remove_NA()
remove rows/columns that contain missing values.replace_NA
replaces missing values values.
Usage
remove_NA(x, ...)
replace_NA(x, ...)
# S4 method for ANY
remove_NA(x, margin = 1, all = FALSE)
# S4 method for matrix
replace_NA(x, value = 0)
Arguments
- x
An object (should be a
matrix
or adata.frame
).- ...
Currently not used.
- margin
A vector giving the subscripts which the function will be applied over (
1
indicates rows,2
indicates columns).- 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 possible replacement value.
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,] 2 5 9 10 6
#> [2,] 3 1 5 7 NA
#> [3,] 4 5 8 NA 8
#> [4,] 10 7 1 3 9
#> [5,] 2 NA 8 4 1
## Count missing values in rows
count(X, f = is.na, margin = 1)
#> [1] 0 1 1 0 1
## Count non-missing values in columns
count(X, f = is.na, margin = 2, negate = TRUE)
#> [1] 5 4 5 4 4
## Find row with NA
detect(X, f = is.na, margin = 1)
#> [1] FALSE TRUE TRUE FALSE TRUE
## Find column without any NA
detect(X, f = is.na, margin = 2, negate = TRUE, all = TRUE)
#> [1] TRUE FALSE TRUE FALSE FALSE
## Keep row without any NA
keep(X, f = is.na, margin = 1, negate = TRUE, all = TRUE)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 2 5 9 10 6
#> [2,] 10 7 1 3 9
## Keep row without any NA
keep(X, f = is.na, margin = 2, negate = TRUE, all = TRUE)
#> [,1] [,2]
#> [1,] 2 9
#> [2,] 3 5
#> [3,] 4 8
#> [4,] 10 1
#> [5,] 2 8
## Remove row with any NA
discard(X, f = is.na, margin = 1, all = FALSE)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 2 5 9 10 6
#> [2,] 10 7 1 3 9
## Remove column with any NA
discard(X, f = is.na, margin = 2, all = FALSE)
#> [,1] [,2]
#> [1,] 2 9
#> [2,] 3 5
#> [3,] 4 8
#> [4,] 10 1
#> [5,] 2 8
## Replace NA with zeros
replace_NA(X, value = 0)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 2 5 9 10 6
#> [2,] 3 1 5 7 0
#> [3,] 4 5 8 0 8
#> [4,] 10 7 1 3 9
#> [5,] 2 0 8 4 1