R/aggregate_and_summarize_elasticity_by_date_v1.R

In msxakk89/dat: Data Analyst Toolkit (dat): a library of tools for data analysts and scientists

#' Aggregate and summarize elasticity
#' 
#' @param elasticityData A data frame.
#' @param base_aggregation_on A character vector specifying variables on which aggregation will be based.
#' @param aggregation_parameters A character vector specifying the variable variants on which aggregation will be based. Must be of length equal to the lenght of 'base_aggregation_on' and variable's variants must be in the order as variables. In this version only one variant of the variable can be supplied.
#' @return An aggregated data.
#' @export 

aggregate_and_summarize_elasticity_by_date_v1 = function(elasticityData, base_aggregation_on, aggregation_parameters){
  aggregated = aggregate_elasticity_data_v1(elasticityData=elasticityData, 
                                            base_aggregation_on=base_aggregation_on, 
                                            aggregation_parameters=aggregation_parameters)
  print("Computing summaries per date...")
  uniqueDates = unique(aggregated$date)
  o = as.data.frame(matrix(nrow = 1,ncol = 7))
  for(i in 1:length(uniqueDates)){
    data_per_date = aggregated[aggregated$date==uniqueDates[i],]
    Ns_per_date_sums = colSums(data_per_date[,12:15])
    bookingCounts = nrow(data_per_date)
    average_sell_price = mean(data_per_date$SELL_PRC)
    addedUp_sell_price = sum(data_per_date$SELL_PRC)
    rowPerBooking = c(Ns_per_date_sums,bookingCounts,average_sell_price,addedUp_sell_price)
    o = rbind(o, rowPerBooking)
  } 
  o = o[-1,]
  row.names(o) = uniqueDates
  colnames(o) = c("N_PAX","N_ADU","N_CHD","N_INF","bookingCounts", "average_sell_price", "addedUp_sell_price")
  print("Done")
  return(o)
}