R/load_file.R

In d-ghale/easyDataScience: Data Exploration and Data Modeling made easy

#' Function to load packages already installed otherwise install then load, may require rJava if not already installed
#'
#' @param pkg name of packages as a vector
#' @return check if packages are already installed, install necessary packages and load them
#' @import plyr utils ggplot2 tidyverse lubridate data.table openxlsx stringr dataPreparation corrplot caret e1071 rmarkdown gridExtra bit64
#' @export
tidy_packages <- function(pkg){
	new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
	if(length(new.pkg)) {
		utils::install.packages(new.pkg, dependencies = TRUE)
		}
	sapply(pkg, require, character.only = TRUE)
}

#' Function to load packages without warnings
#'
#' @param pkg name of packages as a vector
#' @param suppress warnings, messages or both
#' @export
quietly_packages <- function(pkg, suppress = "both"){
	stopifnot(suppress %in% c("both", "messages", "warnings"))
	if(suppress == "warnings") {
		suppressWarnings(utils::install.packages(pkg))
	} else if (suppress == "messages") {
		suppressMessages(utils::install.packages(pkg))
	} else {
		suppressWarnings(suppressMessages(utils::install.packages(pkg)))
	}
}

#' Function to load a file: replaces blank space by NA using na.string and check if there are duplicate column names
#'
#' @param file name of file along with its location
#' @param check.names should names starting with a number, or that are duplicated be treated? TRUE or FALSE
#' @return loads the data as data.table
#' @export
load_datatable <- function( file, check.names = TRUE) {
	data.table::fread(file = file,
										na.strings = c("", "NA"),
										check.names = check.names)
}

#' Function to load excel file: check if there are duplicate column names and find columns that are dates
#'
#' @param file name of excel file along with its location
#' @return loads the data as excel
#' @export
load_excel <- function( file, check.names = FALSE, detectDates = TRUE) {
	openxlsx::read.xlsx(file, check.names = check.names, detectDates = detectDates)
}

#' Function saves the data, useful after data wrangling and manipulation
#'
#' @param data write the name of data
#' @param file_location write the location where to save the data and in which format
#' @return saves data as the format defined in file_location
#' @export
save_data <- function(data, file_location){
	data.table::fwrite(data, file = file_location, quote = TRUE, row.names = FALSE)
}