pkg/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 ? Integer, in hours. Default: 0
  17 #'
  18 #' @return An object of class Data
  19 #'
  20 #' @examples
  21 #' ts_data = read.csv(system.file("extdata","pm10_mesures_H_loc.csv",package="talweg"))
  22 #' exo_data = read.csv(system.file("extdata","meteo_extra_noNAs.csv",package="talweg"))
  23 #' getData(ts_data, exo_data, input_tz="Europe/Paris", working_tz="Europe/Paris", limit=150)
  24 #' @export
  25 getData = function(ts_data, exo_data, input_tz="GMT", date_format="%d/%m/%Y %H:%M",
  26     working_tz="GMT", predict_at=0, limit=Inf)
  27 {
  28     # Sanity checks (not full, but sufficient at this stage)
  29     if (!is.character(input_tz) || !is.character(working_tz))
  30         stop("Bad timezone (see ?timezone)")
  31     input_tz = input_tz[1]
  32     working_tz = working_tz[1]
  33     if ( (!is.data.frame(ts_data) && !is.character(ts_data)) ||
  34             (!is.data.frame(exo_data) && !is.character(exo_data)) )
  35         stop("Bad time-series / exogenous input (data frame or CSV file)")
  36     if (is.character(ts_data))
  37         ts_data = ts_data[1]
  38     if (is.character(exo_data))
  39         exo_data = exo_data[1]
  40     predict_at = as.integer(predict_at)[1]
  41     if (predict_at<0 || predict_at>23)
  42         stop("Bad predict_at (0-23)")
  43     if (!is.character(date_format))
  44         stop("Bad date_format (character)")
  45     date_format = date_format[1]
  46
  47     ts_df =
  48         if (is.character(ts_data))
  49             read.csv(ts_data)
  50         else
  51             ts_data
  52     exo_df =
  53         if (is.character(exo_data))
  54             read.csv(exo_data)
  55         else
  56             exo_data
  57     # Convert to the desired timezone (usually "GMT" or "Europe/Paris")
  58     formatted_dates_POSIXlt = strptime(as.character(ts_df[,1]), date_format, tz=input_tz)
  59     ts_df[,1] = format(as.POSIXct(formatted_dates_POSIXlt), tz=working_tz, usetz=TRUE)
  60
  61     line = 1 #index in PM10 file (24 lines for 1 cell)
  62     nb_lines = nrow(ts_df)
  63     nb_exos = ( ncol(exo_df) - 1 ) / 2
  64     data = list() #new("Data")
  65     i = 1 #index of a cell in data
  66     while (line <= nb_lines)
  67     {
  68         time = c()
  69         serie = c()
  70         repeat
  71         {
  72             {
  73                 time = c(time, ts_df[line,1])
  74                 serie = c(serie, ts_df[line,2])
  75                 line = line + 1
  76             };
  77             if (line >= nb_lines + 1 || as.POSIXlt(ts_df[line-1,1])$hour == predict_at)
  78                 break
  79         }
  80
  81         # NOTE: if predict_at does not cut days at midnight, exogenous vars need to be shifted
  82         exo_hat = as.data.frame( exo_df[
  83             ifelse(predict_at>0,max(1,i-1),i) , (1+nb_exos+1):(1+2*nb_exos) ] )
  84         exo = as.data.frame( exo_df[ ifelse(predict_at>0,max(1,i-1),i) , 2:(1+nb_exos) ] )
  85         level = mean(serie, na.rm=TRUE)
  86         centered_serie = serie - level
  87         #data$append(time, centered_serie, level, exo_hat, exo_Jm1) #too slow; TODO: use R6 class
  88         data[[length(data)+1]] = list("time"=time, "serie"=centered_serie, "level"=level,
  89             "exo_hat"=exo_hat, "exo"=exo)
  90         if (i >= limit)
  91             break
  92         i = i + 1
  93     }
  94     new("Data",data=data)
  95 }