This class allows to define statistical tables and perturb both count and numerical variables.
ck_setup(x, rkey, dims, w = NULL, countvars = NULL, numvars = NULL)
an object coercible to a data.frame
either a column name within x
referring to a variable containing record keys
or a single integer(ish) number > 5
that referns to the number of digits for record keys that
will be generated internally.
a list containing slots for each variable that should be
tabulated. Each slot consists should be created/modified using sdcHierarchies::hier_create()
,
sdcHierarchies::hier_add()
and other functionality from package sdcHierarchies
.
(character) a scalar character referring to a variable in x
holding sampling
weights. If w
is NULL
(the default), all weights are assumed to be 1
(character) an optional vector containing names of binary (0/1 coded)
variables withing x
that should be included in the problem instance.
These variables can later be perturbed.
(character) an optional vector of numerical variables that can later be tabulated.
A new cellkey_obj
object. Such objects (internally) contain the fully computed
statistical tables given input microdata (x
), the hierarchical definitionals (dims
) as
well as the remaining inputs. Intermediate results are stored internally and can only be
modified / accessed via the exported public methods described below.
Such objects are typically generated using ck_setup()
.
new()
Create a new table instance
ck_class$new(x, rkey, dims, w = NULL, countvars = NULL, numvars = NULL)
x
an object coercible to a data.frame
rkey
either a column name within x
referring to a variable containing record keys
or a single integer(ish) number > 5
that referns to the number of digits for record keys that
will be generated internally.
dims
a list containing slots for each variable that should be
tabulated. Each slot consists should be created/modified using sdcHierarchies::hier_create()
,
sdcHierarchies::hier_add()
and other functionality from package sdcHierarchies
.
w
(character) a scalar character referring to a variable in x
holding sampling
weights. If w
is NULL
(the default), all weights are assumed to be 1
countvars
(character) an optional vector containing names of binary (0/1 coded)
variables withing x
that should be included in the problem instance.
These variables can later be perturbed.
numvars
(character) an optional vector of numerical variables that can later be tabulated.
A new cellkey_obj
object. Such objects (internally) contain the fully computed
statistical tables given input microdata (x
), the hierarchical definitionals (dims
) as
well as the remaining inputs. Intermediate results are stored internally and can only be
modified / accessed via the exported public methods described below.
freqtab()
Extract results from already perturbed count variables as a
data.table
v
a vector of variable names for count variables. If NULL
(the default), the results are returned for all available count
variables. For variables that have not yet perturbed, columns
puwc
and pwc
are filled with NA
.
path
if not NULL
, a scalar character defining a (relative
or absolute) path to which the result table should be written. A csv
file will be generated and, if specified, path
must have
".csv" as file-ending
This method returns a data.table
containing all combinations of the dimensional variables in
the first n columns. Additionally, the following columns are shown:
vname
: name of the perturbed variable
uwc
: unweighted counts
wc
: weighted counts
puwc
: perturbed unweighted counts or NA
if vname
was not yet perturbed
pwc
: perturbed weighted counts or NA
if vname
was not yet perturbed
numtab()
Extract results from already perturbed continuous variables
as a data.table
.
v
a vector of variable names of continuous variables. If NULL
(the default), the results are returned for all available numeric variables.
mean_before_sum
(logical); if TRUE
, the perturbed values are adjusted
by a factor ((n+p))⁄n
with
n
: the original weighted cell value
p
: the perturbed cell value
This makes sense if the the accuracy of the variable mean is considered to be
more important than accuracy of sums of the variable. The default value is
FALSE
(no adjustment is done)
path
if not NULL
, a scalar character defining a (relative or absolute)
path to which the result table should be written. A csv
file will be generated
and, if specified, path
must have ".csv" as file-ending
This method returns a data.table
containing all combinations of the
dimensional variables in the first n columns. Additionally, the following
columns are shown:
vname
: name of the perturbed variable
uws
: unweighted sum of the given variable
ws
: weighted cellsum
pws
: perturbed weighted sum of the given cell or NA
if vname
has not not perturbed
measures_cnts()
Utility measures for perturbed count variables
v
name of a count variable for which utility measures should be computed.
exclude_zeros
should empty (zero) cells in the original values be excluded when computing distance measures
This method returns a list
containing a set of utility
measures based on some distance functions. For a detailed description
of the computed measures, see ck_cnt_measures()
hierarchy_info()
Information about hierarchies
a list
(for each dimensional variable) with
information on the hierarchies. This may be used to restrict output tables to
specific levels or codes. Each list element is a data.table
containing
the following variables:
code
: the name of a code within the hierarchy
level
: number defining the level of the code; the higher the number,
the lower the hierarchy with 1
being the overall total
is_leaf
: if TRUE
, this code is a leaf node which means no other codes
contribute to it
parent
: name of the parent code
supp_freq()
Identify sensitive cells based on minimum frequency rule
v
a single variable name of a continuous variable (see method numvars()
)
n
a number defining the threshold. All cells <= n
are considered as unsafe.
weighted
if TRUE
, the weighted number of contributors to a cell are compared to
the threshold specified in n
(default); else the unweighted number of contributors is used.
supp_val()
Identify sensitive cells based on weighted or unweighted cell value
v
a single variable name of a continuous variable (see method numvars()
)
n
a number defining the threshold. All cells <= n
are considered as unsafe.
weighted
if TRUE
, the weighted cell value of variable v
is compared to
the threshold specified in n
(default); else the unweighted number is used.
supp_cells()
Identify sensitive cells based on their names
v
a single variable name of a continuous variable (see method numvars()
)
inp
a data.frame
where each colum represents a dimensional variable. Each row of
this input is then used to compute the relevant cells to be identified as sensitive where
NA
-values are possible and used to match any characteristics of the dimensional variable.
supp_p()
Identify sensitive cells based on the p%-rule rule. Please note that this rule can only be applied to positive-only variables.
supp_pq()
Identify sensitive cells based on the pq-rule. Please note that this rule can only be applied to positive-only variables.
supp_nk()
Identify sensitive cells based on the nk-dominance rule. Please note that this rule can only be applied to positive-only variables.
params_cnts_set()
Set perturbation parameters for count variables
val
a perturbation object created with ck_params_cnts()
v
a character vector (or NULL
). If NULL
(the default),
the perturbation parameters provided in val
are set for all
count variables; otherwise one may specify the names of
the count variables for which the parameters should be set.
reset_cntvars()
reset results and parameters for already perturbed count variables
reset_numvars()
reset results and parameters for already perturbed numerical variables
params_nums_set()
set perturbation parameters for continuous variables.
