Objective

The objective is to analyze and visualize global economic and social indicators, including life expectancy, GDP per capita, and population, across different countries and years. The findings will reveal disparities, trends, and patterns in global development.

library(gapminder)
## Warning: package 'gapminder' was built under R version 4.4.2
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.4.2
## 
## 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(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.4.2
data("gapminder")

# Data Preprocessing
gapminder_filtered <- gapminder %>%
  select(country, year, lifeExp, gdpPercap, pop, continent) %>%
  filter(year >= 2000) %>%  # Focus on data from the year 2000 onwards
  mutate(gdp_billion = gdpPercap * pop / 1e9)  # Calculate GDP in billion dollars

# Summary Table by Continent
summary_table <- gapminder_filtered %>%
  group_by(continent) %>%
  summarise(
    avg_lifeExp = mean(lifeExp, na.rm = TRUE),
    avg_gdpPercap = mean(gdpPercap, na.rm = TRUE),
    total_pop = sum(pop, na.rm = TRUE)
  ) %>%
  arrange(desc(total_pop))

# Display the top rows
head(summary_table)
## # A tibble: 5 × 4
##   continent avg_lifeExp avg_gdpPercap  total_pop
##   <fct>           <dbl>         <dbl>      <dbl>
## 1 Asia             70.0        11324. 7413756030
## 2 Africa           54.1         2844. 1763263608
## 3 Americas         73.0        10145. 1748643946
## 4 Europe           77.2        23383. 1164322398
## 5 Oceania          80.2        28374.   48004776
# Life Expectancy Trend Over Time (2000 onwards)
ggplot(gapminder_filtered, aes(x = year, y = lifeExp, color = continent, group = continent)) +
  geom_line() +
  labs(
    title = "Life Expectancy Trend Over Time (2000 onwards)",
    x = "Year",
    y = "Life Expectancy"
  ) +
  scale_color_manual(values = c("Asia" = "#FF6347", "Europe" = "#4682B4", "Africa" = "#32CD32", "Americas" = "#FFD700", "Oceania" = "#8A2BE2")) + # Custom colors
  theme_minimal()

# GDP per Capita Distribution by Continent
ggplot(gapminder_filtered, aes(x = gdpPercap, fill = continent)) +
  geom_density(alpha = 0.7) +
  scale_x_log10() +  # Log scale for GDP per capita
  labs(
    title = "GDP per Capita Distribution by Continent",
    x = "GDP per Capita (log scale)",
    y = "Density"
  ) +
  scale_fill_manual(values = c("Asia" = "#FF6347", "Europe" = "#4682B4", "Africa" = "#32CD32", "Americas" = "#FFD700", "Oceania" = "#8A2BE2")) + # Custom colors
  theme_minimal()

# Life Expectancy Distribution by Continent
ggplot(gapminder_filtered, aes(x = continent, y = lifeExp, fill = continent)) +
  geom_boxplot() +
  labs(
    title = "Life Expectancy Distribution by Continent",
    x = "Continent",
    y = "Life Expectancy"
  ) +
  scale_fill_manual(values = c("Asia" = "#FF6347", "Europe" = "#4682B4", "Africa" = "#32CD32", "Americas" = "#FFD700", "Oceania" = "#8A2BE2")) + # Custom colors
  theme_minimal()

options(timeout = 600)  # Increase the timeout limit (in seconds)

Key Findings

Life Expectancy Trends: Over time, life expectancy has generally risen across the globe, with notable progress observed in Asia and Latin America. However, significant disparities remain between continents.
GDP Distribution: The density plot highlights a skewed distribution of GDP per capita, where a large portion of the world’s population resides in lower-income regions, particularly in Africa and Asia.
Regional Variability: The box plot indicates that Africa has a broader range of life expectancy values, reflecting substantial disparities within the continent. In contrast, Europe displays a narrower range, signifying more consistent health outcomes across the region.

Conclusion

This analysis sheds light on the economic and social disparities observed across continents. The visualizations underscore key trends, such as the global increase in life expectancy, the concentration of wealth in certain regions, and the differences in health outcomes. Further investigation is required to understand the policies that have influenced these trends and explore how countries can share knowledge and experiences to address these disparities effectively.