R/ck_params_nums.R
ck_flexparams.Rdck_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 46 25.00000 0
#> 2: 2530 28 1 82 25.00000 1
#> 3: 6920 550 1 38 25.00000 0
#> 4: 7960 870 1 37 25.00000 0
#> 5: 9030 20 2 36 16.66667 0
#> ---
#> 4576: 7900 278 1000 65 16.66667 1
#> 4577: 1420 987 1000 62 16.66667 0
#> 4578: 8900 684 1000 31 16.66667 0
#> 4579: 3880 294 1000 34 16.66667 1
#> 4580: 4830 911 1000 73 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 46 25.00000 0
#> 2: 2530 28 1 82 25.00000 1
#> 3: 6920 550 1 38 25.00000 0
#> 4: 7960 870 1 37 25.00000 0
#> 5: 9030 20 2 36 16.66667 0
#> ---
#> 4576: 7900 278 1000 65 16.66667 1
#> 4577: 1420 987 1000 62 16.66667 0
#> 4578: 8900 684 1000 31 16.66667 0
#> 4579: 3880 294 1000 34 16.66667 1
#> 4580: 4830 911 1000 73 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 46 25.00000 0
#> 2: 2530 28 1 82 25.00000 1
#> 3: 6920 550 1 38 25.00000 0
#> 4: 7960 870 1 37 25.00000 0
#> 5: 9030 20 2 36 16.66667 0
#> ---
#> 4576: 7900 278 1000 65 16.66667 1
#> 4577: 1420 987 1000 62 16.66667 0
#> 4578: 8900 684 1000 31 16.66667 0
#> 4579: 3880 294 1000 34 16.66667 1
#> 4580: 4830 911 1000 73 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 46 25.00000 0
#> 2: 2530 28 1 82 25.00000 1
#> 3: 6920 550 1 38 25.00000 0
#> 4: 7960 870 1 37 25.00000 0
#> 5: 9030 20 2 36 16.66667 0
#> ---
#> 4576: 7900 278 1000 65 16.66667 1
#> 4577: 1420 987 1000 62 16.66667 0
#> 4578: 8900 684 1000 31 16.66667 0
#> 4579: 3880 294 1000 34 16.66667 1
#> 4580: 4830 911 1000 73 16.66667 0
#> cnt_males cnt_highincome mixed
#> <num> <num> <int>
#> 1: 1 0 -7
#> 2: 0 0 -9
#> 3: 1 0 2
#> 4: 1 0 -7
#> 5: 1 1 -3
#> ---
#> 4576: 0 0 -6
#> 4577: 1 0 -10
#> 4578: 1 0 9
#> 4579: 0 0 1
#> 4580: 1 0 0
# 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$numvars()
#> [1] "expend" "income" "savings" "mixed"
# create perturbation parameters for "total" variable and
# write to yaml-file
# create a ptable using functionality from the ptable-pkg
f_yaml <- tempfile(fileext = ".yaml")
p_cnts1 <- ck_params_cnts(
ptab = ptable::pt_ex_cnts(),
path = f_yaml)
#> yaml configuration '/tmp/Rtmpu511Tl/file1fa54433c4bb.yaml' successfully written.
