Ditching XML for JSON for imporved speed and 50% less query to Quandl servers

What I did not like in the prior implementation is that the `Quandl` function was querying the servers twice:
  * once to check if the data exists and to identify frequency,
  * then really fetched data.

I wanted to merge these two process, but `XML` parsing seems to be really slow compared to `JSON`, which latter tends to have smaller size too. So suggesting to drop `XML` for the really fast parser of `RSJONIO`.

I have made a quick comparison of the two methods:

```rout
> library(microbenchmark)
> Quandl.XML  <- function() x <- Quandl("NSE/OIL")
> Quandl.JSON <- function() x <- Quandl2("NSE/OIL")
> microbenchmark(Quandl.XML(), Quandl.JSON(), times = 10)
Unit: milliseconds
          expr      min        lq    median        uq      max neval
  Quandl.XML() 993.8316 1053.9598 1094.4323 1110.6291 1192.171    10
 Quandl.JSON() 848.3522  895.3143  908.8844  945.8231 1024.257    10
```
![compare](https://f.cloud.github.com/assets/495736/221428/7d0da5b6-859b-11e2-8d6f-6f72c4a08b16.png)

So we can see some speed improvement, and also less load on your servers for sure.

---

Although I have tested the rewritten parts, it's without any warranty of course :)

Although there are some differences in the results of the functions:

```rout
> identical(Quandl("NSE/OIL"), Quandl2("NSE/OIL"))
 [1] FALSE
```

That's just thanks to the column names, as `read.csv` forces safe names:

```rout
> Quandl.XML()
'data.frame':	857 obs. of  8 variables:
 $ Date                : Date, format: "2009-09-30" "2009-10-01" ...
 $ Open                : num  1096 1102 1152 1150 1154 ...
 $ High                : num  1157 1174 1166 1157 1161 ...
 $ Low                 : num  1090 1102 1137 1132 1140 ...
 $ Last                : num  1135 1167 1143 1143 1141 ...
 $ Close               : num  1141 1166 1141 1145 1142 ...
 $ Total.Trade.Quantity: num  19748012 3074254 919832 627957 698216 ...
 $ Turnover..Lacs.     : num  223877 35464 10581 7186 8033 ...
> Quandl.JSON()
'data.frame':	857 obs. of  8 variables:
 $ Date                : Date, format: "2009-09-30" "2009-10-01" ...
 $ Open                : num  1096 1102 1152 1150 1154 ...
 $ High                : num  1157 1174 1166 1157 1161 ...
 $ Low                 : num  1090 1102 1137 1132 1140 ...
 $ Last                : num  1135 1167 1143 1143 1141 ...
 $ Close               : num  1141 1166 1141 1145 1142 ...
 $ Total Trade Quantity: num  19748012 3074254 919832 627957 698216 ...
 $ Turnover (Lacs)     : num  223877 35464 10581 7186 8033 ...
```

This can be also done with the current method too, although I prefer to use human readable variable names :)

And of course feel free to ignore this pull request if you insist on using `XML`.