val
a perturbation object created with ck_params_nums()
v
a character vector (or NULL
); if NULL
(the default), the
perturbation parameters provided in val
are set for all continuous
variables; otherwise one may specify the names of the numeric variables for
which the parameters should be set.
summary()
some aggregated summary statistics about perturbed variables
print()
prints information about the current table
# \donttest{
x <- ck_create_testdata()
# create some 0/1 variables that should be perturbed later
x[, cnt_females := ifelse(sex == "male", 0, 1)]
#> urbrur roof walls water electcon relat sex age hhcivil expend
#> <int> <int> <int> <int> <int> <int> <fctr> <fctr> <int> <num>
#> 1: 2 4 3 3 1 1 male age_group3 2 9093
#> 2: 2 4 3 3 1 2 female age_group3 2 2734
#> 3: 2 4 3 3 1 3 male age_group1 1 2652
#> 4: 2 4 3 3 1 3 male age_group1 1 1807
#> 5: 2 4 2 3 1 1 male age_group4 2 671
#> ---
#> 4576: 2 4 3 4 1 2 female age_group3 2 3696
#> 4577: 2 4 3 4 1 3 male age_group1 1 282
#> 4578: 2 4 3 4 1 3 male age_group1 1 840
#> 4579: 2 4 3 4 1 3 female age_group1 1 6258
#> 4580: 2 4 3 4 1 3 male age_group1 1 7019
#> income savings ori_hid sampling_weight household_weights cnt_females
#> <num> <num> <int> <int> <num> <num>
#> 1: 5780 12 1 64 25.00000 0
#> 2: 2530 28 1 42 25.00000 1
#> 3: 6920 550 1 95 25.00000 0
#> 4: 7960 870 1 82 25.00000 0
#> 5: 9030 20 2 66 16.66667 0
#> ---
#> 4576: 7900 278 1000 67 16.66667 1
#> 4577: 1420 987 1000 51 16.66667 0
#> 4578: 8900 684 1000 92 16.66667 0
#> 4579: 3880 294 1000 40 16.66667 1
#> 4580: 4830 911 1000 39 16.66667 0
x[, cnt_males := ifelse(sex == "male", 1, 0)]
#> urbrur roof walls water electcon relat sex age hhcivil expend
#> <int> <int> <int> <int> <int> <int> <fctr> <fctr> <int> <num>
#> 1: 2 4 3 3 1 1 male age_group3 2 9093
#> 2: 2 4 3 3 1 2 female age_group3 2 2734
#> 3: 2 4 3 3 1 3 male age_group1 1 2652
#> 4: 2 4 3 3 1 3 male age_group1 1 1807
#> 5: 2 4 2 3 1 1 male age_group4 2 671
#> ---
#> 4576: 2 4 3 4 1 2 female age_group3 2 3696
#> 4577: 2 4 3 4 1 3 male age_group1 1 282
#> 4578: 2 4 3 4 1 3 male age_group1 1 840
#> 4579: 2 4 3 4 1 3 female age_group1 1 6258
#> 4580: 2 4 3 4 1 3 male age_group1 1 7019
#> income savings ori_hid sampling_weight household_weights cnt_females
#> <num> <num> <int> <int> <num> <num>
#> 1: 5780 12 1 64 25.00000 0
#> 2: 2530 28 1 42 25.00000 1
#> 3: 6920 550 1 95 25.00000 0
#> 4: 7960 870 1 82 25.00000 0
#> 5: 9030 20 2 66 16.66667 0
#> ---
#> 4576: 7900 278 1000 67 16.66667 1
#> 4577: 1420 987 1000 51 16.66667 0
#> 4578: 8900 684 1000 92 16.66667 0
#> 4579: 3880 294 1000 40 16.66667 1
#> 4580: 4830 911 1000 39 16.66667 0
#> cnt_males
#> <num>
#> 1: 1
#> 2: 0
#> 3: 1
#> 4: 1
#> 5: 1
#> ---
#> 4576: 0
#> 4577: 1
#> 4578: 1
#> 4579: 0
#> 4580: 1
x[, cnt_highincome := ifelse(income >= 9000, 1, 0)]
#> urbrur roof walls water electcon relat sex age hhcivil expend
#> <int> <int> <int> <int> <int> <int> <fctr> <fctr> <int> <num>
#> 1: 2 4 3 3 1 1 male age_group3 2 9093
#> 2: 2 4 3 3 1 2 female age_group3 2 2734
#> 3: 2 4 3 3 1 3 male age_group1 1 2652
#> 4: 2 4 3 3 1 3 male age_group1 1 1807
#> 5: 2 4 2 3 1 1 male age_group4 2 671
#> ---
#> 4576: 2 4 3 4 1 2 female age_group3 2 3696
#> 4577: 2 4 3 4 1 3 male age_group1 1 282
#> 4578: 2 4 3 4 1 3 male age_group1 1 840
#> 4579: 2 4 3 4 1 3 female age_group1 1 6258
#> 4580: 2 4 3 4 1 3 male age_group1 1 7019
#> income savings ori_hid sampling_weight household_weights cnt_females
#> <num> <num> <int> <int> <num> <num>
#> 1: 5780 12 1 64 25.00000 0
#> 2: 2530 28 1 42 25.00000 1
#> 3: 6920 550 1 95 25.00000 0
#> 4: 7960 870 1 82 25.00000 0
#> 5: 9030 20 2 66 16.66667 0
#> ---
#> 4576: 7900 278 1000 67 16.66667 1
#> 4577: 1420 987 1000 51 16.66667 0
#> 4578: 8900 684 1000 92 16.66667 0
#> 4579: 3880 294 1000 40 16.66667 1
#> 4580: 4830 911 1000 39 16.66667 0
#> cnt_males cnt_highincome
#> <num> <num>
#> 1: 1 0
#> 2: 0 0
#> 3: 1 0
#> 4: 1 0
#> 5: 1 1
#> ---
#> 4576: 0 0
#> 4577: 1 0
#> 4578: 1 0
#> 4579: 0 0
#> 4580: 1 0
# a variable with positive and negative contributions
x[, mixed := sample(-10:10, nrow(x), replace = TRUE)]
#> urbrur roof walls water electcon relat sex age hhcivil expend
#> <int> <int> <int> <int> <int> <int> <fctr> <fctr> <int> <num>
#> 1: 2 4 3 3 1 1 male age_group3 2 9093
#> 2: 2 4 3 3 1 2 female age_group3 2 2734
#> 3: 2 4 3 3 1 3 male age_group1 1 2652
#> 4: 2 4 3 3 1 3 male age_group1 1 1807
#> 5: 2 4 2 3 1 1 male age_group4 2 671
#> ---
