rtauargus: Using Tau-Argus from R

#' data - Turnover broken down by business sector and size of French companies (fake values).
#'
#' A tabular dataset containing the turnover broken down by Business sector
#' and Size of companies. Useful for playing with tab_ functions.
#'
#' @format A tibble/data frame with 414 rows and 5 variables:
#' \describe{
#'  \item{ACTIVITY}{business sector, hierarchical variables with three levels described
#'   in the activity_corr_table dataset. The root is noted "Total"}
#'   \item{SIZE}{size of the companies (Number of employees in three categories
#'   and overall category "Total")}
#'   \item{N_OBS}{Frequency, number of companies}
#'   \item{TOT}{turnover value in euros}
#'   \item{MAX}{turnover of the company which contributes the most to the cell.}
#' }
#' @seealso activity_corr_table
"turnover_act_size"

#' data - Turnover broken down by business sector and type of companies (fake values).
#'
#' A tabular dataset containing the turnover broken down by Business sector
#' and Type of companies. Useful for playing with tab_ functions.
#'
#' @format A tibble/data frame with 406 rows and 5 variables:
#' \describe{
#'   \item{ACTIVITY}{business sector, hierarchical variables with three levels described
#'   in the activity_corr_table dataset. The root is noted "Total"}
#'   \item{CJ}{Type of companies (3 categories + overall category "Total")}
#'   \item{N_OBS}{Frequency, number of companies}
#'   \item{TOT}{turnover}
#'   \item{MAX}{turnover of the company which contributes the most to the cell.}
#' }
#' @seealso activity_corr_table
"turnover_act_cj"

#' data - Turnover broken down by NUTS and size of French companies (fake values).
#'
#' A tabular dataset containing the turnover broken down by NUTS geographical localisation
#' and Size of companies. Useful for playing with tab_ functions.
#'
#' @format A tibble/data frame with 460 rows and 5 variables:
#' \describe{
#'  \item{NUTS}{nuts - european denomination of administrative levels.
#'  Hierarchical variables with two levels (nuts2 and nuts3) described
#'   in the nuts23_fr_corr_table dataset. The root is noted "Total"}
#'   \item{SIZE}{size of the companies (Number of employees in three categories
#'   and overall category "Total")}
#'   \item{N_OBS}{Frequency, number of companies}
#'   \item{TOT}{turnover value in euros}
#'   \item{MAX}{turnover of the company which contributes the most to the cell.}
#' }
#' @seealso nuts23_fr_corr_table
"turnover_nuts_size"

#' data - Turnover broken down by NUTS and size of French companies (fake values).
#'
#' A tabular dataset containing the turnover broken down by NUTS geographical localisation
#' and Type of companies. Useful for playing with tab_ functions.
#'
#' @format A tibble/data frame with 452 rows and 5 variables:
#' \describe{
#'  \item{NUTS}{nuts - european denomination of administrative levels.
#'  Hierarchical variables with two levels (nuts2 and nuts3) described
#'   in the nuts23_fr_corr_table dataset. The root is noted "Total"}
#'   \item{CJ}{Type of companies (3 categories + overall category "Total")}
#'   \item{N_OBS}{Frequency, number of companies}
#'   \item{TOT}{turnover value in euros}
#'   \item{MAX}{turnover of the company which contributes the most to the cell.}
#' }
#' @seealso nuts23_fr_corr_table
"turnover_nuts_cj"

#' data - Correspondence table describing the business sectors hierarchy.
#'
#' A dataset describing the nesting of three levels of business sectors, useful
#' when working with the ACTIVITY variables in the turnover_ datasets.
#'
#' @format A data frame with 92 rows and 3 variables:
#' \describe{
#'   \item{A10}{business sectors in 10 categories}
#'   \item{A21}{business sectors in 21 categories}
#'   \item{A88}{business sectors in 88 categories}
#' }
#' @details Use the `write_hrc2` function to create a .hrc file from this
#' correspondence table.
"activity_corr_table"

#' data - Correspondence table describing the NUTS hierarchy.
#'
#' A dataset describing the nesting of NUTS2 and NUTS3 levels for Metropolitan France, useful
#' when working with the NUTS variables in the turnover_ datasets.
#'
#' @format A data frame with 92 rows and 3 variables:
#' \describe{
#'   \item{NUTS2}{NUTS2 levels in France - equivalent of French "Régions"}
#'   \item{NUTS3}{NUTS3 levels in France - equivalent of French "Départements"}
#' }
#' @details Use the `write_hrc2` function to create a .hrc file from this
#' correspondence table.
"nuts23_fr_corr_table"

#' data - Turnover broken down by business sector, NUTS, and size of French companies (fake values).
#'
#' A tabular dataset containing the turnover broken down by Business sector, NUTS
#' (administrative areas) and Size of companies. The data is restricted to
#' only three NUTS2 of France (codes FR41, FR42 and FR43) and their
#' corresponding NUTS3 areas. Useful for playing with tab_ functions.
#'
#' @format A tibble/data frame with 3 168 rows and 6 variables:
#' \describe{
#'  \item{ACTIVITY}{business sector, hierarchical variables with three levels described
#'   in the activity_corr_table dataset. The root is noted "Total"}
#'   \item{NUTS}{nuts - european denomination of administrative levels.
#'  Hierarchical variables with two levels (nuts2 and nuts3) described
#'   in the nuts23_fr_corr_table dataset. Only "FR41", "FR42" and "FR43" NUTS2
#'   areas and their corresponding NUTS3 areas are in the data.
#'   The root is noted "Total_EAST"}
#'   \item{SIZE}{size of the companies (Number of employees in three categories
#'   and overall category "Total")}
#'   \item{N_OBS}{Frequency, number of companies}
#'   \item{TOT}{turnover value in euros}
#'   \item{MAX}{turnover of the company which contributes the most to the cell.}
#' }
#' @seealso
#' activity_corr_table
#' nuts23_fr_corr_table
"turnover_act_nuts_size"


