`Reduce`

uses a binary function to successively combine the
elements of a given vector and a possibly given initial value.
`Filter`

extracts the elements of a vector for which a predicate
(logical) function gives true. `Find`

and `Position`

give
the first or last such element and its position in the vector,
respectively. `Map`

applies a function to the corresponding
elements of given vectors. `Negate`

creates the negation of a
given function.

```
Reduce(f, x, init, right = FALSE, accumulate = FALSE)
Filter(f, x)
Find(f, x, right = FALSE, nomatch = NULL)
Map(f, ...)
Negate(f)
Position(f, x, right = FALSE, nomatch = NA_integer_)
```

f

a function of the appropriate arity (binary for
`Reduce`

, unary for `Filter`

, `Find`

and
`Position`

, \(k\)-ary for `Map`

if this is called with
\(k\) arguments). An arbitrary predicate function for
`Negate`

.

x

a vector.

init

an R object of the same kind as the elements of
`x`

.

right

a logical indicating whether to proceed from left to right (default) or from right to left.

accumulate

a logical indicating whether the successive reduce combinations should be accumulated. By default, only the final combination is used.

nomatch

the value to be returned in the case when “no match” (no element satisfying the predicate) is found.

…

vectors.

If `init`

is given, `Reduce`

logically adds it to the start
(when proceeding left to right) or the end of `x`

, respectively.
If this possibly augmented vector \(v\) has \(n > 1\) elements,
`Reduce`

successively applies \(f\) to the elements of \(v\)
from left to right or right to left, respectively. I.e., a left
reduce computes \(l_1 = f(v_1, v_2)\), \(l_2 = f(l_1, v_3)\), etc.,
and returns \(l_{n-1} = f(l_{n-2}, v_n)\), and a right reduce does
\(r_{n-1} = f(v_{n-1}, v_n)\), \(r_{n-2} = f(v_{n-2}, r_{n-1})\)
and returns \(r_1 = f(v_1, r_2)\). (E.g., if \(v\) is the
sequence (2, 3, 4) and \(f\) is division, left and right reduce give
\((2 / 3) / 4 = 1/6\) and \(2 / (3 / 4) = 8/3\), respectively.)
If \(v\) has only a single element, this is returned; if there are
no elements, `NULL`

is returned. Thus, it is ensured that
`f`

is always called with 2 arguments.

The current implementation is non-recursive to ensure stability and scalability.

`Reduce`

is patterned after Common Lisp's `reduce`

. A
reduce is also known as a fold (e.g., in Haskell) or an accumulate
(e.g., in the C++ Standard Template Library). The accumulative
version corresponds to Haskell's scan functions.

`Filter`

applies the unary predicate function `f`

to each
element of `x`

, coercing to logical if necessary, and returns the
subset of `x`

for which this gives true. Note that possible
`NA`

values are currently always taken as false; control over
`NA`

handling may be added in the future. `Filter`

corresponds to `filter`

in Haskell or `remove-if-not`

in
Common Lisp.

`Find`

and `Position`

are patterned after Common Lisp's
`find-if`

and `position-if`

, respectively. If there is an
element for which the predicate function gives true, then the first or
last such element or its position is returned depending on whether
`right`

is false (default) or true, respectively. If there is no
such element, the value specified by `nomatch`

is returned. The
current implementation is not optimized for performance.

`Map`

is a simple wrapper to `mapply`

which does not
attempt to simplify the result, similar to Common Lisp's `mapcar`

(with arguments being recycled, however). Future versions may allow
some control of the result type.

`Negate`

corresponds to Common Lisp's `complement`

. Given a
(predicate) function `f`

, it creates a function which returns the
logical negation of what `f`

returns.

Function `clusterMap`

and `mcmapply`

(not
Windows) in package parallel provide parallel versions of `Map`

.

# NOT RUN { ## A general-purpose adder: add <- function(x) Reduce("+", x) add(list(1, 2, 3)) ## Like sum(), but can also used for adding matrices etc., as it will ## use the appropriate '+' method in each reduction step. ## More generally, many generics meant to work on arbitrarily many ## arguments can be defined via reduction: FOO <- function(...) Reduce(FOO2, list(...)) FOO2 <- function(x, y) UseMethod("FOO2") ## FOO() methods can then be provided via FOO2() methods. ## A general-purpose cumulative adder: cadd <- function(x) Reduce("+", x, accumulate = TRUE) cadd(seq_len(7)) ## A simple function to compute continued fractions: cfrac <- function(x) Reduce(function(u, v) u + 1 / v, x, right = TRUE) ## Continued fraction approximation for pi: cfrac(c(3, 7, 15, 1, 292)) ## Continued fraction approximation for Euler's number (e): cfrac(c(2, 1, 2, 1, 1, 4, 1, 1, 6, 1, 1, 8)) ## Iterative function application: Funcall <- function(f, ...) f(...) ## Compute log(exp(acos(cos(0)))) Reduce(Funcall, list(log, exp, acos, cos), 0, right = TRUE) ## n-fold iterate of a function, functional style: Iterate <- function(f, n = 1) function(x) Reduce(Funcall, rep.int(list(f), n), x, right = TRUE) ## Continued fraction approximation to the golden ratio: Iterate(function(x) 1 + 1 / x, 30)(1) ## which is the same as cfrac(rep.int(1, 31)) ## Computing square root approximations for x as fixed points of the ## function t |-> (t + x / t) / 2, as a function of the initial value: asqrt <- function(x, n) Iterate(function(t) (t + x / t) / 2, n) asqrt(2, 30)(10) # Starting from a positive value => +sqrt(2) asqrt(2, 30)(-1) # Starting from a negative value => -sqrt(2) ## A list of all functions in the base environment: funs <- Filter(is.function, sapply(ls(baseenv()), get, baseenv())) ## Functions in base with more than 10 arguments: names(Filter(function(f) length(formals(f)) > 10, funs)) ## Number of functions in base with a '...' argument: length(Filter(function(f) any(names(formals(f)) %in% "..."), funs)) # } # NOT RUN { ## Find all objects in the base environment which are *not* functions: Filter(Negate(is.function), sapply(ls(baseenv()), get, baseenv())) # }

Run the code above in your browser using DataCamp Workspace