This function provides functionality for suppressing magnitude tables based on
the few contributors rule (NContributorsRule).
Usage
SuppressFewContributors(
data,
maxN,
numVar = NULL,
dimVar = NULL,
hierarchies = NULL,
formula = NULL,
contributorVar = NULL,
removeCodes = character(0),
remove0 = TRUE,
candidatesVar = NULL,
...,
spec = PackageSpecs("fewContributorsSpec")
)Arguments
- data
Input data, typically a data frame, tibble, or data.table. If
datais not a classic data frame, it will be coerced to one internally unlesspreAggregateisTRUEandaggregatePackageis"data.table".- maxN
Suppression parameter. Cells with frequency
<= maxNare set as primary suppressed. Using the defaultprimaryfunction,maxNis by default set to3. See details.- numVar
Numerical variable to be aggregated. Any
candidatesVarthat is specified and not included innumVarwill be aggregated accordingly. Additionally, ifremove0is specified as a variable name and it is not included innumVar, it will also be aggregated accordingly. See parameterscandidatesVarandremove0below.- dimVar
The main dimensional variables and additional aggregating variables. This parameter can be useful when hierarchies and formula are unspecified.
- hierarchies
List of hierarchies, which can be converted by
AutoHierarchies. Thus, the variables can also be coded by"rowFactor"or"", which correspond to using the categories in the data.- formula
A model formula
- contributorVar
Extra variables to be used as grouping elements when counting contributors. Typically, the variable contains the contributor IDs.
- removeCodes
Vector of codes to be omitted when counting contributors. With empty
contributorVarrow indices are assumed and conversion to integer is performed.- remove0
When set to
TRUE(default), data rows in which the firstnumVar(if any) is zero are excluded from the count of contributors. Alternatively,remove0can be specified as one or more variable names. In this case, all data rows with a zero in any of the specified variables are omitted from the contributor count. Specifyingremove0as variable name(s) is useful for avoiding warning when there are multiplenumVarvariables.- candidatesVar
Variable to be used in the candidate function to prioritize cells for publication and thus not suppression. The first
numVarvariable will be used if it is not specified.- ...
Further arguments to be passed to the supplied functions and to
ModelMatrix(such asinputInOutputandremoveEmpty).- spec
NULLor a named list of arguments that will act as default values.
Value
data.frame containing aggregated data and supppression information.
Columns nRule and nAll contain the number of contributors.
In the former, removeCodes is taken into account.
Examples
num <- c(100,
90, 10,
80, 20,
70, 30,
50, 25, 25,
40, 20, 20, 20,
25, 25, 25, 25)
v1 <- c("v1",
rep(c("v2", "v3", "v4"), each = 2),
rep("v5", 3),
rep(c("v6", "v7"), each = 4))
sweight <- c(1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1)
d <- data.frame(v1 = v1, num = num, sweight = sweight)
SuppressFewContributors(d, formula = ~v1, maxN = 1, numVar = "num")
#> [extraAggregate 18*2->7*3] Checking ....
#> GaussSuppression_numttHTT: .......
#> v1 num nRule nAll primary suppressed
#> 1 Total 700 18 18 FALSE FALSE
#> 2 v1 100 1 1 TRUE TRUE
#> 3 v2 100 2 2 FALSE FALSE
#> 4 v3 100 2 2 FALSE FALSE
#> 5 v4 100 2 2 FALSE TRUE
#> 6 v5 100 3 3 FALSE FALSE
#> 7 v6 100 4 4 FALSE FALSE
#> 8 v7 100 4 4 FALSE FALSE
SuppressFewContributors(d, formula = ~v1, maxN = 2, numVar = "num")
#> [extraAggregate 18*2->7*3] Checking ....
#> GaussSuppression_numttHTT: ....
