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Modifiers

Modifiers are services that process and transform data at the column level.

Coordinate truncation Modifier

A transformation function that allows users to reduce the geographic precision of coordinate values by truncating decimal places, helping anonymize location data while preserving approximate positioning.

Common Examples:

  • Reduce to ~1km precision: Input: 45.464664, Decimal Places: 2, Output: 45.46
  • Reduce to ~111m precision: Input: 45.464664, Decimal Places: 3, Output: 45.464
  • Anonymize to city level: Input: 45.464664, Decimal Places: 1, Output: 45.4
  • Strip all decimals (country level): Input: 45.464664, Decimal Places: 0, Output: 45

Precision Reference:

  • 4 decimal places → ~11 meter precision
  • 3 decimal places → ~111 meter precision
  • 2 decimal places → ~1.1 km precision
  • 1 decimal place  → ~11 km precision
  • 0 decimal places → ~111 km precision

Note: This technique is a form of geographic masking — lower decimal values mean stronger anonymization but less spatial accuracy. Choose the precision level that balances your privacy and analytical needs.

Input format: Each row should contain a latitude and longitude separated by a comma (e.g. 45.464664,9.188540). Use the Decimal places field below to truncate each coordinate to the desired number of decimal digits.

Data Cleaning

A transformation function that allows users to clean and normalize textual data by applying basictext operations such as trimming whitespace, changing case (lowercase, uppercase, titlecase).

Date Formatter

A transformation function that converts date-like values in the selected column(s) into a standardized or custom date format, using date-fns library for date parsing and formatting.

Pseudoanonymization

Pseudoanonymize or de-anonymize data in the selected column using encryption service. Choose between encrypting original values or decrypting vault keys.

Regular Expression Modifier

A transformation function that allows users to apply regular expression operations on text data, including pattern matching, replacement, and extraction of matched values.

Common Examples:

  • Extract numbers with up to 2 decimals: Pattern: \\d\\.\\d{1,2} (without anchors)
  • Truncate to 2 decimals: Operation: Replace, Pattern: ^(\\d\\.\\d{2})\\d*$, Replacement: $1
  • Extract email addresses: Pattern: \\w@\\w\\.\\w
  • Remove special characters: Operation: Replace, Pattern: [^a-zA-Z0-9\\s], Replacement: (empty)

Note: Use anchors (^ and $) only when you want to match the ENTIRE cell value. Without anchors, the pattern will match anywhere within the text.

Text to columns / Columns to text

A transformation function that allows joining multiple columns into one or splitting a single column into multiple columns using a separator defined by the user or by extracting the first or last portion of the cell value.

Text to rows

A transformation function that allows splitting the values of a single column into multiple rows using a custom separator defined by the user. For each split value, a new row is created and the values of the other columns are duplicated.