#> 4576: 2 4 3 4 1 2 female age_group3 2 3696
#> 4577: 2 4 3 4 1 3 male age_group1 1 282
#> 4578: 2 4 3 4 1 3 male age_group1 1 840
#> 4579: 2 4 3 4 1 3 female age_group1 1 6258
#> 4580: 2 4 3 4 1 3 male age_group1 1 7019
#> income savings ori_hid sampling_weight household_weights cnt_females
#> <num> <num> <int> <int> <num> <num>
#> 1: 5780 12 1 64 25.00000 0
#> 2: 2530 28 1 42 25.00000 1
#> 3: 6920 550 1 95 25.00000 0
#> 4: 7960 870 1 82 25.00000 0
#> 5: 9030 20 2 66 16.66667 0
#> ---
#> 4576: 7900 278 1000 67 16.66667 1
#> 4577: 1420 987 1000 51 16.66667 0
#> 4578: 8900 684 1000 92 16.66667 0
#> 4579: 3880 294 1000 40 16.66667 1
#> 4580: 4830 911 1000 39 16.66667 0
#> cnt_males cnt_highincome mixed
#> <num> <num> <int>
#> 1: 1 0 -6
#> 2: 0 0 2
#> 3: 1 0 -2
#> 4: 1 0 0
#> 5: 1 1 8
#> ---
#> 4576: 0 0 -2
#> 4577: 1 0 5
#> 4578: 1 0 -4
#> 4579: 0 0 7
#> 4580: 1 0 -9
# create record keys
x$rkey <- ck_generate_rkeys(dat = x)
# define required inputs
# hierarchy with some bogus codes
d_sex <- hier_create(root = "Total", nodes = c("male", "female"))
d_sex <- hier_add(d_sex, root = "female", "f")
d_sex <- hier_add(d_sex, root = "male", "m")
d_age <- hier_create(root = "Total", nodes = paste0("age_group", 1:6))
d_age <- hier_add(d_age, root = "age_group1", "ag1a")
d_age <- hier_add(d_age, root = "age_group2", "ag2a")
# define the cell key object
countvars <- c("cnt_females", "cnt_males", "cnt_highincome")
numvars <- c("expend", "income", "savings", "mixed")
tab <- ck_setup(
x = x,
rkey = "rkey",
dims = list(sex = d_sex, age = d_age),
w = "sampling_weight",
countvars = countvars,
numvars = numvars)
#> computing contributing indices | rawdata <--> table; this might take a while
# show some information about this table instance
tab$print() # identical with print(tab)
#> ── Table Information ───────────────────────────────────────────────────────────
#> ✔ 45 cells in 2 dimensions ('sex', 'age')
#> ✔ weights: yes
#> ── Tabulated / Perturbed countvars ─────────────────────────────────────────────
#> ☐ 'total'
#> ☐ 'cnt_females'
#> ☐ 'cnt_males'
#> ☐ 'cnt_highincome'
#> ── Tabulated / Perturbed numvars ───────────────────────────────────────────────
#> ☐ 'expend'
#> ☐ 'income'
#> ☐ 'savings'
#> ☐ 'mixed'
# information about the hierarchies
tab$hierarchy_info()
#> $sex
#> code level is_leaf parent
#> <char> <int> <lgcl> <char>
#> 1: Total 1 FALSE Total
#> 2: male 2 FALSE Total
#> 3: m 3 TRUE male
#> 4: female 2 FALSE Total
#> 5: f 3 TRUE female
#>
#> $age
#> code level is_leaf parent
#> <char> <int> <lgcl> <char>
#> 1: Total 1 FALSE Total
#> 2: age_group1 2 FALSE Total
#> 3: ag1a 3 TRUE age_group1
#> 4: age_group2 2 FALSE Total
#> 5: ag2a 3 TRUE age_group2
#> 6: age_group3 2 TRUE Total
#> 7: age_group4 2 TRUE Total
#> 8: age_group5 2 TRUE Total
#> 9: age_group6 2 TRUE Total
#>
# which variables have been defined?
tab$allvars()
#> $cntvars
#> [1] "total" "cnt_females" "cnt_males" "cnt_highincome"
#>
#> $numvars
#> [1] "expend" "income" "savings" "mixed"
#>
# count variables
tab$cntvars()
#> [1] "total" "cnt_females" "cnt_males" "cnt_highincome"
# continuous variables
tab$numvars()
#> [1] "expend" "income" "savings" "mixed"
# create perturbation parameters for "total" variable and
# write to yaml-file
# create a ptable using functionality from the ptable-pkg
f_yaml <- tempfile(fileext = ".yaml")
p_cnts1 <- ck_params_cnts(
ptab = ptable::pt_ex_cnts(),
path = f_yaml)
#> yaml configuration '/tmp/Rtmpu511Tl/file1fa549201ce2.yaml' successfully written.
# read parameters from yaml-file and set them for variable `"total"`
p_cnts1 <- ck_read_yaml(path = f_yaml)
tab$params_cnts_set(val = p_cnts1, v = "total")
#> --> setting perturbation parameters for variable 'total'
# create alternative perturbation parameters by specifying parameters
para2 <- ptable::create_cnt_ptable(
D = 8, V = 3, js = 2, create = FALSE)
p_cnts2 <- ck_params_cnts(ptab = para2)
# use these ptable it for the remaining variables
tab$params_cnts_set(val = p_cnts2, v = countvars)
#> --> setting perturbation parameters for variable 'cnt_females'
#> --> setting perturbation parameters for variable 'cnt_males'
#> --> setting perturbation parameters for variable 'cnt_highincome'
# perturb a variable
tab$perturb(v = "total")
#> Count variable 'total' was perturbed.
# multiple variables can be perturbed as well
tab$perturb(v = c("cnt_males", "cnt_highincome"))
#> Count variable 'cnt_males' was perturbed.
#> Count variable 'cnt_highincome' was perturbed.
