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Parallel any() and all()

Source: R/parallel.R
parallel-operators.Rd

These functions are variants of any() and all() that work in parallel on multiple inputs at once. They work similarly to how pmin() and pmax() are parallel variants of min() and max().

Usage

vec_pany(
  ...,
  .missing = NA,
  .size = NULL,
  .arg = "",
  .error_call = current_env()
)

vec_pall(
  ...,
  .missing = NA,
  .size = NULL,
  .arg = "",
  .error_call = current_env()
)

Arguments

...

Logical vectors of equal size.

.missing

Value to use when a missing value is encountered. One of:

  • NA to propagate missing values. With this, missings are treated the same way as | or &.

  • FALSE to treat missing values as FALSE.

  • TRUE to treat missing values as TRUE.

.size

An optional output size. Only useful to specify if it is possible for no inputs to be provided.

.arg

Argument name used in error messages.

.error_call

The execution environment of a currently running function, e.g. caller_env(). The function will be mentioned in error messages as the source of the error. See the call argument of abort() for more information.

Value

A logical vector the same size as the vectors in ....

Details

vec_pany() and vec_pall() are consistent with any() and all() when there are no inputs to process in parallel:

  • any() returns FALSE with no inputs. Similarly, vec_pany(.size = 1) returns FALSE.

  • all() returns TRUE with no inputs. Similarly, vec_pall(.size = 1) returns TRUE.

Examples

a <- c(TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, NA, NA, NA)
b <- c(TRUE, FALSE, NA, TRUE, FALSE, NA, TRUE, FALSE, NA)

# Default behavior treats missings like `|` does
vec_pany(a, b)
#> [1]  TRUE  TRUE  TRUE  TRUE FALSE    NA  TRUE    NA    NA
a | b
#> [1]  TRUE  TRUE  TRUE  TRUE FALSE    NA  TRUE    NA    NA

# Default behavior treats missings like `&` does
vec_pall(a, b)
#> [1]  TRUE FALSE    NA FALSE FALSE FALSE    NA FALSE    NA
a & b
#> [1]  TRUE FALSE    NA FALSE FALSE FALSE    NA FALSE    NA

# Remove missings from the computation, like `na_rm = TRUE`
vec_pany(a, b, .missing = FALSE)
#> [1]  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE
(a & !is.na(a)) | (b & !is.na(b))
#> [1]  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE

vec_pall(a, b, .missing = TRUE)
#> [1]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE
(a | is.na(a)) & (b | is.na(b))
#> [1]  TRUE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE

# `vec_pall()` can be used to implement a `dplyr::filter()` style API
df <- data_frame(id = seq_along(a), a = a, b = b)

keep_rows <- function(x, ...) {
  vec_slice(x, vec_pall(..., .missing = FALSE))
}
drop_rows <- function(x, ...) {
  vec_slice(x, !vec_pall(..., .missing = FALSE))
}

# "Keep / Drop the rows when both a and b are TRUE"
# These form complements of one another, even with `NA`s.
keep_rows(df, a, b)
#>   id    a    b
#> 1  1 TRUE TRUE
drop_rows(df, a, b)
#>   id     a     b
#> 1  2  TRUE FALSE
#> 2  3  TRUE    NA
#> 3  4 FALSE  TRUE
#> 4  5 FALSE FALSE
#> 5  6 FALSE    NA
#> 6  7    NA  TRUE
#> 7  8    NA FALSE
#> 8  9    NA    NA

# Same empty behavior as `any()` and `all()`
vec_pany(.size = 1)
#> [1] FALSE
any()
#> [1] FALSE

vec_pall(.size = 1)
#> [1] TRUE
all()
#> [1] TRUE