The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
The GaussSuppression package uses a common interface
shared by other SDC packages developed at Statistics Norway (see also SmallCountRounding
and SSBcellKey).
In the background, these packages use a model matrix
representation, which connects the input data to the intended output.
This functionality is provided by the R package SSBtools.
In this vignette, we look at multiple ways of specifying output tables
given different forms of input. Note that this vignette only scratches
the surface of what is possible with the provided interface, and rather
is intended to help users get going with the package.
We begin by importing the necessary dependencies as well as loading a test data set provided in the SSBtools package.
library(SSBtools)
library(GaussSuppression)
dataset <- SSBtools::SSBtoolsData("d2s")
microdata <- SSBtools::MakeMicro(dataset, "freq")
head(dataset)
#> region county size main_income freq
#> 1 A county-1 BIG other 2
#> 2 B county-2 BIG other 3
#> 3 C county-2 BIG other 5
#> 4 D county-1 small other 3
#> 5 E county-3 small other 9
#> 6 F county-3 small other 4
nrow(dataset)
#> [1] 24
head(microdata)
#> region county size main_income freq
#> 1 A county-1 BIG other 1
#> 2 A county-1 BIG other 1
#> 3 B county-2 BIG other 1
#> 4 B county-2 BIG other 1
#> 5 B county-2 BIG other 1
#> 6 C county-2 BIG other 1
nrow(microdata)
#> [1] 338The imported data set is a fictitious data set containing the
variables: region, county, size, main_income, freq, where region,
county, and size are different (non-nested) regional hierarchies.
GaussSuppression can take microdata as input as well, which
we will demonstrate in the following sections.
The table below illustrates this dataset reshaped to wide format with
several freq columns created from the
main_income variable. However, please note that data that
is input and output in the GaussSuppression package is
always in long format.
| region | county | size | other | wages | assistance | pensions |
|---|---|---|---|---|---|---|
| A | county-1 | BIG | 2 | 11 | 55 | 36 |
| B | county-2 | BIG | 3 | 1 | 29 | 18 |
| C | county-2 | BIG | 5 | 8 | 35 | 25 |
| D | county-1 | small | 3 | 0 | 9 | 2 |
| E | county-3 | small | 9 | 0 | 32 | 20 |
| F | county-3 | small | 4 | 2 | 18 | 11 |
Output tables are mainly specified using the following three
parameters: dimVar, hierarchies, and
formula.
dimVarThe most basic way of defining output tables is by using the
dimVar parameter. This generates by default all
combinations of the variables provided, including marginals. For
example, the following function call creates a one dimensional frequency
table over the variable region.
GaussSuppressionFromData(data = dataset,
dimVar = "region",
freqVar = "freq",
primary = FALSE,
protectZeros = FALSE)
#> region freq primary suppressed
#> 1 Total 338 FALSE FALSE
#> 2 A 104 FALSE FALSE
#> 3 B 51 FALSE FALSE
#> 4 C 73 FALSE FALSE
#> 5 D 14 FALSE FALSE
#> 6 E 61 FALSE FALSE
#> 7 F 35 FALSE FALSE
Table 2: dimVar = "region"
| region | |
|---|---|
| A | 104 |
| B | 51 |
| C | 73 |
| D | 14 |
| E | 61 |
| F | 35 |
| Total | 338 |
Note the use of the function GaussSuppressionFromData and the
inclusion of two parameters primary and
protectZeros. The functions in
GaussSuppression are designed to incorporate both table
building and protection into a single function call. Thus, to illustrate
the table building features, we have set that nothing must be protected.
To learn more about the different ways of protecting tables, see the
other vignettes of this package.
In a similar fashion, we can include multiple variables in the
dimVar parameter:
GaussSuppressionFromData(data = dataset,
dimVar = c("region", "main_income"),
freqVar = "freq",
primary = FALSE,
protectZeros = FALSE)
#> region main_income freq primary suppressed
#> 1 Total Total 338 FALSE FALSE
#> 2 Total assistance 178 FALSE FALSE
#> 3 Total other 26 FALSE FALSE
#> 4 Total pensions 112 FALSE FALSE
#> 5 Total wages 22 FALSE FALSE
#> 6 A Total 104 FALSE FALSE
#> 7 A assistance 55 FALSE FALSE
#> 8 A other 2 FALSE FALSE
#> 9 A pensions 36 FALSE FALSE
#> 10 A wages 11 FALSE FALSE
#> 11 B Total 51 FALSE FALSE
#> 12 B assistance 29 FALSE FALSE
#> 13 B other 3 FALSE FALSE
#> 14 B pensions 18 FALSE FALSE
#> 15 B wages 1 FALSE FALSE
#> 16 C Total 73 FALSE FALSE
#> 17 C assistance 35 FALSE FALSE
#> 18 C other 5 FALSE FALSE
#> 19 C pensions 25 FALSE FALSE
#> 20 C wages 8 FALSE FALSE
#> 21 D Total 14 FALSE FALSE
#> 22 D assistance 9 FALSE FALSE
#> 23 D other 3 FALSE FALSE
#> 24 D pensions 2 FALSE FALSE
#> 25 D wages 0 FALSE FALSE
#> 26 E Total 61 FALSE FALSE
#> 27 E assistance 32 FALSE FALSE
#> 28 E other 9 FALSE FALSE
#> 29 E pensions 20 FALSE FALSE
#> 30 E wages 0 FALSE FALSE
#> 31 F Total 35 FALSE FALSE
#> 32 F assistance 18 FALSE FALSE
#> 33 F other 4 FALSE FALSE
#> 34 F pensions 11 FALSE FALSE
#> 35 F wages 2 FALSE FALSEThe same output is shown below as a formatted and reshaped table.
