The GaussSuppression package contains several
easy-to-use wrapper functions and in this vignette we will look at the
SuppressFewContributors and
SuppressDominantCells functions. In these functions,
primary suppression is based on the number of contributors or by
dominance rules. Then, as always in this package, secondary suppression
is performed using the Gauss method.
We begin by loading a dataset to be used below.
library(GaussSuppression)
dataset <- SSBtoolsData("magnitude1")
dataset
#> sector4 sector2 geo eu company value
#> 1 Agriculture private Portugal EU A 75.9
#> 2 Agriculture private Portugal EU B 24.5
#> 3 Agriculture private Spain EU A 96.6
#> 4 Agriculture private Spain EU B 43.2
#> 5 Entertainment private Iceland nonEU B 16.8
#> 6 Entertainment private Portugal EU B 7.1
#> 7 Entertainment private Portugal EU D 2.3
#> 8 Entertainment private Spain EU A 77.4
#> 9 Entertainment private Spain EU B 11.5
#> 10 Entertainment private Spain EU C 16.4
#> 11 Governmental public Portugal EU B 21.6
#> 12 Governmental public Portugal EU D 2.0
#> 13 Governmental public Spain EU C 6.5
#> 14 Governmental public Spain EU D 2.7
#> 15 Industry private Iceland nonEU B 9.6
#> 16 Industry private Iceland nonEU C 8.8
#> 17 Industry private Iceland nonEU D 1.9
#> 18 Industry private Portugal EU B 25.7
#> 19 Industry private Portugal EU D 3.4
#> 20 Industry private Spain EU C 8.4We can imagine the figures in the variable "value"
represent sales value to different sectors from different companies. In
the first examples, we will not use the "company" variable,
but instead assume that each row represents the contribution of a unique
company. Our input data can then be reformatted and illustrated like
this:
| sector4 | Iceland | Portugal | Spain |
|---|---|---|---|
| Agriculture | 75.9, 24.5 | 96.6, 43.2 | |
| Entertainment | 16.8 | 7.1, 2.3 | 77.4, 11.5, 16.4 |
| Governmental | 21.6, 2.0 | 6.5, 2.7 | |
| Industry | 9.6, 8.8, 1.9 | 25.7, 3.4 | 8.4 |
In the first example, we use SuppressFewContributors
with maxN = 1. This means that cells based on a single
contributor are primary suppressed.
SuppressFewContributors(data=dataset,
numVar = "value",
dimVar= c("sector4", "geo"),
maxN=1)
#> [extraAggregate 20*3->10*4] Checking ....
#> GaussSuppression_numttHTT: ..............::::
#> sector4 geo value nRule nAll primary suppressed
#> 1 Total Total 462.3 20 20 FALSE FALSE
#> 2 Total Iceland 37.1 4 4 FALSE FALSE
#> 3 Total Portugal 162.5 8 8 FALSE FALSE
#> 4 Total Spain 262.7 8 8 FALSE FALSE
#> 5 Agriculture Total 240.2 4 4 FALSE FALSE
#> 6 Agriculture Iceland 0.0 0 0 FALSE FALSE
#> 7 Agriculture Portugal 100.4 2 2 FALSE FALSE
#> 8 Agriculture Spain 139.8 2 2 FALSE FALSE
#> 9 Entertainment Total 131.5 6 6 FALSE FALSE
#> 10 Entertainment Iceland 16.8 1 1 TRUE TRUE
#> 11 Entertainment Portugal 9.4 2 2 FALSE TRUE
#> 12 Entertainment Spain 105.3 3 3 FALSE FALSE
#> 13 Governmental Total 32.8 4 4 FALSE FALSE
#> 14 Governmental Iceland 0.0 0 0 FALSE FALSE
#> 15 Governmental Portugal 23.6 2 2 FALSE TRUE
#> 16 Governmental Spain 9.2 2 2 FALSE TRUE
#> 17 Industry Total 57.8 6 6 FALSE FALSE
#> 18 Industry Iceland 20.3 3 3 FALSE TRUE
#> 19 Industry Portugal 29.1 2 2 FALSE TRUE
#> 20 Industry Spain 8.4 1 1 TRUE TRUEIn the output, the number of contributors is in columns
nRule and nAll. The two columns are equal
under normal usage.
