This document serves as an overview for attacking *common*
combinatorial problems in `R`

. One of the goals of
`RcppAlgos`

is to provide a comprehensive and accessible
suite of functionality so that users can easily get to the heart of
their problem. As a bonus, the functions in `RcppAlgos`

are
extremely efficient and are constantly being improved with every
release.

It should be noted that this document only covers common problems.
For more information on other combinatorial problems addressed by
`RcppAlgos`

, see the following vignettes:

- Combinatorial Sampling
- Constraints, Partitions, and Compositions
- Attacking Problems Related to the Subset Sum Problem
- Combinatorial Iterators in RcppAlgos

For much of the output below, we will be using the following function obtained here combining head and tail methods in R (credit to user @flodel)

```
ht <- function(d, m = 5, n = m) {
## print the head and tail together
cat("head -->\n")
print(head(d, m))
cat("--------\n")
cat("tail -->\n")
print(tail(d, n))
}
```

`comboGeneral`

and
`permuteGeneral`

Easily executed with a very simple interface. The output is in lexicographical order.

We first look at getting results without repetition. You can pass an
integer *n* and it will be converted to the sequence
`1:n`

, or you can pass any vector with an atomic type
(i.e. `logical`

, `integer`

, `numeric`

,
`complex`

, `character`

, and `raw`

).

```
library(RcppAlgos)
options(width = 90)
## combn output for reference
combn(4, 3)
#> [,1] [,2] [,3] [,4]
#> [1,] 1 1 1 2
#> [2,] 2 2 3 3
#> [3,] 3 4 4 4
## This is the same as combn expect the output is transposed
comboGeneral(4, 3)
#> [,1] [,2] [,3]
#> [1,] 1 2 3
#> [2,] 1 2 4
#> [3,] 1 3 4
#> [4,] 2 3 4
## Find all 3-tuple permutations without
## repetition of the numbers c(1, 2, 3, 4).
head(permuteGeneral(4, 3))
#> [,1] [,2] [,3]
#> [1,] 1 2 3
#> [2,] 1 2 4
#> [3,] 1 3 2
#> [4,] 1 3 4
#> [5,] 1 4 2
#> [6,] 1 4 3
## If you don't specify m, the length of v (if v is a vector) or v (if v is a
## scalar (see the examples above)) will be used
v <- c(2, 3, 5, 7, 11, 13)
comboGeneral(v)
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 2 3 5 7 11 13
head(permuteGeneral(v))
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 2 3 5 7 11 13
#> [2,] 2 3 5 7 13 11
#> [3,] 2 3 5 11 7 13
#> [4,] 2 3 5 11 13 7
#> [5,] 2 3 5 13 7 11
#> [6,] 2 3 5 13 11 7
## They are very efficient...
system.time(comboGeneral(25, 12))
#> user system elapsed
#> 0.054 0.013 0.067
comboCount(25, 12)
#> [1] 5200300
ht(comboGeneral(25, 12))
#> head -->
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
#> [1,] 1 2 3 4 5 6 7 8 9 10 11 12
#> [2,] 1 2 3 4 5 6 7 8 9 10 11 13
#> [3,] 1 2 3 4 5 6 7 8 9 10 11 14
#> [4,] 1 2 3 4 5 6 7 8 9 10 11 15
#> [5,] 1 2 3 4 5 6 7 8 9 10 11 16
#> --------
#> tail -->
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
#> [5200296,] 13 14 15 16 18 19 20 21 22 23 24 25
#> [5200297,] 13 14 15 17 18 19 20 21 22 23 24 25
#> [5200298,] 13 14 16 17 18 19 20 21 22 23 24 25
#> [5200299,] 13 15 16 17 18 19 20 21 22 23 24 25
#> [5200300,] 14 15 16 17 18 19 20 21 22 23 24 25
## And for permutations... over 8 million instantly
system.time(permuteGeneral(13, 7))
#> user system elapsed
#> 0.023 0.014 0.037
permuteCount(13, 7)
#> [1] 8648640
ht(permuteGeneral(13, 7))
#> head -->
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 1 2 3 4 5 6 7
#> [2,] 1 2 3 4 5 6 8
#> [3,] 1 2 3 4 5 6 9
#> [4,] 1 2 3 4 5 6 10
#> [5,] 1 2 3 4 5 6 11
#> --------
#> tail -->
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [8648636,] 13 12 11 10 9 8 3
#> [8648637,] 13 12 11 10 9 8 4
#> [8648638,] 13 12 11 10 9 8 5
#> [8648639,] 13 12 11 10 9 8 6
#> [8648640,] 13 12 11 10 9 8 7
## Factors are preserved
permuteGeneral(factor(c("low", "med", "high"),
levels = c("low", "med", "high"),
ordered = TRUE))
#> [,1] [,2] [,3]
#> [1,] low med high
#> [2,] low high med
#> [3,] med low high
#> [4,] med high low
#> [5,] high low med
#> [6,] high med low
#> Levels: low < med < high
```

