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

Usage

detect(x, f, ...)

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

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.

Value

A logical vector.

See also

Other data cleaning tools: assign(), count(), remove(), 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,]    4    6    9    8    9
#> [2,]    6    3    1    1    6
#> [3,]   NA    7    5    4    3
#> [4,]    9    6    3    9   NA
#> [5,]    5   NA    5    5    1

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

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

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

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