Tools for Working With Missing Values

• remove_NA() remove rows/columns that contain missing values.

• replace_NA replaces missing values values.

Usage

remove_NA(x, ...)

replace_NA(x, ...)

# S4 method for ANY
remove_NA(x, margin = 1, all = FALSE)

# S4 method for matrix
replace_NA(x, value = 0)

# S4 method for data.frame
replace_NA(x, value = 0)

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.

value

A possible replacement value.

See also

Other data cleaning tools: compact(), count(), detect(), discard(), empty, infinite, keep(), remove_constant(), 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,]   NA    7    7    3    1
#> [2,]    7   NA    9    4    3
#> [3,]    9    6    7    3    5
#> [4,]    4    8    3    9    4
#> [5,]    1   NA   10    7    2

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

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