Remove Rows/Columns with Missing Values

Removes rows/columns that contain missing values.

Usage

remove_NA(x, ...)

# S4 method for ANY
remove_NA(x, margin = 1, all = FALSE, verbose = getOption("arkhe.verbose"))

Arguments

x

An R object (should be a matrix or a data.frame).

...

Currently not used.

margin

A length-one numeric vector giving the subscripts which the function will be applied over (1 indicates rows, 2 indicates columns).

all

A logical scalar. If TRUE, only the rows/columns whose values all meet the condition defined by f are considered. If FALSE (the default), only rows/columns where at least one value validates the condition defined by f are considered.

verbose

A logical scalar: should R report extra information on progress?

See also

Other data cleaning tools: clean_whitespace(), remove_Inf(), remove_constant(), remove_empty(), remove_zero(), replace_Inf(), replace_NA(), replace_empty(), replace_zero()

Author

N. Frerebeau

Examples

## Create a data matrix
X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)

## Add NA
k <- sample(1:25, 3, FALSE)
X[k] <- NA
X
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    6    9    7    8    1
#> [2,]    4    3    8    7   NA
#> [3,]    6    5    5    1   10
#> [4,]    2    7    2    5   NA
#> [5,]    8    8   NA    4    8

## Remove rows with NA
remove_NA(X, margin = 1)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    6    9    7    8    1
#> [2,]    6    5    5    1   10

## Replace NA with zeros
replace_NA(X, value = 0)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    6    9    7    8    1
#> [2,]    4    3    8    7    0
#> [3,]    6    5    5    1   10
#> [4,]    2    7    2    5    0
#> [5,]    8    8    0    4    8