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Create continuous variable for MockData

Source: R/create_con_var.R
create_con_var.Rd

Generates a continuous mock variable based on specifications from metadata.

Usage

create_con_var(
  var,
  databaseStart,
  variables,
  variable_details,
  df_mock = NULL,
  prop_missing = 0,
  n,
  seed = NULL
)

Arguments

var

character. Variable name to generate (column name in output)

databaseStart

character. Database/cycle identifier for filtering metadata (e.g., "cchs2001_p", "minimal-example"). Used to filter variables and variable_details to the specified database.

variables

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

  • variable: Variable names

  • database: Database identifier (optional, for filtering)

  • rType: R output type (integer/double)

  • distribution: Distribution type (uniform/normal/exponential)

  • mean, sd: Normal distribution parameters

  • rate, shape: Exponential/Gompertz parameters

  • garbage_low_prop, garbage_high_prop: Garbage data parameters

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: Valid range in interval notation (e.g., [18,100])

  • recEnd: Classification (copy, NA::a, NA::b)

  • proportion: Category proportion for missing codes

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

df_mock

data.frame. Optional. Existing mock data (to check if variable already exists).

prop_missing

numeric. Proportion of missing values (0-1). Default 0 (no missing).

n

integer. Number of observations to generate.

seed

integer. Optional. Random seed for reproducibility.

Value

data.frame with one column (the generated continuous variable), or NULL (with a message) if the variable already exists in df_mock.

Errors if the variable is not found in the variables metadata. Warns and uses the first row if multiple variables rows match.

Details

v0.3.0 API: This function now accepts full metadata data frames and filters internally for the specified variable and database. This is the "recodeflow pattern" where filtering is handled inside the function.

Generation process:

  1. Filter metadata: Extract rows for specified var + database

  2. Extract distribution parameters: Read from variables.csv

  3. Extract valid range: Parse from variable_details recStart column

  4. Generate population: Based on distribution type (uniform/normal/exponential)

  5. Apply missing codes: If proportions specified in metadata

  6. Apply garbage: Read garbage parameters from variables.csv

  7. Apply rType: Coerce to specified R type (integer/double)

Type coercion (rType): The rType column in variables.csv controls output data type:

  • \"integer\": Rounds and converts to integer (for age, counts)

  • \"double\": Double precision (default for continuous)

Distribution types:

  • \"uniform\": Uniform distribution over [min, max] from recStart

  • \"normal\": Normal distribution (requires mean, sd in variables.csv)

  • \"exponential\": Exponential distribution (requires rate in variables.csv)

Missing data: Missing codes are identified by recEnd containing "NA::":

  • NA::a: Skip codes (not applicable)

  • NA::b: Missing codes (don't know, refusal, not stated)

Garbage data: Garbage parameters are read from variables.csv:

  • garbage_low_prop, garbage_low_range: Below-range invalid values

  • garbage_high_prop, garbage_high_range: Above-range invalid values

See also

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

Examples

variables <- data.frame(
  variable = "age",
  variableType = "Continuous",
  rType = "integer",
  stringsAsFactors = FALSE
)
variable_details <- data.frame(
  variable = "age",
  recStart = "[18,85]",
  recEnd = "copy",
  proportion = 1,
  stringsAsFactors = FALSE
)

age <- create_con_var(
  var = "age",
  databaseStart = "example",
  variables = variables,
  variable_details = variable_details,
  n = 100,
  seed = 123
)
head(age)
#>   age
#> 1  37
#> 2  71
#> 3  45
#> 4  77
#> 5  81
#> 6  21

if (FALSE) { # \dontrun{
# Not run: requires your own metadata CSV files
# With file paths instead of data frames
result <- create_con_var(
  var = "BMI",
  databaseStart = "minimal-example",
  variables = "path/to/variables.csv",
  variable_details = "path/to/variable_details.csv",
  n = 1000
)
} # }