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:
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 5th 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 4th, 5th, and 6th lexicographical combinations. Observe:
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 #> 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
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