Manipulate Data 

Overview 

LabPlot provides a comprehensive set of tools for manipulating column data through the Manipulate Data context menu. These operations modify column values in-place, transforming data for analysis and visualization. All operations support undo/redo and work on multiple selected columns simultaneously.

Usage: Select column(s) → Right-click → Manipulate Data → Choose operation

Basic Arithmetic Operations 

Apply arithmetic operations to all values in selected columns. These operations support both numeric values and time differences.

Add Value 

Add a constant value or computed value to all entries in selected columns.

Options:

Custom Value - Add a user-specified constant
Difference - Add the difference between start and end values (linear trend)

Use cases:

Add offset to measurements
Shift baseline to zero
Apply calibration corrections

Example:

Original: [10, 20, 30, 40]
Add 5 → [15, 25, 35, 45]

Subtract Value 

Subtract values from all entries in selected columns. Includes statistical subtraction options.

Options:

Custom Value - Subtract a constant
Minimum - Subtract the column’s minimum value (shifts minimum to 0)
Maximum - Subtract the column’s maximum value
Median - Subtract the median (centers around 0)
Mean - Subtract the mean (centers around 0, removes DC offset)
Difference - Subtract linear trend
Baseline (arPLS Algorithm) - Advanced baseline subtraction using Asymmetric Reweighted Penalized Least Squares

Use cases:

Center data around zero
Remove baseline drift
Subtract background signal
Normalize to starting value

Example - Remove DC offset:

Original: [15, 25, 35, 45]
Subtract Mean (30) → [-15, -5, 5, 15]

Subtract Baseline 

Advanced baseline subtraction using the arPLS (Asymmetrically Reweighted Penalized Least Squares) algorithm. This is particularly useful for spectroscopy, chromatography, and signal processing where baseline drift needs to be removed.

Parameters:

Lambda (λ) - Smoothness parameter (default: 10⁶)
- Larger values → smoother baseline
- Smaller values → baseline follows data more closely
- Typical range: 10² to 10⁹
Ratio - Weighting termination ratio (0 to 1, default: 0.01)
- Controls how much negative values are allowed
- Smaller values → less tolerant of negative deviations
- 0.01 is suitable for most cases
Iterations - Number of iterations (default: 10)
- More iterations → better convergence
- 10-20 is usually sufficient

Use cases:

Remove baseline drift in spectra
Subtract background in chromatography peaks
Eliminate low-frequency trends in time series

Preview: The dialog includes a live preview showing the original data, estimated baseline, and result after subtraction.

Multiply by Value 

Multiply all entries by a constant factor.

Use cases:

Unit conversion (e.g., meters to kilometers)
Apply scaling factor
Amplify or attenuate signal

Example:

Original: [1, 2, 3, 4]
Multiply by 10 → [10, 20, 30, 40]

Divide by Value 

Divide all entries by a constant value.

Use cases:

Unit conversion (e.g., millimeters to meters)
Normalize by reference value
Scale down large values

Example:

Original: [100, 200, 300, 400]
Divide by 100 → [1, 2, 3, 4]

Warning

Division by zero will result in NaN (Not a Number) values.

Data Reordering 

Reverse 

Reverse the order of values in selected columns. For numeric columns (Double), trailing NaN values are ignored during reversal.

Usage: Select column(s) → Right-click → Manipulate Data → Reverse

Use cases:

Reverse time series to analyze from end to start
Flip spatial data orientation
Reorder measurements

Example:

Original: [10, 20, 30, 40, 50]
Reversed: [50, 40, 30, 20, 10]

Data Filtering 

Drop Values 

Permanently remove values from columns based on criteria. This is useful for cleaning data by removing outliers, invalid measurements, or unwanted ranges.

Operators for numeric columns:

Equal to - Drop values equal to specified value
Between (exclusive) - Drop values in range (min, max)
Between (inclusive) - Drop values in range [min, max]
Greater than - Drop values > threshold
Greater than or equal - Drop values ≥ threshold
Less than - Drop values < threshold
Less than or equal - Drop values ≤ threshold

Operators for text columns:

Equal to - Drop exact matches
Not equal to - Drop all except exact matches
Starts with - Drop values starting with pattern
Ends with - Drop values ending with pattern
Contains - Drop values containing pattern

Operators for datetime columns:

Same comparison operators as numeric columns
Date/time pickers for precise specification

Use cases:

Remove outliers from measurements
Filter out error codes or invalid values
Remove specific ranges for focused analysis
Clean text data by removing unwanted patterns

Example:

Original: [1, 5, 10, 15, 100, 20, 25]
Drop "Greater than 50" → [1, 5, 10, 15, 20, 25]

Mask Values 

Temporarily exclude values from visualization and analysis without deleting them. Masked values remain in the column but are ignored by plots, fits, and statistical calculations. This is preferable to dropping when you want to preserve original data.

