## Usage

remove_Inf(x, ...)

replace_Inf(x, ...)

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

# S4 method for matrix
replace_Inf(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.

Other data cleaning tools: compact(), count(), detect(), discard(), keep(), missing, zero

N. Frerebeau

## Examples

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

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

## Count missing values in rows
count(X, f = is.na, margin = 1)
#> [1] 0 0 2 0 1
## Count non-missing values in columns
count(X, f = is.na, margin = 2, negate = TRUE)
#> [1] 3 5 5 5 4

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

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

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

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