foreach: foreach
In lepennec/foreach: Provides Foreach Looping Construct for R

Description Usage Arguments Details See Also Examples

View source: R/foreach.R

%do% and %dopar% are binary operators that operate on a foreach object and an R expression. The expression, ex, is evaluated multiple times in an environment that is created by the foreach object, and that environment is modified for each evaluation as specified by the foreach object. %do% evaluates the expression sequentially, while %dopar% evalutes it in parallel. The results of evaluating ex are returned as a list by default, but this can be modified by means of the .combine argument.

foreach(..., .combine, .init, .final=NULL, .inorder=TRUE,
       .multicombine=FALSE,
       .maxcombine=if (.multicombine) 100 else 2,
       .errorhandling=c('stop', 'remove', 'pass'),
       .packages=NULL, .export=NULL, .noexport=NULL,
       .verbose=FALSE)
when(cond)
e1 %:% e2
obj %do% ex
obj %dopar% ex
times(n)

`...`	one or more arguments that control how `ex` is evaluated. Named arguments specify the name and values of variables to be defined in the evaluation environment. An unnamed argument can be used to specify the number of times that `ex` should be evaluated. At least one argument must be specified in order to define the number of times `ex` should be executed.
`.combine`	function that is used to process the tasks results as they generated. This can be specified as either a function or a non-empty character string naming the function. Specifying 'c' is useful for concatenating the results into a vector, for example. The values 'cbind' and 'rbind' can combine vectors into a matrix. The values '+' and '*' can be used to process numeric data. By default, the results are returned in a list.
`.init`	initial value to pass as the first argument of the `.combine` function. This should not be specified unless `.combine` is also specified.
`.final`	function of one argument that is called to return final result.
`.inorder`	logical flag indicating whether the `.combine` function requires the task results to be combined in the same order that they were submitted. If the order is not important, then it setting `.inorder` to `FALSE` can give improved performance. The default value is `TRUE`.
`.multicombine`	logical flag indicating whether the `.combine` function can accept more than two arguments. If an arbitrary `.combine` function is specified, by default, that function will always be called with two arguments. If it can take more than two arguments, then setting `.multicombine` to `TRUE` could improve the performance. The default value is `FALSE` unless the `.combine` function is `cbind`, `rbind`, or `c`, which are known to take more than two arguments.
`.maxcombine`	maximum number of arguments to pass to the combine function. This is only relevant if `.multicombine` is `TRUE`.
`.errorhandling`	specifies how a task evalution error should be handled. If the value is "stop", then execution will be stopped via the `stop` function if an error occurs. If the value is "remove", the result for that task will not be returned, or passed to the `.combine` function. If it is "pass", then the error object generated by task evaluation will be included with the rest of the results. It is assumed that the combine function (if specified) will be able to deal with the error object. The default value is "stop".
`.packages`	character vector of packages that the tasks depend on. If `ex` requires a `R` package to be loaded, this option can be used to load that package on each of the workers. Ignored when used with `%do%`.
`.export`	character vector of variables to export. This can be useful when accessing a variable that isn't defined in the current environment. The default value in `NULL`.
`.noexport`	character vector of variables to exclude from exporting. This can be useful to prevent variables from being exported that aren't actually needed, perhaps because the symbol is used in a model formula. The default value in `NULL`.
`.verbose`	logical flag enabling verbose messages. This can be very useful for trouble shooting.
`obj`	`foreach` object used to control the evaluation of `ex`.
`e1`	`foreach` object to merge.
`e2`	`foreach` object to merge.
`ex`	the `R` expression to evaluate.
`cond`	condition to evaluate.
`n`	number of times to evaluate the `R` expression.

The foreach and %do%/%dopar% operators provide a looping construct that can be viewed as a hybrid of the standard for loop and lapply function. It looks similar to the for loop, and it evaluates an expression, rather than a function (as in lapply), but it's purpose is to return a value (a list, by default), rather than to cause side-effects. This faciliates parallelization, but looks more natural to people that prefer for loops to lapply.

The %:% operator is the nesting operator, used for creating nested foreach loops. Type vignette("nested") at the R prompt for more details.

Parallel computation depends upon a parallel backend that must be registered before performing the computation. The parallel backends available will be system-specific, but include doParallel, which uses R's built-in parallel package, doMC, which uses the multicore package, and doSNOW. Each parallel backend has a specific registration function, such as registerDoParallel or registerDoSNOW.

The times function is a simple convenience function that calls foreach. It is useful for evaluating an R expression multiple times when there are no varying arguments. This can be convenient for resampling, for example.

iter

# equivalent to rnorm(3)
times(3) %do% rnorm(1)

# equivalent to lapply(1:3, sqrt)
foreach(i=1:3) %do%
  sqrt(i)

# equivalent to colMeans(m)
m <- matrix(rnorm(9), 3, 3)
foreach(i=1:ncol(m), .combine=c) %do%
  mean(m[,i])

# normalize the rows of a matrix in parallel, with parenthesis used to
# force proper operator precedence
# Need to register a parallel backend before this example will run
# in parallel
foreach(i=1:nrow(m), .combine=rbind) %dopar%
  (m[i,] / mean(m[i,]))

# simple (and inefficient) parallel matrix multiply
library(iterators)
a <- matrix(1:16, 4, 4)
b <- t(a)
foreach(b=iter(b, by='col'), .combine=cbind) %dopar%
  (a %*% b)

# split a data frame by row, and put them back together again without
# changing anything
d <- data.frame(x=1:10, y=rnorm(10))
s <- foreach(d=iter(d, by='row'), .combine=rbind) %dopar% d
identical(s, d)

# a quick sort function
qsort <- function(x) {
  n <- length(x)
  if (n == 0) {
    x
  } else {
    p <- sample(n, 1)
    smaller <- foreach(y=x[-p], .combine=c) %:% when(y <= x[p]) %do% y
    larger  <- foreach(y=x[-p], .combine=c) %:% when(y >  x[p]) %do% y
    c(qsort(smaller), x[p], qsort(larger))
  }
}
qsort(runif(12))