R/ck_params_nums.R
ck_flexparams.Rd
ck_flexparams()
allows to define a flex function that is used to lookup perturbation
magnitudes (percentages) used when perturbing continuous variables.
ck_flexparams(fp, p = c(0.25, 0.05), epsilon = 1, q = 3)
(numeric scalar); at which point should the noise coefficient
function reaches its desired maximum (defined by the first element of p
)
a numeric vector of length 2
where both elements specify a percentage.
The first value refers to the desired maximum perturbation percentage for small
cells (depending on fp
) while the second element refers to the desired maximum
perturbation percentage for large cells. Both values must be between 0
and 1
and
need to be in descending order.
a numeric vector in descending order with all values >= 0
and <= 1
with the first
element forced to equal 1. The length of this vector must correspond with the number top_k
specified in ck_params_nums()
when creating parameters for type == "top_contr"
which is
checked at runtime. This setting allows to use different flex-functions for the largest top_k
contributors.
(numeric scalar); Parameter of the function; q
needs to be >= 1
an object suitable as input for ck_params_nums()
.
details about the flex function can be found in Deliverable D4.2, Part I in SGA "Open Source tools for perturbative confidentiality methods"
# \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 60 25.00000 0
#> 2: 2530 28 1 66 25.00000 1
#> 3: 6920 550 1 30 25.00000 0
#> 4: 7960 870 1 98 25.00000 0
#> 5: 9030 20 2 75 16.66667 0
#> ---
#> 4576: 7900 278 1000 41 16.66667 1
#> 4577: 1420 987 1000 30 16.66667 0
#> 4578: 8900 684 1000 43 16.66667 0
#> 4579: 3880 294 1000 41 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 60 25.00000 0
#> 2: 2530 28 1 66 25.00000 1
#> 3: 6920 550 1 30 25.00000 0
#> 4: 7960 870 1 98 25.00000 0
#> 5: 9030 20 2 75 16.66667 0
#> ---
#> 4576: 7900 278 1000 41 16.66667 1
#> 4577: 1420 987 1000 30 16.66667 0
#> 4578: 8900 684 1000 43 16.66667 0
#> 4579: 3880 294 1000 41 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 60 25.00000 0
#> 2: 2530 28 1 66 25.00000 1
#> 3: 6920 550 1 30 25.00000 0
#> 4: 7960 870 1 98 25.00000 0
#> 5: 9030 20 2 75 16.66667 0
#> ---
#> 4576: 7900 278 1000 41 16.66667 1
#> 4577: 1420 987 1000 30 16.66667 0
#> 4578: 8900 684 1000 43 16.66667 0
#> 4579: 3880 294 1000 41 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 60 25.00000 0
#> 2: 2530 28 1 66 25.00000 1
#> 3: 6920 550 1 30 25.00000 0
#> 4: 7960 870 1 98 25.00000 0
#> 5: 9030 20 2 75 16.66667 0
#> ---
#> 4576: 7900 278 1000 41 16.66667 1
#> 4577: 1420 987 1000 30 16.66667 0
#> 4578: 8900 684 1000 43 16.66667 0
#> 4579: 3880 294 1000 41 16.66667 1
#> 4580: 4830 911 1000 39 16.66667 0
#> cnt_males cnt_highincome mixed
#> <num> <num> <int>
#> 1: 1 0 0
#> 2: 0 0 0
#> 3: 1 0 -5
#> 4: 1 0 3
#> 5: 1 1 2
#> ---
#> 4576: 0 0 10
#> 4577: 1 0 -8
#> 4578: 1 0 -3
#> 4579: 0 0 -8
#> 4580: 1 0 -5
# 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$numvars()
#> [1] "expend" "income" "savings" "mixed"
# create perturbation parameters for "total" variable and
# write to yaml-file
# create a ptable using functionality from the ptable-pkg
f_yaml <- tempfile(fileext = ".yaml")
p_cnts1 <- ck_params_cnts(
ptab = ptable::pt_ex_cnts(),
path = f_yaml)
#> yaml configuration '/tmp/RtmpHZpsuV/file1d2c73f7f41a.yaml' successfully written.