A formatted version of this output is given in Table 2 below. Primary suppressed cells are underlined and labeled in red, while the secondary suppressed cells are labeled in purple.
| sector4 | Iceland | Portugal | Spain | Total |
|---|---|---|---|---|
| Agriculture | 0 (0) | 100.4 (2) | 139.8 (2) | 240.2 (4) |
| Entertainment | 16.8 (1) | 9.4 (2) | 105.3 (3) | 131.5 (6) |
| Governmental | 0 (0) | 23.6 (2) | 9.2 (2) | 32.8 (4) |
| Industry | 20.3 (3) | 29.1 (2) | 8.4 (1) | 57.8 (6) |
| Total | 37.1 (4) | 162.5 (8) | 262.7 (8) | 462.3 (20) |
In the second example, we use SuppressDominantCells with
n = 1 and k = 80. This means that aggregates
are primary suppressed whenever the largest contribution exceeds 80% of
the cell total.
SuppressDominantCells(data=dataset,
numVar = "value",
dimVar= c("sector4", "geo"),
n = 1, k = 80, allDominance = TRUE)
#> [extraAggregate 20*3->10*4] Checking ....
#> GaussSuppression_numttHTT: ..............::
#> sector4 geo value dominant1 max1contributor n_contr n_non0_contr
#> 1 Total Total 462.3 0.2089552 3 20 20
#> 2 Total Iceland 37.1 0.4528302 5 4 4
#> 3 Total Portugal 162.5 0.4670769 1 8 8
#> 4 Total Spain 262.7 0.3677198 3 8 8
#> 5 Agriculture Total 240.2 0.4021649 3 4 4
#> 6 Agriculture Iceland 0.0 0.0000000 NA 0 0
#> 7 Agriculture Portugal 100.4 0.7559761 1 2 2
#> 8 Agriculture Spain 139.8 0.6909871 3 2 2
#> 9 Entertainment Total 131.5 0.5885932 8 6 6
#> 10 Entertainment Iceland 16.8 1.0000000 5 1 1
#> 11 Entertainment Portugal 9.4 0.7553191 6 2 2
#> 12 Entertainment Spain 105.3 0.7350427 8 3 3
#> 13 Governmental Total 32.8 0.6585366 11 4 4
#> 14 Governmental Iceland 0.0 0.0000000 NA 0 0
#> 15 Governmental Portugal 23.6 0.9152542 11 2 2
#> 16 Governmental Spain 9.2 0.7065217 13 2 2
#> 17 Industry Total 57.8 0.4446367 18 6 6
#> 18 Industry Iceland 20.3 0.4729064 15 3 3
#> 19 Industry Portugal 29.1 0.8831615 18 2 2
#> 20 Industry Spain 8.4 1.0000000 20 1 1
#> primary suppressed
#> 1 FALSE FALSE
#> 2 FALSE FALSE
#> 3 FALSE FALSE
#> 4 FALSE FALSE
#> 5 FALSE FALSE
#> 6 FALSE FALSE
#> 7 FALSE FALSE
#> 8 FALSE FALSE
#> 9 FALSE FALSE
#> 10 TRUE TRUE
#> 11 FALSE TRUE
#> 12 FALSE FALSE
#> 13 FALSE TRUE
#> 14 FALSE FALSE
#> 15 TRUE TRUE
#> 16 FALSE TRUE
#> 17 FALSE TRUE
#> 18 FALSE TRUE
#> 19 TRUE TRUE
#> 20 TRUE TRUETo incorporate the percentage of the two largest contributions in the
output, the parameter allDominance = TRUE was utilized. A
formatted version of this output is given in Table 3 below.
| sector4 | Iceland | Portugal | Spain | Total |
|---|---|---|---|---|
| Agriculture | 0 (0%) | 100.4 (76%) | 139.8 (69%) | 240.2 (40%) |
| Entertainment | 16.8 (100%) | 9.4 (76%) | 105.3 (74%) | 131.5 (59%) |
| Governmental | 0 (0%) | 23.6 (92%) | 9.2 (71%) | 32.8 (66%) |
| Industry | 20.3 (47%) | 29.1 (88%) | 8.4 (100%) | 57.8 (44%) |
| Total | 37.1 (45%) | 162.5 (47%) | 262.7 (37%) | 462.3 (21%) |
Note that this table, as well as Table 2, is discussed below in the
section on the singleton problem.
Here we use SuppressDominantCells with
n = 1:2 and k = c(80, 99). This means that
aggregates are primary suppressed whenever the largest contribution
exceeds 80% of the cell total or when the two largest contributions
exceed 99% of the cell total.
In addition, the example below is made even more advanced by including the variables “sector2” and “eu”.
output <- SuppressDominantCells(data=dataset,
numVar = "value",
dimVar= c("sector4", "sector2", "geo", "eu"),
n = 1:2, k = c(80, 99))
#> [extraAggregate 20*5->10*6] Checking ....
