Sample the Cartesian Product

  • Generate a specific (lexicographically) or random sample of the Cartesian product of the input vectors.

  • Produce results in parallel using the nThreads arguments.

  • GMP support allows for exploration where the number of results is large.

expandGridSample(
    ..., n = NULL, sampleVec = NULL, seed = NULL,
    nThreads = NULL, namedSample = FALSE, return_df = FALSE
)

Arguments

...

vectors, factors or a list containing these. (See ?expand.grid).

n

Number of results to return. The default is NULL.

sampleVec

A vector of numbers representing the lexicographical partition of groups to return. Accepts vectors of class bigz as well as vectors of characters

seed

Random seed initialization. The default is NULL. N.B. If the gmp library is needed, this parameter must be set in order to have reproducible results (E.g set.seed() has no effect in these cases).

nThreads

Specific number of threads to be used. The default is NULL.

namedSample

Logical flag. If TRUE, rownames corresponding to the lexicographical result, will be added to the returned matrix. The default is FALSE.

return_df

Logical flag to force the output to be a data.frame. The default is FALSE.

Details

These algorithms rely on efficiently generating the \(n^{th}\) lexicographical result.

Value

When all of the input is of the same type, by default expandGridSample produces a matrix (a data.frame otherwise). This can be ignored by setting the argument return_df = TRUE.

Author

Joseph Wood

Examples

## input vectors
lst = list(factor(state.abb), euro, islands)

## generate 10 random products
expandGridSample(lst, n = 10, seed = 123)
#>    Var1       Var2 Var3
#> 1    OK 1936.27000  306
#> 2    OK   40.33990  306
#> 3    WV    6.55957   13
#> 4    CO 1936.27000   73
#> 5    AR    6.55957   30
#> 6    WI 1936.27000   29
#> 7    CT    5.94573   25
#> 8    MN   13.76030   33
#> 9    GA   13.76030   84
#> 10   IL   40.33990  227

## using sampleVec to generate specific results
expandGridSample(lst, sampleVec = c(1, 100, 1e3))
#>   Var1     Var2  Var3
#> 1   AL 13.76030 11506
#> 2   AL  1.95583  2968
#> 3   AK  2.20371    16

all.equal(expandGridSample(lst, sampleVec = 1:expandGridCount(lst)),
         expandGrid(lst))
#> [1] TRUE

## Examples with enormous number of total results
big_lst = Map(function(x, y) x:y, 8:33, 15:40)
num = expandGridCount(big_lst)
gmp::log2.bigz(num)
#> [1] 78
## [1] 78

first = gmp::urand.bigz(n = 1, size = 78, seed = 123)
#> Seed initialisation
mySamp = do.call(c, lapply(0:10, function(x) gmp::add.bigz(first, x)))

class(mySamp)
#> [1] "bigz"
## [1] "bigz"

## using the sampling function
cartSamp = expandGridSample(big_lst, sampleVec = mySamp)

## using the standard function
cartGeneral = expandGrid(big_lst,
                         lower = first,
                         upper = gmp::add.bigz(first, 10))

identical(cartSamp, cartGeneral)
#> [1] TRUE
## [1] TRUE