# return weighted and unweighted results
tab$freqtab(v = c("total", "cnt_males"))
#> sex age vname uwc wc puwc pwc
#> <char> <char> <char> <num> <num> <num> <num>
#> 1: Total Total total 4580 274707 4582 274826.959
#> 2: Total age_group1 total 1969 118802 1969 118802.000
#> 3: Total ag1a total 1969 118802 1969 118802.000
#> 4: Total age_group2 total 1143 68583 1141 68462.995
#> 5: Total ag2a total 1143 68583 1141 68462.995
#> 6: Total age_group3 total 864 51473 864 51473.000
#> 7: Total age_group4 total 423 25121 423 25121.000
#> 8: Total age_group5 total 168 9970 167 9910.655
#> 9: Total age_group6 total 13 758 13 758.000
#> 10: male Total total 2296 137345 2296 137345.000
#> 11: m Total total 2296 137345 2296 137345.000
#> 12: male age_group1 total 1015 61334 1014 61273.572
#> 13: m age_group1 total 1015 61334 1014 61273.572
#> 14: male ag1a total 1015 61334 1014 61273.572
#> 15: m ag1a total 1015 61334 1014 61273.572
#> 16: male age_group2 total 571 34108 572 34167.734
#> 17: m age_group2 total 571 34108 572 34167.734
#> 18: male ag2a total 571 34108 572 34167.734
#> 19: m ag2a total 571 34108 572 34167.734
#> 20: male age_group3 total 424 24983 424 24983.000
#> 21: m age_group3 total 424 24983 424 24983.000
#> 22: male age_group4 total 195 11748 196 11808.246
#> 23: m age_group4 total 195 11748 196 11808.246
#> 24: male age_group5 total 84 4696 84 4696.000
#> 25: m age_group5 total 84 4696 84 4696.000
#> 26: male age_group6 total 7 476 7 476.000
#> 27: m age_group6 total 7 476 7 476.000
#> 28: female Total total 2284 137362 2284 137362.000
#> 29: f Total total 2284 137362 2284 137362.000
#> 30: female age_group1 total 954 57468 953 57407.761
#> 31: f age_group1 total 954 57468 953 57407.761
#> 32: female ag1a total 954 57468 953 57407.761
#> 33: f ag1a total 954 57468 953 57407.761
#> 34: female age_group2 total 572 34475 571 34414.729
#> 35: f age_group2 total 572 34475 571 34414.729
#> 36: female ag2a total 572 34475 571 34414.729
#> 37: f ag2a total 572 34475 571 34414.729
#> 38: female age_group3 total 440 26490 439 26429.795
#> 39: f age_group3 total 440 26490 439 26429.795
#> 40: female age_group4 total 228 13373 228 13373.000
#> 41: f age_group4 total 228 13373 228 13373.000
#> 42: female age_group5 total 84 5274 84 5274.000
#> 43: f age_group5 total 84 5274 84 5274.000
#> 44: female age_group6 total 6 282 7 329.000
#> 45: f age_group6 total 6 282 7 329.000
#> 46: Total Total cnt_males 2296 137345 2296 137345.000
#> 47: Total age_group1 cnt_males 1015 61334 1014 61273.572
#> 48: Total ag1a cnt_males 1015 61334 1014 61273.572
#> 49: Total age_group2 cnt_males 571 34108 572 34167.734
#> 50: Total ag2a cnt_males 571 34108 572 34167.734
#> 51: Total age_group3 cnt_males 424 24983 424 24983.000
#> 52: Total age_group4 cnt_males 195 11748 196 11808.246
#> 53: Total age_group5 cnt_males 84 4696 85 4751.905
#> 54: Total age_group6 cnt_males 7 476 7 476.000
#> 55: male Total cnt_males 2296 137345 2296 137345.000
#> 56: m Total cnt_males 2296 137345 2296 137345.000
#> 57: male age_group1 cnt_males 1015 61334 1014 61273.572
#> 58: m age_group1 cnt_males 1015 61334 1014 61273.572
#> 59: male ag1a cnt_males 1015 61334 1014 61273.572
#> 60: m ag1a cnt_males 1015 61334 1014 61273.572
#> 61: male age_group2 cnt_males 571 34108 572 34167.734
#> 62: m age_group2 cnt_males 571 34108 572 34167.734
#> 63: male ag2a cnt_males 571 34108 572 34167.734
#> 64: m ag2a cnt_males 571 34108 572 34167.734
#> 65: male age_group3 cnt_males 424 24983 424 24983.000
#> 66: m age_group3 cnt_males 424 24983 424 24983.000
#> 67: male age_group4 cnt_males 195 11748 196 11808.246
#> 68: m age_group4 cnt_males 195 11748 196 11808.246
#> 69: male age_group5 cnt_males 84 4696 85 4751.905
#> 70: m age_group5 cnt_males 84 4696 85 4751.905
#> 71: male age_group6 cnt_males 7 476 7 476.000
#> 72: m age_group6 cnt_males 7 476 7 476.000
#> 73: female Total cnt_males 0 0 0 0.000
#> 74: f Total cnt_males 0 0 0 0.000
#> 75: female age_group1 cnt_males 0 0 0 0.000
#> 76: f age_group1 cnt_males 0 0 0 0.000
#> 77: female ag1a cnt_males 0 0 0 0.000
#> 78: f ag1a cnt_males 0 0 0 0.000
#> 79: female age_group2 cnt_males 0 0 0 0.000
#> 80: f age_group2 cnt_males 0 0 0 0.000
#> 81: female ag2a cnt_males 0 0 0 0.000
#> 82: f ag2a cnt_males 0 0 0 0.000
#> 83: female age_group3 cnt_males 0 0 0 0.000
#> 84: f age_group3 cnt_males 0 0 0 0.000
#> 85: female age_group4 cnt_males 0 0 0 0.000
#> 86: f age_group4 cnt_males 0 0 0 0.000
#> 87: female age_group5 cnt_males 0 0 0 0.000
#> 88: f age_group5 cnt_males 0 0 0 0.000
#> 89: female age_group6 cnt_males 0 0 0 0.000
#> 90: f age_group6 cnt_males 0 0 0 0.000
#> sex age vname uwc wc puwc pwc
# numerical variables (positive variables using flex-function)
# we also write the config to a yaml file
f_yaml <- tempfile(fileext = ".yaml")
# create a ptable using functionality from the ptable-pkg
# a single ptable for all cells
ptab1 <- ptable::pt_ex_nums(parity = TRUE, separation = FALSE)
# a single ptab for all cells except for very small ones
ptab2 <- ptable::pt_ex_nums(parity = TRUE, separation = TRUE)
# different ptables for cells with even/odd number of contributors
# and very small cells
ptab3 <- ptable::pt_ex_nums(parity = FALSE, separation = TRUE)
p_nums1 <- ck_params_nums(
ptab = ptab1,
type = "top_contr",
top_k = 3,
mult_params = ck_flexparams(
fp = 1000,
p = c(0.30, 0.03),
epsilon = c(1, 0.5, 0.2),
q = 3),
mu_c = 2,
same_key = FALSE,
use_zero_rkeys = FALSE,
path = f_yaml)
#> yaml configuration '/tmp/Rtmpu511Tl/file1fa562262870.yaml' successfully written.