#> GaussSuppression_numttHTT: .............::::::
head(output)
#> sector4 geo value primary suppressed
#> 1 Total Total 462.3 FALSE FALSE
#> 2 Total EU 425.2 FALSE FALSE
#> 3 Total nonEU 37.1 FALSE FALSE
#> 4 Total Iceland 37.1 FALSE FALSE
#> 5 Total Portugal 162.5 FALSE FALSE
#> 6 Total Spain 262.7 FALSE FALSE| sector4 | Iceland | Portugal | Spain | nonEU | EU | Total |
|---|---|---|---|---|---|---|
| Agriculture | 0 | 100.4 | 139.8 | 0 | 240.2 | 240.2 |
| Entertainment | 16.8 | 9.4 | 105.3 | 16.8 | 114.7 | 131.5 |
| Governmental | 0 | 23.6 | 9.2 | 0 | 32.8 | 32.8 |
| Industry | 20.3 | 29.1 | 8.4 | 20.3 | 37.5 | 57.8 |
| private | 37.1 | 138.9 | 253.5 | 37.1 | 392.4 | 429.5 |
| public | 0 | 23.6 | 9.2 | 0 | 32.8 | 32.8 |
| Total | 37.1 | 162.5 | 262.7 | 37.1 | 425.2 | 462.3 |
As described in the define-tables vignette hierarchies are here detected automatically. The same output is obtained if we first generate hierarchies by:
dimlists <- SSBtools::FindDimLists(dataset[c("sector4", "sector2", "geo", "eu")])
dimlists
#> $sector4
#> levels codes
#> 1 @ Total
#> 2 @@ private
#> 3 @@@ Agriculture
#> 4 @@@ Entertainment
#> 5 @@@ Industry
#> 6 @@ public
#> 7 @@@ Governmental
#>
#> $geo
#> levels codes
#> 1 @ Total
#> 2 @@ EU
#> 3 @@@ Portugal
#> 4 @@@ Spain
#> 5 @@ nonEU
#> 6 @@@ IcelandAnd thereafter run SuppressDominantCells with these hierarchies as input:
output <- SuppressDominantCells(data=dataset,
numVar = "value",
hierarchies = dimlists,
n = 1:2, k = c(80, 99))
#> [extraAggregate 20*3->10*4] Checking ....
#> GaussSuppression_numttHTT: .............::::::
Using the formula interface is one way to achieve fewer cells in the
output. Below we use SuppressFewContributors with
maxN = 2. This means that table cells based on one or two
contributors are primary suppressed.
output <- SuppressFewContributors(data=dataset,
numVar = "value",
formula = ~sector2*geo + sector4*eu,
maxN=2,
removeEmpty = FALSE)
#> [extraAggregate 20*5->10*6] Checking ....
#> GaussSuppression_numttHTT: ............:::::
head(output)
#> geo sector4 value nRule nAll primary suppressed
#> 1 Total Total 462.3 20 20 FALSE FALSE
#> 2 Total private 429.5 16 16 FALSE FALSE
#> 3 Total public 32.8 4 4 FALSE FALSE
#> 4 Iceland Total 37.1 4 4 FALSE FALSE
#> 5 Portugal Total 162.5 8 8 FALSE FALSE
#> 6 Spain Total 262.7 8 8 FALSE FALSE
tail(output)
#> geo sector4 value nRule nAll primary suppressed
#> 21 EU Entertainment 114.7 5 5 FALSE TRUE
#> 22 nonEU Entertainment 16.8 1 1 TRUE TRUE
#> 23 EU Governmental 32.8 4 4 FALSE FALSE
#> 24 nonEU Governmental 0.0 0 0 FALSE FALSE
#> 25 EU Industry 37.5 3 3 FALSE TRUE
#> 26 nonEU Industry 20.3 3 3 FALSE TRUEIn the formatted version of this output, blank cells indicate that they are not included in the output.
| sector4 | Iceland | Portugal | Spain | nonEU | EU | Total |
|---|---|---|---|---|---|---|
| Agriculture | 0 (0) | 240.2 (4) | 240.2 (4) | |||
| Entertainment | 16.8 (1) | 114.7 (5) | 131.5 (6) | |||
| Governmental | 0 (0) | 32.8 (4) | 32.8 (4) | |||
| Industry | 20.3 (3) | 37.5 (3) | 57.8 (6) | |||
| private | 37.1 (4) | 138.9 (6) | 253.5 (6) | 429.5 (16) | ||
| public | 0 (0) | 23.6 (2) | 9.2 (2) | 32.8 (4) | ||
| Total | 37.1 (4) | 162.5 (8) | 262.7 (8) | 37.1 (4) | 425.2 (16) | 462.3 (20) |
Please note that in order to include the three empty cells with no
contributors, the removeEmpty parameter was set to
FALSE. By default, this parameter is set to
TRUE when using the formula interface.
contributorVar = "company")According to the "company" variable in the data set,
there are only four contribution companies (A, B, C and D). We specify
this using the contributorVar parameter, which corresponds
to a variable within the dataset, in this case, "company".