Cells that also occur as input/inner cells have white background.
| region | other | wages | assistance | pensions | Total |
|---|---|---|---|---|---|
| A | 2 | 11 | 55 | 36 | 104 |
| B | 3 | 1 | 29 | 18 | 51 |
| C | 5 | 8 | 35 | 25 | 73 |
| D | 3 | 0 | 9 | 2 | 14 |
| E | 9 | 0 | 32 | 20 | 61 |
| F | 4 | 2 | 18 | 11 | 35 |
| Total | 26 | 22 | 178 | 112 | 338 |
Note in particular what happens when we provide two regional variables:
GaussSuppressionFromData(data = dataset,
dimVar = c("region", "county"),
freqVar = "freq",
primary = FALSE,
protectZeros = FALSE)
#> region freq primary suppressed
#> 1 Total 338 FALSE FALSE
#> 2 county-1 118 FALSE FALSE
#> 3 county-2 124 FALSE FALSE
#> 4 county-3 96 FALSE FALSE
#> 5 A 104 FALSE FALSE
#> 6 B 51 FALSE FALSE
#> 7 C 73 FALSE FALSE
#> 8 D 14 FALSE FALSE
#> 9 E 61 FALSE FALSE
#> 10 F 35 FALSE FALSE
Table 4:
dimVar = c("region", "county")
| region | |
|---|---|
| A | 104 |
| B | 51 |
| C | 73 |
| D | 14 |
| E | 61 |
| F | 35 |
| county-1 | 118 |
| county-2 | 124 |
| county-3 | 96 |
| Total | 338 |
The function detects hierarchies encoded in dimVar
columns, and collapses them into a single column (with the name of the
most detailed variable). In this way, it is not necessary to specify
hierarchies by hand and include them explicitly in the function call.
This also works for non-nested hierarchies:
GaussSuppressionFromData(data = dataset,
dimVar = c("region", "county", "size"),
freqVar = "freq",
primary = FALSE,
protectZeros = FALSE)
#> region freq primary suppressed
#> 1 BIG 228 FALSE FALSE
#> 2 Total 338 FALSE FALSE
#> 3 county-1 118 FALSE FALSE
#> 4 county-2 124 FALSE FALSE
#> 5 county-3 96 FALSE FALSE
#> 6 small 110 FALSE FALSE
#> 7 A 104 FALSE FALSE
#> 8 B 51 FALSE FALSE
#> 9 C 73 FALSE FALSE
#> 10 D 14 FALSE FALSE
#> 11 E 61 FALSE FALSE
#> 12 F 35 FALSE FALSE
Table 5:
dimVar = c("region", "county", "size")
| region | |
|---|---|
| A | 104 |
| B | 51 |
| C | 73 |
| D | 14 |
| E | 61 |
| F | 35 |
| county-1 | 118 |
| county-2 | 124 |
| county-3 | 96 |
| small | 110 |
| BIG | 228 |
| Total | 338 |
We can combine all the dimensional variables in our example data:
output <- GaussSuppressionFromData(data = dataset,
dimVar = c("region", "county", "size", "main_income"),
freqVar = "freq",
primary = FALSE,
protectZeros = FALSE)
head(output)
#> region main_income freq primary suppressed
#> 1 BIG Total 228 FALSE FALSE
#> 2 BIG assistance 119 FALSE FALSE
#> 3 BIG other 10 FALSE FALSE
#> 4 BIG pensions 79 FALSE FALSE
#> 5 BIG wages 20 FALSE FALSE
#> 6 Total Total 338 FALSE FALSE
Table 6:
dimVar = c("region", "county", "size", "main_income")
| region | other | wages | assistance | pensions | Total |
|---|---|---|---|---|---|
| A | 2 | 11 | 55 | 36 | 104 |
| B | 3 | 1 | 29 | 18 | 51 |
| C | 5 | 8 | 35 | 25 | 73 |
| D | 3 | 0 | 9 | 2 | 14 |
| E | 9 | 0 | 32 | 20 | 61 |
| F | 4 | 2 | 18 | 11 | 35 |
| county-1 | 5 | 11 | 64 | 38 | 118 |
| county-2 | 8 | 9 | 64 | 43 | 124 |
| county-3 | 13 | 2 | 50 | 31 | 96 |
| small | 16 | 2 | 59 | 33 | 110 |
| BIG | 10 | 20 | 119 | 79 | 228 |
| Total | 26 | 22 | 178 | 112 | 338 |
In the background, functions from SSBtools are used to find the
hierarchies. There are multiple ways of inspecting which hierarchies can
be found; users familiar with DimLists used in other SDC packages can
for example use the following:
FindDimLists(dataset[c("region", "county")])
#> $region
#> levels codes
#> 1 @ Total
#> 2 @@ county-1
#> 3 @@@ A
#> 4 @@@ D
#> 5 @@ county-2
#> 6 @@@ B
#> 7 @@@ C
#> 8 @@ county-3
#> 9 @@@ E
#> 10 @@@ F
FindDimLists(dataset[c("region", "county", "size")])
#> $region
#> levels codes
#> 1 @ Total
#> 2 @@ county-1
#> 3 @@@ A
#> 4 @@@ D
#> 5 @@ county-2
#> 6 @@@ B
#> 7 @@@ C
#> 8 @@ county-3
#> 9 @@@ E
#> 10 @@@ F
#>
#> $region
#> levels codes
#> 1 @ Total
#> 2 @@ BIG
#> 3 @@@ A
#> 4 @@@ B
#> 5 @@@ C
#> 6 @@ small
#> 7 @@@ D
#> 8 @@@ E
#> 9 @@@ FNote the last example which contained non-nested hierarchies. Here, a unique DimList is created for each tree-shaped hierarchy in the data set. This avoids the need for specifying non-nested hierarchies as linked tables.
