`vignettes/GeneralCombinatorics.Rmd`

`GeneralCombinatorics.Rmd`

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, Integer 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)
## 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
0.101 0.048 0.151
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(permuteCount(13, 7))
user system elapsed
0 0 0
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 <- c(1,1,1,1,2,2,2,7,7,7,7,7)
system.time(test <- getPermsWithSpecificRepetition(a, 6))
user system elapsed
2.317 0.059 2.385
```

`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!

```
system.time(test2 <- permuteGeneral(unique(a), 6, freqs = rle(a)$lengths))
user system elapsed
0 0 0
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
## Compared to combn using 4 threads
microbenchmark(combn = combn(25, 10),
serAlgos = comboGeneral(25, 10),
parAlgos = comboGeneral(25, 10, nThreads = 4),
times = 10,
unit = "relative")
Unit: relative
expr min lq mean median uq max neval
combn 140.873986 143.411149 66.043305 128.898187 120.543678 18.503645 10
serAlgos 2.295844 2.302306 2.062132 2.117214 7.153547 1.080612 10
parAlgos 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 10
## 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: milliseconds
expr min lq mean median uq max neval
serial 3.031987 2.990349 2.969633 3.000991 2.992326 2.014025 100
parallel 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 100
set.seed(2187)
bigVec <- sort(sample(10^7, 600))
## Use it with any of the combinatorial functions
system.time(permuteGeneral(bigVec, 3, Parallel = T))
user system elapsed
1.067 3.645 0.902
prettyNum(permuteCount(bigVec, 3), big.mark = ",")
[1] "214,921,200"
```

`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 ```
comboGeneral(5, 3, lower = 4, upper = 6)
[,1] [,2] [,3]
[1,] 1 3 4
[2,] 1 3 5
[3,] 1 4 5
## is equivalent to the following:
comboGeneral(5, 3)[4:6, ]
[,1] [,2] [,3]
[1,] 1 3 4
[2,] 1 3 5
[3,] 1 4 5
```

`.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
99.495 49.993 149.467
## 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
125.361 85.916 35.641
```

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
0.008 0.000 0.008
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
f1 6.948574 6.955633 6.960554 6.961681 6.515623 9.688503 100
f2 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 100
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
unlist(comboGeneral(c("", letters[1:3]), 3,
freqs = c(2, rep(1, 3)),
FUN = function(x) paste0(x, collapse = "")))
[1] "a" "b" "c" "ab" "ac" "bc" "abc"
```

Given a list of vectors, *v _{1}*,

For example, lets say we have: `v1 = 1:4`

and `v2 = 2:5`

. The cartestion product is given by `expand.grid(v1, v2)`

:

```
expand.grid(1:4, 2:5)
Var1 Var2
1 1 2
2 2 2
3 3 2 <-- Same as row 6
4 4 2 <-- Same as row 10
5 1 3
6 2 3 <-- Same as row 3
7 3 3
8 4 3 <-- Same as row 11
9 1 4
10 2 4 <-- Same as row 4
11 3 4 <-- Same as row 8
12 4 4
13 1 5
14 2 5
15 3 5
16 4 5
```

If we don’t care about order, the following row pairs are considered equal and can therefore be pruned to obtain our desired results:

- (r3, r6)
- (r4, r10)
- (r8, r11)

With `comboGrid`

no duplicates are generated:

```
comboGrid(1:4, 2:5)
Var1 Var2
[1,] 1 2
[2,] 1 3
[3,] 1 4
[4,] 1 5
[5,] 2 2
[6,] 2 3
[7,] 2 4
[8,] 2 5
[9,] 3 3
[10,] 3 4
[11,] 3 5
[12,] 4 4
[13,] 4 5
```

Note that the order of `expand.grid`

and `comboGrid`

differ. The order of `comboGrid`

is lexicographical meaning that the last column will vary the fastest whereas with `expand.grid`

, the first column will vary the fastest.

