Removes empty rows/columns in an array-like object using a predicate function.
Usage
compact(x, ...)
compact_cols(x, ...)
compact_rows(x, ...)
# S4 method for ANY
compact(x, margin = 1)
# S4 method for ANY
compact_cols(x)
# S4 method for ANY
compact_rows(x)
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).
Details
A row/column is empty if it contains only NA
, zeros (if of type numeric
)
or zero length character strings (if of type character
).
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,] 4 1 6 6 5
#> [2,] 5 3 8 5 9
#> [3,] NA 2 10 NA 3
#> [4,] NA 10 8 9 8
#> [5,] 2 10 10 4 8
## Count missing values in rows
count(X, f = is.na, margin = 1)
#> [1] 0 0 2 1 0
## Count non-missing values in columns
count(X, f = is.na, margin = 2, negate = TRUE)
#> [1] 3 5 5 4 5
## Find row with NA
detect(X, f = is.na, margin = 1)
#> [1] FALSE FALSE TRUE TRUE FALSE
## Find column without any NA
detect(X, f = is.na, margin = 2, negate = TRUE, all = TRUE)
#> [1] FALSE TRUE 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,] 4 1 6 6 5
#> [2,] 5 3 8 5 9
#> [3,] 2 10 10 4 8
## Keep row without any NA
keep(X, f = is.na, margin = 2, negate = TRUE, all = TRUE)
#> [,1] [,2] [,3]
#> [1,] 1 6 5
#> [2,] 3 8 9
#> [3,] 2 10 3
#> [4,] 10 8 8
#> [5,] 10 10 8
## Remove row with any NA
discard(X, f = is.na, margin = 1, all = FALSE)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 4 1 6 6 5
#> [2,] 5 3 8 5 9
#> [3,] 2 10 10 4 8
## Remove column with any NA
discard(X, f = is.na, margin = 2, all = FALSE)
#> [,1] [,2] [,3]
#> [1,] 1 6 5
#> [2,] 3 8 9
#> [3,] 2 10 3
#> [4,] 10 8 8
#> [5,] 10 10 8
## Replace NA with zeros
replace_NA(X, value = 0)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 4 1 6 6 5
#> [2,] 5 3 8 5 9
#> [3,] 0 2 10 0 3
#> [4,] 0 10 8 9 8
#> [5,] 2 10 10 4 8