# read parameters from yaml-file and set them for variable `"total"`
p_cnts1 <- ck_read_yaml(path = f_yaml)
tab$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 275617 4580 275617.0000
#> 2: Total age_group1 total 1969 118674 1969 118674.0000
#> 3: Total ag1a total 1969 118674 1969 118674.0000
#> 4: Total age_group2 total 1143 68018 1144 68077.5083
#> 5: Total ag2a total 1143 68018 1144 68077.5083
#> 6: Total age_group3 total 864 52588 864 52588.0000
#> 7: Total age_group4 total 423 25354 424 25413.9385
#> 8: Total age_group5 total 168 10148 167 10087.5952
#> 9: Total age_group6 total 13 835 13 835.0000
#> 10: male Total total 2296 138577 2296 138577.0000
#> 11: m Total total 2296 138577 2296 138577.0000
#> 12: male age_group1 total 1015 60945 1015 60945.0000
#> 13: m age_group1 total 1015 60945 1015 60945.0000
#> 14: male ag1a total 1015 60945 1015 60945.0000
#> 15: m ag1a total 1015 60945 1015 60945.0000
#> 16: male age_group2 total 571 34245 570 34185.0263
#> 17: m age_group2 total 571 34245 570 34185.0263
#> 18: male ag2a total 571 34245 570 34185.0263
#> 19: m ag2a total 571 34245 570 34185.0263
#> 20: male age_group3 total 424 25845 425 25905.9552
#> 21: m age_group3 total 424 25845 425 25905.9552
#> 22: male age_group4 total 195 11996 196 12057.5179
#> 23: m age_group4 total 195 11996 196 12057.5179
#> 24: male age_group5 total 84 5080 84 5080.0000
#> 25: m age_group5 total 84 5080 84 5080.0000
#> 26: male age_group6 total 7 466 8 532.5714
#> 27: m age_group6 total 7 466 8 532.5714
#> 28: female Total total 2284 137040 2285 137100.0000
#> 29: f Total total 2284 137040 2285 137100.0000
#> 30: female age_group1 total 954 57729 953 57668.4874
#> 31: f age_group1 total 954 57729 953 57668.4874
#> 32: female ag1a total 954 57729 953 57668.4874
#> 33: f ag1a total 954 57729 953 57668.4874
#> 34: female age_group2 total 572 33773 572 33773.0000
#> 35: f age_group2 total 572 33773 572 33773.0000
#> 36: female ag2a total 572 33773 572 33773.0000
#> 37: f ag2a total 572 33773 572 33773.0000
#> 38: female age_group3 total 440 26743 441 26803.7795
#> 39: f age_group3 total 440 26743 441 26803.7795
#> 40: female age_group4 total 228 13358 230 13475.1754
#> 41: f age_group4 total 228 13358 230 13475.1754
#> 42: female age_group5 total 84 5068 84 5068.0000
#> 43: f age_group5 total 84 5068 84 5068.0000
#> 44: female age_group6 total 6 369 6 369.0000
#> 45: f age_group6 total 6 369 6 369.0000
#> 46: Total Total cnt_males 2296 138577 2297 138637.3558
#> 47: Total age_group1 cnt_males 1015 60945 1015 60945.0000
#> 48: Total ag1a cnt_males 1015 60945 1015 60945.0000
#> 49: Total age_group2 cnt_males 571 34245 570 34185.0263
#> 50: Total ag2a cnt_males 571 34245 570 34185.0263
#> 51: Total age_group3 cnt_males 424 25845 425 25905.9552
#> 52: Total age_group4 cnt_males 195 11996 197 12119.0359
#> 53: Total age_group5 cnt_males 84 5080 84 5080.0000
#> 54: Total age_group6 cnt_males 7 466 8 532.5714
#> 55: male Total cnt_males 2296 138577 2297 138637.3558
#> 56: m Total cnt_males 2296 138577 2297 138637.3558
#> 57: male age_group1 cnt_males 1015 60945 1015 60945.0000
#> 58: m age_group1 cnt_males 1015 60945 1015 60945.0000
#> 59: male ag1a cnt_males 1015 60945 1015 60945.0000
#> 60: m ag1a cnt_males 1015 60945 1015 60945.0000
