Wind Farm Operational Capacity by Country and Year (MW)

# Load necessary libraries
library(tidyverse)

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# Load the dataset skipping the first four rows
my_data <- read_csv("GWPT.csv", skip = 4)

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# Remove special characters from column names for easier handling
colnames(my_data) <- make.names(colnames(my_data))

# Rename columns to avoid conflicts
names(my_data)[2] <- "Country"

# Exclude non-numeric columns from pivot operation
my_data_long <- my_data %>%
  pivot_longer(cols = -c(...1, Country, ...26, ...27), names_to = "Year", values_to = "Energy") %>%
  mutate(Year = as.numeric(gsub("[^0-9]", "", Year)))  # Convert Year column to numeric

# Remove rows with missing Year values
my_data_long <- my_data_long[!is.na(my_data_long$Year), ]

# Plotting with adjusted x-axis
ggplot(my_data_long, aes(x = Year, y = Energy, color = ...1)) +
  geom_line() +
  facet_wrap(~Country, scales = "free_y") +
  labs(x = "Year", y = "Operational Capacity: MW", color = "Region/Country") +
  theme_minimal()