Create continuous variable for MockData

Creates a continuous mock variable based on specifications from variable_details.

Usage

create_con_var(
  var_row = NULL,
  details_subset = NULL,
  n = NULL,
  seed = NULL,
  df_mock = NULL,
  var_raw = NULL,
  cycle = NULL,
  variable_details = NULL,
  variables = NULL,
  length = NULL,
  prop_NA = NULL,
  prop_invalid = NULL,
  distribution = "uniform"
)

Arguments

var_row

data.frame. Single row from mock_data_config (contains variable metadata)

details_subset

data.frame. Rows from mock_data_config_details for this variable

n

integer. Number of observations to generate

seed

integer. Random seed for reproducibility. If NULL, uses global seed.

df_mock

data.frame. The current mock data (to check if variable already exists)

Configuration v0.1 format (LEGACY):

var_raw

character. The RAW variable name (as it appears in source data)

cycle

character. The cycle identifier (e.g., "cycle1", "HC1")

variable_details

data.frame. Variable details metadata

variables

data.frame. Variables metadata (optional, for validation)

length

integer. The desired length of the mock data vector

prop_NA

numeric. Optional. Proportion of NA values (0 to 1). If NULL, no NAs introduced.

prop_invalid

numeric. Optional. Proportion of invalid out-of-range values (0 to 1). If NULL, no invalid values generated.

distribution

character. Distribution type: "uniform" (default) or "normal"

Value

data.frame with one column (the new continuous variable), or NULL if:

• Variable details not found (v0.1 only)

• Variable already exists in df_mock

• No valid range found

Details

Configuration v0.2 format (NEW):

v0.2 format (NEW):

• Uses extract_distribution_params() to get distribution parameters from details_subset

• Generates population based on specified distribution (uniform, normal, exponential)

• Applies missing codes with apply_missing_codes()

• Adds garbage using make_garbage() if garbage rows present

• Supports fallback mode: uniform [0, 100] when details_subset is NULL

v0.1 format (LEGACY):

• Uses get_variable_details_for_raw() to find variable specifications

• Parses ranges from recStart using parse_range_notation()

• Supports "uniform" or "normal" distribution via parameter

• Handles prop_NA and prop_invalid parameters

The function auto-detects which format based on parameter names.

Type coercion (rType): If the metadata contains an rType column, values will be coerced to the specified R type:

• "integer": Rounds and converts to integer (e.g., for age, counts, years)

• "double": Converts to double (default for continuous variables)

• Other types are passed through without coercion

This allows age variables to return integers (45L) instead of doubles (45.0), matching real survey data. If rType is not specified, defaults to double.

See also

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

Examples

if (FALSE) { # \dontrun{
# v0.2 format - called by create_mock_data()
config <- read_mock_data_config("mock_data_config.csv")
details <- read_mock_data_config_details("mock_data_config_details.csv")
var_row <- config[config$variable == "ALW_2A1", ]
details_subset <- get_variable_details(details, variable_name = "ALW_2A1")
mock_var <- create_con_var(var_row, details_subset, n = 1000, seed = 123)

# v0.1 format (legacy)
mock_drinks <- create_con_var(
  var_raw = "alcdwky",
  cycle = "cycle1",
  variable_details = variable_details,
  length = 1000,
  df_mock = existing_data,
  prop_NA = 0.02,
  distribution = "normal"
)
} # }