graphs

library(readxl)
library(dplyr)
library(ggplot2)
library(tidyverse)
library(writexl)
library(patchwork)

Export_GDP <- read_excel("Export_GDP.xls")
Export_Partner_Share <- read_excel("Export_Partner_Share.xlsx")
Import_Partner_Share <- read_excel("Import_Partner_Share.xlsx")
Inflation_CPI <- read_excel("Inflation_CPI.xls")
Inflation_Deflator <- read_excel("Inflation_Deflator.xls")
RGDP_Growth <- read_excel("RGDP_Growth.xls")
RGDP <- read_excel("RGDP.xls")
Unemployment_Rate <- read_excel("Unemployment_Rate.xls")

highlight_years <- c(1994, 1999, 2004, 2009, 2014, 2019, 2024)

Real GDP

RGDP_long <- RGDP |>
  filter(`Country Name` == "Singapore") |>
  pivot_longer(cols = -1, names_to = "Year", values_to = "RGDP") |>
  mutate(Year = as.numeric(Year)) |>
  filter(!is.na(RGDP), Year >= 1994)

highlight_data_RGDP <- RGDP_long |>
  filter(Year %in% highlight_years)

ggplot(RGDP_long, aes(x = Year, y = RGDP)) +
  geom_line(color = "steelblue", linewidth = 1) +
  geom_point(data = highlight_data_RGDP, color = "red", size = 3) +
  geom_text(data = highlight_data_RGDP, aes(label = Year), vjust = -1, size = 3.5, color = "red") +
  labs(title = "Singapore Real GDP Over Time", x = "Year", y = "Real GDP (USD)") +
  theme_minimal()

Steady long-term growth since independence driven by trade and finance
Rapid expansion during the 2000s globalization boom
Sharp contraction only during COVID-19 (2020)

Real GDP Growth Rate

RGDP_Growth_long <- RGDP_Growth |>
  filter(`Country Name` == "Singapore") |>
  pivot_longer(cols = -1, names_to = "Year", values_to = "Growth") |>
  mutate(Year = as.numeric(Year)) |>
  filter(!is.na(Growth), Year >= 1994)

highlight_data_RGDP_Growth <- RGDP_Growth_long |>
  filter(Year %in% highlight_years)

ggplot(RGDP_Growth_long, aes(x = Year, y = Growth)) +
  geom_line(color = "steelblue", linewidth = 1) +
  geom_point(data = highlight_data_RGDP_Growth, color = "red", size = 3) +
  geom_text(data = highlight_data_RGDP_Growth, aes(label = Year), vjust = -1, size = 3.5, color = "red") +
  labs(title = "Singapore Real GDP Growth Rate Over Time", x = "Year", y = "RGDP Growth Rate (%)") +
  theme_minimal()

Dips align with global crises due to trade dependence
1998 Asian Financial Crisis, 2001 dot-com bust, 2003 SARS
2009 Global Financial Crisis, 2020 COVID
Recoveries occur quickly once global trade rebounds

Unemployment Rate

Unemployment_Rate_long <- Unemployment_Rate |>
  filter(`Country Name` == "Singapore") |>
  pivot_longer(cols = -1, names_to = "Year", values_to = "Unemployment") |>
  mutate(Year = as.numeric(Year)) |>
  filter(!is.na(Unemployment), Year >= 1994)

highlight_data_Unemployment <- Unemployment_Rate_long |>
  filter(Year %in% highlight_years)

ggplot(Unemployment_Rate_long, aes(x = Year, y = Unemployment)) +
  geom_line(color = "steelblue", linewidth = 1) +
  geom_point(data = highlight_data_Unemployment, color = "red", size = 3) +
  geom_text(data = highlight_data_Unemployment, aes(label = Year), vjust = -1, size = 3.5, color = "red") +
  labs(title = "Singapore Unemployment Rate Over Time", x = "Year", y = "Unemployment Rate (%)") +
  theme_minimal()

Consistently low due to active government labor policies
Job support programs limit layoffs during crises
Small spikes after global downturns (2004, 2009)
One of the lowest unemployment rates globally

