#Problem Definition #Title: Exploring Global Economic and Social Indicators with 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. # Load libraries library(gapminder) library(dplyr)
data(“gapminder”)
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 <- 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))
head(summary_table)
library(ggplot2)
ggplot(gapminder_filtered, aes(x = gdpPercap, y = lifeExp, color = continent, size = pop)) + geom_point(alpha = 0.7) + scale_x_log10() + # Log scale for GDP per capita labs( title = “Life Expectancy vs GDP per Capita (2000 onwards)”, x = “GDP per Capita (log scale)”, y = “Life Expectancy” ) + theme_minimal()
ggplot(summary_table, aes(x = reorder(continent, total_pop), y = total_pop / 1e9, fill = continent)) + geom_bar(stat = “identity”) + labs( title = “Population by Continent”, x = “Continent”, y = “Population (Billion)” ) + theme_minimal()
#Key Findings:
#Life Expectancy vs GDP: Countries with higher GDP per capita generally have higher life expectancy #but some outliers exist where life expectancy is not proportional to GDP. #Population Trends: Asia accounts for the largest population, followed by Africa, with significant implications #for resource allocation and development. #Regional Disparities: Continents show stark differences in average GDP per capita and life expectancy, #highlighting areas needing development focus.