This vignette outlines the complete project setup process and demonstrates the use of auto_gtap_data.

1 Package Overview

This package streamlines the creation of figures and tables from .HAR and .sl4 results, making academic presentations effortless. Some key features are:

Effortless Multi-Plot Generation – Automatically adjusts dimensions, facets, and layout with minimal input.
Smart Plot Adjustments – Fine-tune visuals easily without manual sizing or layout tweaks.
Dual Export Plot Formats – Instantly save high-resolution PNG and PDF outputs for slides, papers, and LaTeX.
Publication-Ready Pivot Tables – Generate clean tables alongside figures—ideal for academic papers.
Streamlined Styling – Customize colors, fonts, and legends through simple, flexible options.
Powerful Yet Simple – Built on ggplot2, with intuitive TRUE/FALSE switches—no advanced coding needed.
Self-Contained Help – Includes a detailed vignette and internal help—no need to search online.

💡 Tip for Non-GTAP Users

If you’re an advanced R user or not working directly with GTAP data, this package still works for you.

However, data preparation may require custom manipulation before using GTAPViz plotting and table functions.

For working with .sl4 and .har files, refer to my companion package: HARplus.

Before proceeding, ensure that GTAPViz is installed and loaded:

if (!requireNamespace("GTAPViz", quietly = TRUE)) {
  devtools::install_github("Bodysbobb/GTAPViz")
}

require(GTAPViz)

1.1 Plot Types

GTAPViz provides four main plotting functions, generating over 10 plot types:

comparison_plot: Compares variables across multiple experiments for selected observations from a specific dimension (e.g., region, sector).
Examples: qgdp, ppriv, EV, GTAP Macros, and more.
detail_plot: Shows a variable across one or two dimensions for each experiment.
Examples: qo, qxw, qmw, etc.
stack_plot: Visualizes the composition of a variable for decomposition analysis—e.g., EV decomposition.

💡 Tip: Plot Catalog

Not sure which plot to use or what it looks like?
Browse the full Plot Catalog for examples and use cases.

1.2 Tables

GTAPViz includes powerful functions for generating structured pivot tables and Excel-ready tables with interactive filters, ideal for academic presentations and reports.

Explore the full Table Catalog for examples.

2 Project Directory

To use GTAPViz efficiently—with little to no manual adjustment—I highly recommend setting up your project directory as follows:

project.folder <- "your/folder/path"

# Optional: You might not need to adjust this
input.folder <- paste0(project.folder, "/in") 
map.folder <- paste0(project.folder, "/map")      
output.folder <- paste0(project.folder, "/out")

📂 project.folder/ 
├── 📂 in/  # Stores all input files
├── 📂 map/ # Stores the mapping file
├── 📂 out/

Example of Project Folder

2.1 Input Folder

All .sl4 and .har input files must be stored in the same folder—by default, <input.folder>, which refers to the /in directory within your project folder.

Below is an example of the expected input folder structure:

📂 in/ 
├── 📄 TAR10.sl4 
├── 📄 TAR10-WEL.har 
├── 📄 SUBT10.sl4 
├── 📄 SUBT10-WEL.har

Example of Input Folder

2.2 Mapping Folder

The mapping XLSX template is available here: OutputMapping.xlsx. It contains three main sheets:

SL4File
HARFile
FilterData

2.2.1 SL4File and HARFile Sheets

• “Variable” specifies the required variable from each file.

• “Description” is optional for defining variables and plot titles.

• “Unit” is required for all figure commands.

Below is an example of mapping file:

Variable	Description	Unit
qgdp	Real GDP Index	percent
EV	Welfare Equivalents	million USD
ppriv	Consumer Price Index	percent
qo	Output
qxw

Example of “SL4File” and “HARFile” Sheet

Note: The Description and Unit columns can be left empty if using GTAP defaults.

Important: You must manually define both the description and unit for all additional variables — non-GTAPv7 variables.

2.2.2 FilterData Sheet

The FilterData sheet is optional and can alternatively be defined directly in R. It contains two columns used to filter data from all loaded data frames:

“Region” – Excludes specified regions by filtering the "REG" column.
“Sector” – Excludes specified sectors by filtering the "COMM" and "ACTS" columns.

Caveat: This option only works with the default column names "REG", "COMM", and "ACTS".
For filtering by other columns, you must manually apply filters in R after importing the data.

Below is an example of the filter data sheet:

Region	Sector
EastAsia
SEAsia
Oceania

Example of “FilterData” Sheet

Note: You may leave it empty to include all entries or redefine the order for sorting the output format.

3 R Environment Configuration

This section configures experiment names, description and unit handling, and output formats for processing GTAP model results.

3.1 Experiment Names

Define <experiment> to specify input file names. The experiment name:

• Appears in plots and is added to the “Experiment” column.

• Sorts figures and tables based on the order of <experiment>.

The following command processes the files and sorts the output with EXP1 before EXP2:

experiment <- c("TAR10", "SUBT10")

# Automatically Processing These Inputs in the Input Folder 
# - TAR10.sl4 and TAR10-WEL.har  
# - SUBT10.sl4 and SUBT10-WEL.har

Note

You can include as many experiments (inputs) as needed, but a higher number will increase processing time.