# we read the parameters from the yaml-file
p_nums1 <- ck_read_yaml(path = f_yaml)
# for variables with positive and negative values
p_nums2 <- ck_params_nums(
ptab = ptab2,
type = "top_contr",
top_k = 3,
mult_params = ck_flexparams(
fp = 1000,
p = c(0.15, 0.02),
epsilon = c(1, 0.4, 0.15),
q = 3),
mu_c = 2,
same_key = FALSE)
# simple perturbation parameters (not using the flex-function approach)
p_nums3 <- ck_params_nums(
ptab = ptab3,
type = "mean",
mult_params = ck_simpleparams(p = 0.25),
mu_c = 2,
same_key = FALSE)
# use `p_nums1` for all variables
tab$params_nums_set(p_nums1, c("savings", "income", "expend"))
#> --> setting perturbation parameters for variable 'savings'
#> --> setting perturbation parameters for variable 'income'
#> --> setting perturbation parameters for variable 'expend'
# use different parameters for variable `mixed`
tab$params_nums_set(p_nums2, v = "mixed")
#> --> setting perturbation parameters for variable 'mixed'
# identify sensitive cells to which extra protection (`mu_c`) is added.
tab$supp_p(v = "income", p = 85)
#> computing contributing indices | rawdata <--> table; this might take a while
#> p%-rule: 0 new sensitive cells (incl. duplicates) found (total: 0)
tab$supp_pq(v = "income", p = 85, q = 90)
#> computing contributing indices | rawdata <--> table; this might take a while
#> pq-rule: 0 new sensitive cells (incl. duplicates) found (total: 0)
tab$supp_nk(v = "income", n = 2, k = 90)
#> computing contributing indices | rawdata <--> table; this might take a while
#> nk-rule: 0 new sensitive cells (incl. duplicates) found (total: 0)
tab$supp_freq(v = "income", n = 14, weighted = FALSE)
#> freq-rule: 5 new sensitive cells (incl. duplicates) found (total: 5)
tab$supp_val(v = "income", n = 10000, weighted = TRUE)
#> val-rule: 0 new sensitive cells (incl. duplicates) found (total: 5)
tab$supp_cells(
v = "income",
inp = data.frame(
sex = c("female", "female"),
"age" = c("age_group1", "age_group3")
)
)
#> cell-rule: 2 new sensitive cells (incl. duplicates) found (total: 7)
# perturb variables
tab$perturb(v = c("income", "savings"))
#> Numeric variable 'income' was perturbed.
#> Numeric variable 'savings' was perturbed.
# extract results
tab$numtab("income", mean_before_sum = TRUE)
#> sex age vname uws ws pws
#> <char> <char> <char> <num> <num> <num>
#> 1: Total Total income 22952978 1377005797 1376945138
#> 2: Total age_group1 income 9810547 590371940 590376154
#> 3: Total ag1a income 9810547 590371940 590376154
#> 4: Total age_group2 income 5692119 340327551 340187928
#> 5: Total ag2a income 5692119 340327551 340187928
#> 6: Total age_group3 income 4406946 263870748 263807267
#> 7: Total age_group4 income 2133543 128635475 128623136
#> 8: Total age_group5 income 848151 50513336 50403682
#> 9: Total age_group6 income 61672 3286747 3051762
#> 10: male Total income 11262049 674250565 674175625
#> 11: m Total income 11262049 674250565 674175625
#> 12: male age_group1 income 4877164 294221368 294321199
#> 13: m age_group1 income 4877164 294221368 294321199
#> 14: male ag1a income 4877164 294221368 294321199
#> 15: m ag1a income 4877164 294221368 294321199
#> 16: male age_group2 income 2811379 166958567 166998608
#> 17: m age_group2 income 2811379 166958567 166998608
#> 18: male ag2a income 2811379 166958567 166998608
#> 19: m ag2a income 2811379 166958567 166998608
#> 20: male age_group3 income 2168169 128987112 129015795
#> 21: m age_group3 income 2168169 128987112 129015795
#> 22: male age_group4 income 978510 60358734 60375054
#> 23: m age_group4 income 978510 60358734 60375054
#> 24: male age_group5 income 393134 21599577 21687227
#> 25: m age_group5 income 393134 21599577 21687227
#> 26: male age_group6 income 33693 2125207 2198004
#> 27: m age_group6 income 33693 2125207 2198004
#> 28: female Total income 11690929 702755232 703051791
#> 29: f Total income 11690929 702755232 703051791
#> 30: female age_group1 income 4933383 296150572 296142272
#> 31: f age_group1 income 4933383 296150572 296142272
#> 32: female ag1a income 4933383 296150572 296142272
#> 33: f ag1a income 4933383 296150572 296142272
#> 34: female age_group2 income 2880740 173368984 173383331
#> 35: f age_group2 income 2880740 173368984 173383331
#> 36: female ag2a income 2880740 173368984 173383331
#> 37: f ag2a income 2880740 173368984 173383331
#> 38: female age_group3 income 2238777 134883636 134914416
#> 39: f age_group3 income 2238777 134883636 134914416
#> 40: female age_group4 income 1155033 68276741 68470582
#> 41: f age_group4 income 1155033 68276741 68470582
#> 42: female age_group5 income 455017 28913759 28953004
#> 43: f age_group5 income 455017 28913759 28953004
#> 44: female age_group6 income 27979 1161540 1111365
#> 45: f age_group6 income 27979 1161540 1111365
#> sex age vname uws ws pws
tab$numtab("income", mean_before_sum = FALSE)
#> sex age vname uws ws pws
#> <char> <char> <char> <num> <num> <num>
#> 1: Total Total income 22952978 1377005797 1376975467
#> 2: Total age_group1 income 9810547 590371940 590374047
#> 3: Total ag1a income 9810547 590371940 590374047
#> 4: Total age_group2 income 5692119 340327551 340257732
#> 5: Total ag2a income 5692119 340327551 340257732
#> 6: Total age_group3 income 4406946 263870748 263839006