There are many problems in combinatorics which require finding
combinations/permutations with repetition. This is easily achieved by
setting `repetition`

to `TRUE`

.

```
fourDays <- weekdays(as.Date("2019-10-09") + 0:3, TRUE)
ht(comboGeneral(fourDays, repetition = TRUE))
#> head -->
#> [,1] [,2] [,3] [,4]
#> [1,] "Wed" "Wed" "Wed" "Wed"
#> [2,] "Wed" "Wed" "Wed" "Thu"
#> [3,] "Wed" "Wed" "Wed" "Fri"
#> [4,] "Wed" "Wed" "Wed" "Sat"
#> [5,] "Wed" "Wed" "Thu" "Thu"
#> --------
#> tail -->
#> [,1] [,2] [,3] [,4]
#> [31,] "Fri" "Fri" "Fri" "Fri"
#> [32,] "Fri" "Fri" "Fri" "Sat"
#> [33,] "Fri" "Fri" "Sat" "Sat"
#> [34,] "Fri" "Sat" "Sat" "Sat"
#> [35,] "Sat" "Sat" "Sat" "Sat"
## When repetition = TRUE, m can exceed length(v)
ht(comboGeneral(fourDays, 8, TRUE))
#> head -->
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
#> [1,] "Wed" "Wed" "Wed" "Wed" "Wed" "Wed" "Wed" "Wed"
#> [2,] "Wed" "Wed" "Wed" "Wed" "Wed" "Wed" "Wed" "Thu"
#> [3,] "Wed" "Wed" "Wed" "Wed" "Wed" "Wed" "Wed" "Fri"
#> [4,] "Wed" "Wed" "Wed" "Wed" "Wed" "Wed" "Wed" "Sat"
#> [5,] "Wed" "Wed" "Wed" "Wed" "Wed" "Wed" "Thu" "Thu"
#> --------
#> tail -->
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
#> [161,] "Fri" "Fri" "Fri" "Fri" "Sat" "Sat" "Sat" "Sat"
#> [162,] "Fri" "Fri" "Fri" "Sat" "Sat" "Sat" "Sat" "Sat"
#> [163,] "Fri" "Fri" "Sat" "Sat" "Sat" "Sat" "Sat" "Sat"
#> [164,] "Fri" "Sat" "Sat" "Sat" "Sat" "Sat" "Sat" "Sat"
#> [165,] "Sat" "Sat" "Sat" "Sat" "Sat" "Sat" "Sat" "Sat"
fibonacci <- c(1L, 2L, 3L, 5L, 8L, 13L, 21L, 34L)
permsFib <- permuteGeneral(fibonacci, 5, TRUE)
ht(permsFib)
#> head -->
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 1 1 1 1 1
#> [2,] 1 1 1 1 2
#> [3,] 1 1 1 1 3
#> [4,] 1 1 1 1 5
#> [5,] 1 1 1 1 8
#> --------
#> tail -->
#> [,1] [,2] [,3] [,4] [,5]
#> [32764,] 34 34 34 34 5
#> [32765,] 34 34 34 34 8
#> [32766,] 34 34 34 34 13
#> [32767,] 34 34 34 34 21
#> [32768,] 34 34 34 34 34
## N.B. class is preserved
class(fibonacci)
#> [1] "integer"
class(permsFib[1, ])
#> [1] "integer"
## Binary representation of all numbers from 0 to 1023
ht(permuteGeneral(0:1, 10, T))
#> head -->
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#> [1,] 0 0 0 0 0 0 0 0 0 0
#> [2,] 0 0 0 0 0 0 0 0 0 1
#> [3,] 0 0 0 0 0 0 0 0 1 0
#> [4,] 0 0 0 0 0 0 0 0 1 1
#> [5,] 0 0 0 0 0 0 0 1 0 0
#> --------
#> tail -->
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#> [1020,] 1 1 1 1 1 1 1 0 1 1
#> [1021,] 1 1 1 1 1 1 1 1 0 0
#> [1022,] 1 1 1 1 1 1 1 1 0 1
#> [1023,] 1 1 1 1 1 1 1 1 1 0
#> [1024,] 1 1 1 1 1 1 1 1 1 1
```

