transform data

gistfile1.r

transformdata <- function(dates,  values, date.form = "%Y-%m-%d", ...) {
  require(lattice)
  require(grid)
  require(chron)
  if (class(dates) == "character" | class(dates) == "factor" ) {
    dates <- strptime(dates, date.form)
  }
  caldat <- data.frame(value = values, dates = dates)
  min.date <- as.Date(paste(format(min(dates), "%Y"),
                            "-1-1",sep = ""))
  max.date <- as.Date(paste(format(max(dates), "%Y"),
                            "-12-31", sep = ""))
  dates.f <- data.frame(date.seq = seq(min.date, max.date, by="days"))
  
  # Merge moves data by one day, avoid
  caldat <- data.frame(date.seq = seq(min.date, max.date, by="days"), value = NA)
  dates <- as.Date(dates) 
  caldat$value[match(dates, caldat$date.seq)] <- values
  
  caldat$dotw <- as.numeric(format(caldat$date.seq, "%w"))
  caldat$woty <- as.numeric(format(caldat$date.seq, "%U")) + 1
  caldat$yr <- as.factor(format(caldat$date.seq, "%Y"))
  caldat$month <- as.numeric(format(caldat$date.seq, "%m"))
  yrs <- as.character(unique(caldat$yr))
  d.loc <- as.numeric()                        
  for (m in min(yrs):max(yrs)) {
    d.subset <- which(caldat$yr == m)  
    sub.seq <- seq(1,length(d.subset))
    d.loc <- c(d.loc, sub.seq)
  }  
  caldat <- cbind(caldat, seq=d.loc)
  caldat
}

##Example

stock <- "MSFT"
start.date <- "2012-01-12"
end.date <- Sys.Date()
quote <- paste("http://ichart.finance.yahoo.com/table.csv?s=", stock, "&a=", substr(start.date,6,7), "&b=", substr(start.date, 9, 10), "&c=", substr(start.date, 1,4), "&d=", substr(end.date,6,7), "&e=", substr(end.date, 9, 10), "&f=", substr(end.date, 1,4), "&g=d&ignore=.csv", sep="")
stock.data <- read.csv(quote, as.is=TRUE)
# convert the continuous var to a categorical var 
stock.data$by <- cut(stock.data$Adj.Close, b = 6, labels = F)
dat <- transformdata(stock.data$Date, stock.data$by)