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)

Arguments

x

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

...

Currently not used.

margin

A 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(), infinite, keep(), zero

Author

N. Frerebeau

Examples

## Create a count 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,]    2   NA    6   NA    9
#> [2,]    7    8    4    5    1
#> [3,]    3    8   10   10    4
#> [4,]    2    3    5   10    7
#> [5,]    6    4    9    3   NA

## Count missing values in rows
count(X, f = is.na, margin = 1)
#> [1] 2 0 0 0 1
## Count non-missing values in columns
count(X, f = is.na, margin = 2, negate = TRUE)
#> [1] 5 4 5 4 4

## Find row with NA
detect(X, f = is.na, margin = 1)
#> [1]  TRUE FALSE FALSE FALSE  TRUE
## Find column without any NA
detect(X, f = is.na, margin = 2, negate = TRUE, all = TRUE)
#> [1]  TRUE FALSE  TRUE FALSE FALSE

## Keep row without any NA
keep(X, f = is.na, margin = 1, negate = TRUE, all = TRUE)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    7    8    4    5    1
#> [2,]    3    8   10   10    4
#> [3,]    2    3    5   10    7
## Keep row without any NA
keep(X, f = is.na, margin = 2, negate = TRUE, all = TRUE)
#>      [,1] [,2]
#> [1,]    2    6
#> [2,]    7    4
#> [3,]    3   10
#> [4,]    2    5
#> [5,]    6    9

## Remove row with any NA
discard(X, f = is.na, margin = 1, all = FALSE)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    7    8    4    5    1
#> [2,]    3    8   10   10    4
#> [3,]    2    3    5   10    7
## Remove column with any NA
discard(X, f = is.na, margin = 2, all = FALSE)
#>      [,1] [,2]
#> [1,]    2    6
#> [2,]    7    4
#> [3,]    3   10
#> [4,]    2    5
#> [5,]    6    9

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