Title: Tools for Statistical Disclosure Control in Research Data Centers
Version: 0.5.1
Description: Tools for researchers to explicitly show that their results comply to rules for statistical disclosure control imposed by research data centers. These tools help in checking descriptive statistics and models and in calculating extreme values that are not individual data. Also included is a simple function to create log files. The methods used here are described in the "Guidelines for the checking of output based on microdata research" by Bond, Brandt, and de Wolf (2015) https://cros.ec.europa.eu/system/files/2024-02/Output-checking-guidelines.pdf.
License: GPL-3
URL: https://github.com/matthiasgomolka/sdcLog
BugReports: https://github.com/matthiasgomolka/sdcLog/issues
Depends: R (≥ 3.5)
Imports: broom (≥ 0.5.5), checkmate (≥ 2.0.0), cli, data.table (≥ 1.12.8), mathjaxr, stats, utils
Suggests: cffr, knitr, lfe, rmarkdown, skimr, spelling, testthat (≥ 3.0.0), tibble
VignetteBuilder: knitr
RdMacros: mathjaxr
Config/testthat/edition: 3
Encoding: UTF-8
Language: en-US
LazyData: true
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-05-03 08:46:49 UTC; matth
Author: Matthias Gomolka [aut, cre], Tim Becker [aut], Pantelis Karapanagiotis [ctb]
Maintainer: Matthias Gomolka <matthias.gomolka@posteo.de>
Repository: CRAN
Date/Publication: 2025-05-03 09:10:02 UTC

arguments

Description

arguments

Arguments

data

data.frame from which the descriptive statistics are calculated.

id_var

character The name of the id variable. Defaults to getOption("sdc.id_var") so that you can provide options(sdc.id_var = "my_id_var") at the top of your script.

val_var

character vector of value variables on which descriptive statistics are computed.

by

character vector of grouping variables.

zero_as_NA

logical If TRUE, zeros in 'val_var' are treated as NA.

fill_id_var

logical Only for very specific use cases. For example:

  • id_var contains NA values which represent missing values in the sense that there actually exist values identifying the entity but are unknown (or deleted for privacy reasons).

  • id_var contains NA values which result from the fact that an observation features more than one confidential identifier and not all of these identifiers are present in each observation. Examples for such identifiers are the role of a broker in a security transaction or the role of a collateral giver in a credit relationship.

If TRUE, NA values within id_var will internally be filled with ⁠<filled_[i]>⁠, assuming that all NA values of id_var can be treated as different small entities for statistical disclosure control purposes. Thus, set TRUE only if this is a reasonable assumption.

Defaults to FALSE.

model

The estimated model object. Can be a model type like lm, glm and various others (anything which can be handled by broom::augment()).

min_obs

integer The minimum number of observations used to calculate the minimum and maximum. Defaults to getOption("sdc.n_ids", 5L). This is not the number of distinct entities.

max_obs

integer The maximum number of observations used to calculate the minimum and maximum. Defaults to nrow(data). This is not the number of distinct entities.

Print methods for SDC objects

Description

These methods print SDC objects. Tables containing information are only printed when relevant.

Usage

## S3 method for class 'sdc_distinct_ids'
print(x, ...)

## S3 method for class 'sdc_dominance'
print(x, ...)

## S3 method for class 'sdc_options'
print(x, ...)

## S3 method for class 'sdc_settings'
print(x, ...)

## S3 method for class 'sdc_descriptives'
print(x, ...)

## S3 method for class 'sdc_model'
print(x, ...)

## S3 method for class 'sdc_min_max'
print(x, ...)

Arguments

x

The object to be printed

...

Ignored.

Disclosure control for descriptive statistics

Description

Checks the number of distinct entities and the (n, k) dominance rule for your descriptive statistics.

That means that sdc_descriptives() checks if there are at least 5 distinct entities and if the largest 2 entities account for 85% or more of val_var. The parameters can be changed using options. For details see vignette("options", package = "sdcLog").