Finally, for illustration purposes, we see that the same function calls work with microdata as input:
GaussSuppressionFromData(data = microdata,
dimVar = c("region", "county", "size"),
freqVar = "freq",
primary = FALSE,
protectZeros = FALSE)
#> region freq primary suppressed
#> 1 BIG 228 FALSE FALSE
#> 2 Total 338 FALSE FALSE
#> 3 county-1 118 FALSE FALSE
#> 4 county-2 124 FALSE FALSE
#> 5 county-3 96 FALSE FALSE
#> 6 small 110 FALSE FALSE
#> 7 A 104 FALSE FALSE
#> 8 B 51 FALSE FALSE
#> 9 C 73 FALSE FALSE
#> 10 D 14 FALSE FALSE
#> 11 E 61 FALSE FALSE
#> 12 F 35 FALSE FALSEThis output is the same as illustrated in Table 5 above.
hierarchiesThe hierarchies parameter allows the explicit
specification of which hierarchies should be used when creating the
output table. This allows for a more fine-grained approach as opposed to
simply using dimVar, as it allows for applying hierarchies
not already present in the data set. Hierarchies can be provided in many
ways. In this vignette, we will exemplify the following three forms: as
a dimlist (as defined in sdcTable), using the hrc format
from TauArgus, and finally with a more general hierarchy specification
(internally, not surprisingly, simply called hierarchy). Any of these
can be provided to the hierarchies parameter, as they are
all translated to the internal hierarchy representation. For the
purposes of this vignette, we will use dimlists, however in the
following example we shall see how these can be translated to one
another using functions from SSBtools. Let us begin by
defining two hierarchies by using dimlists:
region_dim <- data.frame(levels = c("@", "@@", rep("@@@", 2), rep("@@", 4)),
codes = c("Total", "AB", LETTERS[1:6]))
region_dim
#> levels codes
#> 1 @ Total
#> 2 @@ AB
#> 3 @@@ A
#> 4 @@@ B
#> 5 @@ C
#> 6 @@ D
#> 7 @@ E
#> 8 @@ F
income_dim <- data.frame(levels = c("@", "@@", "@@", "@@@", "@@@", "@@@"),
codes = c("Total", "wages", "not_wages", "other", "assistance", "pensions"))
income_dim
#> levels codes
#> 1 @ Total
#> 2 @@ wages
#> 3 @@ not_wages
#> 4 @@@ other
#> 5 @@@ assistance
#> 6 @@@ pensions
SSBtools::DimList2Hrc(income_dim)
#> [1] "wages" "not_wages" "@other" "@assistance" "@pensions"
SSBtools::DimList2Hierarchy(income_dim)
#> mapsFrom mapsTo sign level
#> 1 wages Total 1 2
#> 2 not_wages Total 1 2
#> 3 other not_wages 1 1
#> 4 assistance not_wages 1 1
#> 5 pensions not_wages 1 1We can use these hierarchies to specify our output table. We do this
by supplying a named list to the hierarchies parameter,
where the list names correspond to variables in the data, and the list
elements correspond to hierarchies we wish to include.
