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Counts values by rows/columns according to a given predicate.

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.

See also

Other data cleaning tools: detect(), remove(), replace()

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,]    2    4   NA    2    8
#> [2,]    5    1    6    3   10
#> [3,]    1    8   10    8    4
#> [4,]    2    4    5   NA   NA
#> [5,]    9   10    1    4    2

## Count missing values in rows
count(X, f = is.na, margin = 1)
#> [1] 1 0 0 2 0
## Count non-missing values in columns
count(X, f = is.na, margin = 2, negate = TRUE)
#> [1] 5 5 4 4 4

## Find row with NA
detect(X, f = is.na, margin = 1)
#> [1]  TRUE FALSE FALSE  TRUE FALSE
## Find column without any NA
detect(X, f = is.na, margin = 2, negate = TRUE, all = TRUE)
#> [1]  TRUE  TRUE FALSE FALSE FALSE

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

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