Gapminder Data Analysis

Analysis of Economic Indicators using Gapminder Data

Description: This analysis will explore key economic indicators from the Gapminder dataset.

The objective is to select relevant data, clean it, and perform descriptive analysis to identify key trends. We will visualize our findings using appropriate plots and provide an interpretation of the results.

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

library(gapminder)

## Warning: package 'gapminder' was built under R version 4.4.2

# Data Wrangling
# Select the relevant columns - country, continent, year, lifeExp, gdpPercap, and pop
gapminder_selected <- gapminder %>% 
  select(country, continent, year, lifeExp, gdpPercap, pop)

# Filter the data for the years 2000 and onwards
gapminder_filtered <- gapminder_selected %>% 
  filter(year >= 2000)

# Create a new column for GDP (Gross Domestic Product) by multiplying population with GDP per capita
gapminder_transformed <- gapminder_filtered %>% 
  mutate(GDP = pop * gdpPercap)

# Table Output
# Generate a summary table showing the top 10 countries with the highest average life expectancy from 2000 onwards
life_exp_summary <- gapminder_transformed %>% 
  group_by(country) %>% 
  summarise(avg_lifeExp = mean(lifeExp, na.rm = TRUE)) %>% 
  arrange(desc(avg_lifeExp)) %>% 
  head(10)

print("Top 10 Countries with Highest Average Life Expectancy (2000 Onwards)")

## [1] "Top 10 Countries with Highest Average Life Expectancy (2000 Onwards)"

print(life_exp_summary)

## # A tibble: 10 × 2
##    country          avg_lifeExp
##    <fct>                  <dbl>
##  1 Japan                   82.3
##  2 Hong Kong, China        81.9
##  3 Switzerland             81.2
##  4 Iceland                 81.1
##  5 Australia               80.8
##  6 Sweden                  80.5
##  7 Italy                   80.4
##  8 Spain                   80.4
##  9 Israel                  80.2
## 10 Canada                  80.2

Data Visualization

Create a scatter plot of GDP per capita vs Life Expectancy, colored by continent

plot_gdp_lifeexp <- ggplot(gapminder_transformed, aes(x = gdpPercap, y = lifeExp, color = continent)) + 
  geom_point(alpha = 0.6) + 
  scale_x_log10() +  # Log scale for GDP per capita
  labs(title = "GDP per Capita vs Life Expectancy (2000 Onwards)",
       x = "GDP per Capita (log scale)",
       y = "Life Expectancy",
       color = "Continent") + 
  theme_minimal()

print(plot_gdp_lifeexp)

life_exp_trend <- gapminder_transformed %>% 
  group_by(year, continent) %>% 
  summarise(avg_lifeExp = mean(lifeExp, na.rm = TRUE))

## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.

plot_lifeexp_trend <- ggplot(life_exp_trend, aes(x = year, y = avg_lifeExp, color = continent)) + 
  geom_line(size = 1) + 
  labs(title = "Trend of Average Life Expectancy by Continent (2000 Onwards)",
       x = "Year",
       y = "Average Life Expectancy",
       color = "Continent") + 
  theme_minimal()

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

print(plot_lifeexp_trend)

Key Findings: 1. The countries with the highest average life expectancy since 2000 are dominated by high-income nations.. There is a positive relationship between GDP per capita and life expectancy, with wealthier countries generally experiencing higher life expectancy.. Over the years, all continents have shown an upward trend in life expectancy, with some continents experiencing faster improvements than others.. The visualizations demonstrate the disparities in GDP and life expectancy across continents, highlighting the development gaps.