Recreates the data.frame out of a contingency table x.

Untable(x, ...)

# S3 method for data.frame
Untable(x, freq = "Freq", rownames = NULL, ...)

# S3 method for default
Untable(x, dimnames = NULL, type = NULL, rownames = NULL, colnames = NULL, ...)

Arguments

x

a numeric vector, a matrix, a table or a data.frame. If x is a vector, a matrix or a table it is interpreted as frequencies which are to be inflated to the original list.
If x is a data.frame it is interpreted as a table in frequency form (containing one or more factors and a frequency variable).

dimnames

the dimension names of x to be used for expanding. Can be used to expand a weight vector to its original values. If set to NULL (default) the dimnames of x will be used.

type

defines the data type generated. This allows to directly define factors or ordered factors, but also numeric values. See examples.

rownames

A names vector for the rownames of the resulting data.frame. If set to NULL (default) the names will be defined according to the table's dimnames.

colnames

A names vector for the colnames of the resulting data.frame. If set to NULL (default) the names will be defined according to the table's dimnames.

freq

character, the name of the frequency variable in case x is a data.frame.

...

further arguments passed to or from functions (not used here).

Details

For x being a vector this reduces to rep(..., n) with n as vector (which is not supported by rep()). NAs in the table will be treated as 0 without raising an error.

Value

a data.frame with the detailed data (even if x was a 1-dimensional table)

Author

Andri Signorell <andri@signorell.net>

See also

Examples

d.titanic <- Untable(Titanic)
str(d.titanic)
#> 'data.frame':	2201 obs. of  4 variables:
#>  $ Class   : Factor w/ 4 levels "1st","2nd","3rd",..: 3 3 3 3 3 3 3 3 3 3 ...
#>  $ Sex     : Factor w/ 2 levels "Male","Female": 1 1 1 1 1 1 1 1 1 1 ...
#>  $ Age     : Factor w/ 2 levels "Child","Adult": 1 1 1 1 1 1 1 1 1 1 ...
#>  $ Survived: Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
#>  - attr(*, "out.attrs")=List of 2
#>   ..$ dim     : Named int [1:4] 4 2 2 2
#>   .. ..- attr(*, "names")= chr [1:4] "Class" "Sex" "Age" "Survived"
#>   ..$ dimnames:List of 4
#>   .. ..$ Class   : chr [1:4] "Class=1st" "Class=2nd" "Class=3rd" "Class=Crew"
#>   .. ..$ Sex     : chr [1:2] "Sex=Male" "Sex=Female"
#>   .. ..$ Age     : chr [1:2] "Age=Child" "Age=Adult"
#>   .. ..$ Survived: chr [1:2] "Survived=No" "Survived=Yes"

# ... not the same as:
data.frame(Titanic)
#>    Class    Sex   Age Survived Freq
#> 1    1st   Male Child       No    0
#> 2    2nd   Male Child       No    0
#> 3    3rd   Male Child       No   35
#> 4   Crew   Male Child       No    0
#> 5    1st Female Child       No    0
#> 6    2nd Female Child       No    0
#> 7    3rd Female Child       No   17
#> 8   Crew Female Child       No    0
#> 9    1st   Male Adult       No  118
#> 10   2nd   Male Adult       No  154
#> 11   3rd   Male Adult       No  387
#> 12  Crew   Male Adult       No  670
#> 13   1st Female Adult       No    4
#> 14   2nd Female Adult       No   13
#> 15   3rd Female Adult       No   89
#> 16  Crew Female Adult       No    3
#> 17   1st   Male Child      Yes    5
#> 18   2nd   Male Child      Yes   11
#> 19   3rd   Male Child      Yes   13
#> 20  Crew   Male Child      Yes    0
#> 21   1st Female Child      Yes    1
#> 22   2nd Female Child      Yes   13
#> 23   3rd Female Child      Yes   14
#> 24  Crew Female Child      Yes    0
#> 25   1st   Male Adult      Yes   57
#> 26   2nd   Male Adult      Yes   14
#> 27   3rd   Male Adult      Yes   75
#> 28  Crew   Male Adult      Yes  192
#> 29   1st Female Adult      Yes  140
#> 30   2nd Female Adult      Yes   80
#> 31   3rd Female Adult      Yes   76
#> 32  Crew Female Adult      Yes   20


tab <- table(set1=sample(letters[1:5], size=40, replace=TRUE), 
             set2=sample(letters[11:15], size=40, replace=TRUE))
Untable(tab)
#>    set1 set2
#> 1     a    k
#> 2     a    k
#> 3     b    k
#> 4     d    k
#> 5     d    k
#> 6     d    k
#> 7     d    k
#> 8     a    l
#> 9     a    l
#> 10    c    l
#> 11    c    l
#> 12    c    l
#> 13    c    l
#> 14    c    l
#> 15    c    l
#> 16    d    l
#> 17    e    l
#> 18    a    m
#> 19    a    m
#> 20    b    m
#> 21    c    m
#> 22    d    m
#> 23    b    n
#> 24    b    n
#> 25    b    n
#> 26    c    n
#> 27    d    n
#> 28    e    n
#> 29    b    o
#> 30    b    o
#> 31    c    o
#> 32    c    o
#> 33    c    o
#> 34    c    o
#> 35    c    o
#> 36    d    o
#> 37    e    o
#> 38    e    o
#> 39    e    o
#> 40    e    o


# return a numeric vector by setting type and coerce to a vector by [,]
Untable(c(6,2,2), type="as.numeric")[,]
#>  [1] 1 1 1 1 1 1 2 2 3 3


# how to produce the original list based on frequencies, given as a data.frame
d.freq <- data.frame(xtabs(Freq ~ Sex + Survived, data=Titanic))

# a data list with each individual
d.data <- Untable( xtabs(c(1364, 126, 367, 344) ~ ., 
             expand.grid(levels(d.freq$Sex),levels(d.freq$Survived)))) 
head(d.data)
#>   Var1 Var2
#> 1 Male   No
#> 2 Male   No
#> 3 Male   No
#> 4 Male   No
#> 5 Male   No
#> 6 Male   No

# expand a weights vector
Untable(c(1,4,5), dimnames=list(c("Zurich","Berlin","London")))
#>      Var1
#> 1  Zurich
#> 2  Berlin
#> 3  Berlin
#> 4  Berlin
#> 5  Berlin
#> 6  London
#> 7  London
#> 8  London
#> 9  London
#> 10 London

# and the same with a numeric vector 
Untable(c(1,4,5), dimnames=list(c(5,10,15)), type="as.numeric")[,]
#>  [1]  5 10 10 10 10 15 15 15 15 15
# ... which again is nothing else than
rep(times=c(1,4,5), x=c(5,10,15))
#>  [1]  5 10 10 10 10 15 15 15 15 15

# the data.frame interface
d.freq <- data.frame(f1=c("A","A","B","B"), f2=c("C","D","C","D"), Freq=c(1,2,3,4))
Untable(d.freq)
#>    f1 f2
#> 1   A  C
#> 2   A  D
#> 3   A  D
#> 4   B  C
#> 5   B  C
#> 6   B  C
#> 7   B  D
#> 8   B  D
#> 9   B  D
#> 10  B  D