Using the iterors package

Introduction

An iterator is a special type of object that generalizes the notion of a looping variable. When passed as an argument to a function that knows what to do with it, the iterator supplies a sequence of values. The iterator also maintains information about its state, in particular its current index. The iteror package includes a number of functions for creating iterators, starting iteror, which takes virtually any R object and turns it into an iterator object. The simplest function that operates on iterators is the nextOr function, which when given an iterator, returns the next value of the iterator. For example, here we create an iterator object from the sequence 1 to 10, and then use nextOr to iterate through the values:

library(iterors)
i1 <- iteror(1:10)
nextOr(i1)

## [1] 1

nextOr(i1)

## [1] 2

You can create iterators from matrices and data frames, using the by argument to specify whether to iterate by row or column:

istate <- iteror(state.x77, by='row')
nextOr(istate)

##         Population Income Illiteracy Life Exp Murder HS Grad Frost  Area
## Alabama       3615   3624        2.1    69.05   15.1    41.3    20 50708

nextOr(istate)

##        Population Income Illiteracy Life Exp Murder HS Grad Frost   Area
## Alaska        365   6315        1.5    69.31   11.3    66.7   152 566432

Iterators can also be created from functions, in which case the iterator can be an endless source of values:

ifun <- iteror(function(or) sample(0:9, 4, replace=TRUE))
nextOr(ifun)

## [1] 4 9 9 6

nextOr(ifun)

## [1] 3 1 9 9

For practical applications, iterators can be paired with foreach to obtain parallel results quite easily:

> library(foreach)

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> x <- matrix(rnorm(1e+06), ncol = 10000)
> itx <- iteror(x, by = "row")
> foreach(i = itx, .combine = c) %dopar% mean(i)

  [1] -0.0069652059  0.0161112989  0.0080068074 -0.0120020610  0.0017168149
  [6]  0.0139835943 -0.0078172106 -0.0024762273 -0.0031558268 -0.0072662893
 [11] -0.0055142639  0.0015717907 -0.0100842965 -0.0123601527  0.0136420084
 [16] -0.0242922105 -0.0126416949 -0.0052951152  0.0216329326 -0.0262476648
 [21]  0.0041937609  0.0121253368 -0.0110165729  0.0044267635  0.0080241894
 [26]  0.0042995539 -0.0102826632  0.0051185628 -0.0013970812 -0.0172380786
 [31]  0.0096079613  0.0046837729 -0.0080726970  0.0083781727 -0.0234620163
 [36] -0.0099883966  0.0026883628  0.0029367320  0.0205825899  0.0035303940
 [41]  0.0204990426 -0.0010804987 -0.0033665481 -0.0127492019 -0.0147443195
 [46]  0.0027046346  0.0016449793  0.0155575490 -0.0003488394 -0.0079238019
 [51]  0.0086390030 -0.0039033309  0.0168593825 -0.0067189572 -0.0009925288
 [56] -0.0162907048 -0.0059171838  0.0093806072  0.0100886929 -0.0111677408
 [61]  0.0021754963 -0.0056770907  0.0081200698 -0.0029828717 -0.0163704350
 [66]  0.0057266267 -0.0017156156  0.0214172738 -0.0141379874 -0.0126593342
 [71]  0.0087124575  0.0040231519  0.0038515673  0.0066066908  0.0023586046
 [76] -0.0044167901 -0.0090543553  0.0010806096  0.0102288061  0.0039881702
 [81] -0.0054549319 -0.0127997275 -0.0031697122 -0.0016100996 -0.0143468118
 [86]  0.0035904125 -0.0059399479  0.0085565480 -0.0159064868  0.0054120554
 [91] -0.0084420572  0.0194448129 -0.0103192553 -0.0062924628  0.0215069258
 [96]  0.0015749065  0.0109657488  0.0152237842 -0.0057181022  0.0035530715

Some Special Iterators

The notion of an iterator is new to R, but should be familiar to users of languages such as Python. The iterors package includes a number of special functions that generate iterators for some common scenarios. For example, the irnorm function creates an iterator for which each value is drawn from a specified random normal distribution:

library(iterors)
itrn <- irnorm(10)
nextOr(itrn)

##  [1] -0.3850849 -1.5878696  0.2652963 -1.5334814  1.5643284 -0.2741895
##  [7]  0.6146574  0.4409272  0.3325373  0.6730775

nextOr(itrn)

##  [1] -0.14764142  0.85121106  0.02761249  0.42523665 -0.39851910 -1.07758561
##  [7]  0.48237138 -0.40294240 -0.08557104 -2.30259565

Similarly, the irunif, irbinom, and irpois functions create iterators which draw their values from uniform, binomial, and Poisson distributions, respectively.

We can then use these functions just as we used irnorm:

itru <- irunif(10)
nextOr(itru)

##  [1] 0.2183984239 0.0006660852 0.4938773464 0.0736619264 0.0929143964
##  [6] 0.3709537017 0.3041272718 0.4120525909 0.3554665660 0.8457686948

nextOr(itru)

##  [1] 0.11271092 0.41546911 0.64107741 0.60418096 0.26903868 0.91734659
##  [7] 0.50664253 0.24962355 0.08834084 0.39061742

These random number generators are an indefinite process, but generally, iterators can come to an end. To specify what to do it the iterator ends, give nextOr a second argument, named or.

The icount function returns an iterator that counts starting from one:

it <- icount(3)
nextOr(it, NULL)

## [1] 1

nextOr(it, NULL)

## [1] 2

nextOr(it, NULL)

## [1] 3

nextOr(it, NULL) #is now ended

## NULL

If you call nextOr in a loop you can tell nextOr to break to exit the loop.

t <- icount(10)
total <- 0
repeat {
  total <- total + nextOr(it, break)
}
total # sum of 1:10

## [1] 0

For an index of iteror functions organized by task, see vignette("categories", "iterors")

If you are familiar with packages iterators/itertools/itertools2, some functions have been moved. See vignette("cross-reference", "iterors")

To learn how to build custom iterors, see vignette("writing", "iterors")

- Introduction
- Some Special Iterators