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

1 Comparison Plot

This figure compares one or multiple variables across all experiments for selected observations from a chosen dimension (e.g., region, sector).

Key Features

Designed for comparing selected variables across experiments.
Displays all selected variables in one plot, making it ideal for a few observations; too many can make it dense and unclear.

1.1 Example 1

Plotting qgdp, ppriv, EV, tot, and u generates six separate figures using the following command:

reg_data = sl4.plot.data[["REG"]]

plotA <- comparison_plot(
          # === Input Data ===
          data         = reg_data,
          filter_var   = NULL,
          x_axis_from  = "Region",
          split_by     = "Variable",
          panel_var    = "Experiment",
          variable_col = "Variable",
          unit_col     = "Unit",
          desc_col     = "Description",
        
          # === Plot Behavior ===
          invert_axis     = FALSE,
          separate_figure = FALSE,
        
          # === Variable Display ===
          var_name_by_description = FALSE,
          add_var_info            = FALSE,
        
          # === Export Settings ===
          output_path     = NULL,
          export_picture  = FALSE,
          export_as_pdf   = FALSE,
          export_config   = create_export_config(
            width  = 20,
            height = 12
          ),
        
          # === Styling ===
          plot_style_config = create_plot_style(
            color_tone         = "purdue",
            add_unit_to_title  = TRUE,
            title_format       = create_title_format(
              type = "prefix",
              text = "Impact on"
            ),
            panel_rows = 2
          )
        )

1.2 GTAP Macro Variable Plot

This figure displays aggregated values across all experiments, typically for global economic impacts. It is designed for GTAP Macro variables extracted from the “Macros” header.

Key Features

Compares macroeconomic indicators across experiments.
Works with aggregated data, making it ideal for global-scale analysis.
Primarily used for GTAP Macro variables but can be applied to other aggregated datasets.

To plot this figure—the only difference from previous figures is changing split_by = FALSE—you can use the following command:

macro.plot <- comparison_plot(
  # === Input Data ===
  data         = macro.data[["macros"]],
  filter_var   = c("pgdpwld", "qgdpwld", "vgdpwld"),
  x_axis_from  = "Variable",
  split_by     = FALSE,     # THIS IS THE MOST IMPORTANT PART FOR THIS PLOT

  # === Export Settings ===
  output_path     = NULL,
  export_picture  = FALSE,
  export_as_pdf   = FALSE,
  export_config   = create_export_config(
    width  = 20,
    height = 15
  ),

  # === Styling ===
  plot_style_config = create_plot_style(
    color_tone = "gtap",
    title_format = create_title_format(
      type = "full",
      text = "Global Economic Impacts"
    )
  )
)

GTAP Macro Plot Visualization
(splt_by = FALSE, panel_rows and panel_cols = NULL i.e., default)

2 Detail Plot

This figure visualizes large-scale data, displaying all sectors (COMM, ACTS) or all regions (REG) within each experiment.

Key Features

Supports visualization across two selected dimensions.
Suitable for datasets too large for comparison_plot.
Automatically detects and aligns variables with different dimension names.
Filters data by selecting the most affected variables using top_impact.

2.1 Example 1

The following command plots all variables with sectors on the x-axis for each region:

sector_data <- sl4.plot.data[["COMM*REG"]]

plotB <- detail_plot(
  # === Input Data ===
  data        = sector_data,
  filter_var  = list(Region = c("Oceania")),
  x_axis_from = "Commodity",
  split_by    = "Region",
  panel_var   = "Experiment",
  variable_col = "Variable",
  unit_col     = "Unit",
  desc_col     = "Description",
  
  # === Plot Behavior ===
  invert_axis      = TRUE,
  separate_figure  = FALSE,
  top_impact       = NULL,
  
  # === Variable Display ===
  var_name_by_description = TRUE,
  add_var_info            = FALSE,
  
  # === Export Settings ===
  output_path     = NULL,
  export_picture  = FALSE,
  export_as_pdf   = FALSE,
  export_config   = create_export_config(
    width  = 45,
    height = 20
  ),
  
  # === Styling ===
  plot_style_config = create_plot_style(
    positive_color = "#2E8B57",
    negative_color = "#CD5C5C",
    panel_rows = 1,
    panel_cols = NULL,
    show_axis_titles_on_all_facets = FALSE,
    y_axis_text_size = 25,
    bar_width = 0.6,
    all_font_size = 1.1
  )
)

💡 Tip

You may notice the following adjustments:

positive_color and negative_color: Define colors for positive and negative values, automatically distinguishing impacts by shade.
show_axis_titles_on_all_facets: Suppresses repeated labels across facets.
y_axis_text_size: Increases y-axis label size to 25 from the default setting.
bar_width: Decreases bar width for better visualization of large datasets.
all_font_size: Increases all font sizes to 1.3 from the default setting.

Detail Plot Visualization
(top_impact = NULL, invert_axis = TRUE, panel_rows = 1)

2.2 Example 2

Another powerful feature of detail_plot is the top_impact argument,
which automatically filters and displays the most-affected values (both gains and losses)
based on the x_axis_from grouping.

Detail Plot Visualization (filtered)
(top_impact = 10, invert_axis = TRUE)

2.3 Example 3

Another example involves plotting multi-dimensional data like bilateral trade (qxs).
To do this, specify:

split_by = c("Source", "Destination")

This automatically generates separate plots for each unique pair, e.g., "Oceania-SEAsia", "Oceania-EastAsia", etc.