Exports as % of GDP

Export_GDP_long <- Export_GDP |>
  pivot_longer(cols = -1, names_to = "Year", values_to = "Export_GDP") |>
  mutate(Year = as.numeric(Year)) |>
  filter(!is.na(Export_GDP))

highlight_data <- Export_GDP_long |>
  filter(Year %in% highlight_years)

ggplot(Export_GDP_long, aes(x = Year, y = Export_GDP)) +
  geom_line(color = "steelblue", linewidth = 1) +
  geom_point(data = highlight_data, color = "red", size = 3) +
  geom_text(data = highlight_data, aes(label = Year), vjust = -1, size = 3.5, color = "red") +
  labs(title = "Singapore Exports of Goods and Services (% of GDP)", x = "Year", y = "Export GDP (%)") +
  theme_minimal()

Extremely high due to Singapore’s role as a global trade hub
Goods imported, processed, and re-exported
Surge during peak globalization in the 2000s
Decline after 2008 as global trade slowed

Inflation (CPI)

Inflation_CPI_long <- Inflation_CPI |>
  filter(`Country Name` == "Singapore") |>
  pivot_longer(cols = -1, names_to = "Year", values_to = "CPI") |>
  mutate(Year = as.numeric(Year)) |>
  filter(!is.na(CPI), Year >= 1994)

highlight_data_CPI <- Inflation_CPI_long |>
  filter(Year %in% highlight_years)

ggplot(Inflation_CPI_long, aes(x = Year, y = CPI)) +
  geom_line(color = "steelblue", linewidth = 1) +
  geom_point(data = highlight_data_CPI, color = "red", size = 3) +
  geom_text(data = highlight_data_CPI, aes(label = Year), vjust = -1, size = 3.5, color = "red") +
  labs(title = "Singapore CPI Inflation Rate Over Time", x = "Year", y = "Inflation Rate (CPI %)") +
  theme_minimal()

Inflation heavily influenced by global prices
Major spikes during energy and commodity shocks
Notable increases in 2008 and 2022
Generally stable between global crises

Inflation (GDP Deflator)

Inflation_Deflator_long <- Inflation_Deflator |>
  filter(`Country Name` == "Singapore") |>
  pivot_longer(cols = -1, names_to = "Year", values_to = "Deflator") |>
  mutate(Year = as.numeric(Year)) |>
  filter(!is.na(Deflator), Year >= 1994)

highlight_data_Defl <- Inflation_Deflator_long |>
  filter(Year %in% highlight_years)

ggplot(Inflation_Deflator_long, aes(x = Year, y = Deflator)) +
  geom_line(color = "steelblue", linewidth = 1) +
  geom_point(data = highlight_data_Defl, color = "red", size = 3) +
  geom_text(data = highlight_data_Defl, aes(label = Year), vjust = -1, size = 3.5, color = "red") +
  labs(title = "Singapore Inflation Deflator Rate Over Time", x = "Year", y = "Inflation Rate (Deflator %)") +
  theme_minimal()

Tracks global commodity price cycles
Larger fluctuations than CPI
Deflation during commodity collapses (late 1990s, mid-2010s)
Large spike after COVID supply chain disruptions

Export Partner Share

Export_Partner_long <- Export_Partner_Share |>
  pivot_longer(cols = -c(1:4), names_to = "Year", values_to = "Share") |>
  mutate(Year = as.numeric(Year)) |>
  filter(!is.na(Share), Share > 0)

top5_countries <- Export_Partner_long |>
  filter(Year %in% highlight_years, `Partner Name` != "World") |>
  group_by(`Partner Name`) |>
  summarise(avg_share = mean(Share, na.rm = TRUE)) |>
  slice_max(avg_share, n = 5) |>
  pull(`Partner Name`)

top5_data <- Export_Partner_long |>
  filter(`Partner Name` %in% top5_countries)

highlight_data <- top5_data |>
  filter(Year %in% highlight_years)

ggplot(top5_data, aes(x = Year, y = Share, color = `Partner Name`)) +
  geom_line(linewidth = 1) +
  geom_point(data = highlight_data, size = 3) +
  geom_text(data = highlight_data, aes(label = Year), vjust = -1, size = 2.8) +
  labs(title = "Singapore Export Partner Share (%) - Top 5 Countries",
       x = "Year", y = "Export Partner Share (%)", color = "Partner Country") +
  theme_minimal()

China rose from minor partner to largest trading partner
Reflects China’s emergence as global manufacturing hub
Japan and Hong Kong declined in relative importance
Trade structure mirrors global economic shifts

