fix plotting; TODO: tests, reports
[talweg.git] / pkg / R / getData.R
CommitLineData
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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")
09cf9c19 16#' @param predict_at When does the prediction take place ? Integer, in hours. Default: 0
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17#'
18#' @return An object of class Data
19#'
20#' @export
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21getData = function(ts_data, exo_data, input_tz="GMT", date_format="%d/%m/%Y %H:%M",
22 working_tz="GMT", predict_at=0, limit=Inf)
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23{
24 # Sanity checks (not full, but sufficient at this stage)
25 if (!is.character(input_tz) || !is.character(working_tz))
26 stop("Bad timezone (see ?timezone)")
27 input_tz = input_tz[1]
28 working_tz = working_tz[1]
29 if (!is.data.frame(ts_data) && !is.character(ts_data))
30 stop("Bad time-series input (data frame or CSV file)")
31 if (is.character(ts_data))
32 ts_data = ts_data[1]
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33 predict_at = as.integer(predict_at)[1]
34 if (predict_at<0 || predict_at>23)
35 stop("Bad predict_at (0-23)")
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36 if (!is.character(date_format))
37 stop("Bad date_format (character)")
38 date_format = date_format[1]
39
40 ts_df =
41 if (is.character(ts_data)) {
42 read.csv(ts_data)
43 } else {
44 ts_data
45 }
46 exo_df =
47 if (is.character(exo_data)) {
48 read.csv(exo_data)
49 } else {
50 exo_data
51 }
52 # Convert to the desired timezone (usually "GMT" or "Europe/Paris")
53 formatted_dates_POSIXlt = strptime(as.character(ts_df[,1]), date_format, tz=input_tz)
54 ts_df[,1] = format(as.POSIXct(formatted_dates_POSIXlt), tz=working_tz, usetz=TRUE)
55
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56 line = 1 #index in PM10 file (24 lines for 1 cell)
57 nb_lines = nrow(ts_df)
58 nb_exos = ( ncol(exo_df) - 1 ) / 2
59 data = list() #new("Data")
60 i = 1 #index of a cell in data
61 while (line <= nb_lines)
62 {
63 time = c()
64 serie = c()
09cf9c19 65 repeat
3d69ff21 66 {
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67 {
68 time = c(time, ts_df[line,1])
69 serie = c(serie, ts_df[line,2])
70 line = line + 1
71 };
72 if (line >= nb_lines + 1 || as.POSIXlt(ts_df[line-1,1])$hour == predict_at)
73 break
74 }
3d69ff21 75
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76 # NOTE: if predict_at does not cut days at midnight, exogenous vars need to be shifted
77 exo_hat = as.data.frame( exo_df[
78 ifelse(predict_at>0,max(1,i-1),i) , (1+nb_exos+1):(1+2*nb_exos) ] )
79 exo = as.data.frame( exo_df[ ifelse(predict_at>0,max(1,i-1),i) , 2:(1+nb_exos) ] )
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80 level = mean(serie, na.rm=TRUE)
81 centered_serie = serie - level
1e20780e 82 #data$append(time, centered_serie, level, exo_hat, exo_Jm1) #too slow; TODO: use R6 class
3d69ff21 83 data[[length(data)+1]] = list("time"=time, "serie"=centered_serie, "level"=level,
dea7ff86 84 "exo_hat"=exo_hat, "exo"=exo)
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85 if (i >= limit)
86 break
87 i = i + 1
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88 }
89 new("Data",data=data)
90}