Mask categorical variables with random labels in a data frame

Applies masked labels to multiple categorical variables in a data frame using the mask_labels() function. Each variable gets independent random masked labels by default, or can optionally use the same masked labels across all selected variables.

Usage

mask_variables(data, ..., across_variables = FALSE)

Arguments

data

a data frame

...

Columns to mask. Each can be:

• Bare column names (e.g., var1, var2)

• A tidyselect expression (e.g., starts_with("treat_"))

• A character vector of column names (e.g., c("var1", "var2"))

• Multiple sets can be provided as separate arguments

Only character and factor columns will be processed.

across_variables

logical. If TRUE, all selected variables will use the same set of masked labels. If FALSE (default), each variable gets its own independent set of masked labels using the column name as prefix.

Value

A data frame with the specified categorical columns masked. Only character and factor columns can be processed.

See also

mask_labels for masking a single vector, mask_variables_rowwise for rowwise masking, and mask_names for masking variable names.

Examples

# Create example data
df <- data.frame(
  treatment = c("control", "intervention", "control"),
  outcome = c("success", "failure", "success"),
  score = c(1, 2, 3)  # numeric, won't be masked
)

set.seed(123)
# Independent masking for each variable (default - uses column names as
# prefixes)
# Using bare names
mask_variables(df, treatment, outcome)
#>            treatment          outcome score
#> 1 treatment_group_01 outcome_group_01     1
#> 2 treatment_group_02 outcome_group_02     2
#> 3 treatment_group_01 outcome_group_01     3
# Or using character vector
mask_variables(df, c("treatment", "outcome"))
#>            treatment          outcome score
#> 1 treatment_group_01 outcome_group_02     1
#> 2 treatment_group_02 outcome_group_01     2
#> 3 treatment_group_01 outcome_group_02     3

set.seed(456)
# Shared masking across variables
mask_variables(df, c("treatment", "outcome"), across_variables = TRUE)
#>         treatment         outcome score
#> 1 masked_group_01 masked_group_03     1
#> 2 masked_group_04 masked_group_02     2
#> 3 masked_group_01 masked_group_03     3

# Using tidyselect helpers
mask_variables(df, where(is.character))
#>            treatment          outcome score
#> 1 treatment_group_01 outcome_group_01     1
#> 2 treatment_group_02 outcome_group_02     2
#> 3 treatment_group_01 outcome_group_01     3

# Example with multiple categorical columns
df2 <- data.frame(
  group = c("A", "B", "A", "B"),
  condition = c("ctrl", "test", "ctrl", "test")
)
set.seed(123)
result <- mask_variables(df2, c("group", "condition"))
print(result)
#>            group          condition
#> 1 group_group_01 condition_group_01
#> 2 group_group_02 condition_group_02
#> 3 group_group_01 condition_group_01
#> 4 group_group_02 condition_group_02

# Example with williams dataset (multiple categorical columns)
data(williams)
set.seed(456)
# Using bare names (recommended for interactive use)
williams_masked <- mask_variables(williams, subject, ecology)
head(williams_masked[c("subject", "ecology")])
#> # A tibble: 6 × 2
#>   subject           ecology         
#>   <chr>             <chr>           
#> 1 subject_group_109 ecology_group_02
#> 2 subject_group_101 ecology_group_01
#> 3 subject_group_035 ecology_group_01
#> 4 subject_group_038 ecology_group_02
#> 5 subject_group_085 ecology_group_01
#> 6 subject_group_027 ecology_group_01