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Simulate preprocessing and report changes without modifying data

dry_run.Rd

Simulates the effect of one or more preprocessing steps on a dataset and returns a report describing which variables would be removed, how many observations might be deleted, and other changes. The input data are not altered.

Usage

dry_run(data, steps = c("varidele", "obsedele", "outlier"),
        cols = NULL, group = NULL, date_col = NULL,
        fraction = 0.25, top = 0.995, bottom = 0.0025,
        by = "min", half = 30, method_outlier = "iqr", coef = 1.5,
        verbose = FALSE)

Arguments

data

A data frame to be simulated.

steps

Character vector of steps to simulate. Currently supported: "varidele", "obsedele", and "outlier".

cols

Column indices or names of numeric variables to consider. If NULL, all numeric columns are used.

group

Optional grouping column for outlier detection.

date_col

Optional time column for observation deletion.

fraction

Missing fraction threshold for variable deletion.

top

Top percentile for outlier detection (percentile method).

bottom

Bottom percentile for outlier detection (percentile method).

by

Time unit for consecutive missing deletion (see obsedele).

half

Half window size for consecutive missing deletion.

method_outlier

Outlier detection method: "iqr", "mad", or "percentile".

coef

Coefficient for IQR or MAD outlier detection.

verbose

Logical; if TRUE, prints progress message.

Value

A list with the following components:

  • original_n: number of rows in the original data.

  • original_ncol: number of columns in the original data.

  • varidele: a list containing removed column names and count (if step included).

  • obsedele: a list with rows_before, rows_after, and removed.

  • outlier: a list with na_before, na_after, and added.

  • final_n: number of rows remaining after simulation, reflecting both variable deletion and observation deletion.

  • final_ncol: number of columns remaining after simulation.

Details

This function is useful for understanding the potential impact of preprocessing without changing the dataset. It actually runs varidele, obsedele, and detect_outliers on a copy of the input, and returns precise per-step before/after counts. The caller's data frame is never modified.

Examples

dry_run(data[1:200, c(1, 4, 17:19)], cols = 3:5, steps = c("varidele", "obsedele", "outlier"))
#> $varidele
#> $varidele$removed_columns
#> [1] "3.98" "4.47" "5.01"
#> 
#> $varidele$removed_count
#> [1] 3
#> 
#> 
#> $obsedele
#> $obsedele$rows_before
#> [1] 200
#> 
#> $obsedele$rows_after
#> [1] 200
#> 
#> $obsedele$removed
#> [1] 0
#> 
#> $obsedele$note
#> [1] "Skipped: no numeric columns remain."
#> 
#> 
#> $outlier
#> $outlier$na_before
#> [1] 0
#> 
#> $outlier$na_after
#> [1] 0
#> 
#> $outlier$added
#> [1] 0
#> 
#> $outlier$note
#> [1] "Skipped: no numeric columns remain."
#> 
#> 
#> $original_n
#> [1] 200
#> 
#> $original_ncol
#> [1] 5
#> 
#> $final_n
#> [1] 200
#> 
#> $final_ncol
#> [1] 2
#>