#> 61: male age_group2 cnt_males 571 34245 570 34185.0263
#> 62: m age_group2 cnt_males 571 34245 570 34185.0263
#> 63: male ag2a cnt_males 571 34245 570 34185.0263
#> 64: m ag2a cnt_males 571 34245 570 34185.0263
#> 65: male age_group3 cnt_males 424 25845 425 25905.9552
#> 66: m age_group3 cnt_males 424 25845 425 25905.9552
#> 67: male age_group4 cnt_males 195 11996 197 12119.0359
#> 68: m age_group4 cnt_males 195 11996 197 12119.0359
#> 69: male age_group5 cnt_males 84 5080 84 5080.0000
#> 70: m age_group5 cnt_males 84 5080 84 5080.0000
#> 71: male age_group6 cnt_males 7 466 8 532.5714
#> 72: m age_group6 cnt_males 7 466 8 532.5714
#> 73: female Total cnt_males 0 0 0 0.0000
#> 74: f Total cnt_males 0 0 0 0.0000
#> 75: female age_group1 cnt_males 0 0 0 0.0000
#> 76: f age_group1 cnt_males 0 0 0 0.0000
#> 77: female ag1a cnt_males 0 0 0 0.0000
#> 78: f ag1a cnt_males 0 0 0 0.0000
#> 79: female age_group2 cnt_males 0 0 0 0.0000
#> 80: f age_group2 cnt_males 0 0 0 0.0000
#> 81: female ag2a cnt_males 0 0 0 0.0000
#> 82: f ag2a cnt_males 0 0 0 0.0000
#> 83: female age_group3 cnt_males 0 0 0 0.0000
#> 84: f age_group3 cnt_males 0 0 0 0.0000
#> 85: female age_group4 cnt_males 0 0 0 0.0000
#> 86: f age_group4 cnt_males 0 0 0 0.0000
#> 87: female age_group5 cnt_males 0 0 0 0.0000
#> 88: f age_group5 cnt_males 0 0 0 0.0000
#> 89: female age_group6 cnt_males 0 0 0 0.0000
#> 90: f age_group6 cnt_males 0 0 0 0.0000
#> 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/RtmpHZpsuV/file1d2c5b91f12f.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 1378728411 1378803432
#> 2: Total age_group1 income 9810547 588645646 588641987
#> 3: Total ag1a income 9810547 588645646 588641987
#> 4: Total age_group2 income 5692119 339113890 339004611
#> 5: Total ag2a income 5692119 339113890 339004611
#> 6: Total age_group3 income 4406946 268059460 268125525
#> 7: Total age_group4 income 2133543 128126535 128233857
#> 8: Total age_group5 income 848151 50544273 50558527
#> 9: Total age_group6 income 61672 4238607 4002653
#> 10: male Total income 11262049 682659125 682642380
#> 11: m Total income 11262049 682659125 682642380
#> 12: male age_group1 income 4877164 292653191 292688150
#> 13: m age_group1 income 4877164 292653191 292688150
#> 14: male ag1a income 4877164 292653191 292688150
#> 15: m ag1a income 4877164 292653191 292688150
#> 16: male age_group2 income 2811379 169879170 169710875
#> 17: m age_group2 income 2811379 169879170 169710875
#> 18: male ag2a income 2811379 169879170 169710875
#> 19: m ag2a income 2811379 169879170 169710875
#> 20: male age_group3 income 2168169 134579789 134535983
#> 21: m age_group3 income 2168169 134579789 134535983
#> 22: male age_group4 income 978510 60132299 60243529
#> 23: m age_group4 income 978510 60132299 60243529
#> 24: male age_group5 income 393134 22911025 22891974
#> 25: m age_group5 income 393134 22911025 22891974
#> 26: male age_group6 income 33693 2503651 2611269
#> 27: m age_group6 income 33693 2503651 2611269
#> 28: female Total income 11690929 696069286 696024828
#> 29: f Total income 11690929 696069286 696024828
#> 30: female age_group1 income 4933383 295992455 295984544
#> 31: f age_group1 income 4933383 295992455 295984544
#> 32: female ag1a income 4933383 295992455 295984544
#> 33: f ag1a income 4933383 295992455 295984544