Operators: Same as Drop Values (see above)

Use cases:

Exclude outliers from fitting without losing data
Temporarily hide measurement errors
Focus analysis on specific value ranges
Compare results with/without certain data points

Example:

Data: [1, 5, 10, 100, 15, 20]
Mask "Greater than 50"
Visible in plots: [1, 5, 10, 15, 20]
Original data preserved: [1, 5, 10, 100*, 15, 20]  (* = masked)

Tip

Masked values can be unmasked later using the context menu for the selected cells in the spreadsheet or for the whole spreadsheet.

Normalization 

Normalize data to make different measurements comparable. LabPlot provides 15 normalization methods organized by category.

Basic Normalization 

Divide by Sum

Scale values so they sum to 1 (convert to proportions).

Formula: \(x_i' = \frac{x_i}{\sum x_i}\)

Divide by Min

Scale minimum value to 1.

Formula: \(x_i' = \frac{x_i}{\min(x)}\)

Divide by Max

Scale maximum value to 1 (common in [0,1] normalization).

Formula: \(x_i' = \frac{x_i}{\max(x)}\)

Divide by Count

Divide by number of values (compute average contribution).

Formula: \(x_i' = \frac{x_i}{n}\)

Central Tendency Normalization 

Divide by Mean

Express values as ratio to mean.

Formula: \(x_i' = \frac{x_i}{\bar{x}}\)

Divide by Median

Express values as ratio to median (robust to outliers).

Formula: \(x_i' = \frac{x_i}{\text{median}(x)}\)

Divide by Mode

Express values as ratio to most frequent value.

Formula: \(x_i' = \frac{x_i}{\text{mode}(x)}\)

Spread Normalization 

Divide by Range

Scale to range width.

Formula: \(x_i' = \frac{x_i}{\max(x) - \min(x)}\)

Divide by SD (Standard Deviation)

Scale to standard deviation units (without centering).

Formula: \(x_i' = \frac{x_i}{\sigma}\)

Divide by MAD (Median Absolute Deviation)

Robust spread normalization.

Formula: \(x_i' = \frac{x_i}{\text{MAD}(x)}\)

Divide by IQR (Interquartile Range)

Scale to interquartile range (robust to outliers).

Formula: \(x_i' = \frac{x_i}{Q_3 - Q_1}\)

Standardization (Z-Scores)

Z-Score (SD) - Standard z-score transformation

Center at mean and scale to unit variance.

Formula: \(z_i = \frac{x_i - \bar{x}}{\sigma}\)

Use cases: Compare distributions, standardize for machine learning, detect outliers

Result: Mean = 0, SD = 1

Z-Score (MAD) - Robust z-score using median

Robust alternative using median and MAD.

Formula: \(z_i = \frac{x_i - \text{median}(x)}{\text{MAD}(x)}\)

Use cases: Robust outlier detection, heavy-tailed distributions

Z-Score (IQR) - Robust z-score using quartiles

Another robust alternative using median and IQR.

Formula: \(z_i = \frac{x_i - \text{median}(x)}{Q_3 - Q_1}\)

Rescaling 

Rescale to [a, b]

Map values to arbitrary interval [a, b]. A dialog prompts for min and max values.

Formula: \(x_i' = a + \frac{(x_i - \min(x)) \cdot (b - a)}{\max(x) - \min(x)}\)

Common ranges:

[0, 1] - Unit interval normalization
[−1, 1] - Centered range
[0, 100] - Percentage scale
[0, 255] - Image pixel values

Use cases:

Normalize features for machine learning
Scale data to plot range
Convert to percentage or standardized units

Ladder of Powers Transformations 

Apply Tukey’s Ladder of Powers transformations to improve normality, stabilize variance, or linearize relationships. These transformations are ordered by increasing power.

Available Transformations 

1/x² (Inverse Squared)

Strong transformation for highly right-skewed data.

