Missing values

vec_detect_missing() returns a logical vector the same size as x. For each element of x, it returns TRUE if the element is missing, and FALSE otherwise.
vec_any_missing() returns a single TRUE or FALSE depending on whether or not x has any missing values.

Differences with `is.na()`

Data frame rows are only considered missing if every element in the row is missing. Similarly, record vector elements are only considered missing if every field in the record is missing. Put another way, rows with any missing values are considered incomplete, but only rows with all missing values are considered missing.

List elements are only considered missing if they are NULL.

Usage

vec_detect_missing(x)

vec_any_missing(x)

Arguments

x: A vector

Value

vec_detect_missing() returns a logical vector the same size as x.
vec_any_missing() returns a single TRUE or FALSE.

Dependencies

vec_proxy_equal()

Examples

x <- c(1, 2, NA, 4, NA)

vec_detect_missing(x)
#> [1] FALSE FALSE  TRUE FALSE  TRUE
vec_any_missing(x)
#> [1] TRUE

# Data frames are iterated over rowwise, and only report a row as missing
# if every element of that row is missing. If a row is only partially
# missing, it is said to be incomplete, but not missing.
y <- c("a", "b", NA, "d", "e")
df <- data_frame(x = x, y = y)

df$missing <- vec_detect_missing(df)
df$incomplete <- !vec_detect_complete(df)
df
#>    x    y missing incomplete
#> 1  1    a   FALSE      FALSE
#> 2  2    b   FALSE      FALSE
#> 3 NA <NA>    TRUE       TRUE
#> 4  4    d   FALSE      FALSE
#> 5 NA    e   FALSE       TRUE