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Extract Variable Data from SL4 or HAR Objects

Source: R/get_data.R
get_data_by_var.Rd

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

var_names

Character vector. Variable names to extract. Use "ALL" or NULL to extract all available variables.

...

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.

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

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.

See also

get_data_by_dims, , group_data_by_dims, load_sl4x, load_harx

Author

Pattawee Puangchit

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)