Save CSV as Data Table in R

# Load necessary library
library(data.table)

#' Read CSV and Save as Data Table
#'
#' This function reads a CSV file from a specified path, converts it into a data.table, and saves it to a specified output file.
#'
#' @param input_csv Path to the input CSV file.
#' @param output_file Path to the output file where the data.table will be saved.
#' @param sep Separator used in the CSV file. Default is comma (",").
#' @param header Logical value indicating whether the CSV file contains a header. Default is TRUE.
#' @return The function saves the data.table to the specified output file and returns the data.table object.
#' @examples
#' save_data_table("path/to/input.csv", "path/to/output.rda")
#' @export
save_data_table <- function(input_csv, output_file, sep = ",", header = TRUE) {
  # Validate inputs
  if (!file.exists(input_csv)) {
    stop("The input CSV file does not exist.")
  }
  if (!is.character(output_file) || nchar(output_file) == 0) {
    stop("Please provide a valid output file path.")
  }

  # Read CSV into data.table
  dt <- fread(input_csv, sep = sep, header = header)
  
  # Validate the data.table content
  if (nrow(dt) == 0) {
    stop("The data table is empty.")
  }

  # Save data.table to specified output file
  save(dt, file = output_file)

  # Return the data.table object
  return(dt)
}

# Example Usage
# dt <- save_data_table("data/input.csv", "data/output.rda")

Sections

• Load Necessary Libraries: Ensure you import the required packages.
• Function Definition: Clear documentation of the parameters and return type.
• Input Validation: Ensure user inputs are correct and handle errors gracefully.
• Read Data: Use fread from data.table to read the CSV.
• Save Data Table: Store the data.table in a specified file.
• Example Usage: Example of how to use the function in a real-world scenario.

Explanation

• Input Validation: Ensures the input file exists and the output path is valid, throwing errors if not.
• Data Reading: Utilizes fread for efficient CSV reading into a data.table.
• Data Validation: Checks if the data table contains any rows.
• Saving Data: Saves the data table to a specified .rda file for easy loading later.

By following these practices, you ensure the code is reliable, maintainable, and user-friendly. For more advanced concepts and data manipulation techniques, consider exploring further resources or courses on the Enterprise DNA Platform.