
Tools for working with infinite values
Source:R/AllGenerics.R
, R/discard.R
, R/replace.R
infinite.Rd
remove_Inf()
remove rows/columns that contain infinite values.replace_Inf
replaces infinite values values.
Usage
remove_Inf(x, ...)
replace_Inf(x, ...)
# S4 method for ANY
remove_Inf(x, margin = 1, all = FALSE)
# S4 method for matrix
replace_Inf(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,] 7 5 5 2 3
#> [2,] 4 8 8 1 4
#> [3,] 6 NA 4 1 6
#> [4,] 6 4 8 10 8
#> [5,] NA 9 2 NA 7
## Count missing values in rows
count(X, f = is.na, margin = 1)
#> [1] 0 0 1 0 2
## Count non-missing values in columns
count(X, f = is.na, margin = 2, negate = TRUE)
#> [1] 4 4 5 4 5
## Find row with NA
detect(X, f = is.na, margin = 1)
#> [1] FALSE FALSE TRUE FALSE TRUE
## Find column without any NA
detect(X, f = is.na, margin = 2, negate = TRUE, all = TRUE)
#> [1] FALSE FALSE 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,] 7 5 5 2 3
#> [2,] 4 8 8 1 4
#> [3,] 6 4 8 10 8
## Keep row without any NA
keep(X, f = is.na, margin = 2, negate = TRUE, all = TRUE)
#> [,1] [,2]
#> [1,] 5 3
#> [2,] 8 4
#> [3,] 4 6
#> [4,] 8 8
#> [5,] 2 7
## Remove row with any NA
discard(X, f = is.na, margin = 1, all = FALSE)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 7 5 5 2 3
#> [2,] 4 8 8 1 4
#> [3,] 6 4 8 10 8
## Remove column with any NA
discard(X, f = is.na, margin = 2, all = FALSE)
#> [,1] [,2]
#> [1,] 5 3
#> [2,] 8 4
#> [3,] 4 6
#> [4,] 8 8
#> [5,] 2 7
## Replace NA with zeros
replace_NA(X, value = 0)
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 7 5 5 2 3
#> [2,] 4 8 8 1 4
#> [3,] 6 0 4 1 6
#> [4,] 6 4 8 10 8
#> [5,] 0 9 2 0 7