anyMissing checks for the presence of at least one missing value,
allMissing checks for the presence of at least one non-missing value.
Supported are atomic types (see is.atomic), lists and data frames.
Missingness is defined as NA or NaN for atomic types and data frame columns,
NULL is defined as missing for lists.allMissing applied to a data.frame returns TRUE if at least one column has
only non-missing values. If you want to perform the less frequent check that there is at least
a single non-missing observation present in the data.frame, use
all(sapply(df, allMissing)) instead.
Value
[logical(1)] Returns TRUE if any (anyMissing) or all (allMissing)
elements of x are missing (see details), FALSE otherwise.
Examples
allMissing(1:2)
#> [1] FALSE
allMissing(c(1, NA))
#> [1] FALSE
allMissing(c(NA, NA))
#> [1] TRUE
x = data.frame(a = 1:2, b = NA)
# Note how allMissing combines the results for data frames:
allMissing(x)
#> [1] TRUE
all(sapply(x, allMissing))
#> [1] FALSE
anyMissing(c(1, 1))
#> [1] FALSE
anyMissing(c(1, NA))
#> [1] TRUE
anyMissing(list(1, NULL))
#> [1] TRUE
x = iris
x[, "Species"] = NA
anyMissing(x)
#> [1] TRUE
allMissing(x)
#> [1] TRUE