GaussSuppressionFromData(data = dataset,
hierarchies = list(region = region_dim, main_income = income_dim),
freqVar = "freq",
primary = FALSE,
protectZeros = FALSE)
#> region main_income freq primary suppressed
#> 1 Total Total 338 FALSE FALSE
#> 2 Total not_wages 316 FALSE FALSE
#> 3 Total assistance 178 FALSE FALSE
#> 4 Total other 26 FALSE FALSE
#> 5 Total pensions 112 FALSE FALSE
#> 6 Total wages 22 FALSE FALSE
#> 7 AB Total 155 FALSE FALSE
#> 8 AB not_wages 143 FALSE FALSE
#> 9 AB assistance 84 FALSE FALSE
#> 10 AB other 5 FALSE FALSE
#> 11 AB pensions 54 FALSE FALSE
#> 12 AB wages 12 FALSE FALSE
#> 13 A Total 104 FALSE FALSE
#> 14 A not_wages 93 FALSE FALSE
#> 15 A assistance 55 FALSE FALSE
#> 16 A other 2 FALSE FALSE
#> 17 A pensions 36 FALSE FALSE
#> 18 A wages 11 FALSE FALSE
#> 19 B Total 51 FALSE FALSE
#> 20 B not_wages 50 FALSE FALSE
#> 21 B assistance 29 FALSE FALSE
#> 22 B other 3 FALSE FALSE
#> 23 B pensions 18 FALSE FALSE
#> 24 B wages 1 FALSE FALSE
#> 25 C Total 73 FALSE FALSE
#> 26 C not_wages 65 FALSE FALSE
#> 27 C assistance 35 FALSE FALSE
#> 28 C other 5 FALSE FALSE
#> 29 C pensions 25 FALSE FALSE
#> 30 C wages 8 FALSE FALSE
#> 31 D Total 14 FALSE FALSE
#> 32 D not_wages 14 FALSE FALSE
#> 33 D assistance 9 FALSE FALSE
#> 34 D other 3 FALSE FALSE
#> 35 D pensions 2 FALSE FALSE
#> 36 D wages 0 FALSE FALSE
#> 37 E Total 61 FALSE FALSE
#> 38 E not_wages 61 FALSE FALSE
#> 39 E assistance 32 FALSE FALSE
#> 40 E other 9 FALSE FALSE
#> 41 E pensions 20 FALSE FALSE
#> 42 E wages 0 FALSE FALSE
#> 43 F Total 35 FALSE FALSE
#> 44 F not_wages 33 FALSE FALSE
#> 45 F assistance 18 FALSE FALSE
#> 46 F other 4 FALSE FALSE
#> 47 F pensions 11 FALSE FALSE
#> 48 F wages 2 FALSE FALSE
Table 7:
hierarchies = list(region = region_dim, main_income = income_dim)
| region | other | wages | assistance | pensions | not_wages | Total |
|---|---|---|---|---|---|---|
| A | 2 | 11 | 55 | 36 | 93 | 104 |
| B | 3 | 1 | 29 | 18 | 50 | 51 |
| C | 5 | 8 | 35 | 25 | 65 | 73 |
| D | 3 | 0 | 9 | 2 | 14 | 14 |
| E | 9 | 0 | 32 | 20 | 61 | 61 |
| F | 4 | 2 | 18 | 11 | 33 | 35 |
| AB | 5 | 12 | 84 | 54 | 143 | 155 |
| Total | 26 | 22 | 178 | 112 | 316 | 338 |
As mentioned previously, the GaussSuppression package
supports non-nested hierarchies natively. We achieve this by having
multiple elements with the same name in the hierarchies
list:
region2_dim <- data.frame(levels = c("@", rep(c("@@", rep("@@@", 2)), 2), rep("@@", 2)),
codes = c("Total", "AD", "A", "D",
"BF", "B", "F",
"C", "E"))
region2_dim
#> levels codes
#> 1 @ Total
#> 2 @@ AD
#> 3 @@@ A
#> 4 @@@ D
#> 5 @@ BF
#> 6 @@@ B
#> 7 @@@ F
#> 8 @@ C
#> 9 @@ E
GaussSuppressionFromData(data = dataset,
hierarchies = list(region = region_dim, region = region2_dim),
freqVar = "freq",
primary = FALSE,
protectZeros = FALSE)
#> region freq primary suppressed
#> 1 AB 155 FALSE FALSE
#> 2 AD 118 FALSE FALSE
#> 3 BF 86 FALSE FALSE
#> 4 Total 338 FALSE FALSE
#> 5 A 104 FALSE FALSE
#> 6 B 51 FALSE FALSE
#> 7 C 73 FALSE FALSE
#> 8 D 14 FALSE FALSE
#> 9 E 61 FALSE FALSE
#> 10 F 35 FALSE FALSE
Table 8:
hierarchies = list(region = region_dim, region = region2_dim)
| region | |
|---|---|
| A | 104 |
| B | 51 |
| C | 73 |
| D | 14 |
| E | 61 |
| F | 35 |
| AB | 155 |
| AD | 118 |
| BF | 86 |
| Total | 338 |
Finally, as before, all of this functionality works with microdata as input as well.
GaussSuppressionFromData(data = microdata,
hierarchies = list(region = region_dim, region = region2_dim),
freqVar = "freq",
primary = FALSE,
protectZeros = FALSE)
#> region freq primary suppressed
#> 1 AB 155 FALSE FALSE
#> 2 AD 118 FALSE FALSE
#> 3 BF 86 FALSE FALSE
#> 4 Total 338 FALSE FALSE
#> 5 A 104 FALSE FALSE
#> 6 B 51 FALSE FALSE
#> 7 C 73 FALSE FALSE
#> 8 D 14 FALSE FALSE
#> 9 E 61 FALSE FALSE
#> 10 F 35 FALSE FALSEformulaThe most flexible method for specifying the output of
GaussSuppression is by using the formula interface. This
makes use of model formulas in R, and provides a powerful way of
specifying multiple different tables. Indeed, all of the above
examples—and much more—can be replicated using the formula interface.