#> 34: female age_group2 income 2880740 169234720 169277054
#> 35: f age_group2 income 2880740 169234720 169277054
#> 36: female ag2a income 2880740 169234720 169277054
#> 37: f ag2a income 2880740 169234720 169277054
#> 38: female age_group3 income 2238777 133479671 133524702
#> 39: f age_group3 income 2238777 133479671 133524702
#> 40: female age_group4 income 1155033 67994236 68013035
#> 41: f age_group4 income 1155033 67994236 68013035
#> 42: female age_group5 income 455017 27633248 27436426
#> 43: f age_group5 income 455017 27633248 27436426
#> 44: female age_group6 income 27979 1734956 1884549
#> 45: f age_group6 income 27979 1734956 1884549
#> 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 1378728411 1378765921
#> 2: Total age_group1 income 9810547 588645646 588643817
#> 3: Total ag1a income 9810547 588645646 588643817
#> 4: Total age_group2 income 5692119 339113890 339059246
#> 5: Total ag2a income 5692119 339113890 339059246
#> 6: Total age_group3 income 4406946 268059460 268092490
#> 7: Total age_group4 income 2133543 128126535 128180185
#> 8: Total age_group5 income 848151 50544273 50551399
#> 9: Total age_group6 income 61672 4238607 4118941
#> 10: male Total income 11262049 682659125 682650752
#> 11: m Total income 11262049 682659125 682650752
#> 12: male age_group1 income 4877164 292653191 292670670
#> 13: m age_group1 income 4877164 292653191 292670670
#> 14: male ag1a income 4877164 292653191 292670670
#> 15: m ag1a income 4877164 292653191 292670670
#> 16: male age_group2 income 2811379 169879170 169795002
#> 17: m age_group2 income 2811379 169879170 169795002
#> 18: male ag2a income 2811379 169879170 169795002
#> 19: m ag2a income 2811379 169879170 169795002
#> 20: male age_group3 income 2168169 134579789 134557884
#> 21: m age_group3 income 2168169 134579789 134557884
#> 22: male age_group4 income 978510 60132299 60187888
#> 23: m age_group4 income 978510 60132299 60187888
#> 24: male age_group5 income 393134 22911025 22901497
#> 25: m age_group5 income 393134 22911025 22901497
#> 26: male age_group6 income 33693 2503651 2556894
#> 27: m age_group6 income 33693 2503651 2556894
#> 28: female Total income 11690929 696069286 696047056
#> 29: f Total income 11690929 696069286 696047056
#> 30: female age_group1 income 4933383 295992455 295988500
#> 31: f age_group1 income 4933383 295992455 295988500
#> 32: female ag1a income 4933383 295992455 295988500
#> 33: f ag1a income 4933383 295992455 295988500
#> 34: female age_group2 income 2880740 169234720 169255886
#> 35: f age_group2 income 2880740 169234720 169255886
#> 36: female ag2a income 2880740 169234720 169255886
#> 37: f ag2a income 2880740 169234720 169255886
#> 38: female age_group3 income 2238777 133479671 133502185
#> 39: f age_group3 income 2238777 133479671 133502185
#> 40: female age_group4 income 1155033 67994236 68003635
#> 41: f age_group4 income 1155033 67994236 68003635
#> 42: female age_group5 income 455017 27633248 27534661
#> 43: f age_group5 income 455017 27633248 27534661
#> 44: female age_group6 income 27979 1734956 1808206
#> 45: f age_group6 income 27979 1734956 1808206
#> 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 137026795 137032535.3
#> 2: Total age_group1 savings 982386 59436797 59435344.1
#> 3: Total ag1a savings 982386 59436797 59435344.1