You will also note that the output of `expand.grid`

is a `data.frame`

whereas with `comboGrid`

, we get a `matrix`

. With `comboGrid`

, we only get a `data.frame`

when the classes of each vector are different as generally speaking, working with matrices is preferrable.

With the small example above, we only had to filter out 3 out of 16 total results (less than 20%). That isn’t that bad. If this was the general case, we might as well just stick with `expand.grid`

as it is very efficient. Unfortunately, this is not the general case and as the number of vectors with overlap increases, filtering will become impractical.

Consider the following example:

```
pools = list(c(1, 10, 14, 6),
c(7, 2, 4, 8, 3, 11, 12),
c(11, 3, 13, 4, 15, 8, 6, 5),
c(10, 1, 3, 2, 9, 5, 7),
c(1, 5, 10, 3, 8, 14),
c(15, 3, 7, 10, 4, 5, 8, 6),
c(14, 9, 11, 15),
c(7, 6, 13, 14, 10, 11, 9, 4),
c(6, 3, 2, 14, 7, 12, 9),
c(6, 11, 2, 5, 15, 7))
## If we used expand.grid, we would have to filter
## more than 100 million results
prod(lengths(pools))
[1] 101154816
## With comboGrid, this is no problem
system.time(myCombs <- comboGrid(pools))
user system elapsed
0.958 0.069 1.027
print(object.size(myCombs), unit = "Mb")
92 Mb
ht(myCombs)
head -->
Var1 Var2 Var3 Var4 Var5 Var6 Var7 Var8 Var9 Var10
[1,] 1 2 3 1 1 3 9 4 2 2
[2,] 1 2 3 1 1 3 9 4 2 5
[3,] 1 2 3 1 1 3 9 4 2 6
[4,] 1 2 3 1 1 3 9 4 2 7
[5,] 1 2 3 1 1 3 9 4 2 11
--------
tail -->
Var1 Var2 Var3 Var4 Var5 Var6 Var7 Var8 Var9 Var10
[1205736,] 14 12 15 10 14 15 15 11 12 15
[1205737,] 14 12 15 10 14 15 15 13 12 15
[1205738,] 14 12 15 10 14 15 15 13 14 15
[1205739,] 14 12 15 10 14 15 15 14 12 15
[1205740,] 14 12 15 10 14 15 15 14 14 15
## This is just the time to create the cartesian product
## Generating keys, then filtering will take much more time
system.time(cartProd <- expand.grid(pools))
user system elapsed
7.531 2.899 10.431
## Creates huge object
print(object.size(cartProd), unit = "Mb")
7717.5 Mb
## What if we want results with unique values...
## Simply set repetition = FALSE
system.time(myCombs <- comboGrid(pools, repetition = FALSE))
user system elapsed
0.029 0.007 0.035
ht(myCombsNoRep)
head -->
Var1 Var2 Var3 Var4 Var5 Var6 Var7 Var8 Var9 Var10
[1,] 1 2 3 5 8 4 9 6 7 11
[2,] 1 2 3 5 8 4 9 6 7 15
[3,] 1 2 3 5 8 4 9 6 12 7
[4,] 1 2 3 5 8 4 9 6 12 11
[5,] 1 2 3 5 8 4 9 6 12 15
--------
tail -->
Var1 Var2 Var3 Var4 Var5 Var6 Var7 Var8 Var9 Var10
[2977,] 14 3 4 5 8 15 9 13 12 11
[2978,] 14 3 4 7 5 15 9 13 12 11
[2979,] 14 3 4 7 8 15 9 13 12 11
[2980,] 14 3 5 7 8 15 9 13 12 11
[2981,] 14 4 5 7 8 15 9 13 12 11
```

The function `comboGrid`

was highly inspired by the following question on stackoverflow:

Currenlty, the underlying algorithm is not the gold standard. By that, we mean that results are not generated one by one. Efforts are underway to achieve this, but up until this point it has proven quite difficult (See the comprehensive answer by Tim Peters (yes, that Tim Peters)).