Usage

sdc_descriptives(
  data,
  id_var = getOption("sdc.id_var"),
  val_var = NULL,
  by = NULL,
  zero_as_NA = NULL,
  fill_id_var = FALSE
)

Arguments

data

data.frame from which the descriptive statistics are calculated.

id_var

character The name of the id variable. Defaults to getOption("sdc.id_var") so that you can provide options(sdc.id_var = "my_id_var") at the top of your script.

val_var

character vector of value variables on which descriptive statistics are computed.

by

character vector of grouping variables.

zero_as_NA

logical If TRUE, zeros in 'val_var' are treated as NA.

fill_id_var

logical Only for very specific use cases. For example:

  • id_var contains NA values which represent missing values in the sense that there actually exist values identifying the entity but are unknown (or deleted for privacy reasons).

  • id_var contains NA values which result from the fact that an observation features more than one confidential identifier and not all of these identifiers are present in each observation. Examples for such identifiers are the role of a broker in a security transaction or the role of a collateral giver in a credit relationship.

If TRUE, NA values within id_var will internally be filled with ⁠<filled_[i]>⁠, assuming that all NA values of id_var can be treated as different small entities for statistical disclosure control purposes. Thus, set TRUE only if this is a reasonable assumption.

Defaults to FALSE.

Details

The general form of the \((n, k)\) dominance rule can be formulated as:

\[\sum_{i=1}^{n}x_i > \frac{k}{100} \sum_{i=1}^{N}x_i\]

where \(x_1 \ge x_2 \ge \cdots \ge x_{N}\). \(n\) denotes the number of largest contributions to be considered, \(x_n\) the \(n\)-th largest contribution, \(k\) the maximal percentage these \(n\) contributions may account for, and \(N\) is the total number of observations.

If the statement above is true, the \((n, k)\) dominance rule is violated.

Value

A list of class sdc_descriptives with detailed information about options, settings, and compliance with the criteria distinct entities and dominance.

Examples

sdc_descriptives(
  data = sdc_descriptives_DT,
  id_var = "id",
  val_var = "val_1"
)

sdc_descriptives(
  data = sdc_descriptives_DT,
  id_var = "id",
  val_var = "val_1",
  by = "sector"
)

sdc_descriptives(
  data = sdc_descriptives_DT,
  id_var = "id",
  val_var = "val_1",
  by = c("sector", "year")
)

sdc_descriptives(
  data = sdc_descriptives_DT,
  id_var = "id",
  val_var = "val_2",
  by = c("sector", "year")
)

sdc_descriptives(
  data = sdc_descriptives_DT,
  id_var = "id",
  val_var = "val_2",
  by = c("sector", "year"),
  zero_as_NA = FALSE
)

Example data for sdc_descriptives()

Description

Utilized in the vignette.

Usage

data("sdc_descriptives_DT")

Format

A data.table with 20 rows and 5 columns.

Details

The data.table contains the following columns:

Create Stata-like log files from R Scripts

Description

This function creates Stata-like log files from R Scripts. It can handle several files (in a character vector) at once.

Usage

sdc_log(r_script, destination, replace = FALSE, append = FALSE, local = FALSE)

Arguments

r_script

character Path of the R script to be run with logging.

destination

One of:

  • character Path of the log file to be used.

  • file connection to which the log should be written. This is especially useful, when you have nested calls to sdc_log() and want to write everything into the same log file. Then, create a single file connection and provide this connection to all calls to sdc_log() (and close it afterwards).

replace

logical Indicates whether to replace an existing log file.

append

logical Indicates whether to append an existing log file.

local

One of:

  • logical Indicates whether to evaluate within the global environment (FALSE) or the calling environment (TRUE).

  • environment A specific evaluation environment. Determines the evaluation environment. Useful whenever sdc_log() is called from within a function, or for nested sdc_log() calls. By default (FALSE) evaluation occurs in the global environment. See also source.

Value

character vector holding the path(s) of the written log file(s).

Calculate RDC rule-compliant extreme values

Description

Checks if calculation of extreme values comply to RDC rules. If so, function returns average min and max values according to RDC rules.