# read parameters from yaml-file and set them for variable `"total"`
p_cnts1 <- ck_read_yaml(path = f_yaml)
tab$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 272686 4581 272745.5384
#> 2: Total age_group1 total 1969 117288 1970 117347.5673
#> 3: Total ag1a total 1969 117288 1970 117347.5673
#> 4: Total age_group2 total 1143 68941 1142 68880.6842
#> 5: Total ag2a total 1143 68941 1142 68880.6842
#> 6: Total age_group3 total 864 51132 863 51072.8194
#> 7: Total age_group4 total 423 24549 421 24432.9291
#> 8: Total age_group5 total 168 10075 168 10075.0000
#> 9: Total age_group6 total 13 701 12 647.0769
#> 10: male Total total 2296 137868 2295 137807.9530
#> 11: m Total total 2296 137868 2295 137807.9530
#> 12: male age_group1 total 1015 60570 1015 60570.0000
#> 13: m age_group1 total 1015 60570 1015 60570.0000
#> 14: male ag1a total 1015 60570 1015 60570.0000
#> 15: m ag1a total 1015 60570 1015 60570.0000
#> 16: male age_group2 total 571 34704 571 34704.0000
#> 17: m age_group2 total 571 34704 571 34704.0000
#> 18: male ag2a total 571 34704 571 34704.0000
#> 19: m ag2a total 571 34704 571 34704.0000
#> 20: male age_group3 total 424 25995 424 25995.0000
#> 21: m age_group3 total 424 25995 424 25995.0000
#> 22: male age_group4 total 195 11353 197 11469.4410
#> 23: m age_group4 total 195 11353 197 11469.4410
#> 24: male age_group5 total 84 4788 82 4674.0000
#> 25: m age_group5 total 84 4788 82 4674.0000
#> 26: male age_group6 total 7 458 6 392.5714
#> 27: m age_group6 total 7 458 6 392.5714
#> 28: female Total total 2284 134818 2284 134818.0000
#> 29: f Total total 2284 134818 2284 134818.0000
#> 30: female age_group1 total 954 56718 953 56658.5472
#> 31: f age_group1 total 954 56718 953 56658.5472
#> 32: female ag1a total 954 56718 953 56658.5472
#> 33: f ag1a total 954 56718 953 56658.5472
#> 34: female age_group2 total 572 34237 573 34296.8549
#> 35: f age_group2 total 572 34237 573 34296.8549
#> 36: female ag2a total 572 34237 573 34296.8549
#> 37: f ag2a total 572 34237 573 34296.8549
#> 38: female age_group3 total 440 25137 441 25194.1295
#> 39: f age_group3 total 440 25137 441 25194.1295
#> 40: female age_group4 total 228 13196 227 13138.1228
#> 41: f age_group4 total 228 13196 227 13138.1228
#> 42: female age_group5 total 84 5287 84 5287.0000
#> 43: f age_group5 total 84 5287 84 5287.0000
#> 44: female age_group6 total 6 243 8 324.0000
#> 45: f age_group6 total 6 243 8 324.0000
#> 46: Total Total cnt_males 2296 137868 2295 137807.9530
#> 47: Total age_group1 cnt_males 1015 60570 1015 60570.0000
#> 48: Total ag1a cnt_males 1015 60570 1015 60570.0000
#> 49: Total age_group2 cnt_males 571 34704 570 34643.2224
#> 50: Total ag2a cnt_males 571 34704 570 34643.2224
#> 51: Total age_group3 cnt_males 424 25995 423 25933.6910
#> 52: Total age_group4 cnt_males 195 11353 198 11527.6615
#> 53: Total age_group5 cnt_males 84 4788 81 4617.0000
#> 54: Total age_group6 cnt_males 7 458 5 327.1429
#> 55: male Total cnt_males 2296 137868 2295 137807.9530
#> 56: m Total cnt_males 2296 137868 2295 137807.9530
#> 57: male age_group1 cnt_males 1015 60570 1015 60570.0000
#> 58: m age_group1 cnt_males 1015 60570 1015 60570.0000
#> 59: male ag1a cnt_males 1015 60570 1015 60570.0000
#> 60: m ag1a cnt_males 1015 60570 1015 60570.0000
#> 61: male age_group2 cnt_males 571 34704 570 34643.2224
#> 62: m age_group2 cnt_males 571 34704 570 34643.2224
#> 63: male ag2a cnt_males 571 34704 570 34643.2224
#> 64: m ag2a cnt_males 571 34704 570 34643.2224
#> 65: male age_group3 cnt_males 424 25995 423 25933.6910
#> 66: m age_group3 cnt_males 424 25995 423 25933.6910
#> 67: male age_group4 cnt_males 195 11353 198 11527.6615
