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Group Data by Dimension Patterns in SL4 or HAR Objects

Source: R/get_data.R
group_data_by_dims.Rd

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

patterns

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

...

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

priority

Named list. Specifies priority dimension elements (c("group_name" = c("dim1", "dim2"))).

rename_cols

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

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.

auto_rename

Logical. If TRUE, automatically renames dimensions for consistency. Default is FALSE.

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.

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.

See also

get_data_by_dims, get_data_by_var, load_sl4x, load_harx

Author

Pattawee Puangchit

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
)