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Keeps only joint distribution (removes '.total').

Source: R/distrr_funs.R
only_joint.Rd

Removes all the rows where variables have value .total.

Usage

only_joint(.cube, .total = "Totale", .variables = NULL)

Arguments

.cube

a datacube with 'Totale' modalities

.total

modality to eliminate (filter out) (default: "Totale")

.variables

a character vector with the names of the categorical variables

Value

a subset of the data cube with only the combinations of all variables modalities, without the "margins".

Examples

data(invented_wages)
str(invented_wages)
#> tibble [1,000 × 5] (S3: tbl_df/tbl/data.frame)
#>  $ gender        : Factor w/ 2 levels "men","women": 1 2 1 2 1 1 1 2 2 2 ...
#>  $ sector        : Factor w/ 2 levels "secondary","tertiary": 2 1 2 2 1 1 2 1 2 1 ...
#>  $ education     : Factor w/ 3 levels "I","II","III": 3 2 2 2 2 1 3 1 2 2 ...
#>  $ wage          : num [1:1000] 8400 4200 5100 7400 4300 4900 5400 2900 4500 3000 ...
#>  $ sample_weights: num [1:1000] 105 32 36 12 21 46 79 113 34 32 ...

vars <- c("gender", "education")
tmp <- dcc2(
  .data = invented_wages, 
  .variables = vars, 
  .fun = jointfun_, 
  order_type = extract_unique2
)
#> 
  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |===================================                                   |  50%
  |                                                                            
  |======================================================================| 100%
tmp
#> # A tibble: 12 × 3
#>    gender education     n
#>  * <fct>  <fct>     <int>
#>  1 Totale Totale     1000
#>  2 Totale I           172
#>  3 Totale II          719
#>  4 Totale III         109
#>  5 men    Totale      547
#>  6 men    I            60
#>  7 men    II          409
#>  8 men    III          78
#>  9 women  Totale      453
#> 10 women  I           112
#> 11 women  II          310
#> 12 women  III          31
str(tmp)
#> tibble [12 × 3] (S3: tbl_df/tbl/data.frame)
#>  $ gender   : Factor w/ 3 levels "Totale","men",..: 1 1 1 1 2 2 2 2 3 3 ...
#>  $ education: Factor w/ 4 levels "Totale","I","II",..: 1 2 3 4 1 2 3 4 1 2 ...
#>  $ n        : int [1:12] 1000 172 719 109 547 60 409 78 453 112 ...
#>  - attr(*, ".variables")= chr [1:2] "gender" "education"
only_joint(tmp, .variables = vars)
#> # A tibble: 6 × 3
#>   gender education     n
#>   <fct>  <fct>     <int>
#> 1 men    I            60
#> 2 men    II          409
#> 3 men    III          78
#> 4 women  I           112
#> 5 women  II          310
#> 6 women  III          31

# Compare dimensions (number of groups)
dim(tmp)
#> [1] 12  3
dim(only_joint(tmp, .variables = vars))
#> [1] 6 3