#> 7: Total age_group4 income 2133543 128635475 128629305
#> 8: Total age_group5 income 848151 50513336 50458479
#> 9: Total age_group6 income 61672 3286747 3167076
#> 10: male Total income 11262049 674250565 674213094
#> 11: m Total income 11262049 674250565 674213094
#> 12: male age_group1 income 4877164 294221368 294271279
#> 13: m age_group1 income 4877164 294221368 294271279
#> 14: male ag1a income 4877164 294221368 294271279
#> 15: m ag1a income 4877164 294221368 294271279
#> 16: male age_group2 income 2811379 166958567 166978586
#> 17: m age_group2 income 2811379 166958567 166978586
#> 18: male ag2a income 2811379 166958567 166978586
#> 19: m ag2a income 2811379 166958567 166978586
#> 20: male age_group3 income 2168169 128987112 129001453
#> 21: m age_group3 income 2168169 128987112 129001453
#> 22: male age_group4 income 978510 60358734 60366894
#> 23: m age_group4 income 978510 60358734 60366894
#> 24: male age_group5 income 393134 21599577 21643358
#> 25: m age_group5 income 393134 21599577 21643358
#> 26: male age_group6 income 33693 2125207 2161299
#> 27: m age_group6 income 33693 2125207 2161299
#> 28: female Total income 11690929 702755232 702903496
#> 29: f Total income 11690929 702755232 702903496
#> 30: female age_group1 income 4933383 296150572 296146422
#> 31: f age_group1 income 4933383 296150572 296146422
#> 32: female ag1a income 4933383 296150572 296146422
#> 33: f ag1a income 4933383 296150572 296146422
#> 34: female age_group2 income 2880740 173368984 173376158
#> 35: f age_group2 income 2880740 173368984 173376158
#> 36: female ag2a income 2880740 173368984 173376158
#> 37: f ag2a income 2880740 173368984 173376158
#> 38: female age_group3 income 2238777 134883636 134899025
#> 39: f age_group3 income 2238777 134883636 134899025
#> 40: female age_group4 income 1155033 68276741 68373593
#> 41: f age_group4 income 1155033 68276741 68373593
#> 42: female age_group5 income 455017 28913759 28933375
#> 43: f age_group5 income 455017 28913759 28933375
#> 44: female age_group6 income 27979 1161540 1136176
#> 45: f age_group6 income 27979 1161540 1136176
#> sex age vname uws ws pws
tab$numtab("savings")
#> sex age vname uws ws pws
#> <char> <char> <char> <num> <num> <num>
#> 1: Total Total savings 2273532 136873818 136870309.5
#> 2: Total age_group1 savings 982386 59422265 59420448.2
#> 3: Total ag1a savings 982386 59422265 59420448.2
#> 4: Total age_group2 savings 552336 33246815 33243636.4
#> 5: Total ag2a savings 552336 33246815 33243636.4
#> 6: Total age_group3 savings 437101 26180709 26181760.6
#> 7: Total age_group4 savings 214661 12776589 12774656.3
#> 8: Total age_group5 savings 80451 4886105 4878388.5
#> 9: Total age_group6 savings 6597 361335 353629.9
#> 10: male Total savings 1159816 69662037 69662037.0
#> 11: m Total savings 1159816 69662037 69662037.0
#> 12: male age_group1 savings 517660 31430472 31435293.2
#> 13: m age_group1 savings 517660 31430472 31435293.2
#> 14: male ag1a savings 517660 31430472 31435293.2
#> 15: m ag1a savings 517660 31430472 31435293.2
#> 16: male age_group2 savings 280923 16836241 16836943.0
#> 17: m age_group2 savings 280923 16836241 16836943.0
#> 18: male ag2a savings 280923 16836241 16836943.0
#> 19: m ag2a savings 280923 16836241 16836943.0
#> 20: male age_group3 savings 214970 12682929 12680329.3
#> 21: m age_group3 savings 214970 12682929 12680329.3
#> 22: male age_group4 savings 99420 5966130 5964685.1
#> 23: m age_group4 savings 99420 5966130 5964685.1
#> 24: male age_group5 savings 43233 2508522 2514248.6
#> 25: m age_group5 savings 43233 2508522 2514248.6
#> 26: male age_group6 savings 3610 237743 236468.4
#> 27: m age_group6 savings 3610 237743 236468.4
#> 28: female Total savings 1113716 67211781 67214410.0
#> 29: f Total savings 1113716 67211781 67214410.0
#> 30: female age_group1 savings 464726 27991793 27991815.6
#> 31: f age_group1 savings 464726 27991793 27991815.6
#> 32: female ag1a savings 464726 27991793 27991815.6
#> 33: f ag1a savings 464726 27991793 27991815.6
#> 34: female age_group2 savings 271413 16410574 16411672.7
#> 35: f age_group2 savings 271413 16410574 16411672.7
#> 36: female ag2a savings 271413 16410574 16411672.7
#> 37: f ag2a savings 271413 16410574 16411672.7
#> 38: female age_group3 savings 222131 13497780 13501050.9
#> 39: f age_group3 savings 222131 13497780 13501050.9
#> 40: female age_group4 savings 115241 6810459 6819421.9
#> 41: f age_group4 savings 115241 6810459 6819421.9
#> 42: female age_group5 savings 37218 2377583 2380704.9
#> 43: f age_group5 savings 37218 2377583 2380704.9
#> 44: female age_group6 savings 2987 123592 122549.0
#> 45: f age_group6 savings 2987 123592 122549.0
#> sex age vname uws ws pws
# results can be resetted, too
tab$reset_cntvars(v = "cnt_males")
# we can then set other parameters and perturb again
tab$params_cnts_set(val = p_cnts1, v = "cnt_males")
#> --> setting perturbation parameters for variable 'cnt_males'
tab$perturb(v = "cnt_males")
#> Count variable 'cnt_males' was perturbed.
# write results to a .csv file
tab$freqtab(
v = c("total", "cnt_males"),
path = file.path(tempdir(), "outtab.csv")
)
#> File '/tmp/Rtmpu511Tl/outtab.csv' successfully written to disk.
