Analyzing GDP Growth and Life Expectancy

Problem Definition:

This project uses the Gapminder dataset to analyze global trends in GDP per capita and life expectancy.

I aim to identify correlations between economic growth and life expectancy, visualize these trends and summarize key findings for real-world insights.

DATA WRANGLING

knitr::opts_chunk$set(echo = TRUE)
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

# Filtering data for 2007 and mutating to add GDP in billions
selected_data <- gapminder %>%
  filter(year == 2007) %>%
  select(country, continent, gdpPercap, lifeExp, pop) %>%
  mutate(gdpBillions = (gdpPercap * pop) / 1e9)

# Display the first few rows
head(selected_data)

## # A tibble: 6 × 6
##   country     continent gdpPercap lifeExp      pop gdpBillions
##   <fct>       <fct>         <dbl>   <dbl>    <int>       <dbl>
## 1 Afghanistan Asia           975.    43.8 31889923        31.1
## 2 Albania     Europe        5937.    76.4  3600523        21.4
## 3 Algeria     Africa        6223.    72.3 33333216       207. 
## 4 Angola      Africa        4797.    42.7 12420476        59.6
## 5 Argentina   Americas     12779.    75.3 40301927       515. 
## 6 Australia   Oceania      34435.    81.2 20434176       704.

# Summarizing average GDP per capita and life expectancy by continent
summary_table <- selected_data %>%
  group_by(continent) %>%
  summarise(
    avg_gdpPercap = mean(gdpPercap, na.rm = TRUE),
    avg_lifeExp = mean(lifeExp, na.rm = TRUE),
    total_gdpBillions = sum(gdpBillions, na.rm = TRUE)
  ) %>%
  arrange(desc(avg_gdpPercap))

# Printing the summary table
summary_table

## # A tibble: 5 × 4
##   continent avg_gdpPercap avg_lifeExp total_gdpBillions
##   <fct>             <dbl>       <dbl>             <dbl>
## 1 Oceania          29810.        80.7              807.
## 2 Europe           25054.        77.6            14795.
## 3 Asia             12473.        70.7            20708.
## 4 Americas         11003.        73.6            19418.
## 5 Africa            3089.        54.8             2380.

# Scatter plot of GDP vs Life Expectancy
scatter_plot <- ggplot(selected_data, aes(x = gdpPercap, y = lifeExp, color = continent)) +
  geom_point(size = 3, alpha = 0.7) +
  scale_x_log10() +
  labs(
    title = "GDP per Capita vs Life Expectancy (2007)",
    x = "GDP per Capita (log scale)",
    y = "Life Expectancy",
    color = "Continent"
  ) +
  theme_minimal()

# Display the plot
scatter_plot

# Bar plot of average life expectancy by continent
bar_plot <- ggplot(summary_table, aes(x = continent, y = avg_lifeExp, fill = continent)) +
  geom_bar(stat = "identity", show.legend = FALSE) +
  labs(
    title = "Average Life Expectancy by Continent (2007)",
    x = "Continent",
    y = "Average Life Expectancy"
  ) +
  theme_minimal()

# Display the plot
bar_plot

Key Insights

#1. There is a clear positive correlation between GDP per capita and life expectancy across countries. Higher GDP per capita often corresponds to better living standards, leading to longer life expectancy.

#2. Europe has the highest average GDP per capita and life expectancy, showcasing strong economic and healthcare systems. The Americas also exhibit similar trends but with slightly lower averages.

#3. Africa, while having the lowest average GDP per capita, demonstrates significant diversity in life expectancy. This indicates varying access to healthcare and differing socioeconomic conditions across the continent.

#4. Asia contributes the highest total GDP in billions due to its large population size, even though the GDP per capita is moderate compared to other continents.

#5. The scatter plot reinforces the disparity between continents, with Africa clustered at the lower GDP and life expectancy spectrum, while Europe and the Americas are at the higher end.

Interpretation of Results

#These findings emphasize the link between economic prosperity and health outcomes. Countries with higher GDP per capita often invest more in healthcare infrastructure, education, and public services, resulting in better quality of life and longer lifespans.

#The disparities between continents highlight the need for targeted policies to address socioeconomic and healthcare inequalities, particularly in lower-income regions.

#This analysis underscores the importance of sustainable economic development as a pathway to improving global health outcomes.