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Create mock data from configuration files

Source: R/create_mock_data.R
create_mock_data.Rd

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 simple fallback generation.

n

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

seed

Integer. Optional random seed for reproducibility.

validate

Logical. Whether to use strict generation checks (default TRUE). When TRUE, unsupported variable types and generator errors stop generation. When FALSE, those errors are converted to warnings and the affected variable is skipped.

verbose

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

Value

Data frame with n rows and one column per enabled variable. When the v0.4 mock_spec path is used, the result also carries a mockdata_diagnostics attribute from postprocess_mock_data(). Legacy fallback paths return plain data frames without that attribute.

Details

v0.4.0 transition: In strict mode, this function first attempts to use the v0.4 mock_spec pipeline: mock_spec_from_recodeflow(), generate_mock_data_native(), and postprocess_mock_data(). If the metadata requests a feature not yet supported by the v0.4 native backend, it falls back to the v0.3 create_* dispatch path so existing users can migrate gradually.

The wrapper deliberately stays on the legacy path when validate = FALSE, when variable_details = NULL, when detail-level databaseStart filtering is needed but the variables metadata has no databaseStart column, or when a variable uses a feature not yet supported by the v0.4 native backend. Set verbose = TRUE to see which path was chosen.

In the v0.4 path, seed is used for baseline generation and seed + 1 is used for post-processing. This makes both stages deterministic, but generated values may differ from v0.3.x output for the same seed.

v0.3.0 API: This function 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 has an exact "enabled" token)

  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 simple default generators for enabled variables (two-category categorical values, continuous values from [0, 100], and dates from 2000-01-01 to 2025-12-31).

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

mock_spec_from_recodeflow(), generate_mock_data_native(), postprocess_mock_data(), generate_mock_data_simstudy(), mock_spec()

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

Other mock generation APIs: generate_mock_data_native(), generate_mock_data_simstudy(), postprocess_mock_data()

Examples

# The packaged minimal example includes deliberately messy metadata
# (auto-normalized proportions, survival dates without an anchor): the
# warnings it generates are expected and demonstrate MockData's diagnostics.
mock_data <- create_mock_data(
  databaseStart = "minimal-example",
  variables = system.file("extdata/minimal-example/variables.csv",
    package = "MockData"
  ),
  variable_details = system.file("extdata/minimal-example/variable_details.csv",
    package = "MockData"
  ),
  n = 100,
  seed = 123
)
#> Excluding derived recodeflow variable(s): BMI_derived
#> Warning: Proportions for variable 'BMI' sum to 0.4 (expected 1.0). Auto-normalizing.
#> Warning: Variable 'primary_event_date' is a survival variable (has followup_min/max/event_prop), but df_mock does not contain 'anchor_date' column. Cannot generate survival dates without anchor dates.
#> Warning: Variable 'death_date' is a survival variable (has followup_min/max/event_prop), but df_mock does not contain 'anchor_date' column. Cannot generate survival dates without anchor dates.
#> Warning: Variable 'ltfu_date' is a survival variable (has followup_min/max/event_prop), but df_mock does not contain 'anchor_date' column. Cannot generate survival dates without anchor dates.
#> Warning: Variable 'admin_censor_date' is a survival variable (has followup_min/max/event_prop), but df_mock does not contain 'anchor_date' column. Cannot generate survival dates without anchor dates.
#> Skipped variables during mock data generation: primary_event_date, death_date, ltfu_date, admin_censor_date
str(mock_data)
#> 'data.frame':	100 obs. of  6 variables:
#>  $ age           : int  42 47 73 51 52 76 57 31 40 43 ...
#>  $ smoking       : Factor w/ 4 levels "1","2","3","7": 1 1 3 2 3 1 1 2 1 2 ...
#>  $ BMI           : num  996 996 996 999 999 999 997 996 996 999 ...
#>  $ height        : num  0.331 0.646 0.973 0.363 0.801 ...
#>  $ weight        : num  80.7 79.5 59.9 75.3 58.8 ...
#>  $ interview_date: Date, format: "2005-07-01" "2005-09-11" ...

# Columns with straightforward metadata generate cleanly:
head(mock_data[, c("age", "smoking", "interview_date")])
#>   age smoking interview_date
#> 1  42       1     2005-07-01
#> 2  47       1     2005-09-11
#> 3  73       3     2003-09-25
#> 4  51       2     2005-08-09
#> 5  52       3     2004-07-13
#> 6  76       1     2002-12-14

# Fallback mode: no variable_details, simple default generators
mock_data <- create_mock_data(
  databaseStart = "minimal-example",
  variables = system.file("extdata/minimal-example/variables.csv",
    package = "MockData"
  ),
  variable_details = NULL,
  n = 500
)
#> Warning: No variable_details rows found for variable 'age' and databaseStart 'minimal-example'. Using fallback uniform range [0, 100].
#> Warning: No variable_details rows found for variable 'smoking' and databaseStart 'minimal-example'. Using fallback categories c('1', '2').
#> Warning: No variable_details rows found for variable 'BMI' and databaseStart 'minimal-example'. Using fallback uniform range [0, 100].
#> Warning: No variable_details rows found for variable 'height' and databaseStart 'minimal-example'. Using fallback uniform range [0, 100].
#> Warning: No variable_details rows found for variable 'weight' and databaseStart 'minimal-example'. Using fallback uniform range [0, 100].
#> Warning: No variable_details rows found for variable 'BMI_derived' and databaseStart 'minimal-example'. Using fallback uniform range [0, 100].
#> Warning: No variable_details rows found for variable 'interview_date' and databaseStart 'minimal-example'. Using fallback date range [2000-01-01, 2025-12-31].
#> Warning: No variable_details rows found for variable 'primary_event_date' and databaseStart 'minimal-example'. Using fallback date range [2000-01-01, 2025-12-31].
#> Warning: No variable_details rows found for variable 'death_date' and databaseStart 'minimal-example'. Using fallback date range [2000-01-01, 2025-12-31].
#> Warning: No variable_details rows found for variable 'ltfu_date' and databaseStart 'minimal-example'. Using fallback date range [2000-01-01, 2025-12-31].
#> Warning: No variable_details rows found for variable 'admin_censor_date' and databaseStart 'minimal-example'. Using fallback date range [2000-01-01, 2025-12-31].