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Count values using a predicate

Source: R/AllGenerics.R, R/detect.R
count.Rd

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.

See also

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

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,]    8   10    2   NA    1
#> [2,]    9    7   NA    2    8
#> [3,]    8    6   10    7    3
#> [4,]   NA    4    8    6    9
#> [5,]    8    4    9    7    2

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

## Find row with NA
detect(X, f = is.na, margin = 1)
#> [1]  TRUE  TRUE FALSE  TRUE FALSE
## Find column without any NA
detect(X, f = is.na, margin = 2, negate = TRUE, all = TRUE)
#> [1] FALSE  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    6   10    7    3
#> [2,]    8    4    9    7    2
## Keep row without any NA
keep(X, f = is.na, margin = 2, negate = TRUE, all = TRUE)
#>      [,1] [,2]
#> [1,]   10    1
#> [2,]    7    8
#> [3,]    6    3
#> [4,]    4    9
#> [5,]    4    2

## Remove row with any NA
discard(X, f = is.na, margin = 1, all = FALSE)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    8    6   10    7    3
#> [2,]    8    4    9    7    2
## Remove column with any NA
discard(X, f = is.na, margin = 2, all = FALSE)
#>      [,1] [,2]
#> [1,]   10    1
#> [2,]    7    8
#> [3,]    6    3
#> [4,]    4    9
#> [5,]    4    2

## Replace NA with zeros
replace_NA(X, value = 0)
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    8   10    2    0    1
#> [2,]    9    7    0    2    8
#> [3,]    8    6   10    7    3
#> [4,]    0    4    8    6    9
#> [5,]    8    4    9    7    2