Generate partitions of a vector into groups of equal size. See Create Combinations in R by Groups on http://stackoverflow.com for a direct use case.
Produce results in parallel using the Parallel
or nThreads
arguments.
GMP support allows for exploration where the number of results is large.
The output is in lexicographical order by groups.
comboGroups(v, numGroups, retType = "matrix",
lower = NULL, upper = NULL, Parallel = FALSE,
nThreads = NULL)
Source vector. If v
is a positive integer, it will be converted to the sequence 1:v
. If v
is a negative integer, it will be converted to the sequence v:-1
. All atomic types are supported (See is.atomic
).
An Integer. The number of groups that the vector will be partitioned into. Must divide the length of v (if v is a vector) or v (if v is a scalar).
A string, "3Darray" or "matrix", that determines the shape of the output. The default is "matrix".
The lower bound. Partitions of groups are generated lexicographically, thus utilizing this argument will determine which specific result to start generating from (e.g. comboGroups(8, 2, lower = 30)
is equivalent to comboGroups(8, 2)[30:comboGroupsCount(8, 2), ]
). This argument along with upper
is very useful for generating results in chunks allowing for easy parallelization.
The upper bound. Similar to lower
, however this parameter allows the user to stop generation at a specific result (e.g. comboGroups(8, 2, upper = 5)
is equivalent to comboGroups(8, 2)[1:5, ]
)
Logical value indicating whether results should be generated in parallel using \(n - 1\) threads, where \(n\) is the maximum number of threads. The default is FALSE
. If nThreads
is not NULL
, it will be given preference (e.g. if user has 8 threads with Parallel = TRUE
and nThreads = 4
, only 4 threads will be spawned). If your system is single-threaded, the arguments Parallel
and nThreads
are ignored.
Specific number of threads to be used. The default is NULL
. See Parallel
.
By default, a matrix is returned with column names corresponding to the associated group. If retType = "3Darray"
, a 3D array is returned.
Conceptually, this problem can be viewed as generating all permutations of the vector v
and removing the within group permutations. To illustrate this, let us consider the case of generating partitions of 1:8
into 2 groups of size 4.
To begin, generate the permutations of 1:8
and group the first/last four elements of each row.
Grp1 | Grp2 | |||||||
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | |
R1 | | 1 | 2 | 3 | 4 | | | 5 | 6 | 7 | 8 | |
R2 | | 1 | 2 | 3 | 4 | | | 5 | 6 | 8 | 7 | |
R3 | | 1 | 2 | 3 | 4 | | | 5 | 7 | 6 | 8 | |
R4 | | 1 | 2 | 3 | 4 | | | 5 | 7 | 8 | 6 | |
R5 | | 1 | 2 | 3 | 4 | | | 5 | 8 | 6 | 7 | |
Note that the permutations above are equivalent partitions of 2 groups of size 4 as only the last four elements are permuted. If we look at at the \(25^{th}\) lexicographical permutation, we observe our second distinct partition.
Grp1 | Grp2 | |||||||
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | |
R24 | | 1 | 2 | 3 | 4 | | | 8 | 7 | 6 | 5 | |
R25 | | 1 | 2 | 3 | 5 | | | 4 | 6 | 7 | 8 | |
R26 | | 1 | 2 | 3 | 5 | | | 4 | 6 | 8 | 7 | |
R27 | | 1 | 2 | 3 | 5 | | | 4 | 7 | 6 | 8 | |
Continuing on, we will reach the \(3,457^{th}\) lexicographical permutation, which represents the last result:
Grp1 | Grp2 | |||||||
C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | |
R3454 | | 1 | 6 | 7 | 5 | | |8 | 3 | 4 | 2 | |
R3455 | | 1 | 6 | 7 | 5 | | |8 | 4 | 2 | 3 | |
R3456 | | 1 | 6 | 7 | 5 | | |8 | 4 | 3 | 2 | |
R3457 | | 1 | 6 | 7 | 8 | | | 2 | 3 | 4 | 5 | |
For this small example, the method above will not be that computationally expensive. In fact, there are only 35 total partitions of 1:8
into 2 groups of size 4 out of a possible factorial(8) = 40320
permutations. However, just doubling the size of the vector will make this approach infeasible as there are over 10 trillion permutations of 1:16
.
The algorithm in comboGroups
avoids these duplicate partitions of groups by utilizing an efficient algorithm analogous to the std::next_permutation found in the standard algorithm library in C++.
# NOT RUN {
## return a matrix
comboGroups(8, 2)
## or a 3 dimensional array
temp = comboGroups(8, 2, "3Darray")
## view the first partition
temp[1, , ]
# }
Run the code above in your browser using DataLab