Skip to contents

Simulate a vector from a summary object.

Source: R/simulate_df.R, R/simulate_vec.R
simulate.Rd

Simulate a vector from a summary object.

Usage

# S3 method for class 'summary_ordered'
simulate(summary, output_length, seed = NULL, ...)

# S3 method for class 'summary_treatment_flag'
simulate(summary, ...)

simulate(summary, ...)

# S3 method for class 'summary_character'
simulate(summary, output_length, seed = NULL, ...)

# S3 method for class 'summary_Date'
simulate(summary, output_length, seed = NULL, ...)

# S3 method for class 'summary_POSIXct'
simulate(summary, output_length, seed = NULL, ...)

# S3 method for class 'summary_difftime'
simulate(summary, output_length, seed = NULL, ...)

# S3 method for class 'summary_integer'
simulate(summary, output_length, seed = NULL, ...)

# S3 method for class 'summary_double'
simulate(summary, output_length, seed = NULL, ...)

# S3 method for class 'summary_id'
simulate(summary, output_length, ...)

# S3 method for class 'summary_flag'
simulate(summary, output_length, seed = NULL, ...)

Arguments

summary

summary - output from a glimpse function.

output_length

integer - number of rows to simulate.

seed

integer - random seed for reproducibility.

...

additional arguments passed to methods.

Value

data.frame - the simulated dataset.

Methods (by class)

  • simulate(summary_ordered): simulate ordered columns by sampling from unique combinations.

  • simulate(summary_treatment_flag): simulate treatment and flag columns by preserving exact counts for each combination.

  • simulate(summary_character): simulate a character vector by sampling from unique values.

  • simulate(summary_Date): simulate a Date vector by sampling uniformly between min and max.

  • simulate(summary_POSIXct): simulate a POSIXct vector by sampling uniformly between min and max.

  • simulate(summary_difftime): simulate a difftime vector by sampling uniformly between min and max.

  • simulate(summary_integer): simulate an integerish vector by sampling uniformly between min and max and rounding.

  • simulate(summary_double): simulate a double vector by sampling uniformly between min and max.

  • simulate(summary_id): simulate an ID vector by generating sequential IDs.

  • simulate(summary_flag): simulate a flag vector by sampling Y/N in proportion to input.

Examples

# Summarise a vector with glimpse().
summary <- glimpse(c("A", "B", "B", "C"), col_name = "column_name")
simulate(summary, output_length = 10, seed = 1)
#> # A tibble: 10 × 1
#>    column_name
#>    <chr>      
#>  1 B          
#>  2 B          
#>  3 B          
#>  4 B          
#>  5 B          
#>  6 B          
#>  7 B          
#>  8 B          
#>  9 B          
#> 10 B