sdc_testproblem() returns a sdc-problem instance with 2 hierarchies and
optionally with a single suppressed cell that is used in various examples
and tests.
sdc_testproblem(with_supps = FALSE)if TRUE, a single cell (violating minimal-frquency rule
with n = 2) is marked as primary sensitive.
a problem instance
p1 <- sdc_testproblem(); p1
#> The object is a sdcProblem instance with 15 cells in 2 dimension(s)!
#> Protection: no
#>
#> The dimensions are:
#> - region (2 levels; 5 codes; of these being 1 aggregates)
#> - gender (2 levels; 3 codes; of these being 1 aggregates)
#>
#> Current suppression pattern:
#> - Primary suppressions: 0
#> - Secondary suppressions: 0
#> - Publishable cells: 15
sdcProb2df(p1)
#> strID freq sdcStatus region gender region_o gender_o
#> 1: 0000 100 s 00 00 total total
#> 2: 0001 55 s 00 01 total male
#> 3: 0002 45 s 00 02 total female
#> 4: 0100 20 s 01 00 A total
#> 5: 0101 18 s 01 01 A male
#> 6: 0102 2 s 01 02 A female
#> 7: 0200 33 s 02 00 B total
#> 8: 0201 14 s 02 01 B male
#> 9: 0202 19 s 02 02 B female
#> 10: 0300 22 s 03 00 C total
#> 11: 0301 12 s 03 01 C male
#> 12: 0302 10 s 03 02 C female
#> 13: 0400 25 s 04 00 D total
#> 14: 0401 11 s 04 01 D male
#> 15: 0402 14 s 04 02 D female
# a single protected cell
p2 <- sdc_testproblem(with_supps = TRUE); p2
#> The object is a sdcProblem instance with 15 cells in 2 dimension(s)!
#> Protection: no
#>
#> The dimensions are:
#> - region (2 levels; 5 codes; of these being 1 aggregates)
#> - gender (2 levels; 3 codes; of these being 1 aggregates)
#>
#> Current suppression pattern:
#> - Primary suppressions: 1
#> - Secondary suppressions: 0
#> - Publishable cells: 14
sdcProb2df(p2)
#> strID freq sdcStatus region gender region_o gender_o
#> 1: 0000 100 s 00 00 total total
#> 2: 0001 55 s 00 01 total male
#> 3: 0002 45 s 00 02 total female
#> 4: 0100 20 s 01 00 A total
#> 5: 0101 18 s 01 01 A male
#> 6: 0102 2 u 01 02 A female
#> 7: 0200 33 s 02 00 B total
#> 8: 0201 14 s 02 01 B male
#> 9: 0202 19 s 02 02 B female
#> 10: 0300 22 s 03 00 C total
#> 11: 0301 12 s 03 01 C male
#> 12: 0302 10 s 03 02 C female
#> 13: 0400 25 s 04 00 D total
#> 14: 0401 11 s 04 01 D male
#> 15: 0402 14 s 04 02 D female
# cell status differs in one cell
specs <- c(gender = "female", region = c("A"))
cell_info(p1, specs = specs)
#> id strID region gender freq val sdcStatus is_primsupp is_secondsupp
#> 1: 6 0102 A female 2 20 s FALSE FALSE
cell_info(p2, specs = specs)
#> id strID region gender freq val sdcStatus is_primsupp is_secondsupp
#> 1: 6 0102 A female 2 20 u TRUE FALSE