#> 68: m age_group4 cnt_males 195 11353 198 11527.6615
#> 69: male age_group5 cnt_males 84 4788 81 4617.0000
#> 70: m age_group5 cnt_males 84 4788 81 4617.0000
#> 71: male age_group6 cnt_males 7 458 5 327.1429
#> 72: m age_group6 cnt_males 7 458 5 327.1429
#> 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/Rtmpu511Tl/file1fa56c97848d.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 1367927750 1367774737
#> 2: Total age_group1 income 9810547 586291459 586497067
#> 3: Total ag1a income 9810547 586291459 586497067
#> 4: Total age_group2 income 5692119 341887456 342013470
#> 5: Total ag2a income 5692119 341887456 342013470
#> 6: Total age_group3 income 4406946 260895581 261028817
#> 7: Total age_group4 income 2133543 123691715 123775978
#> 8: Total age_group5 income 848151 51976816 51972573
#> 9: Total age_group6 income 61672 3184723 3026105
#> 10: male Total income 11262049 677390855 677499342
#> 11: m Total income 11262049 677390855 677499342
#> 12: male age_group1 income 4877164 290785366 290749848
#> 13: m age_group1 income 4877164 290785366 290749848
#> 14: male ag1a income 4877164 290785366 290749848
#> 15: m ag1a income 4877164 290785366 290749848
#> 16: male age_group2 income 2811379 171754899 171787838
#> 17: m age_group2 income 2811379 171754899 171787838
#> 18: male ag2a income 2811379 171754899 171787838
#> 19: m ag2a income 2811379 171754899 171787838
#> 20: male age_group3 income 2168169 132170466 132182213
#> 21: m age_group3 income 2168169 132170466 132182213
#> 22: male age_group4 income 978510 57876940 57915815
#> 23: m age_group4 income 978510 57876940 57915815
#> 24: male age_group5 income 393134 22740934 22753298
#> 25: m age_group5 income 393134 22740934 22753298
#> 26: male age_group6 income 33693 2062250 2169036
#> 27: m age_group6 income 33693 2062250 2169036
#> 28: female Total income 11690929 690536895 690529591
#> 29: f Total income 11690929 690536895 690529591
#> 30: female age_group1 income 4933383 295506093 295561848
#> 31: f age_group1 income 4933383 295506093 295561848
#> 32: female ag1a income 4933383 295506093 295561848
#> 33: f ag1a income 4933383 295506093 295561848
#> 34: female age_group2 income 2880740 170132557 170012752
#> 35: f age_group2 income 2880740 170132557 170012752
#> 36: female ag2a income 2880740 170132557 170012752
#> 37: f ag2a income 2880740 170132557 170012752
#> 38: female age_group3 income 2238777 128725115 128680643
#> 39: f age_group3 income 2238777 128725115 128680643
#> 40: female age_group4 income 1155033 65814775 65778356
#> 41: f age_group4 income 1155033 65814775 65778356
#> 42: female age_group5 income 455017 29235882 29125162
#> 43: f age_group5 income 455017 29235882 29125162
#> 44: female age_group6 income 27979 1122473 1135584
#> 45: f age_group6 income 27979 1122473 1135584
#> 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 1367927750 1367851241
#> 2: Total age_group1 income 9810547 586291459 586394254
#> 3: Total ag1a income 9810547 586291459 586394254
#> 4: Total age_group2 income 5692119 341887456 341950457
#> 5: Total ag2a income 5692119 341887456 341950457
#> 6: Total age_group3 income 4406946 260895581 260962191
#> 7: Total age_group4 income 2133543 123691715 123733840
#> 8: Total age_group5 income 848151 51976816 51974694
#> 9: Total age_group6 income 61672 3184723 3104401
#> 10: male Total income 11262049 677390855 677445096
#> 11: m Total income 11262049 677390855 677445096
#> 12: male age_group1 income 4877164 290785366 290767607
#> 13: m age_group1 income 4877164 290785366 290767607
#> 14: male ag1a income 4877164 290785366 290767607
#> 15: m ag1a income 4877164 290785366 290767607
#> 16: male age_group2 income 2811379 171754899 171771367