#> NULL
# show results containing weighted and unweighted results
tab$freqtab(v = c("total", "cnt_males"))
#> sex age vname uwc wc puwc pwc
#> <char> <char> <char> <num> <num> <num> <num>
#> 1: Total Total total 4580 274707 4582 274826.959
#> 2: Total age_group1 total 1969 118802 1969 118802.000
#> 3: Total ag1a total 1969 118802 1969 118802.000
#> 4: Total age_group2 total 1143 68583 1141 68462.995
#> 5: Total ag2a total 1143 68583 1141 68462.995
#> 6: Total age_group3 total 864 51473 864 51473.000
#> 7: Total age_group4 total 423 25121 423 25121.000
#> 8: Total age_group5 total 168 9970 167 9910.655
#> 9: Total age_group6 total 13 758 13 758.000
#> 10: male Total total 2296 137345 2296 137345.000
#> 11: m Total total 2296 137345 2296 137345.000
#> 12: male age_group1 total 1015 61334 1014 61273.572
#> 13: m age_group1 total 1015 61334 1014 61273.572
#> 14: male ag1a total 1015 61334 1014 61273.572
#> 15: m ag1a total 1015 61334 1014 61273.572
#> 16: male age_group2 total 571 34108 572 34167.734
#> 17: m age_group2 total 571 34108 572 34167.734
#> 18: male ag2a total 571 34108 572 34167.734
#> 19: m ag2a total 571 34108 572 34167.734
#> 20: male age_group3 total 424 24983 424 24983.000
#> 21: m age_group3 total 424 24983 424 24983.000
#> 22: male age_group4 total 195 11748 196 11808.246
#> 23: m age_group4 total 195 11748 196 11808.246
#> 24: male age_group5 total 84 4696 84 4696.000
#> 25: m age_group5 total 84 4696 84 4696.000
#> 26: male age_group6 total 7 476 7 476.000
#> 27: m age_group6 total 7 476 7 476.000
#> 28: female Total total 2284 137362 2284 137362.000
#> 29: f Total total 2284 137362 2284 137362.000
#> 30: female age_group1 total 954 57468 953 57407.761
#> 31: f age_group1 total 954 57468 953 57407.761
#> 32: female ag1a total 954 57468 953 57407.761
#> 33: f ag1a total 954 57468 953 57407.761
#> 34: female age_group2 total 572 34475 571 34414.729
#> 35: f age_group2 total 572 34475 571 34414.729
#> 36: female ag2a total 572 34475 571 34414.729
#> 37: f ag2a total 572 34475 571 34414.729
#> 38: female age_group3 total 440 26490 439 26429.795
#> 39: f age_group3 total 440 26490 439 26429.795
#> 40: female age_group4 total 228 13373 228 13373.000
#> 41: f age_group4 total 228 13373 228 13373.000
#> 42: female age_group5 total 84 5274 84 5274.000
#> 43: f age_group5 total 84 5274 84 5274.000
#> 44: female age_group6 total 6 282 7 329.000
#> 45: f age_group6 total 6 282 7 329.000
#> 46: Total Total cnt_males 2296 137345 2296 137345.000
#> 47: Total age_group1 cnt_males 1015 61334 1014 61273.572
#> 48: Total ag1a cnt_males 1015 61334 1014 61273.572
#> 49: Total age_group2 cnt_males 571 34108 572 34167.734
#> 50: Total ag2a cnt_males 571 34108 572 34167.734
#> 51: Total age_group3 cnt_males 424 24983 424 24983.000
#> 52: Total age_group4 cnt_males 195 11748 196 11808.246
#> 53: Total age_group5 cnt_males 84 4696 84 4696.000
#> 54: Total age_group6 cnt_males 7 476 7 476.000
#> 55: male Total cnt_males 2296 137345 2296 137345.000
#> 56: m Total cnt_males 2296 137345 2296 137345.000
#> 57: male age_group1 cnt_males 1015 61334 1014 61273.572
#> 58: m age_group1 cnt_males 1015 61334 1014 61273.572
#> 59: male ag1a cnt_males 1015 61334 1014 61273.572
#> 60: m ag1a cnt_males 1015 61334 1014 61273.572
#> 61: male age_group2 cnt_males 571 34108 572 34167.734
#> 62: m age_group2 cnt_males 571 34108 572 34167.734
#> 63: male ag2a cnt_males 571 34108 572 34167.734
#> 64: m ag2a cnt_males 571 34108 572 34167.734
#> 65: male age_group3 cnt_males 424 24983 424 24983.000
#> 66: m age_group3 cnt_males 424 24983 424 24983.000
#> 67: male age_group4 cnt_males 195 11748 196 11808.246
#> 68: m age_group4 cnt_males 195 11748 196 11808.246
#> 69: male age_group5 cnt_males 84 4696 84 4696.000
#> 70: m age_group5 cnt_males 84 4696 84 4696.000
#> 71: male age_group6 cnt_males 7 476 7 476.000
#> 72: m age_group6 cnt_males 7 476 7 476.000
#> 73: female Total cnt_males 0 0 0 0.000
#> 74: f Total cnt_males 0 0 0 0.000
#> 75: female age_group1 cnt_males 0 0 0 0.000
#> 76: f age_group1 cnt_males 0 0 0 0.000
#> 77: female ag1a cnt_males 0 0 0 0.000
#> 78: f ag1a cnt_males 0 0 0 0.000
#> 79: female age_group2 cnt_males 0 0 0 0.000
#> 80: f age_group2 cnt_males 0 0 0 0.000
#> 81: female ag2a cnt_males 0 0 0 0.000
#> 82: f ag2a cnt_males 0 0 0 0.000
#> 83: female age_group3 cnt_males 0 0 0 0.000
#> 84: f age_group3 cnt_males 0 0 0 0.000
#> 85: female age_group4 cnt_males 0 0 0 0.000
#> 86: f age_group4 cnt_males 0 0 0 0.000
#> 87: female age_group5 cnt_males 0 0 0 0.000
#> 88: f age_group5 cnt_males 0 0 0 0.000
#> 89: female age_group6 cnt_males 0 0 0 0.000
#> 90: f age_group6 cnt_males 0 0 0 0.000
#> sex age vname uwc wc puwc pwc
# utility measures for a count variable
tab$measures_cnts(v = "total", exclude_zeros = TRUE)
#> $overview
#> noise cnt pct
#> <fctr> <int> <num>
#> 1: -2 1 0.02222222
#> 2: -1 8 0.17777778
#> 3: 0 19 0.42222222
#> 4: 1 15 0.33333333
#> 5: 2 2 0.04444444
#>
#> $measures
#> what d1 d2 d3
#> <char> <num> <num> <num>
#> 1: Min 0.000 0.000 0.000