#> v1 num nRule nAll primary suppressed
#> 1 Total 700 18 18 FALSE FALSE
#> 2 v1 100 1 1 TRUE TRUE
#> 3 v2 100 2 2 TRUE TRUE
#> 4 v3 100 2 2 TRUE TRUE
#> 5 v4 100 2 2 TRUE TRUE
#> 6 v5 100 3 3 FALSE FALSE
#> 7 v6 100 4 4 FALSE FALSE
#> 8 v7 100 4 4 FALSE FALSE
SuppressFewContributors(d, formula = ~v1, maxN = 3, numVar = "num")
#> [extraAggregate 18*2->7*3] Checking ....
#> GaussSuppression_numttHTT: ...
#> v1 num nRule nAll primary suppressed
#> 1 Total 700 18 18 FALSE FALSE
#> 2 v1 100 1 1 TRUE TRUE
#> 3 v2 100 2 2 TRUE TRUE
#> 4 v3 100 2 2 TRUE TRUE
#> 5 v4 100 2 2 TRUE TRUE
#> 6 v5 100 3 3 TRUE TRUE
#> 7 v6 100 4 4 FALSE FALSE
#> 8 v7 100 4 4 FALSE FALSE
d2 <- SSBtoolsData("d2")[-5]
set.seed(123)
d2$v <- round(rnorm(nrow(d2))^2, 1)
d2$family_id <- round(2*as.integer(factor(d2$region)) + runif(nrow(d2)))
# Hierarchical region variables are detected automatically -> same output column
SuppressFewContributors(data = d2, maxN = 2, numVar = "v", contributorVar = "family_id",
dimVar = c("region", "county", "k_group"))
#> [preAggregate 44*6->20*6]
#> [extraAggregate 20*6->11*6] Checking .....
#> GaussSuppression_numttHTT: ....::::
#> region freq v nRule nAll primary suppressed
#> 1 1 8 3.2 3 3 FALSE TRUE
#> 2 10 8 10.0 4 4 FALSE TRUE
#> 3 300 32 27.3 14 14 FALSE FALSE
#> 4 4 4 1.5 2 2 TRUE TRUE
#> 5 400 12 10.9 5 5 FALSE FALSE
#> 6 5 8 6.7 3 3 FALSE TRUE
#> 7 6 8 7.1 3 3 FALSE FALSE
#> 8 8 8 9.7 4 4 FALSE FALSE
#> 9 Total 44 38.2 19 19 FALSE FALSE
#> 10 A 4 2.3 2 2 TRUE TRUE
#> 11 B 4 1.5 2 2 TRUE TRUE
#> 12 C 4 3.3 1 1 TRUE TRUE
#> 13 D 4 3.4 2 2 TRUE TRUE
#> 14 E 4 3.9 1 1 TRUE TRUE
#> 15 F 4 3.2 2 2 TRUE TRUE
#> 16 G 4 5.5 2 2 TRUE TRUE
#> 17 H 4 4.2 2 2 TRUE TRUE
#> 18 I 4 0.9 1 1 TRUE TRUE
#> 19 J 4 3.0 2 2 TRUE TRUE
#> 20 K 4 7.0 2 2 TRUE TRUE
# Formula. Hierarchical variables still detected automatically.
# And codes 1:9 not counted
SuppressFewContributors(data = d2, maxN = 1, numVar = "v", contributorVar = "family_id",
formula = ~main_income * k_group + region + county - k_group,
removeCodes = 1:9)
#> [preAggregate 44*6->44*7]
#> [extraAggregate 44*7->44*7] Checking .....
