# Load necessary libraries
library(readr) # For reading CSV files
library(dplyr) # For data manipulation
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(tidyr) # For reshaping data
library(ggplot2) # For data visualization
# -----------------------------------------
# Figure 1: Bolivia's Short-term Debt (% of Total Reserves)
# -----------------------------------------
# Step 1: Load debt data and country metadata
debt_data <- read_csv("API_DT/API_DT.DOD.DSTC.IR.ZS_DS2_en_csv_v2_4023.csv", skip = 3)
## New names:
## • `` -> `...69`
## Rows: 266 Columns: 69
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): Country Name, Country Code, Indicator Name, Indicator Code
## dbl (54): 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, ...
## lgl (11): 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, ...69
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
meta_country_data <- read_csv("API_DT/Metadata_Country_API_DT.DOD.DSTC.IR.ZS_DS2_en_csv_v2_4023.csv")
## New names:
## Rows: 265 Columns: 6
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (5): Country Code, Region, IncomeGroup, SpecialNotes, TableName lgl (1): ...6
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## • `` -> `...6`
# Step 2: Filter metadata to retain only valid countries
valid_countries <- meta_country_data %>%
filter(!is.na(Region)) %>% # Keep rows where Region is not NA
pull(`Country Code`) # Extract the list of valid country codes
# Step 3: Filter debt data to include only valid countries
debt_data_filtered <- debt_data %>%
filter(`Country Code` %in% valid_countries)
# Step 4: Filter data for Bolivia
bolivia_data <- debt_data_filtered %>%
filter(`Country Name` == "Bolivia") %>%
select(`Country Name`, `Country Code`, matches("^\\d{4}$")) # Select year columns
# Step 5: Reshape data to long format for plotting
bolivia_long <- bolivia_data %>%
pivot_longer(
cols = matches("^\\d{4}$"), # Select all year columns
names_to = "Year",
values_to = "Value"
) %>%
mutate(Year = as.numeric(Year)) %>% # Convert Year to numeric
drop_na(Value) # Remove rows with missing values
# Step 6: Plot Bolivia's short-term debt over time
ggplot(bolivia_long, aes(x = Year, y = Value)) +
geom_line(color = "blue") +
geom_point(color = "red") +
labs(
title = "Figure 1: Short-term Debt (% of Total Reserves) for Bolivia",
x = "Year",
y = "Short-term Debt (% of Total Reserves)"
) +
theme_minimal()
# -----------------------------------------
# Figure 2: Proportions of Bolivia's Public Debt by Creditor
# -----------------------------------------
# Step 1: Load Bolivia's debt data by creditor
debt_data <- read_csv("Bolivia Borrowing - Sheet1.csv")
## Rows: 24 Columns: 30
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Creditor
## num (29): 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, ...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Step 2: Reshape data from wide to long format
long_data <- debt_data %>%
pivot_longer(cols = -Creditor, names_to = "Year", values_to = "Value")
# Step 3: Ensure 'Value' is numeric and handle missing values
long_data <- long_data %>%
mutate(Value = as.numeric(Value)) %>%
drop_na(Value)
# Step 4: Reshape data back to wide format for processing
reshaped_data <- long_data %>%
pivot_wider(names_from = Creditor, values_from = Value)
## Warning: Values from `Value` are not uniquely identified; output will contain list-cols.
## • Use `values_fn = list` to suppress this warning.
## • Use `values_fn = {summary_fun}` to summarise duplicates.
## • Use the following dplyr code to identify duplicates.
## {data} |>
## dplyr::summarise(n = dplyr::n(), .by = c(Year, Creditor)) |>
## dplyr::filter(n > 1L)
# Step 5: Clean and categorize debt data
reshaped_data <- reshaped_data %>%
select(-IMF) %>% # Drop the original "IMF" column
rename(
IMF = `IMF (Allocations)`, # Rename "IMF (Allocations)" to "IMF"
China = `People's Republic of China` # Rename "People's Republic of China" to "China"
) %>%
mutate(across(c("Germany", "Spain", "France", "USA", "United Kingdom", "Belgium", "Japan"), as.numeric)) %>%
rowwise() %>%
mutate(
`Bilateral Global North` = sum(c_across(c("Germany", "Spain", "France", "USA", "United Kingdom", "Belgium", "Japan")), na.rm = TRUE),
`Regional Development Banks (CAF & IDB)` = sum(c_across(c("Andean Development Corporation (CAF)", "Inter-American Development Bank (IDB)")), na.rm = TRUE),
`Breton Woods Institutions (IMF & WB)` = sum(c_across(c("IMF", "World Bank")), na.rm = TRUE)
) %>%
ungroup()
# Step 6: Ensure key columns are numeric
columns_to_numeric <- c(
"China",
"Bilateral Global North",
"Regional Development Banks (CAF & IDB)",
"Breton Woods Institutions (IMF & WB)",
"Total"
)
reshaped_data <- reshaped_data %>%
mutate(across(all_of(columns_to_numeric), as.numeric))
# Step 7: Calculate proportions relative to 'Total'
selected_columns <- c(
"China",
"Bilateral Global North",
"Regional Development Banks (CAF & IDB)",
"Breton Woods Institutions (IMF & WB)"
)
proportion_data <- reshaped_data %>%
mutate(across(all_of(selected_columns), ~ (. / Total) * 100, .names = "prop_{col}")) # Calculate proportions as percentages
# Step 8: Reshape data to long format for plotting
long_proportion_data <- proportion_data %>%
pivot_longer(cols = starts_with("prop_"),
names_to = "Category",
values_to = "Proportion") %>%
mutate(Category = gsub("prop_", "", Category), # Remove "prop_" from category names
Year = as.numeric(Year)) # Ensure Year is numeric
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `Year = as.numeric(Year)`.
## Caused by warning:
## ! NAs introduced by coercion
# Step 9: Plot proportions of Bolivia's debt by creditor
ggplot(long_proportion_data, aes(x = Year, y = Proportion, color = Category)) +
geom_line(size = 1) +
labs(
title = "Figure 2: Bolivian Public External Debt Owed to Major Creditors (% of Total)",
x = "Year",
y = "Proportion (%)",
color = "Category"
) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Removed 4 rows containing missing values or values outside the scale range
## (`geom_line()`).