#> 17: m age_group2 income 2811379 171754899 171771367
#> 18: male ag2a income 2811379 171754899 171771367
#> 19: m ag2a income 2811379 171754899 171771367
#> 20: male age_group3 income 2168169 132170466 132176340
#> 21: m age_group3 income 2168169 132170466 132176340
#> 22: male age_group4 income 978510 57876940 57896374
#> 23: m age_group4 income 978510 57876940 57896374
#> 24: male age_group5 income 393134 22740934 22747115
#> 25: m age_group5 income 393134 22740934 22747115
#> 26: male age_group6 income 33693 2062250 2114969
#> 27: m age_group6 income 33693 2062250 2114969
#> 28: female Total income 11690929 690536895 690533243
#> 29: f Total income 11690929 690536895 690533243
#> 30: female age_group1 income 4933383 295506093 295533969
#> 31: f age_group1 income 4933383 295506093 295533969
#> 32: female ag1a income 4933383 295506093 295533969
#> 33: f ag1a income 4933383 295506093 295533969
#> 34: female age_group2 income 2880740 170132557 170072644
#> 35: f age_group2 income 2880740 170132557 170072644
#> 36: female ag2a income 2880740 170132557 170072644
#> 37: f ag2a income 2880740 170132557 170072644
#> 38: female age_group3 income 2238777 128725115 128702877
#> 39: f age_group3 income 2238777 128725115 128702877
#> 40: female age_group4 income 1155033 65814775 65796563
#> 41: f age_group4 income 1155033 65814775 65796563
#> 42: female age_group5 income 455017 29235882 29180469
#> 43: f age_group5 income 455017 29235882 29180469
#> 44: female age_group6 income 27979 1122473 1129009
#> 45: f age_group6 income 27979 1122473 1129009
#> 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 136102860 136097984.5
#> 2: Total age_group1 savings 982386 59040646 59031680.8
#> 3: Total ag1a savings 982386 59040646 59031680.8
#> 4: Total age_group2 savings 552336 33278908 33281863.8
#> 5: Total ag2a savings 552336 33278908 33281863.8
#> 6: Total age_group3 savings 437101 26060353 26065012.5
#> 7: Total age_group4 savings 214661 12539034 12542412.2
#> 8: Total age_group5 savings 80451 4835524 4835218.1
#> 9: Total age_group6 savings 6597 348395 344777.2
#> 10: male Total savings 1159816 70215220 70217700.1
#> 11: m Total savings 1159816 70215220 70217700.1
#> 12: male age_group1 savings 517660 31254331 31250218.6
#> 13: m age_group1 savings 517660 31254331 31250218.6
#> 14: male ag1a savings 517660 31254331 31250218.6
#> 15: m ag1a savings 517660 31254331 31250218.6
#> 16: male age_group2 savings 280923 17085858 17089070.8
#> 17: m age_group2 savings 280923 17085858 17089070.8
#> 18: male ag2a savings 280923 17085858 17089070.8
#> 19: m ag2a savings 280923 17085858 17089070.8
#> 20: male age_group3 savings 214970 13300653 13299129.9
#> 21: m age_group3 savings 214970 13300653 13299129.9
#> 22: male age_group4 savings 99420 5826952 5828408.7
#> 23: m age_group4 savings 99420 5826952 5828408.7
#> 24: male age_group5 savings 43233 2511432 2511919.0
#> 25: m age_group5 savings 43233 2511432 2511919.0
#> 26: male age_group6 savings 3610 235994 236862.2
#> 27: m age_group6 savings 3610 235994 236862.2
#> 28: female Total savings 1113716 65887640 65885982.8
#> 29: f Total savings 1113716 65887640 65885982.8
#> 30: female age_group1 savings 464726 27786315 27785528.7
#> 31: f age_group1 savings 464726 27786315 27785528.7
#> 32: female ag1a savings 464726 27786315 27785528.7
#> 33: f ag1a savings 464726 27786315 27785528.7
#> 34: female age_group2 savings 271413 16193050 16189740.6
#> 35: f age_group2 savings 271413 16193050 16189740.6
#> 36: female ag2a savings 271413 16193050 16189740.6
#> 37: f ag2a savings 271413 16193050 16189740.6
#> 38: female age_group3 savings 222131 12759700 12758283.1
