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Extract Dichotomous (Binary) Variables

Source: R/flags.R
flags.Rd

Identify and extract dichotomous (binary) variables from a data frame or matrix using isDichotomous().

Usage

flags(
  x,
  strict = FALSE,
  na.rm = FALSE,
  output = c("data", "names", "index", "logical")
)

Arguments

x

a data frame or matrix.

strict

logical. If TRUE, only variables with exactly two distinct values are considered dichotomous. If FALSE (default), variables with one or two distinct values are allowed.

na.rm

logical. Should missing values be ignored when checking for dichotomous variables? Default is FALSE.

output

character string specifying the output representation. One of "data" (subset of x containing only dichotomous variables, the default), "names" (names of dichotomous variables), "index" (column indices) or "logical" (logical vector indicating dichotomous variables).

Value

depending on output:

  • "data": data frame or matrix.

  • "names": character vector.

  • "index": integer vector.

  • "logical": logical vector.

Details

Variables with only missing values are not considered dichotomous when na.rm = FALSE.

When na.rm = TRUE, such variables are treated as empty vectors and are considered dichotomous only if strict = FALSE.

Internally, variables with indeterminate dichotomous status (i.e. NA returned by isDichotomous()) are treated as non-dichotomous for filtering purposes.

See also

Other data.predicate: isDichotomous(), isEuclid(), isLowCardinality(), isNumeric(), isWholeLike(), isZero(), nUnique()

Examples

dat <- data.frame(
  a = c(0, 1, 1, 0),
  b = c(1, 2, 3, 4),
  c = c(TRUE, FALSE, TRUE, TRUE),
  d = c(NA, NA, NA, NA)
)

flags(dat)
#>   a     c
#> 1 0  TRUE
#> 2 1 FALSE
#> 3 1  TRUE
#> 4 0  TRUE

# effect of na.rm
flags(dat, na.rm = TRUE)
#>   a     c  d
#> 1 0  TRUE NA
#> 2 1 FALSE NA
#> 3 1  TRUE NA
#> 4 0  TRUE NA

# return variable names
flags(dat, output = "names")
#> [1] "a" "c"

# return column indices
flags(dat, output = "index")
#> a c 
#> 1 3