GTAPViz: Utilities

In GTAPViz: Automating 'GTAP' Data Processing and Visualization

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  message = FALSE,
  eval = requireNamespace("GTAPViz", quietly = TRUE)
)

try(devtools::load_all(".."), silent = TRUE)  # go up one level from /vignettes/

input_path <- system.file("extdata/in", package = "GTAPViz")
sl4.plot.data <- readRDS(file.path(input_path, "sl4.plot.data.rds"))
har.plot.data <- readRDS(file.path(input_path, "har.plot.data.rds"))
macro.data    <- readRDS(file.path(input_path, "macro.data.rds"))

This vignette provides other useful functions in the GTAPViz package.

Adding Unit and Description Columns {#sec:add-info}

If you manually import data or need to adjust the "Description" and "Unit" columns, you can use the following command:

# Create mapping file
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
)

datasets <- add_mapping_info(mapping_df, 
                             external_map = mapping_df, 
                             mapping = "Yes")

Convert Different Units

You can convert all units across all data frames within the data list by using the following code:

your_data <- sl4.plot.data   # Change this to your data list name

your_data <- convert_units(change_unit_from = c("BAHT", "million USD"),
                           change_unit_to = c("USD", "billion USD"),
                           adjustment = c("*34.5", "/1000"))

Explanation

What does this code do?

• Converts BAHT to USD by multiplying the values by 34.5.

• Converts million USD to billion USD by dividing the values by 1,000.

Parameters:

• change_unit_from: The original unit name to be changed in the "Unit" column.
  If your dataset does not yet include a "Unit" column, please refer to adding unit and description columns.

• change_unit_to: The new unit name to be recorded in the "Unit" column.

• adjustment: The mathematical operation used to transform the values.
  Accepts operators such as /, *, +, or -.

Renaming Columns and Data List Names {#sec:rename-column}

To modify column names across all datasets and/or rename data list names automatically—such as changing REG to Region or COMM to Commodity—use the following command:

# Creating Mapping File
rename_col <- data.frame(
  old = c("REG", "COMM", "ACTS"),
  new = c("Region", "Commodity", "Activity")
  )

har.plot.data <- HARplus::rename_dims(har.plot.data, 
                                      mapping_df = rename_col,
                                      rename_list_names = FALSE)

💡 Tip

You can create this mapping dataframe in Excel and import it into R. The column names must be **"old"** and **"new"**.

Sorting Output {#sec:sorting-output}

This function allows you to sort data before generating plots or tables.
For example, you can display EXP2 before EXP1, or SEAsia before Oceania.

It supports:

• Sorting by multiple columns simultaneously for precise output control

• Applying consistent sorting across all data frames within a data list

See ?sort_plot_data for full details.

Key arguments:

• cols specifies the column(s) to sort based on a predefined order (e.g., in sl4.plot.data, Experiment and Region will follow the defined order).

• sort_by_value_desc controls whether sorting is in descending order (TRUE/FALSE/NULL).

To apply sorting, use the following command:

# Using the function
sort_sl4_plot <- sort_plot_data(
  sl4.plot.data,
  sort_columns = list(
    Experiment = c("EXP2", "EXP1"), # Column Name = Sorting Order
    Region = c("SEAsia", "Oceania")
    ),
  sort_by_value_desc = NULL
)

Renaming Values {#sec:rename-value}

To automatically modify values in any column before plotting—such as renaming country names in the REG column (e.g., changing "USA" to "United States" or "CHN" to "China")—use the following command:

# Creating Mapping File
rename.region <- data.frame(
  ColumnName = "REG",
  OldName = c("USA", "CHN"),
  NewName = c("United States", "China"),
  stringsAsFactors = FALSE
)

sl4.plot.data_rename <- rename_value(sl4.plot.data, mapping.file = rename.region)

Another useful example is renaming the components of welfare decomposition before plotting.

# Rename Value if needed
wefare.decomp.rename <- data.frame(
  ColumnName = "COLUMN",
  OldName = c("alloc_A1", "ENDWB1", "tech_C1", "pop_D1", "pref_G1", "tot_E1", "IS_F1"),
  NewName = c("Alloc Eff.", "Endwb", "Tech Chg.", "Pop", "Perf", "ToT", "I-S"),
  stringsAsFactors = FALSE
)

welfare_decomp <- rename_value(har.plot.data, mapping.file = wefare.decomp.rename)

(HARplus) Manual Data Processing {#sec:manualdata}

This section is for non-GTAP models or manual data extraction for plotting. Follow these steps:

1. Use "HARplus::load_sl4x" or "HARplus::load_harx", then "HARplus::group_data_by_dims" to structure the data in the required format.

2. Filter each dataframe using standard R data.frame operations.

3. Add the "Unit" and "Description" columns using "add_mapping_info" (the "Unit" column is required for plotting).

# Step 1: Filtering Data using Dataframe and lapply
manual.data <- lapply(sl4data, function(x) {
  if (is.data.frame(x)) {
    x[x$REG %in% selected_regions & x$COMM %in% selected_sector & x$ACTS %in% selected_sector
      & x$Experiment %in% selected_exp , ]
  } else {
    x  
  }
})

# Step 2: Adding Unit and Description Column
manual.data <- add_mapping_info(manual.data, external_map = "/your/mapping.xlsx",
                                description_info = TRUE,
                                unit_info = TRUE)

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.

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GTAPViz documentation built on June 8, 2025, 11:43 a.m.