The formula’s predictor variables must be variable names occuring in the
data set (the dependent variable is ignored, and thus we leave it
empty). In the following, we create a table based on the region and
county variables. As before, the hierarchical relationship between these
variables is detected automatically:
GaussSuppressionFromData(data = microdata,
formula = ~ region + county,
freqVar = "freq",
primary = FALSE,
protectZeros = FALSE)
#> region freq primary suppressed
#> 1 Total 338 FALSE FALSE
#> 2 A 104 FALSE FALSE
#> 3 B 51 FALSE FALSE
#> 4 C 73 FALSE FALSE
#> 5 D 14 FALSE FALSE
#> 6 E 61 FALSE FALSE
#> 7 F 35 FALSE FALSE
#> 8 county-1 118 FALSE FALSE
#> 9 county-2 124 FALSE FALSE
#> 10 county-3 96 FALSE FALSE
Table 9:
formula = ~ region + county
| region | |
|---|---|
| A | 104 |
| B | 51 |
| C | 73 |
| D | 14 |
| E | 61 |
| F | 35 |
| county-1 | 118 |
| county-2 | 124 |
| county-3 | 96 |
| Total | 338 |
If there is no hierarchical relationship between variables,
multiplication in the formula and specification in
dimVar yield the same results.
GaussSuppressionFromData(data = microdata,
formula = ~ county * main_income,
freqVar = "freq",
primary = FALSE,
protectZeros = FALSE)
#> county main_income freq primary suppressed
#> 1 Total Total 338 FALSE FALSE
#> 2 county-1 Total 118 FALSE FALSE
#> 3 county-2 Total 124 FALSE FALSE
#> 4 county-3 Total 96 FALSE FALSE
#> 5 Total assistance 178 FALSE FALSE
#> 6 Total other 26 FALSE FALSE
#> 7 Total pensions 112 FALSE FALSE
#> 8 Total wages 22 FALSE FALSE
#> 9 county-1 assistance 64 FALSE FALSE
#> 10 county-1 other 5 FALSE FALSE
#> 11 county-1 pensions 38 FALSE FALSE
#> 12 county-1 wages 11 FALSE FALSE
#> 13 county-2 assistance 64 FALSE FALSE
#> 14 county-2 other 8 FALSE FALSE
#> 15 county-2 pensions 43 FALSE FALSE
#> 16 county-2 wages 9 FALSE FALSE
#> 17 county-3 assistance 50 FALSE FALSE
#> 18 county-3 other 13 FALSE FALSE
#> 19 county-3 pensions 31 FALSE FALSE
#> 20 county-3 wages 2 FALSE FALSE
GaussSuppressionFromData(data = microdata,
dimVar = c("county" , "main_income"),
freqVar = "freq",
primary = FALSE,
protectZeros = FALSE)
#> county main_income freq primary suppressed
#> 1 Total Total 338 FALSE FALSE
#> 2 Total assistance 178 FALSE FALSE
#> 3 Total other 26 FALSE FALSE
#> 4 Total pensions 112 FALSE FALSE
#> 5 Total wages 22 FALSE FALSE
#> 6 county-1 Total 118 FALSE FALSE
#> 7 county-1 assistance 64 FALSE FALSE
#> 8 county-1 other 5 FALSE FALSE
#> 9 county-1 pensions 38 FALSE FALSE
#> 10 county-1 wages 11 FALSE FALSE
#> 11 county-2 Total 124 FALSE FALSE
#> 12 county-2 assistance 64 FALSE FALSE
#> 13 county-2 other 8 FALSE FALSE
#> 14 county-2 pensions 43 FALSE FALSE
#> 15 county-2 wages 9 FALSE FALSE
#> 16 county-3 Total 96 FALSE FALSE
#> 17 county-3 assistance 50 FALSE FALSE
#> 18 county-3 other 13 FALSE FALSE
#> 19 county-3 pensions 31 FALSE FALSE
#> 20 county-3 wages 2 FALSE FALSE
Table 10:
formula = ~ county * main_income or
dimVar = c("county" , "main_income")
| county | other | wages | assistance | pensions | Total |
|---|---|---|---|---|---|
| county-1 | 5 | 11 | 64 | 38 | 118 |
| county-2 | 8 | 9 | 64 | 43 | 124 |
| county-3 | 13 | 2 | 50 | 31 | 96 |
| Total | 26 | 22 | 178 | 112 | 338 |
However, formula lets us specify different shapes for our
tables. For example, if we are only interested in marginal values, we
can supply this with the use of the addition operator:
GaussSuppressionFromData(data = microdata,
formula = ~ county + main_income,
freqVar = "freq",
primary = FALSE,
protectZeros = FALSE)
#> county main_income freq primary suppressed
#> 1 Total Total 338 FALSE FALSE
#> 2 county-1 Total 118 FALSE FALSE
#> 3 county-2 Total 124 FALSE FALSE
#> 4 county-3 Total 96 FALSE FALSE
#> 5 Total assistance 178 FALSE FALSE
#> 6 Total other 26 FALSE FALSE
#> 7 Total pensions 112 FALSE FALSE
#> 8 Total wages 22 FALSE FALSE
Table 11:
formula = ~ county + main_income
| county | other | wages | assistance | pensions | Total |
|---|---|---|---|---|---|
| county-1 | 118 | ||||
| county-2 | 124 | ||||
| county-3 | 96 | ||||
| Total | 26 | 22 | 178 | 112 | 338 |
This example demonstrates, in fact, the ability of specifying multiple
linked tables: a one-dimensional table for county linked with a
one-dimensional table for main_income. Similarly, we can use the colon
(“:”) operator to omit row and column marginals:
GaussSuppressionFromData(data = microdata,
formula = ~ county:main_income,
freqVar = "freq",
primary = FALSE,
protectZeros = FALSE)
#> county main_income freq primary suppressed
#> 1 Total Total 338 FALSE FALSE
#> 2 county-1 assistance 64 FALSE FALSE
#> 3 county-1 other 5 FALSE FALSE
#> 4 county-1 pensions 38 FALSE FALSE
#> 5 county-1 wages 11 FALSE FALSE
#> 6 county-2 assistance 64 FALSE FALSE
#> 7 county-2 other 8 FALSE FALSE
#> 8 county-2 pensions 43 FALSE FALSE
#> 9 county-2 wages 9 FALSE FALSE
#> 10 county-3 assistance 50 FALSE FALSE
#> 11 county-3 other 13 FALSE FALSE
#> 12 county-3 pensions 31 FALSE FALSE
#> 13 county-3 wages 2 FALSE FALSE
Table 12:
formula = ~ county:main_income
| county | other | wages | assistance | pensions | Total |
|---|---|---|---|---|---|
| county-1 | 5 | 11 | 64 | 38 | |
| county-2 | 8 | 9 | 64 | 43 | |
| county-3 | 13 | 2 | 50 | 31 | |
| Total | 338 |
Using subtraction, we can omit marginals and other cells from the
output. For example, the intercept (sum over all records) can be omitted
by including - 1 in the formula, like this:
formula = county : main_income - 1.
