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Test whether a vector is numeric or coercible to numeric

Source: R/is-numeric-like.R
is_numeric_like.Rd

is_numeric_like() tests whether an object is "coercible to numeric" by the particular standards of scrutiny. This means:

  • Integer and double vectors are TRUE.

  • Logical vectors are FALSE, as are non-vector objects.

  • Other vectors (most likely strings) are TRUE if all their non-NA values can be coerced to non-NA numeric values, and FALSE otherwise.

  • Factors are first coerced to string, then tested.

  • Lists are tested like atomic vectors unless any of their elements have length greater 1, in which case they are always FALSE.

  • If all values are non-numeric, non-logical NA, the output is also NA.

See details for discussion.

Usage

is_numeric_like(x)

Arguments

x

Object to be tested.

Value

Logical (length 1).

Details

The scrutiny package often deals with "number-strings", i.e., strings that can be coerced to numeric without introducing new NAs. This is a matter of displaying data in a certain way, as opposed to their storage mode.

is_numeric_like() returns FALSE for logical vectors simply because these are displayed as strings, not as numbers, and the usual coercion rules would be misleading in this context. Likewise, the function treats factors like strings because that is how they are displayed: the fact that factors are stored as integers is irrelevant.

Why store numbers as strings or factors? Only these data types can preserve trailing zeros, and only if the data were originally entered as strings. See vignette("wrangling"), section Trailing zeros.

See also

The vctrs package, which provides a serious typing framework for R; in contrast to this rather ad-hoc function.

Examples

# Numeric vectors are `TRUE`:
is_numeric_like(x = 1:5)
#> [1] TRUE
is_numeric_like(x = 2.47)
#> [1] TRUE

# Logical vectors are always `FALSE`:
is_numeric_like(x = c(TRUE, FALSE))
#> [1] FALSE

# Strings are `TRUE` if all of their non-`NA`
# values can be coerced to non-`NA` numbers,
# and `FALSE` otherwise:
is_numeric_like(x = c("42", "0.7", NA))
#> [1] TRUE
is_numeric_like(x = c("42", "xyz", NA))
#> [1] FALSE

# Factors are treated like their
# string equivalents:
is_numeric_like(x = as.factor(c("42", "0.7", NA)))
#> [1] TRUE
is_numeric_like(x = as.factor(c("42", "xyz", NA)))
#> [1] FALSE

# Lists behave like atomic vectors if all of their
# elements have length 1...
is_numeric_like(x = list("42", "0.7", NA))
#> [1] TRUE
is_numeric_like(x = list("42", "xyz", NA))
#> [1] FALSE

# ...but if they don't, they are `FALSE`:
is_numeric_like(x = list("42", "0.7", NA, c(1, 2, 3)))
#> [1] FALSE

# If all values are `NA`, so is the output...
is_numeric_like(x = as.character(c(NA, NA, NA)))
#> [1] NA

# ...unless the `NA`s are numeric or logical:
is_numeric_like(x = as.numeric(c(NA, NA, NA)))
#> [1] TRUE
is_numeric_like(x = c(NA, NA, NA))
#> [1] FALSE