old_C_code/stage2_UNFINISHED/src/00_convertir-donnnes_2009.r

   1 ## File : 00_convertir-donnnes_2009.r
   2 ## Description : Converts flat EDF's 32K data into a full data matrix
   3 ##               layout [individuals, variables]. Rownames are EDF's ids.
   4 ##               We process the original flat file sequentially by lines
   5 ##               to avoid exceding the available RAM memory (and so avoiding
   6 ##               swaping which is a computational burden).
   7
   8
   9 rm(list = ls())
  10
  11 # setwd("~/ownCloud/projects/2014_EDF-Orsay-Lyon2/codes")
  12
  13
  14 ## 1. Read auxiliar data files ####
  15
  16 identifiants <- read.table("identifs.txt")[ ,1]
  17 dates0       <- read.table("datesall.txt")[, 1]
  18 dates        <- dates0[grep("2009", dates0)]
  19 rm(dates0)
  20
  21 n <- length(identifiants)
  22 p <- length(dates)
  23
  24 blocks <- c(rep(6000, 3), 7011)  # We'll process 1000 x p lines at each
  25                                   # iteration of the reading loop
  26
  27 ## 2. Process the large flat file ####
  28 ## We want to check that every time step recorded for each id.
  29
  30 con <- file("~/tmp/data/2009.csv")  # Establish a connection to the file
  31 open(con, "r")                      # Open the connection
  32 rien <- readLines(con = con, n = 1); rm(rien) # Discard 1st line
  33
  34 for(b in seq_along(blocks)){      # Reading loop
  35   nb <- blocks[b]
  36   actual <- readLines(con = con, n = nb * length(dates))
  37   auxmat <- matrix(unlist(strsplit(actual, ",")), ncol = 4, byrow = TRUE)
  38   rm(actual)
  39   auxdf  <- data.frame(id   = as.integer(auxmat[, 1]),
  40                        date = auxmat[, 2],
  41                        val  = as.numeric(
  42                          ifelse(auxmat[,3] == "", auxmat[, 4], auxmat[, 3])))
  43   rm(auxmat) # free up some space
  44
  45   tab <- table(auxdf$id)
  46   idtt <- NULL
  47   for(tt in as.integer(names(which(tab < p)))) {  # id with less than p records!
  48     print(tt)
  49     idtt <- c(idtt, which(auxdf$id == tt))
  50   }
  51
  52   idmat         <- matrix(auxdf$id[-idtt], ncol = p, byrow = TRUE)
  53   alldatesperid <- apply(idmat[-idtt, ], 1, sd) == 0
  54   valmat        <- matrix(auxdf$val[-idtt], ncol = p, byrow = TRUE)
  55
  56   # store separatelly full records from partial records
  57   write.table(file = paste0("~/tmp/2009_full_", b, ".txt"), valmat,
  58               row.names = idmat[, 1], col.names = FALSE)
  59   write.table(file = paste0("~/tmp/2009_partial_", b, ".txt"), auxdf[idtt, ])
  60 }
  61
  62 close(con)                      # close connection to the file
  63
  64 rm(auxdf, idmat, valmat, alldatesperid, b,  # clean up some memory
  65    idtt, blocks, tab, tt, con)
  66
  67
  68 ## 3. Complete partial records ####
  69 ## After analysis, partial records are only 119 clients from which one only
  70 ## time step (01JAN2009:00:00:00) is lacking.
  71
  72 df_partial <- NULL
  73 for(f in list.files("~/tmp/", "2009_partial_*"))
  74   df_partial <- rbind(df_partial, read.table(paste0('~/tmp/', f)))
  75
  76 tab <- table(df_partial$id)
  77 id_incomp <- as.integer(names(which(tab < p))) # Incomplete records
  78
  79 df_partial_full <- rbind(df_partial,
  80                          data.frame(id   = id_incomp,
  81                                     date = "01JAN2009:00:00:00",
  82                                     val  = NA))
  83
  84 rm(df_partial)
  85
  86 # tab2 <- table(df_partial_full$id)  # Check that df_partial_full is full
  87 # head(sort(tab2))
  88
  89
  90 ## 4. Reorder the lines to get the data matrix ####
  91 ## As we paste chunks of partial records and impute some time steps,
  92 ## the original order of the data is broken. We fix it by reordering
  93 ## the ids and then the data.
