-#' @title Acquire data in a clean format
+#' getData
#'
-#' @description Take in input data frames and/or files containing raw data, and
-#' timezones, and output a Data object, roughly corresponding to a list where each cell
-#' contains all value for one day (see \code{?Data}).
+#' Acquire data as a Data object; see ?Data.
+#'
+#' Since series are given in columns (database format), this function builds series one
+#' by one and incrementally grows a Data object which is finally returned.
#'
#' @param ts_data Time-series, as a data frame (DB style: 2 columns, first is date/time,
-#' second is value) or a CSV file
-#' @param exo_data Exogenous variables, as a data frame or a CSV file; first comlumn is
+#' second is value) or a CSV file.
+#' @param exo_data Exogenous variables, as a data frame or a CSV file; first column is
#' dates, next block are measurements for the day, and final block are exogenous
-#' forecasts
-#' @param input_tz Timezone in the input files ("GMT" or e.g. "Europe/Paris")
+#' forecasts (for the same day).
#' @param date_format How date/time are stored (e.g. year/month/day hour:minutes;
-#' see \code{strptime})
-#' @param working_tz Timezone to work with ("GMT" or e.g. "Europe/Paris")
-#' @param predict_at When does the prediction take place ? Integer, in hours. Default: 0
+#' see ?strptime)
#' @param limit Number of days to extract (default: Inf, for "all")
#'
#' @return An object of class Data
#' exo_data = read.csv(system.file("extdata","meteo_extra_noNAs.csv",package="talweg"))
#' data = getData(ts_data, exo_data, limit=120)
#' @export
-getData = function(ts_data, exo_data, input_tz="GMT", date_format="%d/%m/%Y %H:%M",
- working_tz="GMT", predict_at=0, limit=Inf)
+getData = function(ts_data, exo_data, date_format="%d/%m/%Y %H:%M", limit=Inf)
{
# Sanity checks (not full, but sufficient at this stage)
- if (!is.character(input_tz) || !is.character(working_tz))
- stop("Bad timezone (see ?timezone)")
- input_tz = input_tz[1]
- working_tz = working_tz[1]
if ( (!is.data.frame(ts_data) && !is.character(ts_data)) ||
(!is.data.frame(exo_data) && !is.character(exo_data)) )
stop("Bad time-series / exogenous input (data frame or CSV file)")
ts_data = ts_data[1]
if (is.character(exo_data))
exo_data = exo_data[1]
- predict_at = as.integer(predict_at)[1]
- if (predict_at<0 || predict_at>23)
- stop("Bad predict_at (0-23)")
if (!is.character(date_format))
stop("Bad date_format (character)")
date_format = date_format[1]
+ if (!is.numeric(limit) || limit < 0)
+ stop("limit: positive integer")
ts_df =
if (is.character(ts_data))
read.csv(ts_data)
else
ts_data
- # Convert to the desired timezone (usually "GMT" or "Europe/Paris")
- formatted_dates_POSIXlt = strptime(as.character(ts_df[,1]), date_format, tz=input_tz)
- ts_df[,1] = format(
- as.POSIXct(formatted_dates_POSIXlt, tz=input_tz), tz=working_tz, usetz=TRUE)
+ # Convert to GMT (pretend it's GMT; no impact)
+ dates_POSIXlt = strptime(as.character(ts_df[,1]), date_format, tz="GMT")
+ ts_df[,1] = format(as.POSIXct(dates_POSIXlt, tz="GMT"), tz="GMT", usetz=TRUE)
exo_df =
if (is.character(exo_data))
line = line + 1
};
if (line >= nb_lines + 1
- || as.POSIXlt(ts_df[line-1,1],tz=working_tz)$hour == predict_at)
+ || as.POSIXlt(ts_df[line-1,1],tz="GMT")$hour == 0)
{
break
}
}
- hat_exo = as.data.frame( exo_df[i,(1+nb_exos+1):(1+2*nb_exos)] )
- exo = as.data.frame( exo_df[i,2:(1+nb_exos)] )
- data$appendHat(time, hat_exo)
- data$append(serie, exo) #in realtime, this call comes hours later
+ # TODO: 2 modes, "operational" and "testing"; would need PM10 predictions
+ data$append(time=time, value=serie, level_hat=mean(serie,na.rm=TRUE),
+ exo=exo_df[i,2:(1+nb_exos)], exo_hat=exo_df[i,(1+nb_exos+1):(1+2*nb_exos)])
if (i >= limit)
break
i = i + 1
}
- if (length(data$getCenteredSerie(1)) < length(data$getCenteredSerie(2)))
- data$removeFirst()
- if (length(data$getCenteredSerie(data$getSize()))
- < length(data$getCenteredSerie(data$getSize()-1)))
- {
- data$removeLast()
- }
data
}