# Table: A Step-by-Step Guide

#### Pattawee Puangchit

#### 2025-04-17

This vignette illustrates how to set up table configurations and generate outputs.
For a complete list of available tables with sample images, see the table catalog.

# 1 Structured Table

This is an example of how to create a structured pivot table suitable for inclusion in academic presentations.

Important:
The `report_table` function requires a data list—not a data frame.

- This list structure matches the format of `sl4.plot.data` or `har.plot.data`, without delisting individual elements.

- This differs from the plotting functions, which use a data frame as input.

- Multiple tables can be generated in a single setup using a data list.
  For example, you can generate report tables for all data frames within `sl4.plot.data` in one code block.

    report_table(
      data_list = sl4.plot.data,
      pivot_col = list(REG = "Variable",
                       'COMM*REG' = "Commodity"),
      group_by =  list(
        REG = c("Experiment", "Region"),
        'COMM*REG' = c("Experiment", "Region")),
      rename_cols = list("Experiment" = "Scenario"),

      total_column = FALSE,
      decimal = 4,
      subtotal_level = FALSE,
      repeat_label = FALSE,
      include_units = TRUE,

      var_name_by_description = TRUE,
      add_var_info = TRUE,
      add_group_line = FALSE,

      separate_sheet_by = "Unit",
      export_table = FALSE,
      output_path = NULL,
      separate_file = FALSE,
      workbook_name = "Comparison Table"
    )

💡 Tip

`var_name_by_description` replaces variable names with their full descriptions to enhance clarity and give the output a more academic appearance.

# 2 Decomposition Table

The same `report_table` function can be used to generate decomposition tables, including a total column, as shown in the example below:

    report_table(
      data_list = har.plot.data,
      pivot_col = list(A = "COLUMN",
                       E1 = "PRICES"),
      group_by = list(
        A = list("Experiment", "Region"),
        E1 = list("Experiment", "Commodity", "PRICES")
      ),
      rename_cols = list("Experiment" = "Scenario"),

      total_column = TRUE,
      decimal = 6,
      subtotal_level = FALSE,
      repeat_label = FALSE,
      include_units = TRUE,

      var_name_by_description = FALSE,
      add_var_info = FALSE,
      add_group_line = FALSE,

      separate_sheet_by = "Region",
      export_table = FALSE,
      output_path = NULL,
      separate_file = FALSE,
      workbook_name = "Decomposition Table"
    )

# 3 Interactive Pivot Tables

Unlike static tables generated by `report_table()`, pivot tables remain fully interactive in the Excel output. However, they work with a single data frame at a time (just like the plotting functions).

This function offers:

- Comprehensive Filtering
  Add multiple filtering dimensions to enhance flexibility and enable users to explore the data from different perspectives.

- Retention of Raw Data
  The raw dataset is included in the Excel file, allowing downstream users—especially in Excel—to reconstruct or customize pivot tables as needed.

- User-Oriented Design
  Ideal for sharing with non-R users, the output is organized for intuitive exploration and ease of use.

    pivot_table_with_filter(
      data = sl4.plot.data[["COMM*REG"]],
      filter = c("Variable", "Unit"),    # Allow filtering by variable type and unit
      rows = c("Region", "Commodity"),      # Regions and sectors as row fields
      cols = c("Experiment"),            # Experiments as column fields
      data_fields = "Value",             # Values to be aggregated
      raw_sheet_name = "Raw_Data",       # Sheet name for raw data
      pivot_sheet_name = "Sector_Pivot", # Sheet name for pivot table
      export_table = FALSE,
      output_path = NULL,
      workbook_name = "Sectoral_Impact_Analysis.xlsx"
    )

# 4 Sample Data

Sample data used in this vignette is obtained from the GTAPv7 model and utilizes data from the GTAP 11 database. For more details, refer to the GTAP Database Archive.