Gapminder data

Objective

The aim is to analyze and visualize global economic and social indicators such as life expectancy, GDP per capita, and population across various countries and years. The insights will highlight disparities, trends, and patterns in development worldwide.

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

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"
  ) +
  theme_minimal()

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"
  ) +
  theme_minimal()

ggplot(gapminder_filtered, aes(x = continent, y = lifeExp, fill = continent)) +
  geom_boxplot() +
  labs(
    title = "Life Expectancy Distribution by Continent",
    x = "Continent",
    y = "Life Expectancy"
  ) +
  theme_minimal()

##Key Findings Life Expectancy Trends: Life expectancy has increased over time across the globe, with notable improvements in Asia and Latin America. However, large disparities persist between continents. GDP Distribution: The density plot illustrates that the distribution of GDP per capita is highly skewed, with a significant portion of the global population living in lower-income regions, especially in Africa and Asia. Regional Variability: The box plot reveals that Africa has a much wider range of life expectancy values, indicating disparities within the continent. In contrast, Europe shows a narrower range, highlighting more uniform development in health outcomes.

##Conclusion This analysis has provided a clearer view of the disparities in economic and social indicators across different continents. The visualizations highlight significant trends such as the increase in life expectancy globally, the concentration of wealth in certain regions, and the variation in health outcomes. Further research is needed to explore the specific policies that have contributed to these patterns, and how countries can learn from each other to reduce disparitie