Create mock data from configuration files

Main orchestrator function that generates complete mock datasets from configuration files. Reads metadata, filters for enabled variables, dispatches to type-specific create_* functions, and assembles results into a complete data frame.

Usage

create_mock_data(
  databaseStart,
  variables,
  variable_details = NULL,
  n = 1000,
  seed = NULL,
  validate = TRUE,
  verbose = FALSE
)

Arguments

databaseStart

Character. The database identifier (e.g., "cchs2001_p", "minimal-example"). Used to filter variables to those available in the specified database.

variables

data.frame or character. Variable-level metadata containing:

• variable: Variable names

• variableType: Variable type (Categorical/Continuous/Date)

• role: Role tags (enabled, predictor, outcome, etc.)

• position: Display order (optional)

• database: Database filter (optional)

Can also be a file path (character) to variables.csv.

variable_details

data.frame or character. Detail-level metadata containing:

• variable: Variable name (for joining)

• recStart: Category code/range or date interval

• recEnd: Classification (numeric code, "NA::a", "NA::b")

• proportion: Category proportion (for categorical)

• catLabel: Category label/description

Can also be a file path (character) to variable_details.csv. If NULL, uses fallback mode (uniform distributions).

n

Integer. Number of observations to generate (default 1000).

seed

Integer. Optional random seed for reproducibility.

validate

Logical. Whether to validate configuration files (default TRUE).

verbose

Logical. Whether to print progress messages (default FALSE).

Value

Data frame with n rows and one column per enabled variable.

Details

v0.3.0 API: This function now follows the "recodeflow pattern" where it passes full metadata data frames to create_* functions, which handle internal filtering.

Generation process:

1. Load metadata from file paths or accept data frames

2. Filter for enabled variables (role contains "enabled")

3. Set global seed (if provided)

4. Loop through variables in position order: - Dispatch to create_cat_var, create_con_var, or create_date_var - Pass full metadata data frames (functions filter internally) - Merge result into data frame

5. Return complete dataset

Fallback mode: If variable_details = NULL, uses uniform distributions for all enabled variables.

Variable types supported:

• Categorical: create_cat_var()

• Continuous: create_con_var()

• Date: create_date_var()

Configuration schema: For complete documentation of all configuration columns, see vignette("reference-config", package = "MockData").

See also

Other generators: create_cat_var(), create_con_var(), create_date_var(), create_survival_dates(), create_wide_survival_data()

Examples

if (FALSE) { # \dontrun{
# Basic usage with file paths
mock_data <- create_mock_data(
  databaseStart = "minimal-example",
  variables = "inst/extdata/minimal-example/variables.csv",
  variable_details = "inst/extdata/minimal-example/variable_details.csv",
  n = 1000,
  seed = 123
)

# With data frames instead of file paths
variables <- read.csv("inst/extdata/minimal-example/variables.csv",
                      stringsAsFactors = FALSE)
variable_details <- read.csv("inst/extdata/minimal-example/variable_details.csv",
                              stringsAsFactors = FALSE)

mock_data <- create_mock_data(
  databaseStart = "minimal-example",
  variables = variables,
  variable_details = variable_details,
  n = 1000,
  seed = 123
)

# Fallback mode (uniform distributions, no variable_details)
mock_data <- create_mock_data(
  databaseStart = "minimal-example",
  variables = "inst/extdata/minimal-example/variables.csv",
  variable_details = NULL,
  n = 500
)

# View structure
str(mock_data)
head(mock_data)
} # }