stata.get <- function(file, lowernames=FALSE,
                        convert.dates=TRUE, convert.factors=TRUE,
                        missing.type=FALSE, convert.underscore=TRUE,
                        warn.missing.labels=TRUE, force.single=TRUE,
                        allow=NULL, charfactor=FALSE, ...)
  {
    ## depends on the read.dta function from foriegn
    require('foreign')

    ## Function to convert the elements of w into more compact
    ## data storage types.
    convertObjs <- function(x, charfactor, force.single) {
      ## Date is not nessarely a integer but it ignores any
      ## fraction it might have
      if((inherits(x, 'Date') || is.factor(x))
         && storage.mode(x) != 'integer') {
        storage.mode(x) <- 'integer'
      } else if(charfactor && is.character(x)) {
        ## If x is a character and arg charfactor is TRUE then
        ## convert x to a factor if the number of unique values of x is less
        # than half the total number of values in x
        if(length(unique(x)) < length(x) / 2)
          {
            x <- sub(' +$', '', x)  # remove trailing blanks
            x <- factor(x, exclude='')
          }
      } else if(is.numeric(x)) {
        
        if(all(is.na(x))) {
          ## if all values are NA then convert to integer because
          ## it is 4 bytes instead of 8
          storage.mode(x) <- 'integer'
        }
        else if(force.single && max(abs(x), na.rm=TRUE) <= (2^31-1) &&
                all(floor(x) == x, na.rm=TRUE)) {
          ## convert x to integer if arg force.single is TRUE and the maximum
          ## absolute value of x is less then maximum value that an integer
          ## can store.
          storage.mode(x) <- 'integer'
        }
      }

      return(x)
    }

    ## A function to create additional attributes to add to the elements of
    ## w
    create.attribs <- function(var.label, val.label, format, label.table) {
      attribs <- list()
      
      if(format != '') {
        attribs$format <- format
      }

      ## Translate var labels into Hmisc var lables
      if(var.label != '') {
        attribs$label <- var.label
      }

      ## The label.table values are found by looking a the checking to see
      ## if there is a non-empty value in val.labels.  That value corrasponds
      ## a named element in label.table.
      
      ## Check to see if val.label is not empty and it is one of the
      ## names in label.table and that its value is not NULL
      if(val.label != '' && val.label %in% names(label.table) &&
         !is.null(label.table[[val.label]])) {
        attribs$value.label.table <- label.table[[val.label]]
      }

      return(attribs)
    }
    
    ## If file is a url download and set file = to temp file name
    if(length(grep('^http://', file))){
      tf <- tempfile()
      download.file(file, tf, mode='wb', quiet=TRUE)
      file <- tf
    }

    ## Read the stata file into w
    w <- read.dta(file, convert.dates=convert.dates,
                  convert.factors=convert.factors,
                  missing.type=missing.type,
                  convert.underscore=convert.underscore,
                  warn.missing.labels=warn.missing.labels, ...)

    ## extract attributes from w
    a <- attributes(w)
    num.vars <- length(w)

    ## Do translate attributes names into R names
    nam <- makeNames(a$names, unique=TRUE, allow=allow)
    if(lowernames) nam <- casefold(nam, upper=FALSE)
    a$names <- nam

    ## If var.labels is empty then create a empty char vector.
    if(!length(a$var.labels)) {
      a$var.labels <- character(num.vars)
    }

    ## If val.labels is empty then create an empty char vector.
    if(length(a$val.labels)) {
      val.labels <- a$val.labels
    } else {
      val.labels <- character(num.vars)
    }

    ## create list of attributes for the elements in w.  An mapply is faster
    ## then a for loop in large data sets.
    attribs <- mapply(FUN=create.attribs, var.label=a$var.labels,
                      val.label=val.labels, format=a$formats,
                      MoreArgs=list(label.table=a$label.table),
                      SIMPLIFY=FALSE)
    
    ## clear var.labels attribute
    attr(w, 'var.labels') <- NULL

    ## Convert the elements of w as needed
    w <- lapply(w, FUN=convertObjs, force.single=force.single,
                charfactor=charfactor)
    
    ## strip off the naming info for w
    w <- unname(w)

    ## add the new attributes to the current attributes of
    ## the elements of w
    for(i in seq(along.with=w)) {
      ## Set the label for the element
      if('label' %in% names(attribs[[i]])) {
        label(w[[i]]) <- attribs[[i]]$label
        ## clear the label value from attribs[[i]]
        attribs[[i]]$label <- NULL
      }

      ## combine the new attribs with the current attributes
      consolidate(attributes(w[[i]])) <- attribs[[i]]
    }

    ## add the names, rownames, class variables, and some extra stata
    ## info back to w
    stata.info <- a[c('datalabel','version','time.stamp','val.labels','label.table')]
    attributes(w) <- c(a[c('names','row.names','class')],
                       stata.info=list(stata.info))
    return(w)
  }