Keep Rows/Columns Using a Predicate

Keeps rows/columns in an array-like object using a predicate function.

Usage

keep(x, ...)

keep_cols(x, ...)

keep_rows(x, ...)

# S4 method for ANY
keep(
  x,
  f,
  margin = 1,
  negate = FALSE,
  all = FALSE,
  verbose = getOption("arkhe.verbose"),
  ...
)

# S4 method for ANY
keep_rows(
  x,
  f,
  negate = FALSE,
  all = FALSE,
  verbose = getOption("arkhe.verbose"),
  ...
)

# S4 method for ANY
keep_cols(
  x,
  f,
  negate = FALSE,
  all = FALSE,
  verbose = getOption("arkhe.verbose"),
  ...
)

Arguments

x

An R object (should be a matrix or a data.frame).

...

Further arguments to be passed to f.

f

A predicate function.

margin

A length-one numeric vector giving the subscripts which the function will be applied over (1 indicates rows, 2 indicates columns).

negate

A logical scalar: should the negation of f be used instead of f?

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.

verbose

A logical scalar: should R report extra information on progress?

See also

Other data cleaning tools: compact(), count(), detect(), discard(), empty, infinite, missing, remove_constant(), zero

Author

N. Frerebeau

Examples

## Create a 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   10    6    2
#> [2,]   NA    6    2    2   NA
#> [3,]    6    3    4    8    4
#> [4,]    3    5    3    6    2
#> [5,]    3   NA   10    9   10

## Keep row without any NA
keep(X, f = is.na, margin = 1, negate = TRUE, all = TRUE)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    2    5   10    6    2
#> [2,]    6    3    4    8    4
#> [3,]    3    5    3    6    2
## Keep row without any NA
keep(X, f = is.na, margin = 2, negate = TRUE, all = TRUE)
#>      [,1] [,2]
#> [1,]   10    6
#> [2,]    2    2
#> [3,]    4    8
#> [4,]    3    6
#> [5,]   10    9