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Impute missing values in a data.frame or tibble (simple, column-wise)

Source: R/impute_missing.R
impute_missing.Rd

Performs deterministic, per-column imputation for numeric variables:

  • "mean": replace NAs with the column mean

  • "median": replace NAs with the column median

  • "zero": replace NAs with 0

  • "constant": replace NAs with the single value given in constant

Usage

impute_missing(
  data,
  method = c("mean", "median", "zero", "constant"),
  cols = NULL,
  constant = NULL,
  na_warn_prop = 0.2,
  verbose = FALSE
)

Arguments

data

A data.frame or tibble containing missing values.

method

Character; one of c("mean","median","zero","constant").

cols

Optional character vector of column names to impute. Defaults to all numeric columns in data that contain at least one NA.

constant

Numeric; single value to use when method = "constant".

na_warn_prop

Numeric in \([0,1]\); threshold for high-missingness warnings per column. Default 0.2.

verbose

Logical; if TRUE, prints progress and a completion summary. Default FALSE.

Value

A data.frame/tibble of the same dimensions as data, with the specified columns' missing values imputed.

Details

Non-numeric columns are left untouched. If cols = NULL, all numeric columns that have at least one NA are selected automatically. NA positions are the only values modified; non-NA entries are preserved as-is.

Quality checks:

  • Warns for high-missingness columns (>= na_warn_prop).

  • Warns and skips imputation when a column has no non-NA values (mean/median undefined).

  • Coerces only numeric columns; non-numerics in cols are skipped with a warning.

Examples

df <- tibble::tibble(a = c(1, NA, 3), b = c(NA, NA, 2), c = letters[1:3])
impute_missing(df, method = "mean")
#> Warning: impute_missing(): column 'a' has high missingness (33.3%).
#> Warning: impute_missing(): column 'b' has high missingness (66.7%).
#> # A tibble: 3 × 3
#>       a     b c    
#>   <dbl> <dbl> <chr>
#> 1     1     2 a    
#> 2     2     2 b    
#> 3     3     2 c    
impute_missing(df, method = "median", verbose = TRUE)
#> impute_missing(): preparing inputs (3 rows, 2 column(s), method='median')
#> Warning: impute_missing(): column 'a' has high missingness (33.3%).
#> Warning: impute_missing(): column 'b' has high missingness (66.7%).
#> impute_missing(): results: imputed 3 values across 2 columns [a=1, b=2]
#> # A tibble: 3 × 3
#>       a     b c    
#>   <dbl> <dbl> <chr>
#> 1     1     2 a    
#> 2     2     2 b    
#> 3     3     2 c    
impute_missing(df, method = "constant", constant = -1, cols = "a")
#> Warning: impute_missing(): column 'a' has high missingness (33.3%).
#> # A tibble: 3 × 3
#>       a     b c    
#>   <dbl> <dbl> <chr>
#> 1     1    NA a    
#> 2    -1    NA b    
#> 3     3     2 c