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Analyse Missing Values in a Time-Series Dataset

Source: R/missing_analysis.R
missing_analysis.Rd

Computes a per-variable missing-value summary table and draws boxplots for every numeric column. Results are returned invisibly as a list so they can be passed directly into handle_missing().

Usage

missing_analysis(data, plot = TRUE, verbose = TRUE)

Arguments

data

A data.frame or tibble.

plot

Logical. If TRUE (default) boxplots are drawn.

verbose

Logical. If TRUE (default) the summary table is printed.

Value

An invisible list with elements:

summary

A data.frame with columns variable, n_missing, pct_missing, and pct_missing_num.

overall_pct

Overall fraction of missing cells (numeric 0-1).

plot

A ggplot object (boxplots), or NULL.

data

The original data passed in (unchanged).

Examples

data(airquality)
result <- missing_analysis(airquality)
#> 
#> ============================================================
#>   STEP 3/7 : Missing Value Analysis
#> ============================================================
#> 
#>   Rows    : 153  |  Cols: 6  |  Total cells: 918
#>   Missing : 4.79%  (44 cells)  |  2 / 6 cols affected
#>   Action  : DROP  -- missing = 4.79% <= 5% threshold, safe to remove rows
#> 
#> ------------------------------------------------------------
#>   Missing per column
#> ------------------------------------------------------------
#>  variable n_missing pct_missing
#>     Ozone        37      24.18%
#>   Solar.R         7       4.58%
#> 


result$summary
#>         variable n_missing pct_missing_num pct_missing
#> Ozone      Ozone        37      0.24183007      24.18%
#> Solar.R  Solar.R         7      0.04575163       4.58%
#> Wind        Wind         0      0.00000000          0%
#> Temp        Temp         0      0.00000000          0%
#> Month      Month         0      0.00000000          0%
#> Day          Day         0      0.00000000          0%