R/check_pro_dmg.R

In thelayc/layccaSyncCheck: Checking sync status of ETO, SLED, ProActive

#' check_pro_dmg()
#'
#' This function takes two .csv files: 1) ProActive full report, and 2) "[LAYC CA] Students usi report" as inputs, and return a list of records that are not in sync.
#' @param pro_file character vector: the name of the file containing data from ProActive. Can be a full or relative path to the .csv file.
#' @param eto_file character vector: the name of the file containing data from ETO. Can be a full or relative path to the .csv file.
#' @return data frame
#' @export
#' @examples
#' check_pro_dmg(pro_file = './data/proactive.csv', eto_file = './data/eto.csv')

check_pro_dmg <- function(pro_file = 'proactive.csv', eto_file = 'eto_usi.csv') {
  # Load .csv files
  pro <- load_csv(pro_file)
  eto <- load_csv(eto_file)
  
  # Clean datasets
  eto <- clean_eto(eto)
  pro <- clean_pro(pro)
  
  # Compare names
  names <- compare(pro = pro, eto = eto, var = 'id_name')
  # Compare usi
  usi <- compare(pro = pro, eto = eto, var = 'usi')
  # Compare enrollment dates
  start <- compare(pro = pro, eto = eto, var = 'start')
  # Compare exit dates
  exit <- compare(pro = pro, eto = eto, var = 'exit')
  
  # Combine dataframes
  out <- dplyr::rbind_list(names, usi, start, exit)
  
  # Save results in a .csv file
  if(!file.exists('output')) {dir.create('output')}
  write.csv(out, file = 'output/pro_demographics.csv', na = "", row.names = FALSE)
  
  message("Check your working directory: Your results were saved in the output folder") 
}




#' compare()
#'
#' Helper function. Compare students values in both databases
#' @param pro dataframe: proactive dataframe
#' @param eto dataframe: eto dataframe
#' @param var character: variable we are looking at.
#' @return dataframe
#' @keywords internal
#' @noRd

compare <- function(pro = pro, eto = eto, var = 'id_name') {
  # identify usi that are in eto, but not in proactive
  eto_only <- dplyr::anti_join(eto, pro, by = var)
  pro_info <- dplyr::filter(pro, id_name %in% unique(eto_only$id_name))
  # identify usi that are in proactive, but not in eto
  pro_only <- dplyr::anti_join(pro, eto, by = var)
  eto_info <- dplyr::filter(eto, id_name %in% unique(pro_only$id_name))
  # Combine dataframes
  out <- dplyr::rbind_list(eto_only, pro_only, pro_info, eto_info)
  # remove duplicates rows
  out <- unique(out)
  # Sort dataframe by usi
  out <- dplyr::arrange(out, last_name, first_name, database)
  # Add issue type
  if(nrow(out) > 0) {out$error_type <- var}
  
  return(out)  
}