Revised quandl to allow for loading multiple symbols

Quandl_rev1.R

library(RJSONIO)
library(xts)

Quandl <- function(code, env = .GlobalEnv, type = c('raw', 'ts', 'zoo', 'xts'), start_date, end_date, transformation = c('', 'diff', 'rdiff', 'normalize', 'cumul'), collapse = c('', 'weekly', 'monthly', 'quarterly', 'annual'), rows, authcode = Quandl.auth()) {
  
  ## Flag to indicate frequency change due to collapse
  freqflag = FALSE
  
  # remove everything before the "/" for assigning the symbols to variable names
  sym.names <- gsub(".*/", "", code)
  
  ## Check params
  type           <- match.arg(type)
  transformation <- match.arg(transformation)
  collapse       <- match.arg(collapse)
  
  ## Helper function
  frequency2integer <- function(freq) {
    switch(freq,
           'daily'    = 365,
           'monthly'  = 12,
           'quarterly' = 4,
           'yearly'   = 1,
           1)
  }
  
  # Loop through the symbols to download the data and assign to the environment
  for(i in 1:length(code)) {
    
    ## Build API URL and add auth_token if available
    string <- paste("http://www.quandl.com/api/v1/datasets/", code[i], ".json?sort_order=asc&", sep="")
    if (is.na(authcode))
      warning("It would appear you aren't using an authentication token. Please visit http://www.quandl.com/help/r or your usage may be limited.")
    else
      string <- paste(string, "&auth_token=", authcode, sep = "")
    
    ## Add API options
    if (!missing(start_date))
      string <- paste(string, "&trim_start=", as.Date(start_date), sep = "")
    if (!missing(end_date))
      string <- paste(string,"&trim_end=", as.Date(end_date) ,sep = "")
    if (transformation %in% c("diff", "rdiff", "normalize", "cumul"))
      string <- paste(string,"&transformation=", transformation, sep = "")
    if (collapse %in% c("weekly", "monthly", "quarterly", "annual")) {
      string <- paste(string, "&collapse=", collapse, sep = "")
      freq   <- frequency2integer(collapse)
      freqflag = TRUE
    }
    if (!missing(rows))
      string <- paste(string,"&rows=", rows ,sep = "")
    
    ## Download and parse data
    json <- try(fromJSON(string, nullValue = as.numeric(NA)), silent = TRUE)
    
    ## Check if code exists
    if (inherits(json, 'try-error'))
      stop("Code does not exist")
    
    ## Detect frequency
    if (!freqflag)
      freq <- frequency2integer(json$frequency)
    
    ## Shell data from JSON's list
    data        <- as.data.frame(matrix(unlist(json$data), ncol = length(json$column_names), byrow = TRUE),stringsAsFactors=FALSE)
    names(data) <- json$column_names
    data[,1]    <- as.Date(data[, 1])
    
    ## Transform values to numeric
    if (ncol(data) > 2)
      data[, 2:ncol(data)]  <- apply(data[, 2:ncol(data)], 2, as.numeric)
    else
      data[, 2]  <- as.numeric(data[, 2])
    
    ## Returning raw data
    if (type == "raw") {
      fr <- data
      # Assign the symbol to the environment
      assign(sym.names[i], fr, env)
    }
    
    if (type == "ts") {
      ## Returning ts object
      date <- data[1,1]
      year <- 1900+as.POSIXlt(date)$year
      startdate <- 1
      if(freq == 1) {
        start <- year
      }
      else if (freq == 4) {
        quarter <- pmatch(quarters(date), c("Q1","Q2","Q3","Q4"))
        startdate <- c(year, quarter)
      }
      else if (freq == 12) {
        month <- 1+as.POSIXlt(date)$mon
        startdate <- c(year, month)
      }
      else{
        freq <- 1
      }
      fr <- ts(data[, -1], frequency = freq, start = startdate)
      # Assign the symbol to the environment
      assign(sym.names[i], fr, env)
    }
    
    ## Returning zoo object
    if (type == "zoo"){
      fr <- zoo(data[c(-1)], data[,1])
      # Assign the symbol to the environment
      assign(sym.names[i], fr, env)
    } 
    
    ## Returning xts object
    if (type == "xts"){
      fr <- xts(data[c(-1)], data[,1])
      assign(sym.names[i], fr, env)
    }
    
    ## Just in case
    # stop("Invalid Type")
    # This is not necessary and an invalid type will be caught by match.arg
    
  }
}


# Example to load US GDP, Nymex Crude Oil, and the S&P 500

datasets <- c("FRED/GDP", "OFDP/FUTURE_CL1", "YAHOO/INDEX_GSPC")

Quandl(code=datasets, type="foo", authcode="29Mxp9yC8iXyURxXwUys")

Quandl(code=datasets, type="zoo", authcode="29Mxp9yC8iXyURxXwUys")

Quandl(code=datasets, type="ts", authcode="29Mxp9yC8iXyURxXwUys")

Quandl(code=datasets, type="raw", authcode="29Mxp9yC8iXyURxXwUys")

Quandl(code=datasets, type="xts", authcode="29Mxp9yC8iXyURxXwUys")

head(FUTURE_CL1)
head(GDP)
head(INDEX_GSPC)

Not sure if publishing an authcode is a good idea :)

Anyway, to be on-topic, why not simply running something like this with the original Quandl function instead of tweaking that:

for (code in c("FRED/GDP", "OFDP/FUTURE_CL1"))
    assign(make.names(code), Quandl(code))