Each plot shows detailed impacts at the sector (COMM) level for that country pair.

bilateral_data <- bilateral_data[["qxs"]]

plotB3 <- detail_plot(
  # === Input Data ===
  data        = bilateral_data,
  filter_var  = list(Source = c("Oceania"),
                     Destination = c("SEAsia", "EastAsia")),
  x_axis_from = "Commodity",
  split_by    = c("Source", "Destination"),    # The most important part!
  
  # === Plot Behavior ===
  invert_axis      = TRUE,
  separate_figure  = FALSE,
  top_impact       = NULL,
  
  # === Variable Display ===
  var_name_by_description = FALSE,
  add_var_info            = FALSE,
  
  # === Export Settings ===
  output_path     = NULL,
  export_picture  = FALSE,
  export_as_pdf   = FALSE,
  export_config   = create_export_config(
    width  = 45,
    height = 20
  ),
  
  # === Styling ===
  plot_style_config = create_plot_style(
    positive_color = "#2E8B57",
    negative_color = "#CD5C5C",
    panel_rows = 1,
    panel_cols = NULL,
    show_axis_titles_on_all_facets = FALSE,
    y_axis_text_size = 25,
    bar_width = 0.6,
    all_font_size = 1.1
  )
)

3 Stack Plot

This plot visualizes decomposition results, showing how different components contribute to a total value, making it particularly suitable for decomposition analysis.

Key Features

Displays the contribution of components to a total value.
Allows further decomposition by unstacking components instead of using a stacked bar.
Suitable for welfare decomposition, trade decomposition, and other GTAP structural breakdowns.

💡 Tip

Since decomposition data often contains multiple components, renaming and restructuring may be necessary before plotting. You can try the following code:

# 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
)

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

3.1 Example 1

You can use the following command to plot the decomposition figure:

plotC <- stack_plot(
  # === Input Data ===
  data              = har.plot.data[["A"]],
  filter_var        = NULL,
  x_axis_from       = "Region",
  stack_value_from  = "COLUMN",
  split_by          = FALSE,
  panel_var         = "Experiment",
  variable_col      = "Variable",
  unit_col          = "Unit",
  desc_col          = "Description",

  # === Plot Behavior ===
  invert_axis     = FALSE,
  separate_figure = FALSE,
  show_total      = TRUE,
  unstack_plot    = FALSE,
  top_impact      = NULL,

  # === Variable Display ===
  var_name_by_description = TRUE,
  add_var_info            = FALSE,

  # === Export Settings ===
  output_path     = NULL,
  export_picture  = FALSE,
  export_as_pdf   = FALSE,
  export_config   = create_export_config(
    width  = 28,
    height = 15
  ),

  # === Styling ===
  plot_style_config = create_plot_style(
    color_tone                   = "gtap",
    panel_rows                   = 1,
    panel_cols                   = NULL,
    show_legend                  = TRUE,
    show_axis_titles_on_all_facets = FALSE
  )
)

Stack Plot Visualization (stack)
(split_by = FALSE, show_total = TRUE, unstack_plot = FALSE)

3.2 Example 2

To further analyze the decomposition at the component level, set unstack_plot = TRUE to display unstacked plots.

Stack Plot Visualization (unstacked)
(split_by = FALSE, invert_axis = TRUE, unstack_plot = TRUE)

3.3 Example 3

Another practical example is plotting the decomposition of terms of trade (header E1), which is also a good example of multi-dimensional plotting. You may try the following command:

plotE <- stack_plot(
  # === Input Data ===
  data              = har.plot.data[["E1"]],
  filter_var        = list(Region = c("Oceania", "SEAsia")),
  x_axis_from       = "Commodity",
  stack_value_from  = "PRICES",
  split_by          = "Region",
  panel_var         = "Experiment",
  variable_col      = "Variable",
  unit_col          = "Unit",
  desc_col          = "Description",

  # === Plot Behavior ===
  invert_axis     = TRUE,
  separate_figure = FALSE,
  show_total      = TRUE,
  unstack_plot    = FALSE,
  top_impact      = NULL,

  # === Export Settings ===
  output_path     = NULL,
  export_picture  = FALSE,
  export_as_pdf   = FALSE,
  export_config   = create_export_config(
    width  = 50,
    height = 30
  ),

  # === Styling ===
  plot_style_config = create_plot_style(
    title_format = create_title_format(
      type = "prefix",
      text = "Terms of Trade Decomposition",
      sep  = ": "
    ),
    color_tone                   = "winter", 
    panel_rows                   = 1,
    show_axis_titles_on_all_facets = FALSE,
    bar_width                    = 0.5,
    show_legend                  = TRUE
  )
)

Stack Plot Visualization (Multi-Dimension)
(split_by = “Region”, plot_style_config -> adjustments)

4 Tip

4.1 Plot Configurations

Run the following chunks to view all available configurations.

get_all_config()

Rename the list outputs my_export_config and my_style_config to any names you prefer; you can create as many styles as needed.
Enter the name of your custom style list into the plot function, while other settings in (...) remain unchanged, as shown in the sample below:

comparison_plot <- (...,
                    export_config = my_export_config,
                    plot_style_config = my_style_config
                    )

4.2 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
)

4.3 Renaming Values

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)

5 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.

Plot: A Step-by-Step Guide

Pattawee Puangchit

2025-04-17

1 Comparison Plot

1.1 Example 1

1.2 GTAP Macro Variable Plot

2 Detail Plot

2.1 Example 1

2.2 Example 2

2.3 Example 3

3 Stack Plot

3.1 Example 1

3.2 Example 2

3.3 Example 3

4 Tip

4.1 Plot Configurations

4.2 Sorting Output

4.3 Renaming Values

5 Sample Data