Write CLOSURE results to disk (and read them back in)

You can use closure_write() to save the results of closure_generate() on your computer. A message will show the exact location.

The data are saved in a new folder as four separate files, one for each tibble in closure_generate()'s output. The folder is named after the parameters of closure_generate().

closure_read() is the opposite: it reads those files back into R, recreating the original CLOSURE list. This is useful for later analyses if you don't want to re-run a lengthy closure_generate() call.

Usage

closure_write(data, path = ".")

closure_read(path)

Arguments

data: List returned by closure_generate().
path: String (length 1). File path where closure_write() will create a new folder with the results. By default, the current working directory. For closure_read(), the path to that new folder.

Value

closure_write() returns the path to the new folder it created, closure_read() returns a list.

Details

closure_write() saves the first three tibbles as CSVs, but the "results" tibble becomes a Parquet file. This is much faster and takes up far less disk space — roughly 1% of a CSV file with the same data. Speed and disk space can be relevant with large result sets.

Use closure_read() to recreate the CLOSURE list from the folder. One of the reasons why it is convenient is that opening a Parquet file requires a special reader. For a more general tool, see nanoparquet::read_parquet().

Folder name

The new folder's name should be sufficient to recreate its CLOSURE results. Dashes separate values, underscores replace decimal periods. For example:

CLOSURE-3_5-1_0-90-1-5-up_or_down-5

The order is the same as in closure_generate():


  closure_generate(
    mean = "3.5",
    sd = "1.0",
    n = 90,
    scale_min = 1,
    scale_max = 5,
    rounding = "up_or_down",  # default
    threshold = 5             # default
  )

Examples

data <- closure_generate(
  mean = "2.7",
  sd = "0.6",
  n = 45,
  scale_min = 1,
  scale_max = 5
)

# Just for this example -- don't try at home
fake_folder <- tempdir()

# You should write to a real folder instead;
# or just leave `path` unspecified.
path_new_folder <- closure_write(data, path = fake_folder)
#> ✔ All files written to:
#> /tmp/Rtmptr0TYZ/CLOSURE-2_7-0_6-45-1-5-up_or_down-5/

# In a later session, conveniently read the files
# back into R. This returns the original list,
# identical except for floating-point error.
closure_read(path_new_folder)
#> $inputs
#> # A tibble: 1 × 7
#>   mean  sd        n scale_min scale_max rounding   threshold
#>   <chr> <chr> <dbl>     <dbl>     <dbl> <chr>          <dbl>
#> 1 2.7   0.6      45         1         5 up_or_down         5
#> 
#> $metrics
#> # A tibble: 1 × 5
#>   samples_initial samples_all values_all  horns horns_uniform
#>             <int>       <int>      <int>  <dbl>         <dbl>
#> 1              15          54       2430 0.0906           0.5
#> 
#> $frequency
#> # A tibble: 5 × 4
#>   value f_average f_absolute f_relative
#>   <int>     <dbl>      <int>      <dbl>
#> 1     1     1.37          74    0.0305 
#> 2     2    12.7          685    0.282  
#> 3     3    29.4         1585    0.652  
#> 4     4     1.37          74    0.0305 
#> 5     5     0.222         12    0.00494
#> 
#> $results
#> # A tibble: 54 × 2
#>       id sample    
#>    <int> <list>    
#>  1     1 <int [45]>
#>  2     2 <int [45]>
#>  3     3 <int [45]>
#>  4     4 <int [45]>
#>  5     5 <int [45]>
#>  6     6 <int [45]>
#>  7     7 <int [45]>
#>  8     8 <int [45]>
#>  9     9 <int [45]>
#> 10    10 <int [45]>
#> # ℹ 44 more rows
#>