Sometimes, the standard combination/permutation functions don’t quite
get us to our desired goal. For example, one may need all permutations
of a vector with some of the elements repeated a specific number of
times (i.e. a multiset). Consider the following vector
`a <- c(1,1,1,1,2,2,2,7,7,7,7,7)`

and one would like to
find permutations of `a`

of length 6. Using traditional
methods, we would need to generate all permutations, then eliminate
duplicate values. Even considering that `permuteGeneral`

is
very efficient, this approach is clunky and not as fast as it could be.
Observe:

```
getPermsWithSpecificRepetition <- function(z, n) {
b <- permuteGeneral(z, n)
myDupes <- duplicated(b)
b[!myDupes, ]
}
a <- as.integer(c(1, 1, 1, 1, 2, 2, 2, 7, 7, 7, 7, 7))
system.time(test <- getPermsWithSpecificRepetition(a, 6))
#> user system elapsed
#> 1.261 0.022 1.283
```

`freqs`

Situations like this call for the use of the `freqs`

argument. Simply, enter the number of times each unique element is
repeated and Voila!

```
## Using the S3 method for class 'table'
system.time(test2 <- permuteGeneral(table(a), 6))
#> user system elapsed
#> 0.000 0.000 0.001
identical(test, test2)
#> [1] TRUE
```

Here are some more general examples with multisets:

```
## Generate all permutations of a vector with specific
## length of repetition for each element (i.e. multiset)
ht(permuteGeneral(3, freqs = c(1,2,2)))
#> head -->
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 1 2 2 3 3
#> [2,] 1 2 3 2 3
#> [3,] 1 2 3 3 2
#> [4,] 1 3 2 2 3
#> [5,] 1 3 2 3 2
#> --------
#> tail -->
#> [,1] [,2] [,3] [,4] [,5]
#> [26,] 3 2 3 1 2
#> [27,] 3 2 3 2 1
#> [28,] 3 3 1 2 2
#> [29,] 3 3 2 1 2
#> [30,] 3 3 2 2 1
## or combinations of a certain length
comboGeneral(3, 2, freqs = c(1,2,2))
#> [,1] [,2]
#> [1,] 1 2
#> [2,] 1 3
#> [3,] 2 2
#> [4,] 2 3
#> [5,] 3 3
```

Using the parameter `Parallel`

or `nThreads`

,
we can generate combinations/permutations with greater efficiency.

```
library(microbenchmark)
## RcppAlgos uses the "number of threads available minus one" when Parallel is TRUE
RcppAlgos::stdThreadMax()
#> [1] 8
comboCount(26, 13)
#> [1] 10400600
## Compared to combn using 4 threads
microbenchmark(combn = combn(26, 13),
serAlgos = comboGeneral(26, 13),
parAlgos = comboGeneral(26, 13, nThreads = 4),
times = 10,
unit = "relative")
#> Warning in microbenchmark(combn = combn(26, 13), serAlgos = comboGeneral(26, : less
#> accurate nanosecond times to avoid potential integer overflows
#> Unit: relative
#> expr min lq mean median uq max neval cld
#> combn 134.471317 133.255350 100.725417 131.10378 73.933281 66.475301 10 a
#> serAlgos 2.968905 2.973074 2.462118 2.94302 2.117913 1.987334 10 b
#> parAlgos 1.000000 1.000000 1.000000 1.00000 1.000000 1.000000 10 c
## Using 7 cores w/ Parallel = TRUE
microbenchmark(
serial = comboGeneral(20, 10, freqs = rep(1:4, 5)),
parallel = comboGeneral(20, 10, freqs = rep(1:4, 5), Parallel = TRUE),
unit = "relative"
)
#> Unit: relative
#> expr min lq mean median uq max neval cld
#> serial 3.236333 2.853772 2.763475 2.823088 2.77055 1.491217 100 a
#> parallel 1.000000 1.000000 1.000000 1.000000 1.00000 1.000000 100 b
```

