-#' @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}). Current limitation: series (in working_tz) must start at
-#' right after midnight (to keep in sync with exogenous vars)
+#' 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 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")
+#' 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 (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
#'
+#' @examples
+#' ts_data = read.csv(system.file("extdata","pm10_mesures_H_loc.csv",package="talweg"))
+#' 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)")
+ stop("Bad time-series / exogenous input (data frame or CSV file)")
if (is.character(ts_data))
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)) {
- if (ts_data %in% data(package="talweg")$results[,"Item"])
- ts_data =
-
-
-
-
- ############CONTINUE: http://r-pkgs.had.co.nz/data.html
-
-
-
-
-
- read.csv(ts_data)
- } else {
+ if (is.character(ts_data))
+ read.csv(ts_data)
+ else
ts_data
- }
+ # Convert to GMT (pretend it's GMT; no impact)
+ dates_POSIXlt = strptime(as.character(ts_df[,1]), date_format, tz="GMT")
+ ts_df[,1] = format(as.POSIXct(dates_POSIXlt, tz="GMT"), tz="GMT", usetz=TRUE)
+
exo_df =
- if (is.character(exo_data)) {
+ if (is.character(exo_data))
read.csv(exo_data)
- } else {
+ else
exo_data
- }
- # Convert to the desired timezone (usually "GMT" or "Europe/Paris")
- formatted_dates_POSIXlt = strptime(as.character(ts_df[,1]), date_format, tz=input_tz)
- ts_df[,1] = format(as.POSIXct(formatted_dates_POSIXlt), tz=working_tz, usetz=TRUE)
+ # Times in exogenous variables file are ignored: no conversions required
line = 1 #index in PM10 file (24 lines for 1 cell)
nb_lines = nrow(ts_df)
nb_exos = ( ncol(exo_df) - 1 ) / 2
- data = list() #new("Data")
+ data = Data$new()
i = 1 #index of a cell in data
while (line <= nb_lines)
{
serie = c(serie, ts_df[line,2])
line = line + 1
};
- if (line >= nb_lines + 1 || as.POSIXlt(ts_df[line-1,1])$hour == predict_at)
+ if (line >= nb_lines + 1
+ || as.POSIXlt(ts_df[line-1,1],tz="GMT")$hour == 0)
+ {
break
+ }
}
- # NOTE: if predict_at does not cut days at midnight, exogenous vars need to be shifted
- exo_hat = as.data.frame( exo_df[
- ifelse(predict_at>0,max(1,i-1),i) , (1+nb_exos+1):(1+2*nb_exos) ] )
- exo = as.data.frame( exo_df[ ifelse(predict_at>0,max(1,i-1),i) , 2:(1+nb_exos) ] )
- level = mean(serie, na.rm=TRUE)
- centered_serie = serie - level
- #data$append(time, centered_serie, level, exo_hat, exo_Jm1) #too slow; TODO: use R6 class
- data[[length(data)+1]] = list("time"=time, "serie"=centered_serie, "level"=level,
- "exo_hat"=exo_hat, "exo"=exo)
+ # TODO: 2 modes, "operational" and "testing"; would need PM10 predictions
+ data$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
}
- new("Data",data=data)
+ data
}
projects / talweg.git / blobdiff