Compare Variable Structures Across SL4 and HAR Objects

Description

Compares variable structures across multiple SL4 and HAR datasets to ensure compatibility. Identifies matching and mismatched variable structures, helping users diagnose inconsistencies.

Usage

compare_var_structure(variables = NULL, ..., keep_unique = FALSE)

Arguments

Details

• Verifies whether variables have consistent structures across multiple datasets.

• Ensures correct ordering of dimensions and checks for structural compatibility.

• If keep_unique = TRUE, returns a list of unique variable structures instead of performing a direct comparison.

• Useful for merging or aligning datasets before further processing.

• Helps detect differences in variable dimensions, which may arise due to model updates or dataset variations.

Value

A list containing:

• match: A data frame listing variables with identical structures across datasets.

• diff: A data frame listing variables with mismatched structures, useful for debugging and alignment.

• If keep_unique = TRUE, instead of match and diff, returns a data frame with distinct variable structures across datasets.

Author(s)

Pattawee Puangchit

See Also

get_var_structure, get_dim_patterns, get_dim_elements

Examples

# Import sample data:
har_data1 <- load_harx(system.file("extdata", "TAR10-WEL.har", package = "HARplus"))
har_data2 <- load_harx(system.file("extdata", "SUBT10-WEL.har", package = "HARplus"))

# Compare structure for a single variable across multiple datasets
compare_var_structure("A", har_data1, har_data2)

# Compare structure for multiple variables across multiple datasets
comparison_multiple <- compare_var_structure(c("A", "E1"), har_data1, har_data2)

# Extract unique variable structures across multiple datasets
unique_vars <- compare_var_structure("ALL", har_data1, har_data2, keep_unique = TRUE)

Export Data to Various Formats (CSV/STATA/TEXT/RDS/XLSX)

Description

Exports structured SL4 or HAR data to multiple file formats, including CSV, Stata, TXT, RDS, and XLSX. Supports nested lists, automatic subfolder creation, and multi-sheet Excel exports.

Usage

export_data(
  data,
  output_path,
  format = "csv",
  prefix = "",
  create_subfolder = FALSE,
  multi_sheet_xlsx = FALSE,
  xlsx_filename = NULL,
  report_output = FALSE
)

Arguments

Details

• Supports exporting data in "csv", "stata", "txt", "rds", and "xlsx" formats.

• Handles nested lists and exports each data frame individually.

• Optionally creates subfolders for each format (create_subfolder = TRUE).

• Customizes file names using prefix.

• When multi_sheet_xlsx = TRUE, all exported data is stored in a single Excel workbook, with each dataset as a separate sheet.

• If exporting to Stata ("stata" format), column names containing . will be replaced with ⁠_⁠ to ensure compatibility.

• If multi_sheet_xlsx = TRUE, list elements are exported as separate sheets in a single XLSX file.

• The function creates necessary directories if they do not exist.

Value

A list containing the file paths of the exported data.

Author(s)

Pattawee Puangchit

See Also

pivot_data, get_data_by_var, get_data_by_dims

Examples

# Import sample data:
sl4_data <- load_sl4x(system.file("extdata", "TAR10.sl4", package = "HARplus"))

# Extract data
data_multiple <- get_data_by_var(c("qo", "pca"), sl4_data)

# Export
export_data(data_multiple, file.path(tempdir(), "output_directory"), 
           format = c("csv", "xlsx", "stata", "txt", "rds"),
           create_subfolder = TRUE,
           multi_sheet_xlsx = TRUE)

Export Hierarchical Pivot Table to Excel (Internal)

Description

Internal function to export hierarchical pivot tables to Excel with formatted headers.

Usage

export_hierarchy_to_excel(pivot_df, file_path, xlsx_filename = NULL)

Arguments

Value

Invisibly returns NULL.

Extract Data by Dimension Patterns from SL4 or HAR Objects

Description

Retrieves structured data from SL4 or HAR objects based on specified dimension patterns. Supports multiple experiments and merging datasets while maintaining structured dimension metadata.

Usage

get_data_by_dims(
  patterns = NULL,
  ...,
  experiment_names = NULL,
  subtotal_level = FALSE,
  rename_cols = NULL,
  merge_data = FALSE,
  pattern_mix = FALSE
)

Arguments

Details

• Extracts variables matching specified dimension patterns.

