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vazul

vazul is an R package for data 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).

Data 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 

# install.packages("devtools")
devtools::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))

mask_variables_rowwise() - Row-level masking 

# Consistent masking across rows for categorical data
df |> mask_variables_rowwise(c("condition", "treatment"))

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")

scramble_variables_rowwise() - Row-level scrambling 

# Scramble values within each row
df <- data.frame(
  item1 = c(1, 4, 7),
  item2 = c(2, 5, 8), 
  item3 = c(3, 6, 9)
)

df |> scramble_variables_rowwise(c("item1", "item2", "item3"))
#>   item1 item2 item3
#> 1     3     1     2
#> 2     5     4     6  
#> 3     8     9     7

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

Authors

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

License

MIT License - see LICENSE file for details.