Renewable Energy Production Visualization in R - Coding

Due to climate change and decreasing fossil fuels, it is important to focus on renewable energy production to maintain the balance. R is a statistical programming language that provides multiple such libraries, making data analysis and visualization easier. Visualizing renewable energy production trends becomes crucial with the increasing urgency to transition to cleaner energy sources. This article explores renewable energy production using a comprehensive dataset and demonstrates how R Programming Language can be employed for insightful visualizations and analysis.

Global Renewable Energy Production Visualization

Renewable energy production is a critical aspect of the global energy landscape. As countries strive to reduce their carbon footprint and transition to cleaner energy sources, visualizing renewable energy production data becomes essential for understanding trends, making informed decisions, and tracking progress.

Dataset Link: Global Renewable Energy Production

1: Install and Load Required Libraries

Firstly we will install the libraries and packages in R that help in data manipulation and visualization. In this article, ggplot2 will play the most important role as it is a powerful library used for graphs and plots.

# Install necessary packages

install.packages("ggplot2")
install.packages("dplyr")
install.packages("maps")

# Load Libraries
library(ggplot2)
library(dplyr)
library(maps)

2: Prepare the Data

Ensure your data is in the correct format and aggregate the energy data by country.

data<-read.cav(C:\\Users\\GFG19565\\Downloads\\global_renewable_energy_production.csv)

# Check the data
head(data)

Output:

  Year Country SolarEnergy WindEnergy HydroEnergy OtherRenewableEnergy TotalRenewableEnergy
1 2000     USA    437.0861  1435.9286   1544.3897             319.3963             3736.801
2 2001     USA    240.4168   402.7929    398.7421             439.7793             1481.731
3 2002     USA    641.0035  1120.4944    334.9936             486.4594             2582.951
4 2003     USA    849.1984   476.0408    609.1024             132.5320             2066.874
5 2004     USA    373.8180   882.1834   1034.3065             181.0531             2471.361
6 2005     USA    650.6676   381.3420    796.6459             214.8628             2043.518

3. Visualization and Analysis

Now we will visualize the Renewable Energy Production Analysis.

Country-wise Comparison

We can compare the country-wise production of energy to see the highest producer of renewable energy.

# Bar plot for country-wise comparison
ggplot(data, aes(x = reorder(Country, -TotalRenewableEnergy),
                 y = TotalRenewableEnergy)) +
  geom_bar(stat = "identity") +
  labs(title = "Country-wise Renewable Energy Production",
       x = "Country", y = "Total Renewable Energy (GWh)") +
  theme_minimal() +
  coord_flip()

Output:

Renewable Energy Production Visualization in R

We can see that France is the highest producer of total renewable energy.

Distribution of Solar Energy Production

Histograms and scatter plots help in understanding the distribution and relationships between different types of renewable energy.

# Histogram for Solar Energy
ggplot(data, aes(x = SolarEnergy)) +
  geom_histogram(binwidth = 50, fill = "skyblue", color = "black") +
  labs(title = "Distribution of Solar Energy Production",
       x = "Solar Energy (GWh)", y = "Frequency") +
  theme_minimal()

Output:

Distribution of Solar Energy Production

Proportion of Energy country wise

We can create pie charts to show the proportion of each type of renewable energy within a country.

# Filter the data for the year 2000
data_2000 <- data %>%
  filter(Year == 2000) %>%
  select(SolarEnergy, WindEnergy, HydroEnergy, OtherRenewableEnergy)

# Convert the data to long format
data_long <- data_2000 %>%
  pivot_longer(cols = everything(), names_to = "EnergyType", values_to = "Energy")

# Create the pie chart
ggplot(data_long, aes(x = "", y = Energy, fill = EnergyType)) +
  geom_bar(width = 1, stat = "identity") +
  coord_polar(theta = "y") +
  theme_minimal() +
  labs(title = "Renewable Energy Production in the USA (2000)",
       x = NULL, y = NULL, fill = "Energy Type") +
  theme(axis.ticks = element_blank(),
        axis.text = element_blank(),
        panel.grid = element_blank())

Output:

Renewable Energy Production Visualization in R

This code will produce a pie chart that visualizes the proportion of different types of renewable energy production in the USA for the year 2000.

Contribution of Energy country-wise

Visualize the contribution of each type of renewable energy in total production for each country.

# Aggregate by country and energy type
country_long <- data %>%
  group_by(Country) %>%
  summarise(SolarEnergy = sum(SolarEnergy),
            WindEnergy = sum(WindEnergy),
            HydroEnergy = sum(HydroEnergy),
            OtherRenewableEnergy = sum(OtherRenewableEnergy)) %>%
  pivot_longer(cols = -Country, names_to = "EnergyType", values_to = "Energy")

# Plot the stacked bar plot
ggplot(country_long, aes(x = reorder(Country, -Energy), y = Energy, fill = EnergyType)) +
  geom_bar(stat = "identity") +
  labs(title = "Contribution of Each Renewable Energy Type by Country", x = "Country",
       y = "Energy (GWh)") +
  scale_fill_manual(values = c("SolarEnergy" = "lightgreen", "WindEnergy" = "lightblue", 
                               "HydroEnergy" = "cyan", "OtherRenewableEnergy" = "red")) +
  theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
  theme_minimal()

Output:

Contribution of each renewable energy countrywise

Contribution of different types of energy

Now we will visualize the Contribution of different types of energy.

# Pivot longer for box plot
box_data <- data %>%
  pivot_longer(cols = c(SolarEnergy, WindEnergy, HydroEnergy, OtherRenewableEnergy), 
               names_to = "EnergyType", values_to = "Energy")

# Plot the box plot
ggplot(box_data, aes(x = EnergyType, y = Energy, fill = EnergyType)) +
  geom_boxplot() +
  labs(title = "Distribution of Renewable Energy Production by Type", 
       y = "Energy (GWh)") +
  scale_fill_manual(values = c("SolarEnergy" = "yellow", "WindEnergy" = "blue",
                              "HydroEnergy" = "cyan", "OtherRenewableEnergy" = "red")) +
  theme_minimal()

Output:

Renewable Energy Production Visualization in R

Visualize Data on the World Map

Based on the dataset we have we can visualize all kind of energy based on the world map and identify the patterns on global level. First we will analyze the merged data of world.

library(maps)

# Get the world map data
world_map <- map_data("world")

# Check the structure of world_map
head(world_map)

# Merge the world map with energy data
energy_data$region <- tolower(energy_data$Country)
world_map$region <- tolower(world_map$region)
world_energy <- merge(world_map, energy_data, by = "region", all.x = TRUE)

# Plot the total renewable energy on the world map
ggplot(world_energy, aes(x = long, y = lat, group = group,fill =TotalRenewableEnergy)) +
  geom_polygon(color = "black") +
  scale_fill_continuous(name = "Total Renewable Energy (GWh)", low = "lightgreen", 
                        high = "darkgreen", na.value = "grey50") +
  labs(title = "Total Renewable Energy Production by Country", x = "", y = "") +
  theme_minimal() +
  theme(axis.text = element_blank(), axis.ticks = element_blank())

Output:

Renewable Energy Production Visualization in R

Conclusion

This article explored different kinds of visualization related to renewable energy production in R programming language. We observed and got various meaningful insights through different graphs which was based on global energy production.

Reffered: https://www.geeksforgeeks.org

AI ML DS

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Category:	Coding
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