Add garbage specifications to variables data frame

Helper function to add garbage data specifications to a variables data frame. This provides a convenient way to specify invalid/garbage values for quality assurance testing. Works consistently across all variable types (categorical, continuous, date).

Usage

add_garbage(
  variables,
  var,
  garbage_low_prop = NULL,
  garbage_low_range = NULL,
  garbage_high_prop = NULL,
  garbage_high_range = NULL
)

Arguments

variables

Data frame with variable metadata (typically read from variables.csv)

var

Character. Variable name to add garbage specifications to. Must exist in variables$variable.

garbage_low_prop

Numeric. Proportion of observations to generate as low-range garbage (0-1). If NULL, no low-range garbage is added.

garbage_low_range

Character. Interval notation specifying the range for low-range garbage values (e.g., [-2, 0] for categorical, [0, 1.4) for continuous, [1900-01-01, 1950-12-31] for dates). If NULL, no low-range garbage is added.

garbage_high_prop

Numeric. Proportion of observations to generate as high-range garbage (0-1). If NULL, no high-range garbage is added.

garbage_high_range

Character. Interval notation specifying the range for high-range garbage values (e.g., [10, 15] for categorical, [60, 150] for continuous, [2025-01-01, 2099-12-31] for dates). If NULL, no high-range garbage is added.

Value

Modified variables data frame with garbage specifications added. If the garbage columns don't exist, they are created and initialized with NA for all other variables.

Details

Unified garbage API

All variable types use the same garbage specification pattern:

• garbage_low_prop + garbage_low_range for values below valid range

• garbage_high_prop + garbage_high_range for values above valid range

Variable type examples

Categorical (ordinal treatment):

# Valid codes: 1, 2, 3, 7
# Generate codes -2, -1, 0 below valid range
vars <- add_garbage(vars, "smoking",
  garbage_low_prop = 0.02, garbage_low_range = "[-2, 0]")

Continuous:

# Valid range: [18, 100]
# Generate extreme ages above valid range
vars <- add_garbage(vars, "age",
  garbage_high_prop = 0.03, garbage_high_range = "[150, 200]")

Date:

# Valid range: [2000-01-01, 2020-12-31]
# Generate future dates for QA testing
vars <- add_garbage(vars, "death_date",
  garbage_high_prop = 0.03, garbage_high_range = "[2025-01-01, 2099-12-31]")

Pipe-friendly usage

This function returns the modified variables data frame, making it pipe-friendly:

vars_with_garbage <- variables %>%
  add_garbage("age", garbage_high_prop = 0.03, garbage_high_range = "[150, 200]") %>%
  add_garbage("smoking", garbage_low_prop = 0.02, garbage_low_range = "[-2, 0]") %>%
  add_garbage("death_date", garbage_high_prop = 0.03,
    garbage_high_range = "[2025-01-01, 2099-12-31]")

See also

• create_cat_var() for categorical variable generation

• create_con_var() for continuous variable generation

• create_date_var() for date variable generation

• create_mock_data() for batch generation of all variables

Examples

variables <- data.frame(
  variable = c("age", "smoking"),
  variableType = c("Continuous", "Categorical"),
  stringsAsFactors = FALSE
)

# Add high-range garbage to age and low-range garbage to smoking
vars_with_garbage <- variables |>
  add_garbage("age",
    garbage_high_prop = 0.03, garbage_high_range = "[150, 200]"
  ) |>
  add_garbage("smoking",
    garbage_low_prop = 0.02, garbage_low_range = "[-2, 0]"
  )
vars_with_garbage
#>   variable variableType garbage_high_prop garbage_high_range garbage_low_prop
#> 1      age   Continuous              0.03         [150, 200]               NA
#> 2  smoking  Categorical                NA               <NA>             0.02
#>   garbage_low_range
#> 1              <NA>
#> 2           [-2, 0]

# Pass the result as the `variables` argument of create_mock_data() —
# see ?create_mock_data.