Analysis Blinding Tools.

vazul

vazul is an R package for analyis blinding in research contexts. It offers two main approaches to anonymize data while preserving analytical validity: masking (replacing values with anonymous labels) and scrambling (randomizing the order of existing values).

Analysis Blinding Approaches

Masking replaces original values with anonymous labels, completely hiding the original information:

treatment <- c("control", "treatment", "control")
mask_labels(treatment)
#> "masked_group_01" "masked_group_02" "masked_group_01"

Scrambling preserves all original values but randomizes their order:

scramble_values(treatment) 
#> "treatment" "control" "control"  # Same values, different order

Installation

You can install the released version of vazul from CRAN with:

install.packages("vazul")

Or the development version from GitHub with:

remotes::install_github("nthun/vazul")

Functions

Masking Functions

Replace categorical values with anonymous labels to completely hide original information.

mask_labels() - Mask vector values

library(vazul)

# Basic masking
treatment <- c("control", "treatment", "control", "treatment")
set.seed(123)
mask_labels(treatment)
#> "masked_group_01" "masked_group_02" "masked_group_01" "masked_group_02"

# Custom prefix
mask_labels(treatment, prefix = "group_")
#> "group_01" "group_02" "group_01" "group_02"

mask_variables() - Mask data frame columns

df <- data.frame(
  condition = c("A", "B", "A", "B"),
  treatment = c("ctrl", "test", "ctrl", "test"),
  score = c(85, 92, 78, 88)
)

# Mask multiple columns
mask_variables(df, c("condition", "treatment"))

# Use tidyselect helpers
mask_variables(df, where(is.character))

The .across_variables parameter allows for consistent masking across multiple columns (e.g., longitudinal data in wide format).

df <- data.frame(
  wave_1 = c("A", "B", "A"),
  wave_2 = c("B", "A", "B"),
  score = c(10, 20, 30)
)

# Mask across variables consistently
mask_variables(df, starts_with("wave_"), .across_variables = TRUE)

Scrambling Functions

Randomize the order of values while preserving the original data content.

scramble_values() - Scramble vector order

# Numeric data
set.seed(123) 
scramble_values(1:5)
#> [1] 3 2 5 4 1

# Categorical data
scramble_values(c("A", "B", "C", "A", "B"))
#> [1] "B" "A" "C" "B" "A"

scramble_variables() - Scramble data frame columns

df <- data.frame(x = 1:6, group = rep(c("A", "B"), each = 3))

# Scramble across entire column
scramble_variables(df, "x")

# Scramble within groups
scramble_variables(df, "x", .groups = "group")

# Using dplyr grouping
library(dplyr)
df |> group_by(group) |> scramble_variables("x")

Row-wise scrambling: Use .byrow = TRUE to shuffle values within each row across the selected columns.

df_items <- data.frame(
  item1 = c(1, 4, 7),
  item2 = c(2, 5, 8), 
  item3 = c(3, 6, 9)
)

# Shuffles values horizontally within each row
scramble_variables(df_items, item1:item3, .byrow = TRUE)

Datasets

MARP Dataset

Many Analysts Religion Project data: 10,535 participants across 24 countries studying religiosity and well-being.

Williams Dataset

Experimental study data: 112 participants examining risk-taking behavior under different wealth conditions.

Explanation of the package name

Vazul was a Hungarian prince in the 11. century. He was blinded by the king to become unfit for the throne. More info: https://en.wikipedia.org/wiki/Vazul

Documentation

• Function help: ?mask_labels, ?scramble_values, etc.
• Package website: https://nthun.github.io/vazul/

Authors

• Tamás Nagy - Package author and maintainer
• Márton Kovács - Author
• Alexandra Sarafoglou - Data contributor and author

Citation

Nagy, T., Kovács, M., & Sarafoglou, A. (2026). vazul: An R package for analysis blinding. Zenodo. https://doi.org/10.5281/zenodo.18269711

License

MIT License - see LICENSE file for details.