#' data crossing 4 categorical variables, none are hierarchical.
#'
#' @format A tibble/data frame with 689 rows and 12 variables:
#' \describe{
#'  \item{A10}{business sector, not hierarchical}
#'   \item{cj}{legal category, not hierarchical}
#'   \item{type_distrib}{type of distribution, not hierarchical}
#'   \item{treff}{Number of employees (categorical), not hierarchical}
#'   \item{nb_obs}{Frequency, number of companies}
#'  \item{nb_obs_rnd}{Frequency rounded, number of companies}
#'   \item{pizzas_tot}{turnover value in euros}
#'   \item{pizzas_tot_abs}{turnover absolute value in euros}
#'    \item{pizzas_max}{turnover max value in euros}
#'    \item{is_secret_freq}{Boolean, TRUE if primary secret for frequency rule}
#'    \item{is_secret_dom}{Boolean, TRUE if primary secret for dominance rule}
#'   \item{is_secret_prim}{Boolean, TRUE if primary secret for any rule}
#'
#' }
"datatest1"

#' data crossing 5 categorical variables, none are hierarchical.
#'
#' @format A tibble/data frame with 5 612 rows and 15 variables:
#' \describe{
#'  \item{A10}{business sector, not hierarchical}
#'  \item{cj}{legal category, not hierarchical}
#'  \item{type_distrib}{type of distribution, not hierarchical}
#'  \item{treff}{Number of employees (categorical), not hierarchical}
#'  \item{nuts1}{NUTS region, no hierarchical}
#'  \item{nb_obs}{Frequency, number of companies}
#'  \item{nb_obs_rnd}{Frequency rounded, number of companies}
#'  \item{pizzas_tot}{turnover value in euros}
#'  \item{pizzas_tot_abs}{turnover absolute value in euros}
#'  \item{pizzas_max}{turnover max value in euros}
#'  \item{is_secret_freq}{Boolean, TRUE if primary secret for frequency rule}
#'  \item{is_secret_dom}{Boolean, TRUE if primary secret for dominance rule}
#'  \item{is_secret_prim}{Boolean, TRUE if primary secret for any rule}
#'
#' }
"datatest2"

#' Companies data at individual level.
#'
#' @format A data.table with 9 786 rows and 12 variables:
#' \describe{
#'  \item{A10}{business sector, not hierarchical}
#'  \item{A21}{business sector, not hierarchical but nested in A10}
#'  \item{A88}{business sector, not hierarchical but nested in A21}
#'  \item{CJ}{legal category, not hierarchical}
#'  \item{TYPE}{type of distribution, not hierarchical}
#'  \item{SIZE}{Number of employees (categorical), not hierarchical}
#'  \item{NUTS1}{NUTS 1 level of European administrative regions, not hierarchical}
#'  \item{NUTS2}{NUTS 2 level of European administrative regions, not hierarchical}
#'  \item{NUTS3}{NUTS 3 level of European administrative regions, not hierarchical}
#'  \item{WEIGHT}{Weight of the companies, numeric}
#'  \item{TURNOVER}{Turnover, numeric}
#'  \item{PRODUCTION}{Production, numeric}
#' }
"indiv_dt"

#' Extract of SBS Eurostat template.
#'
#' A dataset describing all the cells expected by Eurostat.
#'
#' @format A data frame with 3 168 rows and 5 variables:
#' \describe{
#'   \item{TIME_PERIOD}{Integer. The year of observation (e.g., 2022).}
#'   \item{INDICATOR}{Character. The type of indicator (e.g., "SAL").}
#'   \item{ACTIVITY}{Character. The sector of activity coded using a standard classification (e.g., "B").}
#'   \item{NUMBER_EMPL}{Character. Employment size category (e.g., "E0", "E1T4", "E5T9", "EGE10").}
#'   \item{LEGAL_FORM}{Character. Legal form of the enterprise, where "_T" represents all legal forms combined.}
#' }
"enterprise_template"

#' Metadata for pizza and lettuce dataset.
#'
#' A dataset containing metadata information for various tables and indicators related to
#' enterprises turnover selling pizzas and lettuces in France.
#'
#' @format A data frame with 12 rows and 9 variables:
#' \describe{
#'   \item{table_name}{Character. Name of the table (e.g., "T1", "T2").}
#'   \item{field}{Character. Data source or category (e.g., "france_entreprises_2023").}
#'   \item{hrc_field}{Logical. Indicates if hierarchical classification is applied to the field (NA if not applicable).}
#'   \item{indicator}{Character. The indicator being measured (e.g., "to_pizza").}
#'   \item{hrc_indicator}{Character. Hierarchical classification for the indicator (NA if not applicable).}
#'   \item{spanning_1}{Character. First spanning variable (e.g., "nuts2", "nuts3", "a10").}
#'   \item{hrc_spanning_1}{Character. Hierarchical classification for spanning_1 (e.g., "hrc_nuts", "hrc_naf").}
#'   \item{spanning_2}{Character. Second spanning variable (e.g., "size", "nuts2", "nuts3").}
#'   \item{hrc_spanning_2}{Character. Hierarchical classification for spanning_2 (NA if not applicable).}
#' }
"metadata_pizza_lettuce"