R/getData.R

   1 #' @title Acquire data in a clean format
   2 #'
   3 #' @description Take in input data frames and/or files containing raw data, and timezones, and
   4 #'   output a Data object, roughly corresponding to a list where each cell contains all value
   5 #'   for one day (see \code{?Data}). Current limitation: series (in working_tz) must start at
   6 #'   right after midnight (to keep in sync with exogenous vars)
   7 #'
   8 #' @param ts_data Time-series, as a data frame (DB style: 2 columns, first is date/time,
   9 #'   second is value) or a CSV file
  10 #' @param exo_data Exogenous variables, as a data frame or a CSV file; first comlumn is dates,
  11 #'   next block are measurements for the day, and final block are exogenous forecasts
  12 #' @param input_tz Timezone in the input files ("GMT" or e.g. "Europe/Paris")
  13 #' @param date_format How date/time are stored (e.g. year/month/day hour:minutes;
  14 #'   see \code{strptime})
  15 #' @param working_tz Timezone to work with ("GMT" or e.g. "Europe/Paris")
  16 #' @param predict_at When does the prediction take place ? ab[:cd][:ef] where a,b,c,d,e,f
  17 #'   in (0,9) and define an hour[minute[second]]; time must be present in the file
  18 #'
  19 #' @return An object of class Data
  20 #'
  21 #' @export
  22 getData = function(ts_data, exo_data,
  23     input_tz="GMT", date_format="%d/%m/%Y %H:%M", working_tz="GMT", predict_at="00")
  24 {
  25     # Sanity checks (not full, but sufficient at this stage)
  26     if (!is.character(input_tz) || !is.character(working_tz))
  27         stop("Bad timezone (see ?timezone)")
  28     input_tz = input_tz[1]
  29     working_tz = working_tz[1]
  30     if (!is.data.frame(ts_data) && !is.character(ts_data))
  31         stop("Bad time-series input (data frame or CSV file)")
  32     if (is.character(ts_data))
  33         ts_data = ts_data[1]
  34     pattern_index_in_predict_at = grep("^[0-9]{2}(:[0-9]{2}){0,2}$", predict_at)
  35     if (!is.character(predict_at) || length(pattern_index_in_predict_at) == 0)
  36         stop("Bad predict_at ( ^[0-9]{2}(:[0-9]{2}){0,2}$ )")
  37     predict_at = predict_at[ pattern_index_in_predict_at[1] ]
  38     if (!is.character(date_format))
  39         stop("Bad date_format (character)")
  40     date_format = date_format[1]
  41
  42     ts_df =
  43         if (is.character(ts_data)) {
  44             read.csv(ts_data)
  45         } else {
  46             ts_data
  47         }
  48     exo_df =
  49         if (is.character(exo_data)) {
  50             read.csv(exo_data)
  51         } else {
  52             exo_data
  53         }
  54     # Convert to the desired timezone (usually "GMT" or "Europe/Paris")
  55     formatted_dates_POSIXlt = strptime(as.character(ts_df[,1]), date_format, tz=input_tz)
  56     ts_df[,1] = format(as.POSIXct(formatted_dates_POSIXlt), tz=working_tz, usetz=TRUE)
  57
  58     if (nchar(predict_at) == 2)
  59         predict_at = paste(predict_at,":00",sep="")
  60     if (nchar(predict_at) == 5)
  61         predict_at = paste(predict_at,":00",sep="")
  62
  63     line = 1 #index in PM10 file (24 lines for 1 cell)
  64     nb_lines = nrow(ts_df)
  65     nb_exos = ( ncol(exo_df) - 1 ) / 2
  66     data = list() #new("Data")
  67     i = 1 #index of a cell in data
  68     while (line <= nb_lines)
  69     {
  70         time = c()
  71         serie = c()
  72         repeat {
  73         {
  74             time = c(time, ts_df[line,1])
  75             serie = c(serie, ts_df[line,2])
  76             line = line + 1
  77         };
  78         if (line >= nb_lines + 1
  79             # NOTE: always second part of date/time, because it has been formatted
  80             || strsplit(as.character(ts_df[line-1,1])," ")[[1]][2] == predict_at)
  81         {
  82             break
  83         }}
  84
  85         # NOTE: if predict_at does not cut days at midnight,
  86         # for the exogenous to be synchronized they need to be shifted
  87         if (predict_at != "00:00:00")
  88         {
  89             exo_hat = as.data.frame(exo_df[max(1,i-1),(1+nb_exos+1):(1+2*nb_exos)])
  90             exo_Dm1 = if (i>=3) as.data.frame(exo_df[i-1,2:(1+nb_exos)]) else NA
  91         } else
  92         {
  93             exo_hat = as.data.frame(exo_df[i,(1+nb_exos+1):(1+2*nb_exos)])
  94             exo_Dm1 = if (i>=2) as.data.frame(exo_df[i-1,2:(1+nb_exos)]) else NA
  95         }
  96         i = i + 1
  97         #center data
  98         level = mean(serie, na.rm=TRUE)
  99         centered_serie = serie - level
 100 #       data$append(time, centered_serie, level, exo_hat, exo_Jm1) #TODO: slow: why ?
 101         data[[length(data)+1]] = list("time"=time, "serie"=centered_serie, "level"=level,
 102             "exo_hat"=exo_hat, "exo_Dm1"=exo_Dm1)
 103     }
 104     new("Data",data=data)
 105 }