#> 2: Q10 0.000 0.000 0.000
#> 3: Q20 0.000 0.000 0.000
#> 4: Q30 0.000 0.000 0.000
#> 5: Q40 0.000 0.000 0.000
#> 6: Mean 0.644 0.008 0.020
#> 7: Median 1.000 0.001 0.016
#> 8: Q60 1.000 0.001 0.016
#> 9: Q70 1.000 0.002 0.021
#> 10: Q80 1.000 0.002 0.022
#> 11: Q90 1.000 0.004 0.033
#> 12: Q95 1.800 0.006 0.038
#> 13: Q99 2.000 0.167 0.196
#> 14: Max 2.000 0.167 0.196
#>
#> $cumdistr_d1
#> cat cnt pct
#> <char> <int> <num>
#> 1: 0 19 0.4222222
#> 2: 1 42 0.9333333
#> 3: 2 45 1.0000000
#>
#> $cumdistr_d2
#> cat cnt pct
#> <char> <int> <num>
#> 1: [0,0.02] 43 0.9555556
#> 2: (0.02,0.05] 43 0.9555556
#> 3: (0.05,0.1] 43 0.9555556
#> 4: (0.1,0.2] 45 1.0000000
#> 5: (0.2,0.3] 45 1.0000000
#> 6: (0.3,0.4] 45 1.0000000
#> 7: (0.4,0.5] 45 1.0000000
#> 8: (0.5,Inf] 45 1.0000000
#>
#> $cumdistr_d3
#> cat cnt pct
#> <char> <int> <num>
#> 1: [0,0.02] 28 0.6222222
#> 2: (0.02,0.05] 43 0.9555556
#> 3: (0.05,0.1] 43 0.9555556
#> 4: (0.1,0.2] 45 1.0000000
#> 5: (0.2,0.3] 45 1.0000000
#> 6: (0.3,0.4] 45 1.0000000
#> 7: (0.4,0.5] 45 1.0000000
#> 8: (0.5,Inf] 45 1.0000000
#>
#> $false_zero
#> [1] 0
#>
#> $false_nonzero
#> [1] 0
#>
#> $exclude_zeros
#> [1] TRUE
#>
# modifications for perturbed count variables
tab$mod_cnts()
#> sex age row_nr pert ckey countvar
#> <char> <char> <num> <int> <num> <char>
#> 1: Total Total 17 2 0.95532247 total
#> 2: Total age_group1 15 0 0.44707334 total
#> 3: Total ag1a 15 0 0.44707334 total
#> 4: Total age_group2 13 -2 0.01327944 total
#> 5: Total ag2a 13 -2 0.01327944 total
#> ---
#> 131: f age_group4 -1 0 0.00000000 cnt_males
#> 132: female age_group5 -1 0 0.00000000 cnt_males
#> 133: f age_group5 -1 0 0.00000000 cnt_males
#> 134: female age_group6 -1 0 0.00000000 cnt_males
#> 135: f age_group6 -1 0 0.00000000 cnt_males
# display a summary about utility measures
tab$summary()
#> ┌──────────────────────────────────────────────┐
#> │Utility measures for perturbed count variables│
#> └──────────────────────────────────────────────┘
#> ── Distribution statistics of perturbations ────────────────────────────────────
#> countvar Min Q10 Q20 Q30 Q40 Mean Median Q60 Q70 Q80
#> <char> <num> <num> <num> <num> <num> <num> <num> <num> <num> <num>
#> 1: total -2 -1 -1 -1 0.0 -0.200 0 0 0 0.2
#> 2: cnt_highincome -4 -3 -2 -2 -1.4 -0.778 -1 0 0 1.0
#> 3: cnt_males -1 -1 0 0 0.0 0.067 0 0 0 0.2
#> Q90 Q95 Q99 Max
#> <num> <num> <num> <num>
#> 1: 1 1 1.56 2
#> 2: 2 2 2.00 2
#> 3: 1 1 1.00 1
#>
#> ── Distance-based measures ─────────────────────────────────────────────────────
#> ✔ Variable: 'total'
#>
#> what d1 d2 d3
#> <char> <num> <num> <num>
#> 1: Min 0.000 0.000 0.000
#> 2: Q10 0.000 0.000 0.000
#> 3: Q20 0.000 0.000 0.000
#> 4: Q30 0.000 0.000 0.000
#> 5: Q40 0.000 0.000 0.000
#> 6: Mean 0.644 0.008 0.020
#> 7: Median 1.000 0.001 0.016
#> 8: Q60 1.000 0.001 0.016
#> 9: Q70 1.000 0.002 0.021
#> 10: Q80 1.000 0.002 0.022
#> 11: Q90 1.000 0.004 0.033
#> 12: Q95 1.800 0.006 0.038
#> 13: Q99 2.000 0.167 0.196
#> 14: Max 2.000 0.167 0.196
#>
#> ✔ Variable: 'cnt_males'
#>
#> what d1 d2 d3
#> <char> <num> <num> <num>
#> 1: Min 0.000 0.000 0.000
#> 2: Q10 0.000 0.000 0.000
#> 3: Q20 0.000 0.000 0.000
#> 4: Q30 0.000 0.000 0.000
#> 5: Q40 0.000 0.000 0.000
#> 6: Mean 0.556 0.001 0.012
#> 7: Median 1.000 0.001 0.016
#> 8: Q60 1.000 0.001 0.016
#> 9: Q70 1.000 0.002 0.021
#> 10: Q80 1.000 0.002 0.021
#> 11: Q90 1.000 0.003 0.027
#> 12: Q95 1.000 0.005 0.036
#> 13: Q99 1.000 0.005 0.036
#> 14: Max 1.000 0.005 0.036
#>
#> ✔ Variable: 'cnt_highincome'
#>
#> what d1 d2 d3
#> <char> <num> <num> <num>
#> 1: Min 0.000 0.000 0.000
#> 2: Q10 1.000 0.005 0.034
#> 3: Q20 1.000 0.012 0.072
#> 4: Q30 1.000 0.020 0.084
#> 5: Q40 1.600 0.024 0.100
#> 6: Mean 1.775 0.043 0.119
#> 7: Median 2.000 0.024 0.106
#> 8: Q60 2.000 0.030 0.118
#> 9: Q70 2.000 0.035 0.126
#> 10: Q80 2.000 0.039 0.144
#> 11: Q90 3.100 0.062 0.200
#> 12: Q95 4.000 0.150 0.210
#> 13: Q99 4.000 0.286 0.410
#> 14: Max 4.000 0.286 0.410
#>
#> ┌──────────────────────────────────────────────────┐
#> │Utility measures for perturbed numerical variables│
#> └──────────────────────────────────────────────────┘
#> ── Distribution statistics of perturbations ────────────────────────────────────
#> Warning: no non-missing arguments to min; returning Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to min; returning Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> vname Min Q10 Q20 Q30 Q40 Mean
#> <char> <num> <num> <num> <num> <num> <num>
#> 1: expend Inf NA NA NA NA NaN
#> 2: income -119670.898 -37471.122 -25364.211 -4149.947 7173.574 12287.54
#> 3: savings -7716.547 -2947.004 -1816.778 -1228.303 13.560 699.44
#> 4: mixed Inf NA NA NA NA NaN
#> Median Q60 Q70 Q80 Q90 Q95 Q99
#> <num> <num> <num> <num> <num> <num> <num>
#> 1: NA NA NA NA NA NA NA
#> 2: 8159.516 17079.81 20019.388 43780.682 49911.087 96851.629 148263.749
#> 3: 702.049 1070.46 2322.902 3270.909 4821.238 5726.561 8962.924
#> 4: NA NA NA NA NA NA NA
#> Max
#> <num>
#> 1: -Inf
#> 2: 148263.749
#> 3: 8962.924
#> 4: -Inf
# }