In general, this variable refers to the holding information to be used
by the suppression method. When this is taken into account, the primary
suppression rules will be applied to data that, within each cell, is
aggregated within each contributor. Our example data aggregated in this
way is shown below.
| sector4 | Iceland | Portugal | Spain | nonEU | EU | Total |
|---|---|---|---|---|---|---|
| Agriculture | A=75.9, B=24.5 | A=96.6, B=43.2 | A=172.5, B=67.7 | A=172.5, B=67.7 | ||
| Entertainment | B=16.8 | B=7.1, D=2.3 | A=77.4, B=11.5, C=16.4 | B=16.8 | A=77.4, B=18.6, C=16.4, D=2.3 | A=77.4, B=35.4, C=16.4, D=2.3 |
| Governmental | B=21.6, D=2.0 | C=6.5, D=2.7 | B=21.6, C=6.5, D=4.7 | B=21.6, C=6.5, D=4.7 | ||
| Industry | B=9.6, C=8.8, D=1.9 | B=25.7, D=3.4 | C=8.4 | B=9.6, C=8.8, D=1.9 | B=25.7, C=8.4, D=3.4 | B=35.3, C=17.2, D=5.3 |
| private | B=26.4, C=8.8, D=1.9 | A=75.9, B=57.3, D=5.7 | A=174.0, B=54.7, C=24.8 | B=26.4, C=8.8, D=1.9 | A=249.9, B=112.0, C=24.8, D=5.7 | A=249.9, B=138.4, C=33.6, D=7.6 |
| public | B=21.6, D=2.0 | C=6.5, D=2.7 | B=21.6, C=6.5, D=4.7 | B=21.6, C=6.5, D=4.7 | ||
| Total | B=26.4, C=8.8, D=1.9 | A=75.9, B=78.9, D=7.7 | A=174.0, B=54.7, C=31.3, D=2.7 | B=26.4, C=8.8, D=1.9 | A=249.9, B=133.6, C=31.3, D=10.4 | A=249.9, B=160.0, C=40.1, D=12.3 |
Below we take into account contributor IDs when using few contributors primary suppression.
output <- SuppressFewContributors(data=dataset,
numVar = "value",
dimVar = c("sector4", "sector2", "geo", "eu"),
maxN=2,
contributorVar = "company")
#> [preAggregate 20*6->20*7]
#> [extraAggregate 20*7->10*7] Checking .....
#> GaussSuppression_numttHTT: ............::::::
head(output)
#> sector4 geo freq value nRule nAll primary suppressed
#> 1 Total Total 20 462.3 4 4 FALSE FALSE
#> 2 Total EU 16 425.2 4 4 FALSE FALSE
#> 3 Total nonEU 4 37.1 3 3 FALSE FALSE
#> 4 Total Iceland 4 37.1 3 3 FALSE FALSE
#> 5 Total Portugal 8 162.5 3 3 FALSE FALSE
#> 6 Total Spain 8 262.7 4 4 FALSE FALSE| sector4 | Iceland | Portugal | Spain | nonEU | EU | Total |
|---|---|---|---|---|---|---|
| Agriculture | 0 (0) | 100.4 (2) | 139.8 (2) | 0 (0) | 240.2 (2) | 240.2 (2) |
| Entertainment | 16.8 (1) | 9.4 (2) | 105.3 (3) | 16.8 (1) | 114.7 (4) | 131.5 (4) |
| Governmental | 0 (0) | 23.6 (2) | 9.2 (2) | 0 (0) | 32.8 (3) | 32.8 (3) |
| Industry | 20.3 (3) | 29.1 (2) | 8.4 (1) | 20.3 (3) | 37.5 (3) | 57.8 (3) |
| private | 37.1 (3) | 138.9 (3) | 253.5 (3) | 37.1 (3) | 392.4 (4) | 429.5 (4) |
| public | 0 (0) | 23.6 (2) | 9.2 (2) | 0 (0) | 32.8 (3) | 32.8 (3) |
| Total | 37.1 (3) | 162.5 (3) | 262.7 (4) | 37.1 (3) | 425.2 (4) | 462.3 (4) |
Below we take into account contributor IDs when using dominant cell primary suppression.
output <- SuppressDominantCells(data=dataset,
numVar = "value",
formula = ~sector2*geo + sector4*eu,
contributorVar = "company",
n = 1:2, k = c(80, 99))
#> [preAggregate 20*6->20*7]
#> [extraAggregate 20*7->10*7] Checking .....