• Allows for flexible pattern matching (pattern_mix = TRUE).

• Supports merging data across multiple experiments (merge_data = TRUE).

• Provides column renaming functionality (rename_cols).

• Handles subtotal filtering (subtotal_level), controlling whether "TOTAL" or decomposed values are retained.

Value

A structured list of extracted data:

• If merge_data = FALSE, returns a named list where each element corresponds to an experiment.

• If merge_data = TRUE, returns a named list of all merged data

Author(s)

Pattawee Puangchit

See Also

get_data_by_var, group_data_by_dims

Examples

# Import sample data:
sl4_data <- load_sl4x(
  system.file("extdata", "TAR10.sl4", package = "HARplus")
)
sl4_data1 <- load_sl4x(
  system.file("extdata", "SUBT10.sl4", package = "HARplus")
)

# Extract data for a single dimension pattern
data_single_pattern <- get_data_by_dims(
  "comm*reg",
  sl4_data
)

# Extract multiple dimension patterns
data_multiple_patterns <- get_data_by_dims(
  c("comm*reg", "REG*ACTS"),
  sl4_data
)

# Extract all dimension patterns separately from multiple datasets
data_all_patterns <- get_data_by_dims(
  NULL,
  sl4_data, sl4_data1,
  merge_data = FALSE
)

# Merge data for identical patterns across multiple datasets
data_merged_patterns <- get_data_by_dims(
  NULL,
  sl4_data, sl4_data1,
  merge_data = TRUE
)

# Merge data while allowing interchangeable dimensions (e.g., A*B = B*A)
data_pattern_mixed <- get_data_by_dims(
  NULL,
  sl4_data, sl4_data1,
  merge_data = TRUE,
  pattern_mix = TRUE
)

# Retain only "TOTAL" values
data_total_only <- get_data_by_dims(
  "comm*reg",
  sl4_data,
  subtotal_level = "total"
)
data_total_only_alt <- get_data_by_dims(
  "comm*reg",
  sl4_data,
  subtotal_level = FALSE
)

# Retain only decomposed components
data_decomposed_only <- get_data_by_dims(
  "comm*reg",
  sl4_data,
  subtotal_level = "decomposed"
)

# Retain all value levels
data_all_decomp <- get_data_by_dims(
  "comm*reg",
  sl4_data,
  subtotal_level = "all"
)
data_all_decomp_alt <- get_data_by_dims(
  "comm*reg",
  sl4_data,
  subtotal_level = TRUE
)

# Rename specific columns
data_renamed <- get_data_by_dims(
  "comm*reg",
  sl4_data,
  rename_cols = c(REG = "Region", COMM = "Commodity")
)

# Merge data with custom experiment names
data_merged_experiments <- get_data_by_dims(
  "comm*reg",
  sl4_data, sl4_data1,
  experiment_names = c("EXP1", "EXP2"),
  merge_data = TRUE
)

Extract Variable Data from SL4 or HAR Objects

Description

Extracts structured data for one or more variables from SL4 or HAR objects, transforming array-like data into a tidy format.

Usage

get_data_by_var(
  var_names = NULL,
  ...,
  experiment_names = NULL,
  subtotal_level = FALSE,
  rename_cols = NULL,
  merge_data = FALSE
)

Arguments

Details

• Retrieves specific variables, multiple variables, or all available variables from SL4 or HAR datasets.

• Supports merging data from multiple experiments (merge_data = TRUE).

• Allows renaming of column names (rename_cols).

• Handles subtotal filtering (subtotal_level), controlling whether "TOTAL" or decomposed values are retained.

Value

A list of structured data:

• If merge_data = FALSE, returns a named list where each element corresponds to an experiment.

• If merge_data = TRUE, returns a named list of all merged data

Author(s)

Pattawee Puangchit

See Also

get_data_by_dims, , group_data_by_dims, load_sl4x, load_harx

Examples

# Import sample data:
sl4_data <- load_sl4x(system.file("extdata", "TAR10.sl4", package = "HARplus"))
sl4_data1 <- load_sl4x(system.file("extdata", "SUBT10.sl4", package = "HARplus"))

# Extract a single variable
data_qo <- get_data_by_var("qo", sl4_data)

# Extract multiple variables
data_multiple <- get_data_by_var(c("qo", "qgdp"), sl4_data)

# Extract all variables separately from multiple datasets
data_all <- get_data_by_var(NULL, sl4_data, sl4_data1, merge_data = FALSE)

# Merge variable data across multiple datasets
data_merged <- get_data_by_var(NULL, sl4_data, sl4_data1, merge_data = TRUE)

# Retain only "TOTAL" values, removing decomposed components (subtotal_level = "total" or FALSE)
data_total_only <- get_data_by_var("qo", sl4_data, subtotal_level = "total")
data_total_only_alt <- get_data_by_var("qo", sl4_data, subtotal_level = FALSE)

# Retain only decomposed components, removing "TOTAL" (subtotal_level = "decomposed")
data_decomposed_only <- get_data_by_var("qo", sl4_data, subtotal_level = "decomposed")

# Retain all value levels (subtotal_level = "all" or TRUE)
data_all_decomp <- get_data_by_var("qo", sl4_data, subtotal_level = "all")
data_all_decomp_alt <- get_data_by_var("qo", sl4_data, subtotal_level = TRUE)

# Rename specific columns
data_renamed <- get_data_by_var("qo", sl4_data, rename_cols = c(REG = "Region", COMM = "Commodity"))

# Merge data across multiple datasets with custom experiment names
data_merged_experiments <- get_data_by_var("qo", sl4_data, sl4_data1,
experiment_names = c("EXP1", "EXP2"),
merge_data = TRUE)

Get Dimension Elements from SL4 and HAR Objects

Description

Extracts and lists unique dimension elements (e.g., REG, COMM, ACTS) from one or more datasets.

Usage

get_dim_elements(..., keep_unique = FALSE)

Arguments

Value

A data frame containing unique dimension elements.

Author(s)

Pattawee Puangchit

See Also

get_dim_patterns, get_var_structure

Examples

# Import sample data:
sl4_data1 <- load_sl4x(system.file("extdata", "TAR10.sl4", package = "HARplus"))
sl4_data2 <- load_sl4x(system.file("extdata", "SUBT10.sl4", package = "HARplus"))


# Extract dimension elements from a single dataset
get_dim_elements(sl4_data1)

# Extract dimension elements from multiple datasets
get_dim_elements(sl4_data1, sl4_data2)

# Extract unique dimension elements across datasets
get_dim_elements(sl4_data1, sl4_data2, keep_unique = TRUE)

Extract and Organize Dimension Metadata (Internal)

Description

A helper function that extracts and structures dimension-related metadata from a given dimension structure. Used internally in get_var_structure() and compare_var_structure().

Usage

get_dim_info(dim_info)

Arguments

Details

• Retrieves structured metadata for variables in SL4 and HAR datasets.

• Computes data shape and ensures consistency in dimension structures.

• Helps determine observation counts and column sizes for variable summaries.

Value

A structured list containing:

• dimension_string: The original dimension string.

• dim_size: The number of dimensions.

• data_shape: A formatted string representing the data shape (e.g., "10x20x30").

• col_size: The product of all dimension sizes except the first, representing column count.

• n_obs: The first dimension size, typically representing the number of observations.

Author(s)

Pattawee Puangchit

See Also

get_var_structure, compare_var_structure

Get Dimension Patterns from SL4 and HAR Objects

Description

Extracts and lists unique dimension patterns (e.g., REG*COMM, REG*REG*ACTS) from one or more datasets.

Usage

get_dim_patterns(..., keep_unique = FALSE)

Arguments

Details

• Extracts dimension structure details from the dataset.

• If multiple datasets are provided, combines their dimension information.

• If keep_unique = TRUE, returns only distinct dimension patterns.

Value

A data frame containing:

• DimPattern: The unique dimension patterns.

Author(s)

Pattawee Puangchit

See Also

get_dim_elements, get_var_structure

Examples

# Import sample data:
sl4_data <- load_sl4x(system.file("extdata", "TAR10.sl4", package = "HARplus"))
sl4_data2 <- load_sl4x(system.file("extdata", "SUBT10.sl4", package = "HARplus"))

# Extract dimension patterns
get_dim_patterns(sl4_data)

# Extract only unique dimension patterns across datasets
get_dim_patterns(sl4_data, sl4_data2, keep_unique = TRUE)

Retrieve the Original Dimension Pattern (Internal)

Description

A helper function that finds the original dimension pattern name in an SL4 or HAR dataset that matches a given pattern. Used internally in get_data_by_dims().

Usage

get_original_pattern(pattern, data_obj, mix_patterns = FALSE)

Arguments

Details

• Performs a case-insensitive comparison to identify matching dimension patterns.

• Supports flexible pattern matching when mix_patterns = TRUE, allowing dimension order to vary.

• Returns the standardized dimension pattern name as stored in the dataset.

Value

The original dimension pattern name as a character string, or NULL if no match is found.

Author(s)

Pattawee Puangchit

See Also

pattern_match, process_pattern, get_data_by_dims

Get Variable Structure Summary from SL4 and HAR Objects

Description

Generates a summary of the variables within one or more SL4 or HAR objects, listing their dimension sizes, structures, and optionally, column and observation counts.

Usage

get_var_structure(variables = NULL, ..., include_col_size = FALSE)

Arguments

Details

• Extracts dimension structures for variables in one or more SL4 or HAR datasets.

• If include_col_size = TRUE, adds column and observation counts.

• Supports multiple datasets and returns results as a named list, with each dataset’s summary stored separately.

• Can summarize specific variables or "ALL".

Value

A named list, where each element contains a data frame with:

• Variable: The variable name.

• Dimensions: The associated dimensions.

• DimSize: The number of dimensions.

• DataShape: The shape of the data (e.g., ⁠10x20x30⁠).

• No.Col: (Optional) The number of columns.

• No.Obs: (Optional) The number of observations.

Author(s)

Pattawee Puangchit

See Also

get_dim_patterns, get_dim_elements

Examples

# Import data sample:
sl4_data <- load_sl4x(system.file("extdata", "TAR10.sl4", package = "HARplus"))
sl4_data1 <- load_sl4x(system.file("extdata", "SUBT10.sl4", package = "HARplus"))

# Get summary for all variables in a single dataset
get_var_structure(data_obj = sl4_data)

# Get summary for specific variables
get_var_structure(c("gdp", "trade"), sl4_data)

# Include column and observation counts
get_var_structure("ALL", sl4_data, include_col_size = TRUE)

# Compare structures across multiple datasets
get_var_structure("ALL", sl4_data, sl4_data1)

# Include column and observation counts across multiple datasets
get_var_structure("ALL", sl4_data, sl4_data1, include_col_size = TRUE)

Group Data by Dimension Patterns in SL4 or HAR Objects

Description

Groups extracted SL4 or HAR data based on specified dimension structures and priority rules. Supports automatic renaming, merging, subtotal filtering, and structured metadata handling.

Usage

group_data_by_dims(
  patterns = NULL,
  ...,
  priority,
  rename_cols = NULL,
  experiment_names = NULL,
  subtotal_level = FALSE,
  auto_rename = FALSE
)

Arguments

Details

• Groups extracted variables based on dimension elements.

• Applies predefined priority rules to structure the data.

• Allows automatic renaming of dimensions (auto_rename = TRUE).

• Supports merging of grouped data across multiple experiments.

• Handles subtotal filtering (subtotal_level), controlling whether "TOTAL" or decomposed values are retained.

Value

A structured list of grouped data:

• A named list where each element corresponds to a dimension size group (e.g., "2D", "3D").

• Each group contains dimension-grouped data based on priority rules.

• If unmerged data exists, includes a report attribute detailing merge issues.

Author(s)

Pattawee Puangchit

See Also

get_data_by_dims, get_data_by_var, load_sl4x, load_harx

Examples

# Import sample data
sl4_data1 <- load_sl4x(system.file("extdata", "TAR10.sl4", package = "HARplus"))
sl4_data2 <- load_sl4x(system.file("extdata", "SUBT10.sl4", package = "HARplus"))

# Case 1: Multiple priority levels (Sector then Region) with auto_rename
priority_list <- list(
  "Sector" = c("COMM", "ACTS"),
  "Region" = c("REG")
)
grouped_data_multiple <- group_data_by_dims(
  patterns = "ALL",
  sl4_data1,
  priority = priority_list,
  auto_rename = TRUE
)

# Case 2: Single priority (Region only) with auto_rename
priority_list <- list("Region" = c("REG"))
grouped_data_single <- group_data_by_dims(
  patterns = "ALL",
  sl4_data1, sl4_data2,
  priority = priority_list,
  auto_rename = TRUE
)

# Case 3: Multiple priorities without auto_rename
priority_list <- list(
  "Sector" = c("COMM", "ACTS"),
  "Region" = c("REG")
)
grouped_data_no_rename <- group_data_by_dims(
  patterns = "ALL",
  sl4_data1,
  priority = priority_list,
  auto_rename = FALSE
)

Load and Process GEMPACK HAR Files (Internal)

Description

Reads a GEMPACK HAR file and efficiently extracts structured data while maintaining compatibility with standard HAR formats. This implementation builds upon the foundational work of the HARr package, reorganizing the process for improved execution speed, memory management, and handling of sparse data structures.

Usage

load_harplus(con, coefAsname = FALSE, lowercase = TRUE, select_header = NULL)

Arguments

Details

• Efficient File Reading: Reads large HAR files in chunks for better performance.

• Optimized Memory Usage: Reduces unnecessary allocations and improves cleanup.

• Streamlined Header Processing: Ensures accurate extraction of dimension metadata.

• Supports Sparse Data Structures: Handles RESPSE and REFULL headers efficiently.

Supported HAR Header Types:

• ⁠1CFULL⁠: Character headers

• ⁠2IFULL⁠: Integer headers

• ⁠2RFULL⁠: Real headers

• REFULL: Real headers with extended metadata

• RESPSE: Sparse real headers

Value

A structured list where:

• Each element corresponds to a header in the HAR file.

• Names are either header names or coefficient names (if coefAsname = TRUE).

• Data maintains its original dimensions and attributes.

Author(s)

Pattawee Puangchit

See Also

load_sl4x, load_harx

Load and Process HAR Files with Header Selection

Description

Reads a GEMPACK HAR file and extracts structured data while maintaining compatibility with standard HAR formats. Provides flexibility in naming conventions and header selection.

Usage

load_harx(
  file_path,
  coefAsname = FALSE,
  lowercase = FALSE,
  select_header = NULL
)

Arguments

Details

• Uses load_harplus() internally for efficient HAR file reading.

• Allows optional conversion of variable names to lowercase (lowercase = TRUE).

• Supports coefficient-based naming (coefAsname = TRUE).

• Enables selective header extraction via select_header = c("A", "E1").

• Returns structured data with explicit dimension names and sizes.

Value

A structured list containing:

• data: Extracted HAR variable data stored as matrices, arrays, or vectors.

• dimension_info: A list with:

  • dimension_string: A textual representation of dimensions (e.g., "REGCOMMYEAR").

  • dimension_names: The names of each dimension.

  • dimension_sizes: The size of each dimension.

Author(s)

Pattawee Puangchit

See Also

load_sl4x, get_data_by_var, get_data_by_dims

Examples

# Path to example files
har_path <- system.file("extdata", "TAR10-WEL.har", package = "HARplus")

# Basic loading
har_data <- load_harx(har_path)

# Load with coefficient names
har_data_coef <- load_harx(har_path, coefAsname = TRUE)

# Load with lowercase names
har_data_lower <- load_harx(har_path, lowercase = TRUE)

# Load specific headers
har_selected <- load_harx(har_path, select_header = c("A", "E1"))

# Load with multiple options
har_combined <- load_harx(har_path,
                         coefAsname = TRUE,
                         lowercase = TRUE,
                         select_header = c("A", "E1"))

Load and Process SL4 Files with Enhanced Options

Description

Reads an SL4 file and processes its structured data into an enhanced SL4 object. Extracts structured variable information, dimensions, and handles subtotal columns.

Usage

load_sl4x(file_path, lowercase = FALSE, select_header = NULL)

Arguments

Details

• Uses load_harplus() internally for optimized SL4 file reading.

• Extracts variable names, dimension structures, and metadata.

• Converts variable names to lowercase if lowercase = TRUE.

• Allows the selection of specific headers using select_header.

• Returns structured data with explicit dimension names and sizes.

Value

A structured list containing:

• data: Extracted SL4 variable data, stored as arrays or matrices.

• dimension_info: A list with:

  • dimension_string: A textual representation of dimensions (e.g., "REGCOMMYEAR").

  • dimension_names: The names of each dimension.

  • dimension_sizes: The size of each dimension.

Author(s)

Pattawee Puangchit

See Also

load_harx, get_data_by_var, get_data_by_dims

Examples

# Path to example files
sl4_path <- system.file("extdata", "TAR10.sl4", package = "HARplus")

# Basic loading
sl4_data <- load_sl4x(sl4_path)

# Load with lowercase names
sl4_data_lower <- load_sl4x(sl4_path, lowercase = TRUE)

# Load specific headers
sl4_selected <- load_sl4x(sl4_path, select_header = c("qo", "qgdp"))

Match Patterns with Optional Mixing (Internal)

Description

Compares two patterns to determine if they match, with an option to allow flexible dimension order.

Usage

pattern_match(pattern1, pattern2, mix_patterns = FALSE)

Arguments

Details

• Performs case-insensitive pattern matching.

• If mix_patterns = TRUE, allows patterns to match even if dimensions are in different order.

Value

Logical; TRUE if the patterns match, FALSE otherwise.

Author(s)

Pattawee Puangchit

See Also

get_original_pattern, process_pattern, get_data_by_dims

Pivot Data from SL4 or HAR Objects

Description

Transforms long-format SL4 or HAR data into wide format by pivoting selected columns. Supports both single data frames and nested lists.

Usage

pivot_data(data_obj, pivot_cols, name_repair = "unique")

Arguments

Details

• Uses tidyr::pivot_wider() internally to reshape data.

• Allows multiple columns to be pivoted simultaneously.

• Recursively processes nested lists, ensuring all data frames are transformed.

Value

A transformed data object where the specified pivot_cols are pivoted into wide format.

Author(s)

Pattawee Puangchit

See Also

get_data_by_var, get_data_by_dims

Examples

# Import sample data:
sl4_data <- load_sl4x(system.file("extdata", "TAR10.sl4", package = "HARplus"))

# Extract multiple variables
data_multiple <- get_data_by_var(c("qo", "qxs"), sl4_data)

# Pivot a single column
pivoted_data <- pivot_data(data_multiple, pivot_cols = "REG")

# Pivot multiple columns
pivoted_data_multi <- pivot_data(data_multiple, pivot_cols = c("REG", "COMM"))

Create Hierarchical Pivot Table from SL4 or HAR Objects

Description

Creates hierarchical pivot tables from structured SL4 or HAR data, with optional Excel export. Supports both single data frames and nested lists, preserving dimension hierarchies.

Usage

pivot_data_hierarchy(
  data_obj,
  pivot_cols,
  name_repair = "unique",
  export = FALSE,
  file_path = NULL,
  xlsx_filename = NULL
)

Arguments

Details

• Transforms data into hierarchical pivot format with nested column headers.

• Supports multiple pivot columns in specified order (e.g., REG > COMM).

• Handles both single data frames and nested list structures.

• Optional direct export to Excel with formatted hierarchical headers.

• Uses efficient data processing with tidyr and openxlsx.

Value

A pivoted data object with hierarchical structure:

• If input is a data frame: Returns a hierarchical pivot table.

• If input is a list: Returns a nested list of hierarchical pivot tables.

• If export = TRUE: Invisibly returns the pivoted object after Excel export.

Author(s)

Pattawee Puangchit

See Also

pivot_data

Examples

# Import sample data:
sl4_data <- load_sl4x(system.file("extdata", "TAR10.sl4", package = "HARplus"))

# Extract data
data_multiple <- get_data_by_var(c("qo", "pca"), sl4_data)

# Create hierarchical pivot without export
pivot_hier <- pivot_data_hierarchy(data_multiple, 
                                   pivot_cols = c("REG", "COMM"))

# Create and export to Excel in one step
pivot_export <- pivot_data_hierarchy(data_multiple, 
                                     pivot_cols = c("REG", "COMM"),
                                     export = TRUE,
                                     file_path = file.path(tempdir(), "pivot_output.xlsx"))

Process Decomposition Levels in Data Frames (Internal)

Description

A helper function that filters data based on decomposition levels in the "Subtotal" column. Used internally in get_data_by_var(), get_data_by_dims(), and group_data_by_dims().

Usage

process_decomp_level(df, subtotal_level)

Arguments

Details

• If subtotal_level = "total", keeps only "TOTAL" values, removing all decomposed components.

• If subtotal_level = "decomposed", keeps only decomposed components, removing "TOTAL".

• If subtotal_level = "all", keeps both "TOTAL" and decomposed values (no filtering).

• If subtotal_level = TRUE (logical), it is equivalent to "all", meaning all values are kept.

• If subtotal_level = FALSE (logical), it is equivalent to "total", meaning only "TOTAL" values are kept, and decomposed components are removed.

• Filtering is applied only when both "TOTAL" and decomposed values exist.

Value

A filtered data frame based on the specified decomposition level.

Author(s)

Pattawee Puangchit

See Also

get_data_by_var, get_data_by_dims, group_data_by_dims

Process and Export Report (Internal)

Description

Generates a summary report of variables and their corresponding output files from structured SL4 or HAR data. The report is saved as an Excel file.

Usage

process_export_report(data, output_path, prefix = "", data_name = "data")

Arguments

Details

• Extracts unique variable names and their associated export filenames.

• Handles nested list structures by recursively traversing them.

• The output report is saved as an Excel file named "Report_<data_name>.xlsx".

Author(s)

Pattawee Puangchit

See Also

export_data

Extract and Process Pattern-Matched Variables (Internal)

Description

A helper function that extracts and processes variables matching a specified pattern within an SL4 or HAR data object. Used internally in get_data_by_dims().

Usage

process_pattern(pattern, data_obj, exp_name, pattern_mix = FALSE)

Arguments

Details

• Searches for variables whose dimension structures match the given pattern.

• Supports flexible pattern matching when pattern_mix = TRUE, allowing dimension order to vary.

• Extracted data is converted into a tidy format, preserving dimension structures.

• Standardizes "Subtotal" column naming for consistency across datasets.

• Ensures only non-empty extracted variables are retained.

Value

A data frame containing processed data for the matching pattern, or NULL if no matches are found.

Author(s)

Pattawee Puangchit

See Also

get_original_pattern, pattern_match, get_data_by_dims

Rename Columns in a Data Frame (Internal)

Description

A helper function that renames columns in a data frame based on a specified mapping. Used internally in get_var_structure(), get_data_by_dims(), and rename_dims().

Usage

rename_col(df, rename_cols)

Arguments

Details

• Replaces column names according to the provided rename_cols mapping.

• Ensures no duplicate column names by appending numerical suffixes when necessary.

• Helps standardize column names across SL4 and HAR datasets.

Value

A modified data frame with renamed columns.

Author(s)

Pattawee Puangchit

See Also

get_var_structure, get_data_by_dims, rename_dims

Rename Dimensions in SL4 or HAR Objects

Description

Renames dimension and list names in structured SL4 or HAR objects.

Usage

rename_dims(data_obj, mapping_df, rename_list_names = FALSE)

Arguments

Details

• Replaces old dimension names with new ones as specified in mapping_df.

• If rename_list_names = TRUE, renames list element names as well.

• Ensures consistency across SL4 and HAR datasets.

Value

The modified SL4 or HAR object with updated dimension names and, optionally, updated list names.

Author(s)

Pattawee Puangchit

See Also

get_data_by_var, get_data_by_dims

Examples

# Import sample data:
sl4_data <- load_sl4x(system.file("extdata", "TAR10.sl4", package = "HARplus"))

# Define a renaming map
mapping_df <- data.frame(
  old = c("REG", "COMM"),
  new = c("Region", "Commodity")
)

# Rename columns in the dataset
rename_dims(sl4_data, mapping_df)

# Rename both columns and list names
rename_dims(sl4_data, mapping_df, rename_list_names = TRUE)