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- Required Packages:
tidyverse - Inputs: None
- Outputs: None
library(tidyverse)
Required Packages: None Inputs: None Outputs: data - a data frame containing the airquality dataset R Copy code data <- airquality Data Cleaning - Remove Rows with NA Required Packages: None Inputs: data - a data frame containing the airquality dataset Outputs: data - a cleaned data frame with NA rows removed R Copy code data <- data %>% filter(!is.na(Ozone), !is.na(Solar.R), !is.na(Wind), !is.na(Temp), !is.na(Month), !is.na(Day)) Calculate Average Solar Radiation for Each Month Required Packages: tidyverse Inputs: data - a data frame containing the airquality dataset, month - the month for which to calculate the average Outputs: A numeric value representing the average Solar Radiation for the specified month R Copy code calculate_average_solar <- function(data, month) { data %>% filter(Month == month) %>% summarise(Average_Solar_Radiation = mean(Solar.R, na.rm = TRUE)) } Calculate Average Solar Radiation for Each Month Using purrr::map_dbl Required Packages: tidyverse, purrr Inputs: months - a vector of months (e.g., c(5, 6, 7, 8, 9) Outputs: avg_solar - a numeric vector of average Solar Radiation values for each month R Copy code months <- 5:9 avg_solar <- map_dbl(months, ~ calculate_average_solar(data, .x)$Average_Solar_Radiation) Print the Results Required Packages: None Inputs: months - a vector of months (e.g., c(5, 6, 7, 8, 9), avg_solar - a numeric vector of average Solar Radiation values Outputs: None R Copy code map2(months, avg_solar, ~ cat("Average Solar Radiation for Month", .x, ":", .y, "\n")) Calculate Correlation for Each Month Required Packages: tidyverse Inputs: data - a data frame containing the airquality dataset, month - the month for which to calculate the correlation Outputs: A numeric value representing the correlation between Ozone and Solar Radiation for the specified month R Copy code calculate_correlation <- function(data, month) { data %>% filter(Month == month) %>% summarise(Correlation = cor(Ozone, Solar.R, use = "complete.obs")) } Calculate Correlation for Each Month Using purrr::map_dbl Required Packages: tidyverse, purrr Inputs: months - a vector of months (e.g., c(5, 6, 7, 8, 9) Outputs: correlation - a numeric vector of correlation values for each month R Copy code correlation <- map_dbl(months, ~ calculate_correlation(data, .x)$Correlation) Print the Results Required Packages: None Inputs: months - a vector of months (e.g., c(5, 6, 7, 8, 9), correlation - a numeric vector of correlation values Outputs: None R Copy code map2(months, correlation, ~ cat("Correlation for Month", .x, ":", .y, "\n")) Define a Function to Save Plots Required Packages: tidyverse Inputs: data - a data frame containing the airquality dataset, month - the month for which to save the plot, filename - the filename for the saved plot Outputs: None R Copy code save_monthly_plot <- function(data, month, filename) { plot_data <- data %>% filter(Month == month)
g <- ggplot(plot_data, aes(x = Solar.R, y = Ozone, shape = factor(Month))) + geom_point() + ggtitle(paste("Month", month))
ggsave(filename, g) } Save Plots for Each Month Using purrr::walk Required Packages: tidyverse, purrr Inputs: months - a vector of months (e.g., c(5, 6, 7, 8, 9), month_names - a vector of month names (e.g., c("May", "June", "July", "August", "September") Outputs: None R Copy code map2(months, c("May", "June", "July", "August", "September"), ~ save_monthly_plot(data, .x, paste("plot_", .x, ".png"))) Save Data Required Packages: None Inputs: data - a data frame to be saved, filename - the filename for the saved CSV Outputs: None R Copy code write.csv(data, "cleaned_data.csv", row.names = FALSE) The comments describe the purpose of each function, the required packages, and the inputs and outputs of each function. This documentation should make the code more understandable and maintainable.