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Removes rows/columns in an array-like object according to a given predicate.

Usage

compact(x, f, ...)

remove_NA(x, ...)

remove_Inf(x, ...)

remove_zero(x, ...)

remove_empty(x, ...)

# S4 method for ANY,`function`
compact(x, f, margin = 1, negate = FALSE, all = FALSE)

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

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

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

# S4 method for ANY
remove_empty(x, margin = 1)

Arguments

x

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

f

A predicate function.

...

Currently not used.

margin

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

negate

A logical scalar: should the negation of f be used instead of f?

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.

Details

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

  • remove_Inf() remove rows/columns that contain infinite values.

  • remove_zero() remove rows/columns that contain zero.

  • remove_empty() is a special case that remove empty rows/columns. A row/column is empty if it contains only NA, zeros (if of type numeric) or zero length character strings (if of type character).

See also

Other data cleaning tools: count(), detect(), replace()

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

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

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

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

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