Removes rows/columns in an array-like object using a predicate function.
Usage
discard(x, f, ...)
discard_cols(x, f, ...)
discard_rows(x, f, ...)
# S4 method for ANY,`function`
discard(x, f, margin = 1, negate = FALSE, all = FALSE)
# S4 method for ANY,`function`
discard_rows(x, f, negate = FALSE, all = FALSE)
# S4 method for ANY,`function`
discard_cols(x, f, 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.
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,] 5 NA 3 4 10
#> [2,] 6 6 7 5 6
#> [3,] 4 6 10 5 1
#> [4,] 10 3 8 3 6
#> [5,] 2 NA NA 1 9
## Count missing values in rows
count(X, f = is.na, margin = 1)
#> [1] 1 0 0 0 2
## Count non-missing values in columns
count(X, f = is.na, margin = 2, negate = TRUE)
#> [1] 5 3 4 5 5
## Find row with NA
detect(X, f = is.na, margin = 1)
#> [1] TRUE FALSE FALSE FALSE TRUE
## Find column without any NA
detect(X, f = is.na, margin = 2, negate = TRUE, all = TRUE)
#> [1] TRUE FALSE FALSE TRUE TRUE
## Keep row without any NA
keep(X, f = is.na, margin = 1, negate = TRUE, all = TRUE)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 6 6 7 5 6
#> [2,] 4 6 10 5 1
#> [3,] 10 3 8 3 6
## Keep row without any NA
keep(X, f = is.na, margin = 2, negate = TRUE, all = TRUE)
#> [,1] [,2] [,3]
#> [1,] 5 4 10
#> [2,] 6 5 6
#> [3,] 4 5 1
#> [4,] 10 3 6
#> [5,] 2 1 9
## Remove row with any NA
discard(X, f = is.na, margin = 1, all = FALSE)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 6 6 7 5 6
#> [2,] 4 6 10 5 1
#> [3,] 10 3 8 3 6
## Remove column with any NA
discard(X, f = is.na, margin = 2, all = FALSE)
#> [,1] [,2] [,3]
#> [1,] 5 4 10
#> [2,] 6 5 6
#> [3,] 4 5 1
#> [4,] 10 3 6
#> [5,] 2 1 9
## Replace NA with zeros
replace_NA(X, value = 0)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 5 0 3 4 10
#> [2,] 6 6 7 5 6
#> [3,] 4 6 10 5 1
#> [4,] 10 3 8 3 6
#> [5,] 2 0 0 1 9