Generates a mock person table and integrates it into an existing CDM object.

This function creates a mock person table with specified characteristics for each individual, including a randomly assigned date of birth within a given range and gender based on specified proportions. It populates the CDM object's person table with these entries, ensuring each record is uniquely identified.

Usage

mockPerson(
  cdm = mockCdmReference(),
  nPerson = 10,
  birthRange = as.Date(c("1950-01-01", "2000-12-31")),
  proportionFemale = 0.5,
  seed = NULL
)

Arguments

cdm

A cdm_reference object that serves as the base structure for adding the person table. This parameter should be an existing or newly created CDM object that does not yet contain a 'person' table.

nPerson

An integer specifying the number of mock persons to create in the person table. This defines the scale of the simulation and allows for the creation of datasets with varying sizes.

birthRange

A date range within which the birthdays of the mock persons will be randomly generated. This should be provided as a vector of two dates (as.Date format), specifying the start and end of the range.

proportionFemale

A numeric value between 0 and 1 indicating the proportion of the persons who are female. For example, a value of 0.5 means approximately 50% of the generated persons will be female. This helps simulate realistic demographic distributions.

seed

An optional integer used to set the seed for random number generation, ensuring reproducibility of the generated data. If provided, this seed allows the function to produce consistent results each time it is run with the same inputs. If 'NULL', the seed is not set, which can lead to different outputs on each run.

Value

A modified cdm object with the new 'person' table added. This table includes simulated person data for each generated individual, with unique identifiers and demographic attributes.

Examples

# \donttest{
library(omock)
cdm <- mockPerson(cdm = mockCdmReference(), nPerson = 10)

# View the generated person data
print(cdm$person)
#> # A tibble: 10 × 18
#>    person_id gender_concept_id year_of_birth month_of_birth day_of_birth
#>  *     <int>             <int>         <int>          <int>        <int>
#>  1         1              8532          1963              2           27
#>  2         2              8532          1964              7            4
#>  3         3              8532          1981              5           31
#>  4         4              8532          1973              1           26
#>  5         5              8532          1976             10           26
#>  6         6              8507          1990              2           11
#>  7         7              8532          1994             10           25
#>  8         8              8532          1981             12           12
#>  9         9              8532          1990              2           19
#> 10        10              8532          1971              5           23
#> # ℹ 13 more variables: race_concept_id <int>, ethnicity_concept_id <int>,
#> #   birth_datetime <dttm>, location_id <int>, provider_id <int>,
#> #   care_site_id <int>, person_source_value <chr>, gender_source_value <chr>,
#> #   gender_source_concept_id <int>, race_source_value <chr>,
#> #   race_source_concept_id <int>, ethnicity_source_value <chr>,
#> #   ethnicity_source_concept_id <int>
# }