Counts values by rows/columns using a predicate function.

## Usage

count(x, f, ...)

# S4 method for matrix,function
count(x, f, margin = 1, negate = FALSE)

# S4 method for data.frame,function
count(x, f, margin = 1, negate = FALSE)

## Arguments

x

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

f

A predicate function.

...

Currently not used.

margin

A 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?

## Value

A numeric vector.

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

N. Frerebeau

## Examples

## Create a count data matrix
X <- matrix(sample(1:10, 25, TRUE), nrow = 5, ncol = 5)

k <- sample(1:25, 3, FALSE)
X[k] <- NA
X
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    6    4    8    2    9
#> [2,]    6   NA    6    3    3
#> [3,]    1   NA    5    5    8
#> [4,]    6    9   10    6    8
#> [5,]   NA    6    6    6    2

## 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] 4 3 5 5 5

## 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] FALSE FALSE  TRUE  TRUE  TRUE

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

## Remove row with any NA
discard(X, f = is.na, margin = 1, all = FALSE)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    6    4    8    2    9
#> [2,]    6    9   10    6    8
## Remove column with any NA
discard(X, f = is.na, margin = 2, all = FALSE)
#>      [,1] [,2] [,3]
#> [1,]    8    2    9
#> [2,]    6    3    3
#> [3,]    5    5    8
#> [4,]   10    6    8
#> [5,]    6    6    2

## Replace NA with zeros
replace_NA(X, value = 0)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    6    4    8    2    9
#> [2,]    6    0    6    3    3
#> [3,]    1    0    5    5    8
#> [4,]    6    9   10    6    8
#> [5,]    0    6    6    6    2