`lower`

and `upper`

There are arguments `lower`

and `upper`

that
can be utilized to generate chunks of combinations/permutations without
having to generate all of them followed by subsetting. As the output is
in lexicographical order, these arguments specify where to start and
stop generating. For example, `comboGeneral(5, 3)`

outputs 10
combinations of the vector `1:5`

chosen 3 at a time. We can
set `lower`

to 5 in order to start generation from the
*5 ^{th}* lexicographical combination. Similarly, we can
set

`upper`

to 4 in order to only generate the first 4
combinations. We can also use them together to produce only a certain
chunk of combinations. For example, setting `lower`

to 4 and
`upper`

to 6 only produces the `.Machine$integer.max`

In addition to being useful by avoiding the unnecessary overhead of generating all combination/permutations followed by subsetting just to see a few specific results, lower and upper can be utilized to generate large number of combinations/permutations in parallel (see this stackoverflow post for a real use case). Observe:

```
## Over 3 billion results
comboCount(35, 15)
#> [1] 3247943160
## 10086780 evenly divides 3247943160, otherwise you need to ensure that
## upper does not exceed the total number of results (E.g. see below, we
## would have "if ((x + foo) > 3247943160) {myUpper = 3247943160}" where
## foo is the size of the increment you choose to use in seq()).
system.time(lapply(seq(1, 3247943160, 10086780), function(x) {
temp <- comboGeneral(35, 15, lower = x, upper = x + 10086779)
## do something
x
}))
#> user system elapsed
#> 26.438 11.890 38.333
## Enter parallel
library(parallel)
system.time(mclapply(seq(1, 3247943160, 10086780), function(x) {
temp <- comboGeneral(35, 15, lower = x, upper = x + 10086779)
## do something
x
}, mc.cores = 6))
#> user system elapsed
#> 30.185 15.874 9.810
```

The arguments `lower`

and `upper`

are also
useful when one needs to explore combinations/permutations where the
number of results is large:

```
set.seed(222)
myVec <- rnorm(1000)
## HUGE number of combinations
comboCount(myVec, 50, repetition = TRUE)
#> Big Integer ('bigz') :
#> [1] 109740941767310814894854141592555528130828577427079559745647393417766593803205094888320
## Let's look at one hundred thousand combinations in the range (1e15 + 1, 1e15 + 1e5)
system.time(b <- comboGeneral(myVec, 50, TRUE,
lower = 1e15 + 1,
upper = 1e15 + 1e5))
#> user system elapsed
#> 0.003 0.002 0.004
b[1:5, 45:50]
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 0.5454861 0.4787456 0.7797122 2.004614 -1.257629 -0.7740501
#> [2,] 0.5454861 0.4787456 0.7797122 2.004614 -1.257629 0.1224679
#> [3,] 0.5454861 0.4787456 0.7797122 2.004614 -1.257629 -0.2033493
#> [4,] 0.5454861 0.4787456 0.7797122 2.004614 -1.257629 1.5511027
#> [5,] 0.5454861 0.4787456 0.7797122 2.004614 -1.257629 1.0792094
```

You can also pass user defined functions by utilizing the argument
`FUN`

. This feature’s main purpose is for convenience,
however it is somewhat more efficient than generating all
combinations/permutations and then using a function from the
`apply`

family (N.B. the argument `Parallel`

has
no effect when `FUN`

is employed).

```
funCustomComb = function(n, r) {
combs = comboGeneral(n, r)
lapply(1:nrow(combs), function(x) cumprod(combs[x,]))
}
identical(funCustomComb(15, 8), comboGeneral(15, 8, FUN = cumprod))
#> [1] TRUE
microbenchmark(f1 = funCustomComb(15, 8),
f2 = comboGeneral(15, 8, FUN = cumprod), unit = "relative")
#> Unit: relative
#> expr min lq mean median uq max neval cld
#> f1 5.21106 4.97984 4.616931 4.989338 5.036799 2.015578 100 a
#> f2 1.00000 1.00000 1.000000 1.000000 1.000000 1.000000 100 b
comboGeneral(15, 8, FUN = cumprod, upper = 3)
#> [[1]]
#> [1] 1 2 6 24 120 720 5040 40320
#>
#> [[2]]
#> [1] 1 2 6 24 120 720 5040 45360
#>
#> [[3]]
#> [1] 1 2 6 24 120 720 5040 50400
## An example involving the powerset... Note, we could
## have used the FUN.VALUE parameter here instead of
## calling unlist. See the next section.
unlist(comboGeneral(c("", letters[1:3]), 3,
freqs = c(2, rep(1, 3)),
FUN = function(x) paste(x, collapse = "")))
#> [1] "a" "b" "c" "ab" "ac" "bc" "abc"
```