#> GaussSuppression_numttHTT: .......:::::::::::::::::::
#> main_income region freq v nRule nAll primary suppressed
#> 1 Total Total 44 38.2 12 19 FALSE FALSE
#> 2 assistance Total 11 9.5 6 10 FALSE FALSE
#> 3 other Total 11 9.7 6 9 FALSE FALSE
#> 4 pensions Total 11 9.3 5 9 FALSE FALSE
#> 5 wages Total 11 9.7 6 9 FALSE FALSE
#> 6 Total A 4 2.3 0 2 FALSE TRUE
#> 7 Total B 4 1.5 0 2 FALSE FALSE
#> 8 Total C 4 3.3 0 1 FALSE FALSE
#> 9 Total D 4 3.4 0 2 FALSE FALSE
#> 10 Total E 4 3.9 1 1 TRUE TRUE
#> 11 Total F 4 3.2 2 2 FALSE TRUE
#> 12 Total G 4 5.5 2 2 FALSE TRUE
#> 13 Total H 4 4.2 2 2 FALSE TRUE
#> 14 Total I 4 0.9 1 1 TRUE TRUE
#> 15 Total J 4 3.0 2 2 FALSE TRUE
#> 16 Total K 4 7.0 2 2 FALSE TRUE
#> 17 Total 1 8 3.2 1 3 TRUE TRUE
#> 18 Total 4 4 1.5 0 2 FALSE FALSE
#> 19 Total 5 8 6.7 0 3 FALSE FALSE
#> 20 Total 6 8 7.1 3 3 FALSE TRUE
#> 21 Total 8 8 9.7 4 4 FALSE FALSE
#> 22 Total 10 8 10.0 4 4 FALSE FALSE
#> 23 assistance 300 8 8.4 3 7 FALSE FALSE
#> 24 assistance 400 3 1.1 3 3 FALSE FALSE
#> 25 other 300 8 7.5 3 6 FALSE FALSE
#> 26 other 400 3 2.2 3 3 FALSE FALSE
#> 27 pensions 300 8 3.0 3 7 FALSE TRUE
#> 28 pensions 400 3 6.3 2 2 FALSE TRUE
#> 29 wages 300 8 8.4 4 7 FALSE FALSE
#> 30 wages 400 3 1.3 2 2 FALSE FALSE
# With hierarchies created manually
ml <- data.frame(levels = c("@", "@@", "@@@", "@@@", "@@@", "@@"),
codes = c("Total", "not_assistance", "other", "pensions", "wages", "assistance"))
SuppressFewContributors(data = d2, maxN = 2, numVar = "v", contributorVar = "family_id",
hierarchies = list(main_income = ml, k_group = "Total_Norway"))
#> [preAggregate 44*6->44*5]
#> [extraAggregate 44*5->8*5] Checking .....
#> GaussSuppression_numttHTT: ................
#> main_income k_group freq v nRule nAll primary suppressed
#> 1 Total Total_Norway 44 38.2 19 19 FALSE FALSE
#> 2 Total 300 32 27.3 14 14 FALSE FALSE
#> 3 Total 400 12 10.9 5 5 FALSE FALSE
#> 4 not_assistance Total_Norway 33 28.7 18 18 FALSE FALSE
#> 5 not_assistance 300 24 18.9 13 13 FALSE FALSE
#> 6 not_assistance 400 9 9.8 5 5 FALSE FALSE
#> 7 assistance Total_Norway 11 9.5 10 10 FALSE FALSE
#> 8 assistance 300 8 8.4 7 7 FALSE FALSE
#> 9 assistance 400 3 1.1 3 3 FALSE FALSE
#> 10 other Total_Norway 11 9.7 9 9 FALSE FALSE
#> 11 other 300 8 7.5 6 6 FALSE FALSE
#> 12 other 400 3 2.2 3 3 FALSE FALSE
#> 13 pensions Total_Norway 11 9.3 9 9 FALSE FALSE
#> 14 pensions 300 8 3.0 7 7 FALSE TRUE
#> 15 pensions 400 3 6.3 2 2 TRUE TRUE
#> 16 wages Total_Norway 11 9.7 9 9 FALSE FALSE
#> 17 wages 300 8 8.4 7 7 FALSE TRUE
#> 18 wages 400 3 1.3 2 2 TRUE TRUE