r library tidyverse

# Step 1: Install and load the tidyverse package
install.packages("tidyverse")
library(tidyverse)

# Step 2: Import data
data <- read.csv("your_data.csv")

# Step 3: Explore the data
summary(data)
str(data)

# Step 4: Data cleaning (if needed)
# Example: Remove missing values
data_cleaned <- na.omit(data)

# Step 5: Data manipulation using dplyr functions
# Example: Filter rows based on a condition
data_filtered <- data_cleaned %>% filter(variable > 10)

# Step 6: Data transformation using dplyr functions
# Example: Create a new variable
data_transformed <- data_filtered %>% mutate(new_variable = variable * 2)

# Step 7: Data visualization using ggplot2
# Example: Create a scatter plot
ggplot(data_transformed, aes(x = variable, y = new_variable)) +
  geom_point() +
  labs(title = "Scatter Plot", x = "Variable X", y = "Variable Y")

# Step 8: Data summarization using dplyr functions
# Example: Calculate mean by group
summary_data <- data_transformed %>% group_by(group_variable) %>% summarize(mean_variable = mean(variable))

# Step 9: Export the final data or visualization (if needed)
write.csv(data_transformed, "final_data.csv")
ggsave("scatter_plot.png")