Using these features, we can define more complicated linked tables. To illustrate this, let us assume we wish to publish the following:
To do this, we begin by adding a column encoding whether the main source of income was “wages” or “not_wages”.
dataset$income2 <- ifelse(dataset$main_income == "wages", "wages", "not_wages")
microdata$income2 <- ifelse(microdata$main_income == "wages", "wages", "not_wages")
head(dataset)
#> region county size main_income freq income2
#> 1 A county-1 BIG other 2 not_wages
#> 2 B county-2 BIG other 3 not_wages
#> 3 C county-2 BIG other 5 not_wages
#> 4 D county-1 small other 3 not_wages
#> 5 E county-3 small other 9 not_wages
#> 6 F county-3 small other 4 not_wagesThen we can specify the desired output with the following formula:
GaussSuppressionFromData(data = dataset,
formula = ~ region * income2 + (county + size) * main_income,
freqVar = "freq",
primary = FALSE,
protectZeros = FALSE)
#> region main_income freq primary suppressed
#> 1 Total Total 338 FALSE FALSE
#> 2 A Total 104 FALSE FALSE
#> 3 B Total 51 FALSE FALSE
#> 4 C Total 73 FALSE FALSE
#> 5 D Total 14 FALSE FALSE
#> 6 E Total 61 FALSE FALSE
#> 7 F Total 35 FALSE FALSE
#> 8 Total not_wages 316 FALSE FALSE
#> 9 Total wages 22 FALSE FALSE
#> 10 county-1 Total 118 FALSE FALSE
#> 11 county-2 Total 124 FALSE FALSE
#> 12 county-3 Total 96 FALSE FALSE
#> 13 BIG Total 228 FALSE FALSE
#> 14 small Total 110 FALSE FALSE
#> 15 Total assistance 178 FALSE FALSE
#> 16 Total other 26 FALSE FALSE
#> 17 Total pensions 112 FALSE FALSE
#> 18 Total wages 22 FALSE FALSE
#> 19 A not_wages 93 FALSE FALSE
#> 20 A wages 11 FALSE FALSE
#> 21 B not_wages 50 FALSE FALSE
#> 22 B wages 1 FALSE FALSE
#> 23 C not_wages 65 FALSE FALSE
#> 24 C wages 8 FALSE FALSE
#> 25 D not_wages 14 FALSE FALSE
#> 26 D wages 0 FALSE FALSE
#> 27 E not_wages 61 FALSE FALSE
#> 28 E wages 0 FALSE FALSE
#> 29 F not_wages 33 FALSE FALSE
#> 30 F wages 2 FALSE FALSE
#> 31 county-1 assistance 64 FALSE FALSE
#> 32 county-1 other 5 FALSE FALSE
#> 33 county-1 pensions 38 FALSE FALSE
#> 34 county-1 wages 11 FALSE FALSE
#> 35 county-2 assistance 64 FALSE FALSE
#> 36 county-2 other 8 FALSE FALSE
#> 37 county-2 pensions 43 FALSE FALSE
#> 38 county-2 wages 9 FALSE FALSE
#> 39 county-3 assistance 50 FALSE FALSE
#> 40 county-3 other 13 FALSE FALSE
#> 41 county-3 pensions 31 FALSE FALSE
#> 42 county-3 wages 2 FALSE FALSE
#> 43 BIG assistance 119 FALSE FALSE
#> 44 BIG other 10 FALSE FALSE
#> 45 BIG pensions 79 FALSE FALSE
#> 46 BIG wages 20 FALSE FALSE
#> 47 small assistance 59 FALSE FALSE
#> 48 small other 16 FALSE FALSE
#> 49 small pensions 33 FALSE FALSE
#> 50 small wages 2 FALSE FALSE
Table 13:
formula = ~ region * income2 + (county + size) * main_income
| region | other | wages | assistance | pensions | not_wages | Total |
|---|---|---|---|---|---|---|
| A | 11 | 93 | 104 | |||
| B | 1 | 50 | 51 | |||
| C | 8 | 65 | 73 | |||
| D | 0 | 14 | 14 | |||
| E | 0 | 61 | 61 | |||
| F | 2 | 33 | 35 | |||
| county-1 | 5 | 11 | 64 | 38 | 118 | |
| county-2 | 8 | 9 | 64 | 43 | 124 | |
| county-3 | 13 | 2 | 50 | 31 | 96 | |
| small | 16 | 2 | 59 | 33 | 110 | |
| BIG | 10 | 20 | 119 | 79 | 228 | |
| Total | 26 | 22 | 178 | 112 | 316 | 338 |
In this manner, we can specify multiple linked tables, each of which can use different non-nested hierarchies. This allows the suppression algorithm to protect all of these tables simultaneously (indeed, they are treated as a single table internally), avoiding the need for a stratified protection paradigm. Furthermore, the fine-grained specification of which cells are to be published allows the secondary suppression algorithm to protect with respect to precisely those cells that will be published. If row and column marginals are not published, for example, the suppression algorithm does not need to secondary suppress with respect to these marginals. See the other vignettes in this package for more details on setting up the protection methods.
