Data cube creation

Usage

dcc6(
  .data,
  .variables,
  .funs_list = list(n = ~dplyr::n()),
  .total = "Totale",
  order_type = extract_unique4,
  .all = TRUE
)

dcc6_fixed(
  .data,
  .variables,
  .funs_list = list(n = ~dplyr::n()),
  .total = "Totale",
  order_type = extract_unique5,
  .all = TRUE,
  fixed_variable = NULL
)

Arguments

.data: data frame to be processed.
.variables: variables to split data frame by, as a character vector (c("var1", "var2")).
.funs_list: a list of function calls in the form of right-hand formula.
.total: character string with the name to give to the subset of data that includes all the observations of a variable (default: "Totale").
order_type: a function like extract_unique or extract_unique2.
.all: logical, indicating if functions have to be evaluated on the complete dataset.
fixed_variable: name of the variable for which you do not want to estimate the total

Examples

dcc6(
  invented_wages,
  .variables = c("gender", "sector"), 
  .funs_list = list(n = ~dplyr::n()),
  .all = TRUE
)
#> 
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#> # A tibble: 9 × 3
#>   gender sector        n
#> * <fct>  <fct>     <int>
#> 1 Totale Totale     1000
#> 2 Totale secondary   455
#> 3 Totale tertiary    545
#> 4 men    Totale      547
#> 5 men    secondary   289
#> 6 men    tertiary    258
#> 7 women  Totale      453
#> 8 women  secondary   166
#> 9 women  tertiary    287
     
dcc6(
  invented_wages,
  .variables = c("gender", "sector"), 
  .funs_list = list(n = ~dplyr::n()),
  .all = FALSE
)
#> 
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#> # A tibble: 8 × 3
#>   gender sector        n
#>   <fct>  <fct>     <int>
#> 1 Totale secondary   455
#> 2 Totale tertiary    545
#> 3 men    Totale      547
#> 4 men    secondary   289
#> 5 men    tertiary    258
#> 6 women  Totale      453
#> 7 women  secondary   166
#> 8 women  tertiary    287