#'
#' @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)
+#' for one day (see \code{?Data}).
#'
#' @param ts_data Time-series, as a data frame (DB style: 2 columns, first is date/time,
#' second is value) or a CSV file
#' 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
+#' @param limit Number of days to extract (default: Inf, for "all")
#'
-#' @return An object of class Data
+#' @return A list where data[[i]] contains
+#' \itemize{
+#' \item time: vector of times
+#' \item centered_serie: centered serie
+#' \item level: corresponding level
+#' \item exo: exogenous variables
+#' \item exo_hat: predicted exogenous variables
+#' }
#'
+#' @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)
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))
date_format = date_format[1]
ts_df =
- if (is.character(ts_data)) {
- if (ts_data %in% data(package="talweg")$results[,"Item"])
- ts_data =
-
-
-
-
- ############CONTINUE: http://r-pkgs.had.co.nz/data.html
-
-
-
-
-
- read.csv(ts_data)
- } else {
+ if (is.character(ts_data))
+ read.csv(ts_data)
+ else
ts_data
- }
exo_df =
- if (is.character(exo_data)) {
+ if (is.character(exo_data))
read.csv(exo_data)
- } else {
+ else
exo_data
- }
# Convert to the desired timezone (usually "GMT" or "Europe/Paris")
formatted_dates_POSIXlt = strptime(as.character(ts_df[,1]), date_format, tz=input_tz)
ts_df[,1] = format(as.POSIXct(formatted_dates_POSIXlt), tz=working_tz, usetz=TRUE)
line = 1 #index in PM10 file (24 lines for 1 cell)
nb_lines = nrow(ts_df)
nb_exos = ( ncol(exo_df) - 1 ) / 2
- data = list() #new("Data")
+ data = list()
i = 1 #index of a cell in data
while (line <= nb_lines)
{
break
}
- # NOTE: if predict_at does not cut days at midnight, exogenous vars need to be shifted
- exo_hat = as.data.frame( exo_df[
- ifelse(predict_at>0,max(1,i-1),i) , (1+nb_exos+1):(1+2*nb_exos) ] )
- exo = as.data.frame( exo_df[ ifelse(predict_at>0,max(1,i-1),i) , 2:(1+nb_exos) ] )
+ exo = as.data.frame( exo_df[i,2:(1+nb_exos)] )
+ exo_hat = as.data.frame( exo_df[i,(1+nb_exos+1):(1+2*nb_exos)] )
level = mean(serie, na.rm=TRUE)
centered_serie = serie - level
- #data$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)
+ data[[i]] = list("time"=time, "centered_serie"=centered_serie, "level"=level,
+ "exo"=exo, "exo_hat"=exo_hat)
if (i >= limit)
break
i = i + 1
}
- new("Data",data=data)
+ start = 1
+ end = length(data)
+ if (length(data[[1]]$centered_serie) < length(data[[2]]$centered_serie))
+ start = 2
+ if (length(data[[length(data)]]$centered_serie) <
+ length(data[[length(data)-1]]$centered_serie))
+ {
+ end = end-1
+ }
+ if (start>1 || end<length(data))
+ data = data[start:end]
+ data
}