---
title: "GTAPViz: Utilities"
author: "Pattawee Puangchit"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float:
collapsed: true
smooth_scroll: true
css: mystyle.css
number_sections: true
code_folding: show
self_contained: false
vignette: >
%\VignetteIndexEntry{GTAPViz: Utilities}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE, eval = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
warning = FALSE,
message = FALSE,
eval = requireNamespace("GTAPViz", quietly = TRUE)
)
```
```{r Dev Period, include = FALSE, eval = 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:
```{r Add Info, eval = FALSE}
# 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:
```{r Converting Unit, eval = FALSE}
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"))
```
<details>
<summary class="toggle-summary">Explanation</summary>
<div class="tip-box">
**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](#sec:add-info).
- `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 `-`.
</div>
</details>
# 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:
```{r Rename Column, eval = FALSE}
# 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)
```
<details>
<summary class="toggle-summary">💡 Tip</summary>
<div class="tip-box">
You can create this mapping dataframe in Excel and import it into R. The column names must be **"old"** and **"new"**.
</div>
</details>
# 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:
```{r Sorting Data Function, eval = FALSE}
# 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:
```{r Rename Value, eval = FALSE}
# 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)
```
<br>
Another useful example is renaming the components of welfare decomposition before plotting.
```{r, eval = FALSE}
# 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).
```{r Manual Data Filtering, eval = FALSE}
# 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](https://www.gtap.agecon.purdue.edu/databases/archives.asp).
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