  94
  95 idx_ordered <- order(df_partial_full$id)             # order ids
  96 df_partial_full2 <- df_partial_full[idx_ordered, ]
  97 rm(df_partial_full)
  98
  99 # Order data values following the correct dates (as the date is a factor
 100 # we need to seek for each value: this is computationnaly innefficient).
 101
 102 valmat  <- matrix(df_partial_full2$val,  ncol = p, byrow = TRUE)
 103 datemat <- matrix(df_partial_full2$date, ncol = p, byrow = TRUE)
 104 idmat   <- matrix(df_partial_full2$id,   ncol = p, byrow = TRUE)
 105
 106 # Use this for as a check by running it twice. On the second run no
 107 # printing should be done (because records should be ordered).
 108 for(line in 1:nrow(datemat)) {
 109   if(any(datemat[line, ] != dates)) { # TRUE is line is not ordered
 110     cat(sprintf("\nline %i is not ordered", line))
 111
 112     neworder         <- match(dates, datemat[line, ])
 113     valmat[line , ]  <- valmat[ line, neworder]
 114     datemat[line , ] <- datemat[line, neworder]
 115   }
 116 }
 117
 118
 119 ## 5. Write on disk the full data matrix of partial records ####
 120
 121 write.table(file = "~/tmp/2009_full_Z.txt", valmat,
 122             row.names = idmat[, 1], col.names = FALSE)
 123 rm(list = ls())
 124 gc()
 125
 126 ## 6. Compile data files in BASH ####
 127
 128 # cat 2009_full*.txt > 2009_full.txt
 129 # rm 2009_full_*.txt 2009_partial_*.txt
 130
 131
 132 ## A. data.table & reshape2 ####
 133 ## When large RAM memory is available, one could use this code to process
 134 ## everything in memory.
 135
 136 #library(data.table)
 137 #library(reshape2)
 138
 139 #dt <- fread(input  = "~/tmp/data/2009_chunk.csv")
 140
 141 #dt[, charge := ifelse(is.na(CPP_PUISSANCE_CORRIGEE),
 142 #                      CPP_PUISSANCE_BRUTE,
 143 #                      CPP_PUISSANCE_CORRIGEE), ]
 144 #dt[, CPP_PUISSANCE_CORRIGEE := NULL]
 145 #dt[, CPP_PUISSANCE_BRUTE := NULL]
 146
 147 #dt2 <- dcast.data.table(data = dt, CPP_DATE_PUISSANCE + FK_CCU_ID ~ charge)
 148
 149
 150 ## Z. Probably stuff to be deleted
 151
 152 # searchpos <- function(row) {
 153 #   str  <- strsplit(row, ",")
 154 #
 155 #   auxmat <- matrix(unlist(str), ncol = 4, byrow = TRUE); rm(str)
 156 #
 157 #   auxdf  <- data.frame(id   = as.integer(auxmat[, 1]),
 158 #                        date = auxmat[, 2],
 159 #                        val  = as.numeric(
 160 #                          ifelse(auxmat[,3] == "", auxmat[, 4], auxmat[, 3]))
 161 #   )
 162 #   rm(auxmat)
 163 #
 164 #   idmat <- matrix(auxdf$id, ncol = length(dates), byrow = TRUE)
 165 #   alldatesperid <- apply(idmat, 1, sd) == 0
 166 #
 167 #
 168 #   #  lines <- match(auxdf$id, identifiants)
 169 #   #  cols  <- match(auxdf$date, dates)
 170 #
 171 #   return(cbind(lines, cols, auxdf$val))
 172 # }