Data transformation #

The Data transformation view lets you modify your data through a rule-based system. Rules are defined once, applied in order, and can be edited or removed at any time. The original data is always preserved — transformations produce a new working copy.

For a practical walkthrough of scoring questionnaires (recoding, reversing items, computing scales), see the questionnaire scoring guide.

The rule editor #

Click one of the five buttons at the top to create a rule. A three-panel editor opens:

• Left panel — select input variables (click or drag to multi-select)
• Center panel — configure the transformation
• Right panel — choose where the results go

Give each rule a name (required) and optionally a description, then click Save rule. The transformation is applied immediately.

Rule types #

Value recode #

Maps specific values to new ones. When you select input variables, DataSuite scans the data and pre-fills the Original value column with all unique values it finds. Fill in the New value for each entry. You can add extra rows manually. Values not listed in the mapping stay unchanged.

Range recode #

Maps numeric ranges to output values. Each row defines a Min, Max, and Output. Both boundaries are inclusive — a value that falls within the range gets the specified output. Values outside all defined ranges stay unchanged.

Formula #

A mathematical expression evaluated row by row. Selected input variables are referenced as v1, v2, v3, etc. — reference badges appear next to each variable in the input panel.

(v1 + v2) / 2

The range syntax v1:v7 expands to all variables in that span, useful with aggregate functions:

sum(v1:v7) / 7

Three special variables are available:

• i — the current row number (1-based)
• v — the current value of the variable being processed (when replacing in place)
• c — the full column array of the current variable

Multi-variable formulas let you create several new variables in a single rule using @ declarations:

@Total = v1 + v2 + v3
@Average = @Total / 3
@Centered = @Average - mean(c)

Each line creates (or overwrites) a variable. Later lines can reference earlier ones. The editor shows whether each output will be new or will replace an existing variable.

The formula editor provides syntax highlighting, bracket matching, and autocomplete — start typing a variable name or function and a suggestion list appears (press Ctrl+Space to open it manually). Syntax errors are underlined as you type.

For the full list of operators and functions, see the formula reference.

Regex replace #

Applies a regular expression search-and-replace to text data in each cell separately. You provide a search pattern and a replacement string (supporting $1, $2, etc. for captured groups).

Options:

• Global — replace all matches, not just the first
• Case sensitive
• Multiline mode

A live preview shows the first matching value with matches highlighted and capture groups in distinct colors.

For pattern syntax details, see the regex reference.

Standardize #

Converts numeric values to a standardized scale:

• Z-score — transforms to mean = 0, standard deviation = 1
• Min-max scaling — transforms to a 0–1 range

Missing values remain missing. If all values are identical, z-score returns 0 and min-max returns 0.5.

Output options #

Every rule (except multi-variable formulas) offers three output modes:

• Replace original values — modifies the input variables in place
• Replace another variable — writes results into a different existing variable (input and output counts must match)
• Create new variables — specify a name for the new variable; when multiple inputs are selected, each gets its own output with the base name as prefix

Managing rules #

Saved rules appear in the Transformation rules list, showing:

• a colored badge for the rule type
• the rule name and description
• how many variables the rule applies to
• Edit and Delete buttons

Rules are applied in the order they appear. Editing a rule re-opens the editor with its current settings. Deleting a rule removes it and any variables it created (after confirmation).

Rule library #

The rule library stores transformation rules persistently in your browser, independent of any loaded data file. This lets you reuse rules across different projects and sessions.

Note: library rules are stored locally in your browser’s database. If you plan to switch to a different computer or browser, use the import and export feature to transfer them.

Click Rule library in the transform view to open a two-pane modal:

• Left pane (file rules) — rules in the current project
• Right pane (library) — rules saved in your browser’s persistent storage

Each rule shows a checkmark if it already exists on the other side (matched by content, not name).

Moving rules #

• Push to library — select rules in the file pane and push them to persistent storage
• Pull to file — select library rules and add them to the current project (applied immediately)

If a rule with the same name but different content already exists, a conflict dialog lets you overwrite, keep both (with a numbered suffix), skip, or cancel.

Import and export #

• Import — load rules from a JSON file previously exported from DataSuite
• Export — download selected library rules as a shareable JSON file

Table converter #

The table converter reshapes data from wide format to long (stacked) format — typically needed when repeated measurements are stored as separate columns and need to be combined for analysis (e.g. for repeated-measures tests).

Configuration #

• Stacking pattern — whether source columns are grouped by condition (all variables for condition 1, then condition 2, …) or grouped by variable (each variable’s conditions are adjacent)
• Number of conditions — how many repeated measurements each variable has (2–20)
• Condition labels — a name for each condition (e.g. “Before”, “After”). DataSuite attempts to auto-detect these from variable names.
• Subject identifier — generate row numbers automatically, or use an existing column
• Condition column name — the name of the new column that holds the condition labels (default: “Condition”)
• Variables to stack — click or drag to select which variables to reshape. A warning appears if the count doesn’t divide evenly by the number of conditions.

A live preview on the right side updates as you adjust the settings, showing the first and last rows of the result.

Note: applying the table converter replaces the current dataset. The original data is not preserved — save a project file first if you want to keep it.