Read the values an exposure takes

The three readings detect_exposure_type() classifies from, exported so that a package taking its own decision from the same facts reads them the same way.

observed_values() returns the distinct values a vector takes. has_two_levels() reports whether there are exactly two of them. is_categorical() applies the unique-value heuristic that separates a categorical numeric exposure from a continuous one.

Usage

observed_values(.x)

has_two_levels(.x)

is_categorical(.exposure)

Arguments

.x

A vector.

.exposure

An exposure vector.

Value

observed_values() returns the distinct observed values, as a vector of the same type as its input. has_two_levels() and is_categorical() each return a single logical value.

Details

A missing value is not a level. observed_values() drops the missing values before taking the distinct ones, and that is what the other two count, so an exposure recorded with missing values is read as the exposure it is. Counting the missing values as a value of their own would give a binary exposure three levels, which takes it off the binary path entirely, and would make a categorical one look as though it had one category more than it has.

is_categorical() treats a vector as categorical when the number of values it takes is less than 20 percent of the number of observations that are not missing. The threshold is strict, so a ratio of exactly 0.2 is not categorical. An exposure with nothing observed has no ratio to take and is not categorical, which leaves it on the continuous branch of detection rather than returning a missing value into the classification.

The distinct values are returned in the order they first appear rather than sorted, since they are only ever counted and sorting them would impose an ordering on a categorical exposure that nothing else here honors. The values keep the type they came in as, so the observed values of a factor are a factor over the same declared levels.

See also

detect_exposure_type(), which combines these into a type.

Examples

observed_values(c(0, 1, NA, 1))
#> [1] 0 1
has_two_levels(c(0, 1, NA, 1))
#> [1] TRUE

# 15 values over 300 observations is categorical; 300 over 300 is not.
is_categorical(rep_len(1:15, 300))
#> [1] TRUE
is_categorical(seq_len(300) / 300)
#> [1] FALSE