Convert an ftable or an array Object to a data.table

While convenient methods exist for converting tables and other objects to data.tabless, such methods do not exist for converting an ftable to a data.table. An ftable is essentially a matrix with attributes for the rows and columns, which can be nested.

ftable2dt(inarray, direction = "wide")

Arguments

inarray	The input ftable or array.
direction	Should the reslut be "wide" (with multiple measurement. columns) or "long" (with a single measurement column)? Defaults to "wide".

Value

A data.table

Note

If the array has no dimnames, names would be added using the provideDimnames function. Defaults to reshape2::melt() if the input is a simple matrix and not a multidimensional array.

References

http://stackoverflow.com/a/11143126/1270695

Author

Ananda Mahto

Examples

x <- ftable(Titanic, row.vars = 1:3)
x
#>                    Survived  No Yes
#> Class Sex    Age                   
#> 1st   Male   Child            0   5
#>              Adult          118  57
#>       Female Child            0   1
#>              Adult            4 140
#> 2nd   Male   Child            0  11
#>              Adult          154  14
#>       Female Child            0  13
#>              Adult           13  80
#> 3rd   Male   Child           35  13
#>              Adult          387  75
#>       Female Child           17  14
#>              Adult           89  76
#> Crew  Male   Child            0   0
#>              Adult          670 192
#>       Female Child            0   0
#>              Adult            3  20
ftable2dt(x)
#>     Class    Sex   Age  No Yes
#>  1:   1st Female Adult   4 140
#>  2:   1st Female Child   0   1
#>  3:   1st   Male Adult 118  57
#>  4:   1st   Male Child   0   5
#>  5:   2nd Female Adult  13  80
#>  6:   2nd Female Child   0  13
#>  7:   2nd   Male Adult 154  14
#>  8:   2nd   Male Child   0  11
#>  9:   3rd Female Adult  89  76
#> 10:   3rd Female Child  17  14
#> 11:   3rd   Male Adult 387  75
#> 12:   3rd   Male Child  35  13
#> 13:  Crew Female Adult   3  20
#> 14:  Crew Female Child   0   0
#> 15:  Crew   Male Adult 670 192
#> 16:  Crew   Male Child   0   0
ftable2dt(x, direction = "long")
#>     Class    Sex   Age Survived value
#>  1:   1st   Male Child       No     0
#>  2:   1st   Male Adult       No   118
#>  3:   1st Female Child       No     0
#>  4:   1st Female Adult       No     4
#>  5:   2nd   Male Child       No     0
#>  6:   2nd   Male Adult       No   154
#>  7:   2nd Female Child       No     0
#>  8:   2nd Female Adult       No    13
#>  9:   3rd   Male Child       No    35
#> 10:   3rd   Male Adult       No   387
#> 11:   3rd Female Child       No    17
#> 12:   3rd Female Adult       No    89
#> 13:  Crew   Male Child       No     0
#> 14:  Crew   Male Adult       No   670
#> 15:  Crew Female Child       No     0
#> 16:  Crew Female Adult       No     3
#> 17:   1st   Male Child      Yes     5
#> 18:   1st   Male Adult      Yes    57
#> 19:   1st Female Child      Yes     1
#> 20:   1st Female Adult      Yes   140
#> 21:   2nd   Male Child      Yes    11
#> 22:   2nd   Male Adult      Yes    14
#> 23:   2nd Female Child      Yes    13
#> 24:   2nd Female Adult      Yes    80
#> 25:   3rd   Male Child      Yes    13
#> 26:   3rd   Male Adult      Yes    75
#> 27:   3rd Female Child      Yes    14
#> 28:   3rd Female Adult      Yes    76
#> 29:  Crew   Male Child      Yes     0
#> 30:  Crew   Male Adult      Yes   192
#> 31:  Crew Female Child      Yes     0
#> 32:  Crew Female Adult      Yes    20
#>     Class    Sex   Age Survived value

if (FALSE) {
dims <- c(2, 1, 2, 3, 2)
set.seed(1)
M <- `dim<-`(sample(100, prod(dims), TRUE), dims)
N <- O <- `dimnames<-`(M, lapply(dims, function(x) 
                                 c(letters, LETTERS)[seq_len(x)]))
names(attributes(O)$dimnames) <- c("first", "second", "third", 
                                   "fourth", "fifth")

ftable2dt(M)
ftable2dt(N)
ftable2dt(O)
ftable2dt(M, "long")
ftable2dt(N, "long")
ftable2dt(O, "long")
}