Final Report: Global Climate Change and Carbon Emissions Analysis

Introduction

This report analyzes global climate change trends, carbon emissions, renewable energy usage, and economic indicators using a series of visualizations.

Import Data

climate_data <- tibble(
  Year = c(2000, 2005, 2010, 2015, 2020),
  CO2_Emissions = c(25.1, 29.3, 33.1, 35.5, 34.8)
)

country_data <- tibble(
  Country = c("China", "United States", "India", "Russia", "Japan"),
  CO2 = c(10668, 4713, 2442, 1577, 1061),
  Renewable = c(29.3, 12.5, 24.5, 18.1, 20.3),
  GDP = c(12720, 70248, 2389, 12194, 39312)
)

country_data

## # A tibble: 5 × 4
##   Country         CO2 Renewable   GDP
##   <chr>         <dbl>     <dbl> <dbl>
## 1 China         10668      29.3 12720
## 2 United States  4713      12.5 70248
## 3 India          2442      24.5  2389
## 4 Russia         1577      18.1 12194
## 5 Japan          1061      20.3 39312

Figure 1: Global CO₂ Emissions Over Time

ggplot(climate_data, aes(x = Year, y = CO2_Emissions)) +
  geom_line() +
  geom_point() +
  labs(
    title = "Global CO₂ Emissions Over Time",
    x = "Year",
    y = "CO₂ Emissions (Billion Tons)"
  )

Figure 2: Top Countries by CO₂ Emissions

ggplot(country_data, aes(x = Country, y = CO2)) +
  geom_col() +
  labs(
    title = "Top Countries by CO₂ Emissions",
    x = "Country",
    y = "CO₂ Emissions (Million Tons)"
  )

Figure 3: GDP vs CO₂ Emissions

ggplot(country_data, aes(x = GDP, y = CO2, size = Renewable)) +
  geom_point() +
  labs(
    title = "GDP vs CO₂ Emissions",
    x = "GDP Per Capita",
    y = "CO₂ Emissions"
  )

Figure 4: Renewable Energy Usage

ggplot(country_data, aes(x = Country, y = Renewable)) +
  geom_col() +
  labs(
    title = "Renewable Energy Usage by Country",
    x = "Country",
    y = "Renewable Energy (%)"
  )

Figure 5: Area Chart of Global Emissions

ggplot(climate_data, aes(x = Year, y = CO2_Emissions)) +
  geom_area() +
  labs(
    title = "Area Chart of Global CO₂ Emissions",
    x = "Year",
    y = "CO₂ Emissions"
  )

Figure 6: Boxplot of CO₂ Emissions

ggplot(country_data, aes(y = CO2)) +
  geom_boxplot() +
  labs(
    title = "Boxplot of Country CO₂ Emissions",
    y = "CO₂ Emissions"
  )

Figure 7: Histogram of Renewable Energy

ggplot(country_data, aes(x = Renewable)) +
  geom_histogram(bins = 5) +
  labs(
    title = "Distribution of Renewable Energy Usage",
    x = "Renewable Energy (%)",
    y = "Frequency"
  )

Figure 8: Interactive Visualization

interactive_plot <- plot_ly(
  country_data,
  x = ~Renewable,
  y = ~GDP,
  type = "scatter",
  mode = "markers",
  color = ~Country,
  size = ~CO2
) %>%
  layout(
    title = "Interactive Climate Comparison",
    xaxis = list(title = "Renewable Energy (%)"),
    yaxis = list(title = "GDP Per Capita")
  )

interactive_plot