Usage

sdc_min_max(
  data,
  id_var = getOption("sdc.id_var"),
  val_var,
  by = NULL,
  max_obs = nrow(data),
  fill_id_var = FALSE
)

Arguments

data

data.frame from which the descriptive statistics are calculated.

id_var

character The name of the id variable. Defaults to getOption("sdc.id_var") so that you can provide options(sdc.id_var = "my_id_var") at the top of your script.

val_var

character vector of value variables on which descriptive statistics are computed.

by

character vector of grouping variables.

max_obs

integer The maximum number of observations used to calculate the minimum and maximum. Defaults to nrow(data). This is not the number of distinct entities.

fill_id_var

logical Only for very specific use cases. For example:

  • id_var contains NA values which represent missing values in the sense that there actually exist values identifying the entity but are unknown (or deleted for privacy reasons).

  • id_var contains NA values which result from the fact that an observation features more than one confidential identifier and not all of these identifiers are present in each observation. Examples for such identifiers are the role of a broker in a security transaction or the role of a collateral giver in a credit relationship.

If TRUE, NA values within id_var will internally be filled with ⁠<filled_[i]>⁠, assuming that all NA values of id_var can be treated as different small entities for statistical disclosure control purposes. Thus, set TRUE only if this is a reasonable assumption.

Defaults to FALSE.

Value

A list list of class sdc_min_max with detailed information about options, settings and the calculated extreme values (if possible).

Examples

sdc_min_max(sdc_min_max_DT, id_var = "id", val_var = "val_1")
sdc_min_max(sdc_min_max_DT, id_var = "id", val_var = "val_2")
sdc_min_max(sdc_min_max_DT, id_var = "id", val_var = "val_3", max_obs = 10)
sdc_min_max(sdc_min_max_DT, id_var = "id", val_var = "val_1", by = "year")
sdc_min_max(
  sdc_min_max_DT, id_var = "id", val_var = "val_1", by = c("sector", "year")
)

Example data for sdc_min_max()

Description

Utilized in the vignette

Usage

data("sdc_min_max_DT")

Format

A data.table with 20 rows and 6 columns.

Details

The data.table contains the following columns:

Disclosure control for models

Description

Checks if your model complies to RDC rules. Checks for overall number of entities and number of entities for each level of dummy variables.

Usage

sdc_model(data, model, id_var = getOption("sdc.id_var"), fill_id_var = FALSE)

Arguments

data

data.frame which was used to build the model.

model

The estimated model object. Can be a model type like lm, glm and various others (anything which can be handled by broom::augment()).

id_var

character The name of the id variable. Defaults to getOption("sdc.id_var") so that you can provide options(sdc.id_var = "my_id_var") at the top of your script.

fill_id_var

logical Only for very specific use cases. For example:

  • id_var contains NA values which represent missing values in the sense that there actually exist values identifying the entity but are unknown (or deleted for privacy reasons).

  • id_var contains NA values which result from the fact that an observation features more than one confidential identifier and not all of these identifiers are present in each observation. Examples for such identifiers are the role of a broker in a security transaction or the role of a collateral giver in a credit relationship.

If TRUE, NA values within id_var will internally be filled with ⁠<filled_[i]>⁠, assuming that all NA values of id_var can be treated as different small entities for statistical disclosure control purposes. Thus, set TRUE only if this is a reasonable assumption.

Defaults to FALSE.

Value

A list of class sdc_model with detailed information about options, settings, and compliance with the distinct entities criterion.

Examples

# Check simple models
model_1 <- lm(y ~ x_1 + x_2, data = sdc_model_DT)
sdc_model(data = sdc_model_DT, model = model_1, id_var = "id")

model_2 <- lm(y ~ x_1 + x_2 + x_3, data = sdc_model_DT)
sdc_model(data = sdc_model_DT, model = model_2, id_var = "id")

model_3 <- lm(y ~ x_1 + x_2 + dummy_3, data = sdc_model_DT)
sdc_model(data = sdc_model_DT, model = model_3, id_var = "id")

Example data for sdc_model()

Description

Utilized in the vignette

Usage

data("sdc_model_DT")

Format

A data.table with 80 rows and 9 columns.

Details

The data.table contains the following columns: