HARplus (version 1.0.1)

get_data_by_dims: 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
)

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

Arguments

patterns

Character vector. Dimension patterns to extract. Use "ALL" or NULL to extract all available patterns.

...

One or more SL4 or HAR data objects loaded using load_sl4x() or load_harx().

experiment_names

Character vector. Names assigned to each dataset. If NULL, names are inferred.

subtotal_level

Character or logical. Determines which decomposition levels to retain:

• "total": Keeps only "TOTAL" values.

• "decomposed": Keeps only decomposed values (excludes "TOTAL").

• "all": Keeps all rows.

• TRUE: Equivalent to "all", retaining both "TOTAL" and decomposed values.

• FALSE: Equivalent to "total", keeping only "TOTAL" values.

rename_cols

Named vector. Column name replacements (c("old_name" = "new_name")).

merge_data

Logical. If TRUE, attempts to merge data across multiple experiments. Default is FALSE.

pattern_mix

Logical. If TRUE, allows flexible pattern matching, ignoring dimension order. Default is FALSE.

Author

Pattawee Puangchit

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.

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
)