Looking at the output data above Table 13, you will see that row 9 is
duplicated on row 18. The reason is that the code wages is
used both in the main_income variable and in the
income2 variable. Currently, the formula interface does not
do any special checking for this phenomenon. The recommended practice is
to avoid such duplicate codes. When running FindDimLists,
you will see that this function performs checking.
In addition to defining the dimensions of the output tables, we need
to decide whether they should be frequency tables (where we count
contributing records) or magnititude tables (where we add contributing
records’ numerical values for a given variable). All of the above
examples have been frequency tables. However, the process is exactly the
same if one wishes to construct magnititude tables; the only difference
is that one must specify the numerical variable with the help of the
parameter numVar.
Since most magnitude table suppression methods are based on comparing units’ contributions, the input data will most likely be supplied as microdata. Therefore, let us add a fake numerical variable to our microdata:
set.seed(12345)
microdata$num <- sample(0:1000, nrow(microdata), replace = TRUE) Then in order to construct a volume table where records’
contributions to num are aggregated, we supply this as a
parameter to GaussSuppressionFromData:
GaussSuppressionFromData(data = microdata,
formula = ~ region * income2 + (county + size) * main_income,
numVar = "num",
primary = FALSE,
protectZeros = FALSE)
#> [preAggregate 338*7->22*7]
#> region main_income freq.1 num primary suppressed
#> 1 Total Total 338 168843 FALSE FALSE
#> 2 A Total 104 50640 FALSE FALSE
#> 3 B Total 51 27386 FALSE FALSE
#> 4 C Total 73 35826 FALSE FALSE
#> 5 D Total 14 5730 FALSE FALSE
#> 6 E Total 61 30295 FALSE FALSE
#> 7 F Total 35 18966 FALSE FALSE
#> 8 Total not_wages 316 157199 FALSE FALSE
#> 9 Total wages 22 11644 FALSE FALSE
#> 10 county-1 Total 118 56370 FALSE FALSE
#> 11 county-2 Total 124 63212 FALSE FALSE
#> 12 county-3 Total 96 49261 FALSE FALSE
#> 13 BIG Total 228 113852 FALSE FALSE
#> 14 small Total 110 54991 FALSE FALSE
#> 15 Total assistance 178 91989 FALSE FALSE
#> 16 Total other 26 13000 FALSE FALSE
#> 17 Total pensions 112 52210 FALSE FALSE
#> 18 Total wages 22 11644 FALSE FALSE
#> 19 A not_wages 93 45268 FALSE FALSE
#> 20 A wages 11 5372 FALSE FALSE
#> 21 B not_wages 50 26500 FALSE FALSE
#> 22 B wages 1 886 FALSE FALSE
#> 23 C not_wages 65 32111 FALSE FALSE
#> 24 C wages 8 3715 FALSE FALSE
#> 25 D not_wages 14 5730 FALSE FALSE
#> 26 E not_wages 61 30295 FALSE FALSE
#> 27 F not_wages 33 17295 FALSE FALSE
#> 28 F wages 2 1671 FALSE FALSE
#> 29 county-1 assistance 64 33024 FALSE FALSE
#> 30 county-1 other 5 1260 FALSE FALSE
#> 31 county-1 pensions 38 16714 FALSE FALSE
#> 32 county-1 wages 11 5372 FALSE FALSE
#> 33 county-2 assistance 64 33424 FALSE FALSE
#> 34 county-2 other 8 4696 FALSE FALSE
#> 35 county-2 pensions 43 20491 FALSE FALSE
#> 36 county-2 wages 9 4601 FALSE FALSE
#> 37 county-3 assistance 50 25541 FALSE FALSE
#> 38 county-3 other 13 7044 FALSE FALSE
#> 39 county-3 pensions 31 15005 FALSE FALSE
#> 40 county-3 wages 2 1671 FALSE FALSE
#> 41 BIG assistance 119 62407 FALSE FALSE
#> 42 BIG other 10 4887 FALSE FALSE
#> 43 BIG pensions 79 36585 FALSE FALSE
#> 44 BIG wages 20 9973 FALSE FALSE
#> 45 small assistance 59 29582 FALSE FALSE
#> 46 small other 16 8113 FALSE FALSE
#> 47 small pensions 33 15625 FALSE FALSE
#> 48 small wages 2 1671 FALSE FALSE| region | other | wages | assistance | pensions | not_wages | Total |
|---|---|---|---|---|---|---|
| A | 5372 ( 11) | 45268 ( 93) | 50640 (104) | |||
| B | 886 ( 1) | 26500 ( 50) | 27386 ( 51) | |||