Export Partner Composition

pie_data <- Export_Partner_Share |>
  pivot_longer(cols = -c(1:4), names_to = "Year", values_to = "Share") |>
  mutate(Year = as.numeric(Year)) |>
  filter(!is.na(Share), Share > 0,
         `Partner Name` %in% top5_countries,
         Year %in% highlight_years)

available_years <- intersect(highlight_years, unique(pie_data$Year))

plots_list <- lapply(seq_along(available_years), function(i) {
  yr <- available_years[i]
  df <- pie_data |>
    filter(Year == yr) |>
    arrange(desc(Share)) |>
    mutate(pct = Share / sum(Share) * 100,
           label = paste0(round(pct, 1), "%"),
           pos = cumsum(pct) - pct / 2)
  p <- ggplot(df, aes(x = "", y = pct, fill = `Partner Name`)) +
    geom_col(width = 1, color = "white") +
    coord_polar(theta = "y") +
    geom_text(aes(y = pos, label = label), color = "white", size = 3, fontface = "bold") +
    labs(title = as.character(yr), fill = "Country") +
    theme_void() +
    theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 12))
  if (i != length(available_years)) p <- p + theme(legend.position = "none")
  p
})

wrap_plots(plots_list, ncol = 4) +
  plot_annotation(title = "Singapore Export Partner Share - Top 5 Countries") +
  plot_layout(guides = "collect")

China’s share grows significantly from 1994 to 2019
Reflects global rise of China in trade networks
Many countries show similar shifts toward China
Singapore diversifying trade toward India, Middle East, Europe

Import Partner Share

Import_Partner_long <- Import_Partner_Share |>
  pivot_longer(cols = -c(1:4), names_to = "Year", values_to = "Share") |>
  mutate(Year = as.numeric(Year)) |>
  filter(!is.na(Share), Share > 0)

top5_import <- Import_Partner_long |>
  filter(Year %in% highlight_years, `Partner Name` != "World") |>
  group_by(`Partner Name`) |>
  summarise(avg_share = mean(Share, na.rm = TRUE)) |>
  slice_max(avg_share, n = 5) |>
  pull(`Partner Name`)

top5_import_data <- Import_Partner_long |>
  filter(`Partner Name` %in% top5_import)

highlight_import <- top5_import_data |>
  filter(Year %in% highlight_years)

ggplot(top5_import_data, aes(x = Year, y = Share, color = `Partner Name`)) +
  geom_line(linewidth = 1) +
  geom_point(data = highlight_import, size = 3) +
  geom_text(data = highlight_import, aes(label = Year), vjust = -1, size = 2.8) +
  labs(title = "Singapore Import Partner Share (%) - Top 5 Countries",
       x = "Year", y = "Import Partner Share (%)", color = "Partner Country") +
  theme_minimal()

China became main import source as manufacturing shifted there
Japan’s share declined with long-term economic slowdown
Malaysia remains stable due to geographic proximity
Strong regional supply chain integration

Import Partner Composition

pie_import <- Import_Partner_Share |>
  pivot_longer(cols = -c(1:4), names_to = "Year", values_to = "Share") |>
  mutate(Year = as.numeric(Year)) |>
  filter(!is.na(Share), Share > 0,
         `Partner Name` %in% top5_import,
         Year %in% highlight_years)

available_import_years <- intersect(highlight_years, unique(pie_import$Year))

plots_import <- lapply(seq_along(available_import_years), function(i) {
  yr <- available_import_years[i]
  df <- pie_import |>
    filter(Year == yr) |>
    arrange(desc(Share)) |>
    mutate(pct = Share / sum(Share) * 100,
           label = paste0(round(pct, 1), "%"),
           pos = cumsum(pct) - pct / 2)
  p <- ggplot(df, aes(x = "", y = pct, fill = `Partner Name`)) +
    geom_col(width = 1, color = "white") +
    coord_polar(theta = "y") +
    geom_text(aes(y = pos, label = label), color = "white", size = 3, fontface = "bold") +
    labs(title = as.character(yr), fill = "Country") +
    theme_void() +
    theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 12))
  if (i != length(available_import_years)) p <- p + theme(legend.position = "none")
  p
})

wrap_plots(plots_import, ncol = 4) +
  plot_annotation(title = "Singapore Import Partner Share - Top 5 Countries") +
  plot_layout(guides = "collect")

China’s share grows significantly from 1994 to 2019
Reflects global rise of China in trade networks
Many countries show similar shifts toward China
Singapore diversifying trade toward India, Middle East, Europe