This manual provides an explanation of the file
3.TableGens.R
1.ProjectSetup with plot_data <-
TRUE before running this script.
3.TableGens.R.
If you are unsure, please refer to: Table Catalogs
You may define a custom output folder to create separate directories for each plot.
# Define new output location (or leave it as deafult)
output.folder <- output.folderdata_list)For this table, a data list is required, this data
definition should match the output in sl4.plot.data and
har.plot.data.
This is different to the data used in plotting, which is a data frame.
Multiple tables based on a single data list can be created in one
setup (e.g., all data frames within sl4.plot.data
can be generated in a single code block).
data_list <- sl4.plot.data
# However, if you used "group_data_by_dims", you may need to de-list one level as follows:
# data_list <- sl4.plot.data[["Sector"]](1) pivot_col
Define the pivot column for each dataframe within the data list. For example,
REG and COMM*REG are dataframes within
sl4.plot.data,
Variable and Commodity are the columns
to be pivoted in each respective dataframe
pivot_col = list( # DO NOT CHANGE THIS LIST LINE; MAKE YOUR CHANGES BELOW
REG = "Variable",
COMM*REG = "Commodity"
) # DO NOT REMOVE THIS BRACKET(2) group_by
Define the main group columns (used as rows in the structured table) for each dataframe. The order matters: the first listed will be shown as the first (leftmost) column
group_by = list( # DO NOT CHANGE THIS LIST LINE; MAKE YOUR CHANGES BELOW
REG = list("Experiment", "Region"),
COMM*REG = list("Experiment", "Variable", "Region")
) # DO NOT REMOVE THIS BRACKET(3) configs
rename_cols = list("Experiment" = "Scenario"), # Rename "Experiment" to "Scenario"
total_column = FALSE, # Add row totals (e.g., for decomposition)
decimal = 4, # Number of decimal places
subtotal_level = FALSE, # Include subtotal rows from CGE if available
repeat_label = FALSE, # Repeat group labels in all rows
include_units = TRUE, # Append unit to column names (e.g., (%), (million USD))
var_name_by_description = TRUE, # Use variable description instead of GTAP code
add_var_info = TRUE, # Append code or description in parentheses
add_group_line = FALSE, # Add line between groups based on first column
separate_sheet_by = "Unit", # Split sheets by this column
export_table = TRUE, # Export to Excel
output_path = output.folder, # Output directory
separate_file = FALSE, # Split sheets into separate workbooks
workbook_name = "Comparison Table 1D" # Workbook file nameA default code provided in `3.TableGens.R`.
# Your data list
data_list <- sl4.plot.data
report_table(
data_list = data_list,
pivot_col = list( # See manual or run `?report_table` for more details
REG = "Variable",
'COMM*REG' = "Commodity"
),
group_by = list( # See manual or run `?report_table` for more details
REG = list("Experiment", "Region"),
'COMM*REG' = list("Experiment", "Variable", "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 = TRUE,
output_path = output.folder,
separate_file = FALSE,
workbook_name = "Comparison Table Default"
)
A one-dimensional structure groups data by a single dimension, such as REG, COMM, or ACTS.
# Your data list
data_list <- sl4.plot.data
report_table(
data_list = data_list,
pivot_col = list( # See manual or run `?report_table` for more details
REG = "Variable"
),
group_by = list( # See manual or run `?report_table` for more details
REG = list("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 = TRUE,
output_path = output.folder,
separate_file = FALSE,
workbook_name = "Comparison Table 1D"
)
A two-dimensional structure groups data by two dimensions, such as REG*COMM or REG*ACTS.
# Your data list
data_list <- sl4.plot.data
report_table(
data_list = data_list,
pivot_col = list( # See manual or run `?report_table` for more details
COMM*REG = "Commodity"
),
group_by = list( # See manual or run `?report_table` for more details
COMM*REG = list("Experiment", "Variable", "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 = TRUE,
output_path = output.folder,
separate_file = FALSE,
workbook_name = "Comparison Table 2D"
)
A multi-dimensional structure groups data by more than two dimensions, such as COMM*REG*REG for bilateral trade at the sector level.
# Your data list
data_list <- bilateral_data
report_table(
data_list = bilateral_data,
pivot_col = list( # See manual or run `?report_table` for more details
qxs = "Commodity"
),
group_by = list( # See manual or run `?report_table` for more details
qxs = list("Experiment", "Source", "Destination")
),
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 = TRUE,
separate_sheet_by = "Unit",
export_table = TRUE,
output_path = output.folder,
separate_file = FALSE,
workbook_name = "Comparison Table 3D"
)
This pivot table requires a data frame similar to the one used for plotting. Therefore, you can create only one table per setup—unlike structured tables, which support multiple tables within a single setup.
A default code provided in `3.TableGens.R`.
# Your data frame
data_pivot_table <- sl4.plot.data[["REG"]]
pivot_table_with_filter( # DO NOT CHANGE THIS FUCNTION NAME; MAKE YOUR CHANGES BELOW
data = data_pivot_table,
filter = c("Variable", "Unit"), # Allow filtering by variable type and unit
rows = c("Region", "Sector"), # 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
dims = "A3", # Starting cell for pivot table
export = TRUE,
output_path = output.folder,
workbook_name = "Sectoral_Impact_Analysis.xlsx"
)
# Your data frame
data_pivot_table <- bilateral_data[["qxs"]]
pivot_table_with_filter( # DO NOT CHANGE THIS FUCNTION NAME; MAKE YOUR CHANGES BELOW
data = data_pivot_table,
filter = c("Experiment", "Commodity", "Unit"), # Allow filtering by variable type and unit
rows = c("Source"), # Regions and sectors as row fields
cols = c("Destination"), # 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
dims = "A5", # Starting cell for pivot table
export = TRUE,
output_path = output.folder,
workbook_name = "Sectoral_Impact_Analysis.xlsx"
)