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 adata.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 off
be used instead off
?- all
A
logical
scalar. IfTRUE
, only the rows/columns whose values all meet the condition defined byf
are considered. IfFALSE
(the default), only rows/columns where at least one value validates the condition defined byf
are considered.
Value
A logical
vector.
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,] NA 5 7 8 8
#> [2,] 5 1 10 5 NA
#> [3,] 1 6 7 6 6
#> [4,] 3 2 7 2 7
#> [5,] NA 6 6 6 1
## Count missing values in rows
count(X, f = is.na, margin = 1)
#> [1] 1 1 0 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] TRUE TRUE FALSE 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,] 1 6 7 6 6
#> [2,] 3 2 7 2 7
## Keep row without any NA
keep(X, f = is.na, margin = 2, negate = TRUE, all = TRUE)
#> [,1] [,2] [,3]
#> [1,] 5 7 8
#> [2,] 1 10 5
#> [3,] 6 7 6
#> [4,] 2 7 2
#> [5,] 6 6 6
## Remove row with any NA
discard(X, f = is.na, margin = 1, all = FALSE)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 1 6 7 6 6
#> [2,] 3 2 7 2 7
## Remove column with any NA
discard(X, f = is.na, margin = 2, all = FALSE)
#> [,1] [,2] [,3]
#> [1,] 5 7 8
#> [2,] 1 10 5
#> [3,] 6 7 6
#> [4,] 2 7 2
#> [5,] 6 6 6
## Replace NA with zeros
replace_NA(X, value = 0)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0 5 7 8 8
#> [2,] 5 1 10 5 0
#> [3,] 1 6 7 6 6
#> [4,] 3 2 7 2 7
#> [5,] 0 6 6 6 1