#> 4: Total age_group2 savings 552336 32886105 32875905.7
#> 5: Total ag2a savings 552336 32886105 32875905.7
#> 6: Total age_group3 savings 437101 26457789 26452807.1
#> 7: Total age_group4 savings 214661 13014851 13024613.8
#> 8: Total age_group5 savings 80451 4819415 4819584.3
#> 9: Total age_group6 savings 6597 411838 406425.0
#> 10: male Total savings 1159816 70055883 70056754.7
#> 11: m Total savings 1159816 70055883 70056754.7
#> 12: male age_group1 savings 517660 31197472 31200201.3
#> 13: m age_group1 savings 517660 31197472 31200201.3
#> 14: male ag1a savings 517660 31197472 31200201.3
#> 15: m ag1a savings 517660 31197472 31200201.3
#> 16: male age_group2 savings 280923 16723727 16719188.0
#> 17: m age_group2 savings 280923 16723727 16719188.0
#> 18: male ag2a savings 280923 16723727 16719188.0
#> 19: m ag2a savings 280923 16723727 16719188.0
#> 20: male age_group3 savings 214970 13109917 13108526.7
#> 21: m age_group3 savings 214970 13109917 13108526.7
#> 22: male age_group4 savings 99420 6192071 6202017.0
#> 23: m age_group4 savings 99420 6192071 6202017.0
#> 24: male age_group5 savings 43233 2619083 2618672.9
#> 25: m age_group5 savings 43233 2619083 2618672.9
#> 26: male age_group6 savings 3610 213613 213375.7
#> 27: m age_group6 savings 3610 213613 213375.7
#> 28: female Total savings 1113716 66970912 66962502.2
#> 29: f Total savings 1113716 66970912 66962502.2
#> 30: female age_group1 savings 464726 28239325 28241487.4
#> 31: f age_group1 savings 464726 28239325 28241487.4
#> 32: female ag1a savings 464726 28239325 28241487.4
#> 33: f ag1a savings 464726 28239325 28241487.4
#> 34: female age_group2 savings 271413 16162378 16165437.9
#> 35: f age_group2 savings 271413 16162378 16165437.9
#> 36: female ag2a savings 271413 16162378 16165437.9
#> 37: f ag2a savings 271413 16162378 16165437.9
#> 38: female age_group3 savings 222131 13347872 13350884.5
#> 39: f age_group3 savings 222131 13347872 13350884.5
#> 40: female age_group4 savings 115241 6822780 6824733.3
#> 41: f age_group4 savings 115241 6822780 6824733.3
#> 42: female age_group5 savings 37218 2200332 2190989.1
#> 43: f age_group5 savings 37218 2200332 2190989.1
#> 44: female age_group6 savings 2987 198225 200052.5
#> 45: f age_group6 savings 2987 198225 200052.5
#> 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/RtmpHZpsuV/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 275617 4580 275617.0000
#> 2: Total age_group1 total 1969 118674 1969 118674.0000
#> 3: Total ag1a total 1969 118674 1969 118674.0000
#> 4: Total age_group2 total 1143 68018 1144 68077.5083
#> 5: Total ag2a total 1143 68018 1144 68077.5083
#> 6: Total age_group3 total 864 52588 864 52588.0000
#> 7: Total age_group4 total 423 25354 424 25413.9385
#> 8: Total age_group5 total 168 10148 167 10087.5952
#> 9: Total age_group6 total 13 835 13 835.0000
#> 10: male Total total 2296 138577 2296 138577.0000
#> 11: m Total total 2296 138577 2296 138577.0000
#> 12: male age_group1 total 1015 60945 1015 60945.0000
#> 13: m age_group1 total 1015 60945 1015 60945.0000
#> 14: male ag1a total 1015 60945 1015 60945.0000
#> 15: m ag1a total 1015 60945 1015 60945.0000
#> 16: male age_group2 total 571 34245 570 34185.0263
#> 17: m age_group2 total 571 34245 570 34185.0263
