 Finds rows/columns in an array-like object using a predicate function.

## 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.

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

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

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

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

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

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