`FUN.VALUE`

As of version `2.5.0`

, we can make use of
`FUN.VALUE`

which serves as a template for the return value
from `FUN`

. The behavior is nearly identical to
`vapply`

:

```
## Example from earlier involving the power set
comboGeneral(c("", letters[1:3]), 3, freqs = c(2, rep(1, 3)),
FUN = function(x) paste(x, collapse = ""), FUN.VALUE = "a")
#> [1] "a" "b" "c" "ab" "ac" "bc" "abc"
comboGeneral(15, 8, FUN = cumprod, upper = 3, FUN.VALUE = as.numeric(1:8))
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
#> [1,] 1 2 6 24 120 720 5040 40320
#> [2,] 1 2 6 24 120 720 5040 45360
#> [3,] 1 2 6 24 120 720 5040 50400
## Fun example with binary representations... consider the following:
permuteGeneral(0:1, 3, TRUE)
#> [,1] [,2] [,3]
#> [1,] 0 0 0
#> [2,] 0 0 1
#> [3,] 0 1 0
#> [4,] 0 1 1
#> [5,] 1 0 0
#> [6,] 1 0 1
#> [7,] 1 1 0
#> [8,] 1 1 1
permuteGeneral(c(FALSE, TRUE), 3, TRUE, FUN.VALUE = 1,
FUN = function(x) sum(2^(which(rev(x)) - 1)))
#> [1] 0 1 2 3 4 5 6 7
```

`...`

As of version `2.8.3`

, we have added the ability to pass
further arguments to `FUN`

via `...`

.

```
## Again, same example with the power set only this time we
## conveniently pass the additional arguments to paste via '...'
comboGeneral(c("", letters[1:3]), 3, freqs = c(2, rep(1, 3)),
FUN = paste, collapse = "", FUN.VALUE = "a")
#> [1] "a" "b" "c" "ab" "ac" "bc" "abc"
```

This concludes our discussion around user defined functions. There are several nice features that allow the user to more easily get the desired output with fewer function calls as well as fewer keystrokes. This was most clearly seen in our example above with the power set.

We started with wrapping our call to `comboGeneral`

with
`unlist`

, which was alleviated by the parameter
`FUN.VALUE`

. We then further simplified our usage of
`FUN`

by allowing additional arguments to be passed via
`...`

.

As of version `2.8.3`

, we have added several S3 methods
for convenience.

Take our earlier example where we were talking about multisets.

```
a <- as.integer(c(1, 1, 1, 1, 2, 2, 2, 7, 7, 7, 7, 7))
## Explicitly utilizing the freqs argument and determining the unique
## values for v... Still works, but clunky
t1 <- permuteGeneral(rle(a)$values, 6, freqs = rle(a)$lengths)
## Now using the table method... much cleaner
t2 <- permuteGeneral(table(a), 6)
identical(t1, t2)
#> [1] TRUE
```

There are other S3 methods defined that simplify the interface. Take
for example the case when we want to pass a character vector. We know
underneath the hood, character vectors are not thread safe so the
`Parallel`

and `nThreads`

argument are ignored. We
also know that the constraints parameters are only applicable to numeric
vectors. For these reason, our default method’s interface is greatly
simplified:

We see only the necessary options. With numeric types, the options are more numerous:

There is also a `list`

method that allows one to find
combinations or permutations of lists:

```
comboGeneral(
list(
numbers = rnorm(4),
states = state.abb[1:5],
some_data = data.frame(a = c('a', 'b'), b = c(10, 100))
),
m = 2
)
#> [[1]]
#> [[1]]$numbers
#> [1] -1.9376332 0.2583997 -0.7198657 -0.8985872
#>
#> [[1]]$states
#> [1] "AL" "AK" "AZ" "AR" "CA"
#>
#>
#> [[2]]
#> [[2]]$numbers
#> [1] -1.9376332 0.2583997 -0.7198657 -0.8985872
#>
#> [[2]]$some_data
#> a b
#> 1 a 10
#> 2 b 100
#>
#>
#> [[3]]
#> [[3]]$states
#> [1] "AL" "AK" "AZ" "AR" "CA"
#>
#> [[3]]$some_data
#> a b
#> 1 a 10
#> 2 b 100
```

This feature was inspired by ggrothendieck here: Issue 20.