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