3.2 Description and Unit

<mapping_info> controls how the “Description” and “Unit” columns are included in the output:

"Yes" → Uses descriptions and units from the mapping file.
"No" → Excludes "Description" and "Unit".
"GTAPv7"→ Applies default definitions and units from GTAP Model Version 7.
"Mix" → Uses manual values when available; otherwise, applies GTAP defaults.

mapping_info <- "Mix"

Note: GTAP defaults apply only to variables included in the GTAPv7 model. Any additional variables must be manually defined.

3.3 Output Formats

Select the required output formats ("Yes" = export, "No" = skip):

CSV (csv.output) → "No"
STATA (stata.output) → "No"
R (r.output) → "Yes"
Text (txt.output) → "Yes"

You can also choose to export only the organized raw data, with or without visualization, by setting plot_data to TRUE or FALSE.

The following command exports all formats and generates data for plotting:

csv.output <- "YES"    
stata.output <- "YES"  
r.output <- "YES"
txt.output <- "YES"   

plot_data = TRUE

# Convert units (optional)
# Options: "mil2bil", "bil2mil", "pct2frac", "frac2pct" — see details in `?convert_units`
sl4_convert_unit <- c("mil2bil")
har_convert_unit <- c("mil2bil")

💡 Tip: Unit Conversion

You can convert result units for ‘sl4’ and ‘har’ independently by using the following automatic options, see ?convert_units:

"mil2bil": converts million USD to billion USD.
"bil2mil": converts billion USD to million USD.
"pct2frac": converts percentages to fractions.
"frac2pct": converts fractions to percentages.
NULL: No conversion

4 R Configuration Summary

In summary, the entire R setup is captured in the following chunk:

# 1. Project Directory
project.folder <- "your/project/folder"

# 2. Define the Input Names 
experiment <- c("TAR10", "SUBT10")

# 3. Adding Description / Unit (Yes/No/GTAPv7/Mix)
mapping_info <- "Mix"

# 4. Choosing Output: (CSV, STATA, R, TEXT) 
csv.output <- "No"    
stata.output <- "No"  
r.output <- "No"
txt.output <- "No"   

# 5. For Plotting: (TRUE/FALSE)
plot_data = TRUE

💡 Tip

Once this process is complete, you can use the same format for future documents—saving you time and ensuring consistency.

You can simply run the following code to setup subdirectories without modification if you followed all the previous instructions:

# Default Subdirectories:
input.folder <- paste0(project.folder, "/in")
map.folder <- paste0(project.folder, "/map")
output.folder <- paste0(project.folder, "/out")

# Default Mapping File:
sl4map <- readxl::read_xlsx(paste0(map.folder, "/OutputMapping.xlsx"), sheet = "SL4File")
harmap <- readxl::read_xlsx(paste0(map.folder, "/OutputMapping.xlsx"), sheet = "HARFile")
filter.map <- readxl::read_xlsx(paste0(map.folder, "/OutputMapping.xlsx"), sheet = "FilterData")

# Filtering Data:
selected_regions <- if(length(filter.map$Region) > 0) filter.map$Region else NULL
selected_sector  <- if(length(filter.map$Sector) > 0) filter.map$Sector else NULL

Note: If you’re familiar with R and prefer a more flexible directory structure, you can customize any of these. However, you must also define them in the function.

5 Automated Data Extraction

To streamline figure generation from GTAP results, I developed an automated data extraction method using the following command:

auto_gtap_data(
  experiment = experiment,
  process_sl4_vars = sl4map, 
  process_har_vars = harmap,
  mapping_info = mapping_info,
  sl4_mapping_info = sl4map,
  har_mapping_info = harmap,
  sl4_convert_unit ="mil2bil",
  har_convert_unit = "mil2bil",
  region_select = selected_regions,
  sector_select = selected_sector,
  subtotal_level = FALSE,
  rename_columns = TRUE,
  decimals = 4,
  project_path = project.folder,
  plot_data = plot_data,
  output_formats = list(
    "csv" = csv.output,
    "stata" = stata.output,
    "rds" = r.output,
    "txt" = txt.output))

W # Tips

5.1 Manual Mapping Files

You can easily create a filter file (Filter) using the following code:

selected_regions <- c("EastAsia", "SEAsia", "Oceania")
selected_sector  <- NULL

You can also manually create a mapping file that replicates the structure of the SL4File and HARFile sheets using the following code:

mapping_df <- data.frame(
  Variable = c("qgdp", "EV", "ppriv"),
  Description = c("Real GDP Index", "Welfare Equivalents", "Consumer Price Index"),
  Unit = c("Percent", "million USD", "percent"),
  stringsAsFactors = FALSE
)

5.2 Sorting Rules

These predefined lists determine the display order of outputs, i.e., figures and tables:

<experiment> sorts the column "Experiment", i.e., your input files.
<selected_region> sorts the column for defined countries by GTAP; the default is "REG".
<selected_sectors> sorts the column for defined sectors by GTAP; the default is "COMM" and "ACTS".