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Tools for working with zeros

Source: R/AllGenerics.R, R/discard.R, R/replace.R
zero.Rd

  • remove_zero() remove rows/columns that contain zeros.

  • replace_zero replaces zeros.

Usage

remove_zero(x, ...)

replace_zero(x, ...)

# S4 method for ANY
remove_zero(x, margin = 1, all = FALSE)

# S4 method for matrix
replace_zero(x, value)

Arguments

x

An object (should be a matrix or a data.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. If TRUE, only the rows/columns whose values all meet the condition defined by f are considered. If FALSE (the default), only rows/columns where at least one value validates the condition defined by f are considered.

value

A possible replacement value.

See also

Other data cleaning tools: compact(), count(), detect(), discard(), infinite, keep(), missing

Author

N. Frerebeau

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,]   10    2    7   NA    2
#> [2,]    4    5   NA    4    1
#> [3,]    8    4    9    5   10
#> [4,]    5    3    4    4    2
#> [5,]   10   10    8   NA    1

## 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 4 3 5

## 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 FALSE FALSE  TRUE

## Keep row without any NA
keep(X, f = is.na, margin = 1, negate = TRUE, all = TRUE)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    8    4    9    5   10
#> [2,]    5    3    4    4    2
## Keep row without any NA
keep(X, f = is.na, margin = 2, negate = TRUE, all = TRUE)
#>      [,1] [,2] [,3]
#> [1,]   10    2    2
#> [2,]    4    5    1
#> [3,]    8    4   10
#> [4,]    5    3    2
#> [5,]   10   10    1

## Remove row with any NA
discard(X, f = is.na, margin = 1, all = FALSE)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    8    4    9    5   10
#> [2,]    5    3    4    4    2
## Remove column with any NA
discard(X, f = is.na, margin = 2, all = FALSE)
#>      [,1] [,2] [,3]
#> [1,]   10    2    2
#> [2,]    4    5    1
#> [3,]    8    4   10
#> [4,]    5    3    2
#> [5,]   10   10    1

## Replace NA with zeros
replace_NA(X, value = 0)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]   10    2    7    0    2
#> [2,]    4    5    0    4    1
#> [3,]    8    4    9    5   10
#> [4,]    5    3    4    4    2
#> [5,]   10   10    8    0    1