Build tables and apply Cell Key Method on a list

This function constructs multiple tables from microdata and applies the Cell Key Method to each table based on a description data frame specifying the table structure.

Usage

tabulate_and_apply_ckm_list(
  df,
  rk_var = "rkey",
  desc_tab,
  marge_label = "Total",
  prefix = "tab",
  D,
  V,
  js = 0,
  I = NULL,
  J = NULL,
  ...
)

Arguments

df

data.frame. Input microdata

rk_var

Character. Individual key variable (NULL for no keys)

desc_tab

data.frame. Table description with 3 columns (TAB, VAR, HRC) describing the tables to construct

marge_label

Character. Margin label (default: "Total")

prefix

character. Prefix to add to table names provided in desc_tab (default: "tab")

D

integer. Deviation parameter (must be strictly positive)

V

numeric. Noise variance (must be strictly positive)

js

integer. Threshold for sensitive values (default: 0). If js=0, only value 0 will be forbidden

I

integer vector. Original values to consider

J

integer vector. Perturbed values to consider

...

Additional parameters passed to transition matrix creation

Value

A list containing:

• tab: list of tables (tibbles) with CKM applied to each

• ptab: transition matrix used for calculations

• risque: tibble with risk measures for each table

• utilite: tibble with utility measures for each table

Details

The desc_tab data frame must have the following structure:

• TAB: Table name or number

• VAR: Variable name for the table

• HRC: Hierarchy name if the variable has hierarchical relationship, NA otherwise

One row corresponds to one variable in a given table. Variables with hierarchical relationships should be listed in decreasing order of hierarchy (from broadest to finest level). Two variables (e.g., REGION and DEPARTEMENT) in the same table with a hierarchical relationship must have the same value in the HRC column.

Examples

if (FALSE) { # \dontrun{
data("dtest")
set.seed(123)
dtest_avec_cles <- build_individual_keys(dtest)

# Define two tables:
# tab1: DIPLOME * SEXE * AGE
# tab2: DIPLOME * TYPE * REG * DEP, where REG > DEP
desc_tableaux <- data.frame(
  TAB = c(rep(1,3), rep(2,4)),
  VAR = c("DIPLOME", "SEXE", "AGE", "DIPLOME", "TYPE", "REG", "DEP"),
  HRC = c(rep(NA, 5), rep("GEO",2))
)

res_ckm <- tabulate_and_apply_ckm_list(
  df = dtest_avec_cles,
  desc_tab = desc_tableaux,
  marge_label = "Total",
  D = 10, V = 15, js = 4
)
} # }