Data cube creation (dcc)
Usage
dcc(.data, .variables, .fun = jointfun_, ...)
dcc2(.data, .variables, .fun = jointfun_, order_type = extract_unique2, ...)
dcc5(
.data,
.variables,
.fun = jointfun_,
.total = "Totale",
order_type = extract_unique4,
.all = TRUE,
...
)Arguments
- .data
data frame to be processed
- .variables
variables to split data frame by, as a character vector (
c("var1", "var2")).- .fun
function to apply to each piece (default:
jointfun_)- ...
additional functions passed to
.fun.- order_type
a function like
extract_uniqueorextract_unique2.- .total
character string with the name to give to the subset of data that includes all the observations of a variable (default:
"Totale").- .all
logical, indicating if functions' have to be evaluated on the complete dataset.
Value
a data cube, with a column for each cateogorical variable used, and a row for each combination of all the categorical variables' modalities. In addition to all the modalities, each variable will also have a "Total" possibility, which includes all the others. The data cube will contain marginal, conditional and joint empirical distributions...
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 ...
tmp <- dcc(
.data = invented_wages,
.variables = c("gender", "sector"),
.fun = jointfun_
)
#>
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tmp
#> # 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
str(tmp)
#> tibble [9 × 3] (S3: tbl_df/tbl/data.frame)
#> $ gender: Factor w/ 3 levels "Totale","men",..: 1 1 1 2 2 2 3 3 3
#> $ sector: Factor w/ 3 levels "Totale","secondary",..: 1 2 3 1 2 3 1 2 3
#> $ n : int [1:9] 1000 455 545 547 289 258 453 166 287
#> - attr(*, ".variables")= chr [1:2] "gender" "sector"
tmp2 <- dcc2(
.data = invented_wages,
.variables = c("gender", "education"),
.fun = jointfun_,
order_type = extract_unique2
)
#>
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tmp2
#> # 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(tmp2)
#> 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"
# dcc5 works like dcc2, but has an additional optional argument, .total,
# that can be added to give a name to the groups that include all the
# observations of a variable.
tmp5 <- dcc5(
.data = invented_wages,
.variables = c("gender", "education"),
.fun = jointfun_,
.total = "TOTAL",
order_type = extract_unique2
)
#>
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tmp5
#> # A tibble: 12 × 3
#> gender education n
#> * <fct> <fct> <int>
#> 1 TOTAL TOTAL 1000
#> 2 TOTAL I 172
#> 3 TOTAL II 719
#> 4 TOTAL III 109
#> 5 men TOTAL 547
#> 6 men I 60
#> 7 men II 409
#> 8 men III 78
#> 9 women TOTAL 453
#> 10 women I 112
#> 11 women II 310
#> 12 women III 31