The algorithm in `comboGrid`

leverages The Fundamental Theorem of Arithmetic to efficiently generate keys that will be used in a hash function to determine if a particular combination of elements have been encountered. For greater efficiency, we make use of deduplication as user2357112 suggests.

`comboGroups`

Given a vector of length *n* and *k* groups, where *k* divides *n*, each group is comprised of a combination of the vector chosen *g = n / k* at a time. As is stated in the documentation (see `?comboGroups`

), these can be constructed by first generating all permutations of the vector and subsequently removing entries with permuted groups. Let us consider the following example. Given `v = 1:12`

, generate all partitions `v`

into 3 groups each of size 4.

```
funBruteGrp <- function(myLow = 1, myUp) {
mat <- do.call(rbind, permuteGeneral(12, lower = myLow, upper = myUp,
FUN = function(x) {
sapply(seq(0, 8, 4), function(y) {
paste0(c("(", x[(y + 1):(y + 4)], ")"), collapse = " ")
})
}))
colnames(mat) <- paste0("Grp", 1:3)
rownames(mat) <- myLow:myUp
mat
}
## All of these are the same as only the 3rd group is being permuted
funBruteGrp(myUp = 6)
Grp1 Grp2 Grp3
1 "( 1 2 3 4 )" "( 5 6 7 8 )" "( 9 10 11 12 )"
2 "( 1 2 3 4 )" "( 5 6 7 8 )" "( 9 10 12 11 )"
3 "( 1 2 3 4 )" "( 5 6 7 8 )" "( 9 11 10 12 )"
4 "( 1 2 3 4 )" "( 5 6 7 8 )" "( 9 11 12 10 )"
5 "( 1 2 3 4 )" "( 5 6 7 8 )" "( 9 12 10 11 )"
6 "( 1 2 3 4 )" "( 5 6 7 8 )" "( 9 12 11 10 )"
## We found our second distinct partition
funBruteGrp(myLow = 23, myUp = 26)
Grp1 Grp2 Grp3
23 "( 1 2 3 4 )" "( 5 6 7 8 )" "( 12 11 9 10 )"
24 "( 1 2 3 4 )" "( 5 6 7 8 )" "( 12 11 10 9 )"
25 "( 1 2 3 4 )" "( 5 6 7 9 )" "( 8 10 11 12 )" ## <<-- 2nd distinct partition of groups
26 "( 1 2 3 4 )" "( 5 6 7 9 )" "( 8 10 12 11 )"
funBruteGrp(myLow = 48, myUp = 50)
Grp1 Grp2 Grp3
48 "( 1 2 3 4 )" "( 5 6 7 9 )" "( 12 11 10 8 )"
49 "( 1 2 3 4 )" "( 5 6 7 10 )" "( 8 9 11 12 )" ## <<-- 3rd distinct partition of groups
50 "( 1 2 3 4 )" "( 5 6 7 10 )" "( 8 9 12 11 )"
```

We are starting to see a pattern. Each new partition is exactly 24 spots away. This makes sense as there are `factorial(4) = 24`

permutations of size 4. Now, this is an oversimplification as if we simply generate every *24 ^{th}* permutation, we will still get duplication as they start to carry over to the other groups. Observe:

```
do.call(rbind, lapply(seq(1, 145, 24), function(x) {
funBruteGrp(myLow = x, myUp = x)
}))
Grp1 Grp2 Grp3
1 "( 1 2 3 4 )" "( 5 6 7 8 )" "( 9 10 11 12 )"
25 "( 1 2 3 4 )" "( 5 6 7 9 )" "( 8 10 11 12 )"
49 "( 1 2 3 4 )" "( 5 6 7 10 )" "( 8 9 11 12 )"
73 "( 1 2 3 4 )" "( 5 6 7 11 )" "( 8 9 10 12 )"
97 "( 1 2 3 4 )" "( 5 6 7 12 )" "( 8 9 10 11 )"
121 "( 1 2 3 4 )" "( 5 6 8 7 )" "( 9 10 11 12 )" ## <<-- This is the same as the 1st
145 "( 1 2 3 4 )" "( 5 6 8 9 )" "( 7 10 11 12 )" ## partition. The only difference is
169 "( 1 2 3 4 )" "( 5 6 8 10 )" "( 7 9 11 12 )" ## that the 2nd Grp has been permuted
```

This only gets more muddled as the number of groups increases. It is also very inefficient, however this exercise hopefully serves to better illustrate these structures.