#> 18: male ag2a total 571 34245 570 34185.0263
#> 19: m ag2a total 571 34245 570 34185.0263
#> 20: male age_group3 total 424 25845 425 25905.9552
#> 21: m age_group3 total 424 25845 425 25905.9552
#> 22: male age_group4 total 195 11996 196 12057.5179
#> 23: m age_group4 total 195 11996 196 12057.5179
#> 24: male age_group5 total 84 5080 84 5080.0000
#> 25: m age_group5 total 84 5080 84 5080.0000
#> 26: male age_group6 total 7 466 8 532.5714
#> 27: m age_group6 total 7 466 8 532.5714
#> 28: female Total total 2284 137040 2285 137100.0000
#> 29: f Total total 2284 137040 2285 137100.0000
#> 30: female age_group1 total 954 57729 953 57668.4874
#> 31: f age_group1 total 954 57729 953 57668.4874
#> 32: female ag1a total 954 57729 953 57668.4874
#> 33: f ag1a total 954 57729 953 57668.4874
#> 34: female age_group2 total 572 33773 572 33773.0000
#> 35: f age_group2 total 572 33773 572 33773.0000
#> 36: female ag2a total 572 33773 572 33773.0000
#> 37: f ag2a total 572 33773 572 33773.0000
#> 38: female age_group3 total 440 26743 441 26803.7795
#> 39: f age_group3 total 440 26743 441 26803.7795
#> 40: female age_group4 total 228 13358 230 13475.1754
#> 41: f age_group4 total 228 13358 230 13475.1754
#> 42: female age_group5 total 84 5068 84 5068.0000
#> 43: f age_group5 total 84 5068 84 5068.0000
#> 44: female age_group6 total 6 369 6 369.0000
#> 45: f age_group6 total 6 369 6 369.0000
#> 46: Total Total cnt_males 2296 138577 2296 138577.0000
#> 47: Total age_group1 cnt_males 1015 60945 1015 60945.0000
#> 48: Total ag1a cnt_males 1015 60945 1015 60945.0000
#> 49: Total age_group2 cnt_males 571 34245 570 34185.0263
#> 50: Total ag2a cnt_males 571 34245 570 34185.0263
#> 51: Total age_group3 cnt_males 424 25845 425 25905.9552
#> 52: Total age_group4 cnt_males 195 11996 196 12057.5179
#> 53: Total age_group5 cnt_males 84 5080 84 5080.0000
#> 54: Total age_group6 cnt_males 7 466 8 532.5714
#> 55: male Total cnt_males 2296 138577 2296 138577.0000
#> 56: m Total cnt_males 2296 138577 2296 138577.0000
#> 57: male age_group1 cnt_males 1015 60945 1015 60945.0000
#> 58: m age_group1 cnt_males 1015 60945 1015 60945.0000
#> 59: male ag1a cnt_males 1015 60945 1015 60945.0000
#> 60: m ag1a cnt_males 1015 60945 1015 60945.0000
#> 61: male age_group2 cnt_males 571 34245 570 34185.0263
#> 62: m age_group2 cnt_males 571 34245 570 34185.0263
#> 63: male ag2a cnt_males 571 34245 570 34185.0263
#> 64: m ag2a cnt_males 571 34245 570 34185.0263
#> 65: male age_group3 cnt_males 424 25845 425 25905.9552
#> 66: m age_group3 cnt_males 424 25845 425 25905.9552
#> 67: male age_group4 cnt_males 195 11996 196 12057.5179
#> 68: m age_group4 cnt_males 195 11996 196 12057.5179
#> 69: male age_group5 cnt_males 84 5080 84 5080.0000
#> 70: m age_group5 cnt_males 84 5080 84 5080.0000
#> 71: male age_group6 cnt_males 7 466 8 532.5714
#> 72: m age_group6 cnt_males 7 466 8 532.5714
#> 73: female Total cnt_males 0 0 0 0.0000
#> 74: f Total cnt_males 0 0 0 0.0000
#> 75: female age_group1 cnt_males 0 0 0 0.0000
#> 76: f age_group1 cnt_males 0 0 0 0.0000
#> 77: female ag1a cnt_males 0 0 0 0.0000
#> 78: f ag1a cnt_males 0 0 0 0.0000
#> 79: female age_group2 cnt_males 0 0 0 0.0000
#> 80: f age_group2 cnt_males 0 0 0 0.0000
#> 81: female ag2a cnt_males 0 0 0 0.0000
#> 82: f ag2a cnt_males 0 0 0 0.0000