#> 39: f age_group3 savings 222131 12759700 12758283.1
#> 40: female age_group4 savings 115241 6712082 6712501.1
#> 41: f age_group4 savings 115241 6712082 6712501.1
#> 42: female age_group5 savings 37218 2324092 2318978.4
#> 43: f age_group5 savings 37218 2324092 2318978.4
#> 44: female age_group6 savings 2987 112401 112641.0
#> 45: f age_group6 savings 2987 112401 112641.0
#> 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 272686 4581 272745.5384
#> 2: Total age_group1 total 1969 117288 1970 117347.5673
#> 3: Total ag1a total 1969 117288 1970 117347.5673
#> 4: Total age_group2 total 1143 68941 1142 68880.6842
#> 5: Total ag2a total 1143 68941 1142 68880.6842
#> 6: Total age_group3 total 864 51132 863 51072.8194
#> 7: Total age_group4 total 423 24549 421 24432.9291
#> 8: Total age_group5 total 168 10075 168 10075.0000
#> 9: Total age_group6 total 13 701 12 647.0769
#> 10: male Total total 2296 137868 2295 137807.9530
#> 11: m Total total 2296 137868 2295 137807.9530
#> 12: male age_group1 total 1015 60570 1015 60570.0000
#> 13: m age_group1 total 1015 60570 1015 60570.0000
#> 14: male ag1a total 1015 60570 1015 60570.0000
#> 15: m ag1a total 1015 60570 1015 60570.0000
#> 16: male age_group2 total 571 34704 571 34704.0000
#> 17: m age_group2 total 571 34704 571 34704.0000
#> 18: male ag2a total 571 34704 571 34704.0000
#> 19: m ag2a total 571 34704 571 34704.0000
#> 20: male age_group3 total 424 25995 424 25995.0000
#> 21: m age_group3 total 424 25995 424 25995.0000
#> 22: male age_group4 total 195 11353 197 11469.4410
#> 23: m age_group4 total 195 11353 197 11469.4410
#> 24: male age_group5 total 84 4788 82 4674.0000
#> 25: m age_group5 total 84 4788 82 4674.0000
#> 26: male age_group6 total 7 458 6 392.5714
#> 27: m age_group6 total 7 458 6 392.5714
#> 28: female Total total 2284 134818 2284 134818.0000
#> 29: f Total total 2284 134818 2284 134818.0000
#> 30: female age_group1 total 954 56718 953 56658.5472
#> 31: f age_group1 total 954 56718 953 56658.5472
#> 32: female ag1a total 954 56718 953 56658.5472
#> 33: f ag1a total 954 56718 953 56658.5472
#> 34: female age_group2 total 572 34237 573 34296.8549
#> 35: f age_group2 total 572 34237 573 34296.8549
#> 36: female ag2a total 572 34237 573 34296.8549
#> 37: f ag2a total 572 34237 573 34296.8549
#> 38: female age_group3 total 440 25137 441 25194.1295
#> 39: f age_group3 total 440 25137 441 25194.1295
#> 40: female age_group4 total 228 13196 227 13138.1228
#> 41: f age_group4 total 228 13196 227 13138.1228
#> 42: female age_group5 total 84 5287 84 5287.0000
#> 43: f age_group5 total 84 5287 84 5287.0000
#> 44: female age_group6 total 6 243 8 324.0000
#> 45: f age_group6 total 6 243 8 324.0000
#> 46: Total Total cnt_males 2296 137868 2295 137807.9530
#> 47: Total age_group1 cnt_males 1015 60570 1015 60570.0000
#> 48: Total ag1a cnt_males 1015 60570 1015 60570.0000
#> 49: Total age_group2 cnt_males 571 34704 571 34704.0000
#> 50: Total ag2a cnt_males 571 34704 571 34704.0000
#> 51: Total age_group3 cnt_males 424 25995 424 25995.0000
#> 52: Total age_group4 cnt_males 195 11353 197 11469.4410
#> 53: Total age_group5 cnt_males 84 4788 82 4674.0000
#> 54: Total age_group6 cnt_males 7 458 6 392.5714
#> 55: male Total cnt_males 2296 137868 2295 137807.9530
#> 56: m Total cnt_males 2296 137868 2295 137807.9530
#> 57: male age_group1 cnt_males 1015 60570 1015 60570.0000
#> 58: m age_group1 cnt_males 1015 60570 1015 60570.0000
#> 59: male ag1a cnt_males 1015 60570 1015 60570.0000
#> 60: m ag1a cnt_males 1015 60570 1015 60570.0000
#> 61: male age_group2 cnt_males 571 34704 571 34704.0000
#> 62: m age_group2 cnt_males 571 34704 571 34704.0000
