Changelog

lissyrtools 0.2.0 (2025-06-04)

New functions

🔧 Adjustment & Equivalisation

• apply_iqr_top_bottom_coding(): Applies interquartile range-based top and bottom coding.
• apply_oecd_equivalisation(): Applies the OECD equivalence scale.
• apply_sqrt_equivalisation(): Applies square-root equivalisation.
• apply_ppp_adjustment(): Adjust monetary variables for inflation and PPP

📊 Weighted Statistics

• run_weighted_count(): Computes weighted counts in absolute and percentage terms.
• run_weighted_mean(): Computes weighted means.
• run_weighted_percentiles(): Computes weighted percentiles or shares using type 2 or type 4 definitions.
• run_weighted_ratios(): Computes percentile ratios (e.g. P90/P10).
• run_weighted_gini(): Calculates the Gini coefficient.
• run_weighted_atkinson(): Computes the Atkinson inequality index.
• run_weighted_relative_poverty(): Measures relative poverty based on a median threshold.
• run_weighted_poverty_shortfall(): Computes absolute and relative poverty shortfalls (poverty gap).
• run_weighted_poverty_gap_index(): Calculates the poverty gap index.

📈 Plotting

• structure_to_plot(): Transforms output into tidy data for plotting with ggplot2.

🌍 Country, Year, and Survey Access

• get_countries_lis(): Prints available countries in LIS.
• get_countries_lws(): Prints available countries in LWS.
• get_years_lis(): Prints available years for each country in LIS.
• get_years_lws(): Prints available years for each country in LWS.
• get_surveys_lis(): Prints surveys used for each country in LIS.
• get_surveys_lws(): Prints surveys used for each country in LWS.

🔍 Variable Information

• variable_labels(): Retrieves variable labels.
• variable_country_specific_categories(): Gets country-specific categories for such variables.
• variable_exists(): Checks the years that a variable exists for a given country series.
• variable_has_note(): Checks whether a variable has an associated note.

Major changes

• Expanded Local Sample Data: Increased the number of sample datasets available for local use, supporting more flexible and practical development and testing workflows.
• New Variable and Data Availability Utilities: Introduced a set of functions for local use, to retrieve country/year/survey availability and variable-level metadata, centralizing access to crucial documentation and reducing cognitive fatigue by minimizing tab/window switching.
• Unified Percentile Computation: A new function, run_weighted_percentiles(), now centralizes the computation of weighted percentiles, supporting both Type 2 and Type 4 estimation methods as described in Hyndman & Fan (1996). This ensures consistency across all functions that depend on percentile-based logic, such as run_weighted_ratios(), apply_iqr_top_bottom_coding(), and the one that compute relative poverty figures: run_weighted_relative_poverty(), run_weighted_poverty_shortfall() and run_weighted_poverty_gap_index().
• Group-Level Analysis via by Argument: run_weighted_percentiles(), run_weighted_mean() and run_weighted_count() now accept a by argument, allowing the disaggregation of results by categorical variables (e.g. region, gender) including inum variables in LWS datasets.
• Improved Output and Visualization Readiness:

  • Cleaner Output Structure: Results are now printed in a more compact and intuitive format, grouped by country and sorted by year, in attempt to improve readability.

  • Tidy Data for Plotting: The new structure_to_plot() function standardizes lists to be printed in the console into tidy data frames, making it easy to feed directly into ggplot2 for visualization.

Minor changes

• lis_variables and lws_variables are now external and documented reference objects in the form of character vectors.

• It is no longer possible to adjust scale parameters in the apply_oecd_equivalisation() function.

Lifecycle

Deprecated functions from the early stages of the lissyrtools package. Maintained for reference and backward compatibility.

• bind_lissy_files()

• compute_*()

• determine_*()

• lissy_map()

• merge_dataset_levels()

• plot_indicator()

• print_*()

• read_*()

• transform_*()

lissyrtools 0.1.11

Change in lissyuse()

• The output of lissyuse()now requires explicit assignment to a variable, whereas previously, the function automatically created and assigned a pre-named list (lis_datasets or lws_datasets) to the global environment.

lissyrtools 0.1.10

Introduction of lissyuse()

• A new and more efficient method for loading data is now available through lissyuse(). The previous approach, which utilized read_lissy_files() and merge_dataset_levels(), will be deprecated moving forward.

Inclusion of sample files

• Inclusion of built-in data frames containing sample files from LIS to assist users in developing LISSY code scripts in local environments.

New built-in objects

• These include the datasets data frame, as well as vector objects containing variables categorized into specific groups (e.g., key_vars_household_lis, lis_weight_variables, lws_wealth_categorical_variables, lis_country_specific_variables).

New “show_countries_” and “get_years_” functions

• Four new functions have been added to print the ISO2 codes for each country in LIS and LWS, as well as the available years for a given country in either LIS or LWS.

lissyrtools 0.1.5

Bug fixes

• transform_equivalise_oecd() and respectively implement_equivalise_oecd() were now corrected for the way they classify children in the argument value_children. Previously this argument was dependent on LIS variable nhhmem17, now it uses nhhmem13.