Applies FUN
to all operands in ...
.
Other arguments have similar meaning as binaryapply_byname()
.
See details for more information.
naryapply_byname( FUN, ..., .FUNdots = NULL, match_type = c("all", "matmult", "none"), set_rowcoltypes = TRUE, .organize = TRUE )
FUN | a binary function to be applied "by name" to all operands in |
---|---|
... | the operands for |
.FUNdots | a list of additional named arguments passed to |
match_type | one of "all", "matmult", or "none".
When |
set_rowcoltypes | tells whether to apply row and column types from
operands in |
.organize | a boolean that tells whether or not to automatically
complete operands in |
the result of applying FUN
to all operands in ...
If only one ...
argument is supplied,
FUN
must be capable of handling one argument, and
the call is routed to unaryapply_byname()
.
When set_rowcoltypes
is TRUE
,
the rowcoltypes
argument of unaryapply_byname()
is set to "all",
but when set_rowcoltypes
is FALSE
,
the rowcoltypes
argument of unaryapply_byname()
is set to "none".
If finer control is desired, the caller should use unaryapply_byname()
directly.
If more than one argument is passed in ...
,
FUN
must be a binary function, but its use in by naryapply_byname()
is "n-ary."
Arguments match_type
, set_rowcoltypes
, and .organize
have same meaning as for binaryapply_byname()
.
Thus, all of the operands in ...
must obey the rules of type matching
when match_type
is TRUE
.
naryapply_byname()
and cumapply_byname()
are similar.
Their differences can be described by considering a data frame.
naryapply_byname()
applies FUN
to several columns (variables) of the data frame.
For example, sum_byname()
applied to several variables gives another column
containing the sums across each row of the data frame.
cumapply_byname()
applies FUN
to successive entries in a single column.
For example sum_byname()
applied to a single column gives the sum of all numbers in that column.
naryapply_byname(FUN = sum_byname, 2, 3)#> [1] 5naryapply_byname(FUN = sum_byname, 2, 3, 4, -4, -3, -2)#> [1] 0#> [[1]] #> [1] 1 #> #> [[2]] #> [1] 4 #> #> [[3]] #> [1] 9 #>