The algorithm in `comboGroups`

avoids all of this duplication by implementing a novel algorithm akin to std::next_permutation from the algorithm library in `C++`

.

```
system.time(comboGroups(12, numGroups = 3))
user system elapsed
0 0 0
ht(comboGroups(12, numGroups = 3))
head -->
Grp1 Grp1 Grp1 Grp1 Grp2 Grp2 Grp2 Grp2 Grp3 Grp3 Grp3 Grp3
[1,] 1 2 3 4 5 6 7 8 9 10 11 12
[2,] 1 2 3 4 5 6 7 9 8 10 11 12
[3,] 1 2 3 4 5 6 7 10 8 9 11 12
[4,] 1 2 3 4 5 6 7 11 8 9 10 12
[5,] 1 2 3 4 5 6 7 12 8 9 10 11
--------
tail -->
Grp1 Grp1 Grp1 Grp1 Grp2 Grp2 Grp2 Grp2 Grp3 Grp3 Grp3 Grp3
[5771,] 1 10 11 12 2 5 8 9 3 4 6 7
[5772,] 1 10 11 12 2 6 7 8 3 4 5 9
[5773,] 1 10 11 12 2 6 7 9 3 4 5 8
[5774,] 1 10 11 12 2 6 8 9 3 4 5 7
[5775,] 1 10 11 12 2 7 8 9 3 4 5 6
```

Just as in `combo/permuteGeneral`

, we can utilize the arguments `lower`

, `upper`

, `Parallel`

, and `nThreads`

.

```
comboGroupsCount(30, 6)
Big Integer ('bigz') :
[1] 123378675083039376
system.time(a1 <- comboGroups(30, numGroups = 6,
lower = "123378675000000000",
upper = "123378675005000000"))
user system elapsed
0.299 0.113 0.412
## Use specific number of threads
system.time(a2 <- comboGroups(30, numGroups = 6,
lower = "123378675000000000",
upper = "123378675005000000", nThreads = 4))
user system elapsed
0.358 0.197 0.146
## Use n - 1 number of threads (in this case, there are 7)
system.time(a3 <- comboGroups(30, numGroups = 6,
lower = "123378675000000000",
upper = "123378675005000000", Parallel = TRUE))
user system elapsed
0.466 0.307 0.118
identical(a1, a2)
[1] TRUE
identical(a1, a3)
[1] TRUE
```

There is one additional argument (i.e. `retType`

) not present in the other two general functions that allows the user to specify the type of object returned. The user can select between `"matrix"`

(the default) and `"3Darray"`

. This structure has a natural connection to 3D space. We have a particular result (*1 ^{st}* dimension) broken down into groups (

```
my3D <- comboGroups(factor(month.abb), 4, retType = "3Darray")
my3D[1, , ]
Grp1 Grp2 Grp3 Grp4
[1,] Jan Apr Jul Oct
[2,] Feb May Aug Nov
[3,] Mar Jun Sep Dec
Levels: Apr Aug Dec Feb Jan Jul Jun Mar May Nov Oct Sep
comboGroupsCount(12, 4)
[1] 15400
my3D[15400, , ]
Grp1 Grp2 Grp3 Grp4
[1,] Jan Feb Mar Apr
[2,] Nov Sep Jul May
[3,] Dec Oct Aug Jun
Levels: Apr Aug Dec Feb Jan Jul Jun Mar May Nov Oct Sep
```