Penn World Table Analysis

Problem Definition

The objective of this analysis is to explore and analyze global economic data using the full Penn World Table dataset. We aim to examine GDP growth, population trends, employment levels, and other economic indicators across countries from 2000 to 2019.

Data Wrangling

We start by loading the full dataset and performing preprocessing, including selecting relevant variables, filtering for the desired time range, and calculating additional economic indicators.

# Load necessary libraries
library(dplyr)
library(ggplot2)
library(DT)
library(tidyr)
library(leaflet)
library(readxl)
library(knitr)
library(sf)
library(rworldmap)
library(RColorBrewer)

# Load the dataset
# Adjust the file path to where your pwt1001.xlsx is saved
pwt_data <- read_excel("pwt1001.xlsx", sheet = "Data")

# Data Wrangling
# Select relevant columns
pwt_cleaned <- pwt_data %>%
  select(country, year, pop, emp, rgdpe, hc, avh) %>%
  filter(year >= 2000 & year <= 2019) %>%
  drop_na(country, year, pop, emp, rgdpe) %>%
  mutate(
    gdp_per_capita = rgdpe / pop,
    employment_rate = (emp / pop) * 100
  )

Table Outputs

We summarize the data with detailed tables.

1. Average GDP Per Capita and Total Population by Country

2. Average Employment Rate and Average Hours Worked by Country

Visualizing Data

Several enhanced visualizations.

1. GDP Per Capita Trends Over Time

## 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.

2. GDP Per Capita vs Employment Rate

3. Global Map of Average GDP Per Capita

4. Employment Rate Across Countries

Summary of Findings

• GDP Trends: Countries like Luxembourg, Norway, and Switzerland consistently show high GDP per capita.
• Employment Trends: There is a positive correlation between employment rate and GDP per capita in many countries.
• Global Insights: The global map visualization highlights economic disparities between countries, with higher GDP per capita concentrated in North America and Western Europe.
• Employment Distribution: The boxplot illustrates the variability in employment rates across different countries.

Conclusion

This comprehensive analysis leverages the full Penn World Table dataset to provide insights into global economic performance. The use of detailed tables and advanced visualizations helps in understanding complex economic indicators and their interrelationships.