Mask variable names with anonymous labels

Assigns new masked names to selected variables in a data frame. All selected variables are combined into a single set and renamed with a common prefix. To mask different variable groups with different prefixes, call the function separately for each group.

Usage

mask_names(data, ..., prefix, keep_suffixes = NULL)

Arguments

data

A data frame.

...

Columns to mask using tidyselect semantics. All arguments are combined into a single set. Each can be:

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

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

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

prefix

character string to use as prefix for masked names. This becomes the base prefix, with numeric suffixes appended (e.g., prefix = "treatment_" produces "treatment_01", "treatment_02", etc.). The prefix is used as-is, so include a separator (e.g., underscore) if desired.

keep_suffixes

Optional character vector of suffixes to preserve in masked names. When provided, any selected column whose name ends with one of the supplied strings will have that suffix appended verbatim to its masked name (e.g. treat_1_r becomes A_02_r rather than A_02). Multiple suffixes may be supplied (e.g. c("_r", "_z")); when more than one suffix matches, the longest match takes precedence and a warning reports the affected column(s) and the overlapping suffixes. Duplicate entries are silently removed. Defaults to NULL (no suffixes preserved).

Value

A data frame with the specified variables renamed to masked names. The masked columns are sorted by their base masked name alphabetically within their original positions; downstream code that accesses columns by integer index rather than name may be affected. When keep_suffixes

is provided, columns whose original names end with a matching suffix retain that suffix in their masked name.

See also

mask_labels for masking values in a vector, mask_variables for masking values in multiple variables.

Examples

df <- data.frame(
  treat_1 = c(1, 2, 3),
  treat_2 = c(4, 5, 6),
  outcome_a = c(7, 8, 9),
  outcome_b = c(10, 11, 12),
  id = 1:3
)

# Mask one set of variables
library(dplyr)
mask_names(df, starts_with("treat_"), prefix = "A_")
#>   A_01 A_02 outcome_a outcome_b id
#> 1    1    4         7        10  1
#> 2    2    5         8        11  2
#> 3    3    6         9        12  3

# Using character vectors
mask_names(df, c("treat_1", "treat_2"), prefix = "A_")
#>   A_01 A_02 outcome_a outcome_b id
#> 1    1    4         7        10  1
#> 2    2    5         8        11  2
#> 3    3    6         9        12  3

# Preserve the _r suffix for reverse-scored items
set.seed(42)
df2 <- data.frame(
  treat_1   = c(1, 2, 3),
  treat_1_r = c(3, 2, 1),
  id = 1:3
)
mask_names(df2, starts_with("treat_"), prefix = "A_", keep_suffixes = "_r")
#>   A_01 A_02_r id
#> 1    1      3  1
#> 2    2      2  2
#> 3    3      1  3

# Mask multiple sets separately
# Note that the order of masking matters
# Try to mix up the order of prefixes
# for different sets to ensure proper masking.
df |>
  mask_names(starts_with("treat_"), prefix = "B_") |>
  mask_names(starts_with("outcome_"), prefix = "A_")
#>   B_01 B_02 A_01 A_02 id
#> 1    1    4   10    7  1
#> 2    2    5   11    8  2
#> 3    3    6   12    9  3

# Example with the 'williams' dataset
data(williams)
set.seed(42)

williams |>
  mask_names(starts_with("SexUnres"), prefix = "A_") |>
  mask_names(starts_with("Impul"), prefix = "B_") |>
  colnames()
#>  [1] "subject"             "A_01"                "A_02"               
#>  [4] "A_03"                "A_04"                "A_05"               
#>  [7] "B_01"                "B_02"                "B_03"               
#> [10] "Opport_1"            "Opport_2"            "Opport_3"           
#> [13] "Opport_4"            "Opport_5"            "Opport_6_r"         
#> [16] "InvEdu_1_r"          "InvEdu_2_r"          "InvChild_1"         
#> [19] "InvChild_2_r"        "age"                 "gender"             
#> [22] "ecology"             "duration_in_seconds" "attention_1"        
#> [25] "attention_2"