#> GaussSuppression_numttHTT: .........:::::
head(output)
#> geo sector4 freq value primary suppressed
#> 1 Total Total 20 462.3 FALSE FALSE
#> 2 Total private 16 429.5 FALSE FALSE
#> 3 Total public 4 32.8 FALSE FALSE
#> 4 Iceland Total 4 37.1 FALSE FALSE
#> 5 Portugal Total 8 162.5 FALSE FALSE
#> 6 Spain Total 8 262.7 FALSE FALSEHere we have also made use of the formula interface.
| sector4 | Iceland | Portugal | Spain | nonEU | EU | Total |
|---|---|---|---|---|---|---|
| Agriculture | 240.2 | 240.2 | ||||
| Entertainment | 16.8 | 114.7 | 131.5 | |||
| Governmental | 32.8 | 32.8 | ||||
| Industry | 20.3 | 37.5 | 57.8 | |||
| private | 37.1 | 138.9 | 253.5 | 429.5 | ||
| public | 23.6 | 9.2 | 32.8 | |||
| Total | 37.1 | 162.5 | 262.7 | 37.1 | 425.2 | 462.3 |
Below, the data is suppressed in the same way as in Table 7, but with
a different formula.
output <- SuppressDominantCells(data=dataset,
numVar = "value",
formula = ~sector4*geo + sector2*eu,
contributorVar = "company",
n = 1:2, k = c(80, 99))
#> [preAggregate 20*6->20*7]
#> [extraAggregate 20*7->10*7] Checking .....
#> GaussSuppression_numttHTT: ............
head(output)
#> sector4 geo freq value primary suppressed
#> 1 Total Total 20 462.3 FALSE FALSE
#> 2 Agriculture Total 4 240.2 TRUE TRUE
#> 3 Entertainment Total 6 131.5 FALSE FALSE
#> 4 Governmental Total 4 32.8 FALSE FALSE
#> 5 Industry Total 6 57.8 FALSE TRUE
#> 6 Total Iceland 4 37.1 FALSE FALSE| sector4 | Iceland | Portugal | Spain | nonEU | EU | Total |
|---|---|---|---|---|---|---|
| Agriculture | 100.4 | 139.8 | 240.2 | |||
| Entertainment | 16.8 | 9.4 | 105.3 | 131.5 | ||
| Governmental | 23.6 | 9.2 | 32.8 | |||
| Industry | 20.3 | 29.1 | 8.4 | 57.8 | ||
| private | 37.1 | 392.4 | 429.5 | |||
| public | 32.8 | 32.8 | ||||
| Total | 37.1 | 162.5 | 262.7 | 37.1 | 425.2 | 462.3 |
By using singletonMethod = "none" in this case,
Entertainment:Spain will not be suppressed. This cell is
suppressed due to the default handling of the singleton problem. The
reason is that Entertainment:Iceland has a single contributor.
This contributor can reveal Entertainment:Portugal if
Entertainment:Spain is not suppressed.
Here it might appear that the table contains another issue, Entertainment:Iceland can reveal Industry:Iceland. However, this can be considered ok in this case. Industry:Iceland is secondary suppressed and the only reason for this is to protect Entertainment:Iceland.
Nevertheless, in most cases, secondary suppressed cells introduce further complexity to the handling of singletons. A part of the singleton handling of magnitude tables is to add virtual primary suppressed cells prior to the secondary suppression algorithm. Secondary suppressed cells cannot, therefore, be treated in this way. However, another part of the singleton handling solves many of the remaining problems. This is done within the suppression algorithm.
Here we can observe the effect of this in Tables 2 and 3. By using
singletonMethod = "none" in Table 2,
Industry:Portugal will not be suppressed. In that case,
Industry:Spain can reveal Industry:Iceland and
consequently Entertainment:Iceland. In Table 3,
Governmental:Total and Industry:Total are suppressed
due to advanced singleton handling. In fact, by using
singletonMethod = "none", all tables above will be
suppressed differently.