND Group = "Treatment" AND Site = "London".
You need complex logic with grouped conditions using OR: for example, (Region = "North" AND Sales > 100) OR (Region = "South" AND Sales > 200).
You want to exclude outliers or invalid entries before analysis, such as removing rows where a quality score is below a threshold or where a required field is empty.

Required Inputs

Column -- the column to filter on. You can add multiple filter rows to apply conditions on different columns simultaneously.
Operator -- the comparison type. Available operators include: equals, not equals, greater than, greater than or equal, less than, less than or equal, contains, starts with, ends with, is empty, is not empty, and matches regex.
Value -- the comparison value to test each row against. Not required for the "is empty" and "is not empty" operators. For numeric comparisons, enter a number; for text comparisons, enter a string.

What Changes

Row count decreases because rows that do not match the filter conditions are removed from the dataset.
Column structure is preserved -- no columns are added or removed. The result has the same columns as the input.
The operation is applied in place, replacing the current data. Undo is available to revert to the previous state if the filter was too aggressive or applied incorrectly.
Row indices are reset after filtering. If you had row numbers as a column, those values are preserved, but the grid row positions change.

Common Pitfalls

Applying numeric comparisons (greater than, less than) to a text column compares values lexicographically, which produces incorrect results for numbers stored as strings (e.g., "9" > "80" because "9" > "8"). Convert the column to numeric type first.
String matching is case-insensitive by default. Toggle the "Aa" button to enable case-sensitive matching when the distinction matters (e.g., filtering gene names like "TP53" vs "tp53").
Empty and missing values (NA, N/A, blank cells, NaN) are all treated as empty by the "is empty" operator. Use "is not empty" to exclude them, or be aware that numeric filters silently skip empty cells.
When using OR logic between groups, make sure the parenthesised grouping is correct. (A AND B) OR C is different from A AND (B OR C).

Example: Before & After

Before

Name	Age	Group
Alice	28	Control
Bob	35	Treatment
Carol	22	Control
Dave	41	Treatment

→

After

Name	Age	Group
Bob	35	Treatment
Dave	41	Treatment

Filter: Group = "Treatment". Only the two Treatment rows are retained; Control rows are removed.

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