#> 83: female age_group3 cnt_males 0 0 0 0.0000
#> 84: f age_group3 cnt_males 0 0 0 0.0000
#> 85: female age_group4 cnt_males 0 0 0 0.0000
#> 86: f age_group4 cnt_males 0 0 0 0.0000
#> 87: female age_group5 cnt_males 0 0 0 0.0000
#> 88: f age_group5 cnt_males 0 0 0 0.0000
#> 89: female age_group6 cnt_males 0 0 0 0.0000
#> 90: f age_group6 cnt_males 0 0 0 0.0000
#> 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 2 0.04444444
#> 2: -1 13 0.28888889
#> 3: 0 21 0.46666667
#> 4: 1 9 0.20000000
#>
#> $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 0.578 0.008 0.021
#> 7: Median 1.000 0.000 0.010
#> 8: Q60 1.000 0.001 0.016
#> 9: Q70 1.000 0.002 0.021
#> 10: Q80 1.000 0.002 0.024
#> 11: Q90 1.000 0.006 0.037
#> 12: Q95 1.000 0.009 0.066
#> 13: Q99 2.000 0.143 0.183
#> 14: Max 2.000 0.143 0.183
#>
#> $cumdistr_d1
#> cat cnt pct
#> <char> <int> <num>
#> 1: 0 21 0.4666667
#> 2: 1 43 0.9555556
#> 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] 29 0.6444444
#> 2: (0.02,0.05] 41 0.9111111
#> 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 15 0 0.4894645 total
#> 2: Total age_group1 15 0 0.6373559 total
#> 3: Total ag1a 15 0 0.6373559 total
#> 4: Total age_group2 16 1 0.8031745 total
#> 5: Total ag2a 16 1 0.8031745 total
#> ---
#> 131: f age_group4 -1 0 0.0000000 cnt_males
#> 132: female age_group5 -1 0 0.0000000 cnt_males
#> 133: f age_group5 -1 0 0.0000000 cnt_males
#> 134: female age_group6 -1 0 0.0000000 cnt_males
#> 135: f age_group6 -1 0 0.0000000 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 -1 -1.0 -0.2 0 0 0.178 0 0 1 1.0
#> 2: cnt_highincome -3 -1.6 -1.0 0 0 -0.044 0 0 0 1.0
#> 3: cnt_males -1 -1.0 0.0 0 0 0.067 0 0 0 0.2
#> Q90 Q95 Q99 Max
#> <num> <num> <num> <num>
#> 1: 1 1 2.00 2
#> 2: 2 2 2.56 3
#> 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 0.578 0.008 0.021
#> 7: Median 1.000 0.000 0.010
#> 8: Q60 1.000 0.001 0.016
#> 9: Q70 1.000 0.002 0.021
#> 10: Q80 1.000 0.002 0.024
#> 11: Q90 1.000 0.006 0.037
#> 12: Q95 1.000 0.009 0.066
#> 13: Q99 2.000 0.143 0.183
#> 14: Max 2.000 0.143 0.183
#>
#> ✔ 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 0.017 0.032
#> 7: Median 1.000 0.002 0.021
#> 8: Q60 1.000 0.002 0.021
#> 9: Q70 1.000 0.002 0.024
#> 10: Q80 1.000 0.005 0.033
#> 11: Q90 1.000 0.060 0.095
#> 12: Q95 1.000 0.143 0.183
#> 13: Q99 1.000 0.143 0.183
#> 14: Max 1.000 0.143 0.183
#>
#> ✔ Variable: 'cnt_highincome'
#>
#> what d1 d2 d3
#> <char> <num> <num> <num>
#> 1: Min 0.0 0.000 0.000
#> 2: Q10 0.0 0.000 0.000
#> 3: Q20 0.0 0.000 0.000
#> 4: Q30 0.0 0.000 0.000
#> 5: Q40 1.0 0.005 0.034
#> 6: Mean 1.0 0.038 0.084
#> 7: Median 1.0 0.009 0.053
#> 8: Q60 1.0 0.011 0.066
#> 9: Q70 1.0 0.024 0.131
#> 10: Q80 2.0 0.052 0.154
#> 11: Q90 2.1 0.143 0.190
#> 12: Q95 3.0 0.150 0.266
#> 13: Q99 3.0 0.286 0.410
#> 14: Max 3.0 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 -119666.44 -84168.15 -28712.405 -9527.670 -3955.398 -4277.001
#> 3: savings -10199.26 -8409.78 -4538.963 -1440.378 -306.416 -194.113
#> 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: -1829.318 17479.098 21165.646 24616.965 53486.978 55589.280 73250.364
#> 3: 871.673 2036.928 2615.948 3012.478 3059.882 8958.283 9945.989
#> 4: NA NA NA NA NA NA NA
#> Max
#> <num>
#> 1: -Inf
#> 2: 73250.364
#> 3: 9945.989
#> 4: -Inf
# }