| C | 3715 ( 8) | 32111 ( 65) | 35826 ( 73) | |||
| D | 5730 ( 14) | 5730 ( 14) | ||||
| E | 30295 ( 61) | 30295 ( 61) | ||||
| F | 1671 ( 2) | 17295 ( 33) | 18966 ( 35) | |||
| county-1 | 1260 ( 5) | 5372 ( 11) | 33024 ( 64) | 16714 ( 38) | 56370 (118) | |
| county-2 | 4696 ( 8) | 4601 ( 9) | 33424 ( 64) | 20491 ( 43) | 63212 (124) | |
| county-3 | 7044 ( 13) | 1671 ( 2) | 25541 ( 50) | 15005 ( 31) | 49261 ( 96) | |
| small | 8113 ( 16) | 1671 ( 2) | 29582 ( 59) | 15625 ( 33) | 54991 (110) | |
| BIG | 4887 ( 10) | 9973 ( 20) | 62407 (119) | 36585 ( 79) | 113852 (228) | |
| Total | 13000 ( 26) | 11644 ( 22) | 91989 (178) | 52210 (112) | 157199 (316) | 168843 (338) |
Note that there are two empty cells in the wages column. This means
that these cells are not included in the output data. One reason is that
the removeEmpty parameter to
SSBtools::ModelMatrix has TRUE as default in
the case of a formula interface. By including
removeEmpty = FALSE, zeros will be included in the output.
Another way to achieve this is to use extend0 = TRUE. By
this parameter, zeros are added to the input data after the automatic
aggregation from microdata. As you will see in other vignettes in this
package, the extend0 parameter can be important for
suppression methods.
Note also that a new frequency variable is generated with the above
call. If a frequency variable is already present in the input data, we
can provide it in addition to numVar and the method will
use that information instead:
GaussSuppressionFromData(data = microdata,
formula = ~ region * income2 + (county + size) * main_income,
freqVar = "freq",
numVar = "num",
primary = FALSE,
protectZeros = FALSE)
#> region main_income freq num primary suppressed
#> 1 Total Total 338 168843 FALSE FALSE
#> 2 A Total 104 50640 FALSE FALSE
#> 3 B Total 51 27386 FALSE FALSE
#> 4 C Total 73 35826 FALSE FALSE
#> 5 D Total 14 5730 FALSE FALSE
#> 6 E Total 61 30295 FALSE FALSE
#> 7 F Total 35 18966 FALSE FALSE
#> 8 Total not_wages 316 157199 FALSE FALSE
#> 9 Total wages 22 11644 FALSE FALSE
#> 10 county-1 Total 118 56370 FALSE FALSE
#> 11 county-2 Total 124 63212 FALSE FALSE
#> 12 county-3 Total 96 49261 FALSE FALSE
#> 13 BIG Total 228 113852 FALSE FALSE
#> 14 small Total 110 54991 FALSE FALSE
#> 15 Total assistance 178 91989 FALSE FALSE
#> 16 Total other 26 13000 FALSE FALSE
#> 17 Total pensions 112 52210 FALSE FALSE
#> 18 Total wages 22 11644 FALSE FALSE
#> 19 A not_wages 93 45268 FALSE FALSE
#> 20 A wages 11 5372 FALSE FALSE
#> 21 B not_wages 50 26500 FALSE FALSE
#> 22 B wages 1 886 FALSE FALSE
#> 23 C not_wages 65 32111 FALSE FALSE
#> 24 C wages 8 3715 FALSE FALSE
#> 25 D not_wages 14 5730 FALSE FALSE
#> 26 E not_wages 61 30295 FALSE FALSE
#> 27 F not_wages 33 17295 FALSE FALSE
#> 28 F wages 2 1671 FALSE FALSE
#> 29 county-1 assistance 64 33024 FALSE FALSE
#> 30 county-1 other 5 1260 FALSE FALSE
#> 31 county-1 pensions 38 16714 FALSE FALSE
#> 32 county-1 wages 11 5372 FALSE FALSE
#> 33 county-2 assistance 64 33424 FALSE FALSE
#> 34 county-2 other 8 4696 FALSE FALSE
#> 35 county-2 pensions 43 20491 FALSE FALSE
#> 36 county-2 wages 9 4601 FALSE FALSE
#> 37 county-3 assistance 50 25541 FALSE FALSE
#> 38 county-3 other 13 7044 FALSE FALSE
#> 39 county-3 pensions 31 15005 FALSE FALSE
#> 40 county-3 wages 2 1671 FALSE FALSE
#> 41 BIG assistance 119 62407 FALSE FALSE
#> 42 BIG other 10 4887 FALSE FALSE
#> 43 BIG pensions 79 36585 FALSE FALSE
#> 44 BIG wages 20 9973 FALSE FALSE
#> 45 small assistance 59 29582 FALSE FALSE
#> 46 small other 16 8113 FALSE FALSE
#> 47 small pensions 33 15625 FALSE FALSE
#> 48 small wages 2 1671 FALSE FALSEThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.