#> 63: male ag2a cnt_males 571 34704 571 34704.0000
#> 64: m ag2a cnt_males 571 34704 571 34704.0000
#> 65: male age_group3 cnt_males 424 25995 424 25995.0000
#> 66: m age_group3 cnt_males 424 25995 424 25995.0000
#> 67: male age_group4 cnt_males 195 11353 197 11469.4410
#> 68: m age_group4 cnt_males 195 11353 197 11469.4410
#> 69: male age_group5 cnt_males 84 4788 82 4674.0000
#> 70: m age_group5 cnt_males 84 4788 82 4674.0000
#> 71: male age_group6 cnt_males 7 458 6 392.5714
#> 72: m age_group6 cnt_males 7 458 6 392.5714
#> 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 4 0.08888889
#> 2: -1 9 0.20000000
#> 3: 0 15 0.33333333
#> 4: 1 14 0.31111111
#> 5: 2 3 0.06666667
#>
#> $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 1.000 0.000 0.011
#> 6: Mean 0.822 0.025 0.046
#> 7: Median 1.000 0.001 0.016
#> 8: Q60 1.000 0.001 0.019
#> 9: Q70 1.000 0.002 0.023
#> 10: Q80 1.000 0.006 0.053
#> 11: Q90 2.000 0.056 0.129
#> 12: Q95 2.000 0.143 0.196
#> 13: Q99 2.000 0.333 0.379
#> 14: Max 2.000 0.333 0.379
#>
#> $cumdistr_d1
#> cat cnt pct
#> <char> <int> <num>
#> 1: 0 15 0.3333333
#> 2: 1 38 0.8444444
#> 3: 2 45 1.0000000
#>
#> $cumdistr_d2
#> cat cnt pct
#> <char> <int> <num>
#> 1: [0,0.02] 38 0.8444444
#> 2: (0.02,0.05] 40 0.8888889
#> 3: (0.05,0.1] 41 0.9111111
#> 4: (0.1,0.2] 43 0.9555556
#> 5: (0.2,0.3] 43 0.9555556
#> 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] 27 0.6000000
#> 2: (0.02,0.05] 36 0.8000000
#> 3: (0.05,0.1] 38 0.8444444
#> 4: (0.1,0.2] 43 0.9555556
#> 5: (0.2,0.3] 43 0.9555556
#> 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 16 1 0.7027340 total
#> 2: Total age_group1 16 1 0.6973654 total
#> 3: Total ag1a 16 1 0.6973654 total
#> 4: Total age_group2 14 -1 0.1872797 total
#> 5: Total ag2a 14 -1 0.1872797 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 -2 -1 -1.0 -1 0 -0.067 0 0 0 1
#> 2: cnt_highincome -4 -2 -1.0 0 0 0.289 0 1 1 2
#> 3: cnt_males -2 -1 -0.2 0 0 -0.133 0 0 0 0
#> Q90 Q95 Q99 Max
#> <num> <num> <num> <num>
#> 1: 1 2.0 2 2
#> 2: 2 2.0 5 5
#> 3: 0 1.6 2 2
#>
#> ── 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 1.000 0.000 0.011
#> 6: Mean 0.822 0.025 0.046
#> 7: Median 1.000 0.001 0.016
#> 8: Q60 1.000 0.001 0.019
#> 9: Q70 1.000 0.002 0.023
#> 10: Q80 1.000 0.006 0.053
#> 11: Q90 2.000 0.056 0.129
#> 12: Q95 2.000 0.143 0.196
#> 13: Q99 2.000 0.333 0.379
#> 14: Max 2.000 0.333 0.379
#>
#> ✔ 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 0.667 0.020 0.043
#> 7: Median 0.000 0.000 0.000
#> 8: Q60 1.000 0.000 0.010
#> 9: Q70 1.000 0.010 0.071
#> 10: Q80 1.800 0.021 0.102
#> 11: Q90 2.000 0.071 0.144
#> 12: Q95 2.000 0.143 0.196
#> 13: Q99 2.000 0.143 0.196
#> 14: Max 2.000 0.143 0.196
#>
#> ✔ Variable: 'cnt_highincome'
#>
#> 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 1.000 0.005 0.034
#> 4: Q30 1.000 0.009 0.066
#> 5: Q40 1.000 0.018 0.066
#> 6: Mean 1.475 0.038 0.101
#> 7: Median 1.000 0.020 0.088
#> 8: Q60 2.000 0.022 0.102
#> 9: Q70 2.000 0.025 0.112
#> 10: Q80 2.000 0.051 0.158
#> 11: Q90 2.100 0.143 0.183
#> 12: Q95 4.050 0.143 0.196
#> 13: Q99 5.000 0.230 0.430
#> 14: Max 5.000 0.286 0.579
#>
#> ┌──────────────────────────────────────────────────┐
#> │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 -80322.053 -59913.190 -22237.695 -17759.356 -2733.890 5311.218
#> 3: savings -8965.189 -4570.259 -3716.747 -2978.984 -1459.402 -895.135
#> 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: 6181.025 16468.481 26187.802 44243.43 59497.280 65888.031 102794.835
#> 3: -786.321 311.611 791.997 2480.08 3212.818 3212.818 4095.742
#> 4: NA NA NA NA NA NA NA
#> Max
#> <num>
#> 1: -Inf
#> 2: 102794.835
#> 3: 4659.522
#> 4: -Inf
# }