Speed-up get_download, fix #21, and a range of other improvements

I was looking into the speed of `get_download()` as it seemed to take a while to process the object returned from the Neotoma query: this was taking as long if not longer than the query itself.

The main speed issue was the two calls to `l*ply()` to process the query data into a long data frame containing all the count information resulting in `sample.data` in the code. **plyr** is not know for its speed, rather it is a user-friendly interface to the *split*-*apply*-*combine* idiom.

I was able to replace those two `l*ply()` calls with some base R code that is significantly quicker. This also does away with the `suppressMessages()` call which is dangerous as it zaps all messages not just the ones you want to hide from `melt()`.

I also changed some of the other code producing the other data frames returned:

 1. First I removed the `with()` statement - it is generally best to avoid these sugar functions in code as they are designed for use at the top-level (global environment) and weird things can happen if the evaluation frame isn't where you expect it to be. Replaced with direct indexing.

 2. Simplified creation of `site.data` to a single call

 3. Simplified creation of `sample.meta`. Reduced the six `sapply()` or `ldply()` to three equivalent calls. Advantages here are the reduced number of calls, and processing of `sample.name` can be done in a vectorised fashion rather than a lot of `if else` calls.

Whilst I was at this I also addressed #21 by changing the `xtabs()` call to a call to `reshape2::dcast()` call which results in a data frame to return as `counts` instead of the `"xtabs"` classed object.

Other fixes:

 1. You don't need to process the function call to check for presence etc of arguments. Here, as there is only a single main argument, it is much easier to just use `missing()`. `datasetid` is *required* otherwise the function fails. Here I check if it is missing & return an error message if it is. If supplied, I then do the checks as before, adding one for multiple supplied values in `datasetid`.

 2. There was a bug in the creation of `sample.meta`. This line:

        thickness = sapply(samples, function(x) x$AnalysisUnitName)

    in the `data.frame()` call was addressing the wrong component `AnalysisUnitName`. It should have been addressing `AnalysisUnitThickness`.

 3. A the end of the original version you had

        if(class(aa) == 'try-error') aa <- aa

    which is redundant as `aa` will *still* be of class `"try-error"` as the two previous `if` clauses would not have been entered into. Now I simply check immediately if there was an error and bail out.

 4. I added a `verbose` argument defaulting to `TRUE` to maintain backwards compatibility. This allows the API worked message to be skipped if the user wants.