Formula: \(y = \frac{1}{x^2}\)

Effect: Strong right-to-left redistribution

Note: Undefined for x = 0

1/x (Inverse)

Strong reciprocal transformation.

Formula: \(y = \frac{1}{x}\)

Effect: Reverses scale direction, strong skew correction

Use cases: Rate data, reciprocal relationships

Note: Undefined for x = 0

1/√x (Inverse Square Root)

Moderate inverse transformation.

Formula: \(y = \frac{1}{\sqrt{x}}\)

Effect: Moderate right-to-left redistribution

Note: Undefined for x ≤ 0

log(x) (Logarithm)

Common transformation for right-skewed data.

Formula: \(y = \log_{10}(x)\)

Effect: Compresses large values, expands small values

Use cases:

Data spanning multiple orders of magnitude
Multiplicative relationships → additive
Right-skewed distributions → more symmetric

Note: Undefined for x ≤ 0

√x (Square Root)

Mild transformation for count data or moderate skew.

Formula: \(y = \sqrt{x}\)

Effect: Mild compression of large values

Use cases:

Poisson-distributed count data
Variance proportional to mean
Mild right skew

Note: Undefined for x < 0

x² (Squared)

Expands differences for large values.

Formula: \(y = x^2\)

Effect: Amplifies large values, compresses small values

Use cases:

Left-skewed distributions
Emphasize large deviations
Quadratic relationships

x³ (Cubed)

Strong expansion of large values, preserves sign.

Formula: \(y = x^3\)

Effect: Strong amplification of large values

Use cases:

Cubic relationships
Strong left skew correction
Preserves negative values (unlike even powers)

Choosing a Transformation 

For right-skewed data (long tail to the right):: Move down the ladder: log(x), √x
For left-skewed data (long tail to the left):: Move up the ladder: x², x³

For variance stabilization:

Poisson count data → √x
Proportions/percentages → arcsin(√x) (not in menu, use formula)
Variance proportional to mean → log(x)

Visual guide:

← Stronger left skew correction     Stronger right skew correction →

x³  →  x²  →  √x  →  log(x)  →  1/√x  →  1/x  →  1/x²

Tip

After transformation, check if the data distribution improved using a histogram or normal probability plot.

Warning

Pay attention to domain restrictions:

Square root, log: require x ≥ 0
Inverse: undefined at x = 0
Even powers: lose sign information

Best Practices 

Arithmetic Operations 

Preview before applying - Use preview feature for baseline subtraction to verify parameters
Check for zero - Division operations can produce NaN values
Document operations - Keep notes on applied transformations for reproducibility

Filtering and Masking 

Prefer masking over dropping - Masking preserves original data
Use masking for exploration - Test different filtering criteria without data loss
Drop for final cleanup - Use drop when you’re certain values should be permanently removed

Normalization 

Choose appropriate method - Z-score for comparison, rescale for visualization, divide by max for proportions
Check for zeros - Many normalization methods fail with zero denominators
Robust methods for outliers - Use MAD or IQR-based methods when outliers are present

Transformations 

Understand the goal - Are you linearizing, stabilizing variance, or improving normality?
Check domain - Verify data is positive for log/sqrt transformations
Visualize results - Plot before and after to verify improvement
Consider back-transformation - Remember to interpret results in original scale

Common Workflows 

Outlier Detection and Removal 

Calculate z-score: Manipulate Data → Normalize → (x-Mean)/SD
Identify outliers: Values with |z| > 3
Mask or drop: Manipulate Data → Mask Values (or Drop Values) → “Greater than 3” and “Less than -3”

Baseline Correction 

Select signal column
Manipulate Data → Subtract Baseline
Adjust λ parameter (start with 10⁶)
Use preview to verify baseline fit
Apply subtraction

Preparing Data for Analysis 

Remove obvious errors: Drop or mask invalid values
Transform if needed: Apply log/sqrt for skewed data
Normalize: Apply appropriate normalization for comparison
Center if required: Subtract mean for zero-centered analysis

Unit Conversion 

Identify conversion factor (e.g., mm → m: divide by 1000)
Select column(s)
Manipulate Data → Multiply or Divide by Value
Enter conversion factor

Additional Resources 

For formula-based transformations: Column Formulas
For statistical operations: Column Statistics and Statistics Spreadsheet
For data generation: Spreadsheet → Generate Data