RBbg - Reshaping Time Series Data - r

Question

Is there a better way of reshaping dataframe data?
temp <- bdh(conn,c("AUDUSD Curncy","EURUSD Curncy"),"PX_LAST","20110101")
gives
head(temp)
ticker date PX_LAST
1 AUDUSD Curncy 2011-01-01 NA
2 AUDUSD Curncy 2011-01-02 NA
3 AUDUSD Curncy 2011-01-03 1.0205
4 AUDUSD Curncy 2011-01-04 1.0040
5 AUDUSD Curncy 2011-01-05 1.0014
6 AUDUSD Curncy 2011-01-06 0.9969
and
tail(temp)
ticker date PX_LAST
2127 EURUSD Curncy 2013-11-26 1.3557
2128 EURUSD Curncy 2013-11-27 1.3570
2129 EURUSD Curncy 2013-11-28 1.3596
2130 EURUSD Curncy 2013-11-29 1.3591
2131 EURUSD Curncy 2013-11-30 NA
2132 EURUSD Curncy 2013-12-01 NA
in other words, the data are just vertically tacked on to each other and further processing is necessary in order to get them working. how can i regroup this data into the various tickers, i.e.
head(temp)
AUDUSD.Curncy EURUSD.Curncy
2011-01-01 NA NA
2011-01-02 NA NA
2011-01-03 1.0205 1.3375
2011-01-04 1.0040 1.3315
2011-01-05 1.0014 1.3183
2011-01-06 0.9969 1.3028
All the reshaping questions I googled didnt have the kind of reshaping I wanted. I have implemented my own piecemeal solution given below but for learning's sake I wanted to ask you guys if there is a more elegant solution for this?

Answer 1

You could try read.zoo. Use index.column to specify in which column index/time is stored, and reshape data according to splitcolumnn, . The result is a zoo time series
library(zoo)
z <- read.zoo(text = "ticker date PX_LAST
1 AUDUSD 2011-01-01 NA
2 AUDUSD 2011-01-02 NA
3 AUDUSD 2011-01-03 1.0205
4 AUDUSD 2011-01-04 1.0040
5 AUDUSD 2011-01-05 1.0014
6 AUDUSD 2011-01-06 0.9969
2127 EURUSD 2013-11-26 1.3557
2128 EURUSD 2013-11-27 1.3570
2129 EURUSD 2013-11-28 1.3596
2130 EURUSD 2013-11-29 1.3591
2131 EURUSD 2013-11-30 NA
2132 EURUSD 2013-12-01 NA", index.column = "date", split = "ticker")
z
# AUDUSD EURUSD
# 2011-01-01 NA NA
# 2011-01-02 NA NA
# 2011-01-03 1.0205 NA
# 2011-01-04 1.0040 NA
# 2011-01-05 1.0014 NA
# 2011-01-06 0.9969 NA
# 2013-11-26 NA 1.3557
# 2013-11-27 NA 1.3570
# 2013-11-28 NA 1.3596
# 2013-11-29 NA 1.3591
# 2013-11-30 NA NA
# 2013-12-01 NA NA
str(z)

Answer 2

This is exactly why we have created the RbbgExtension package. It is a wrapper around the Rbbg package that handles many issues when dealing with financial data - issues we have come across in our daily work with backtesting trading strategies etc. for a financial institution.
As you can see the output is a xts object, but if the query is across multiple tickers and multiple fields, then the output will an array - but you can read about why that is in the documentation.
We have made the package open source and publicly available on GitHub. Just use Hadley's devtools' function install_github("pgarnry/RbbgExtension") to get the package. It has a few dependencies including "Rbbg".
> require(RbbgExtension)
Loading required package: RbbgExtension
>
> tickers <- c("AUDUSD", "EURUSD")
>
> prices <- HistData(tickers = tickers,
+ type = "Curncy",
+ fields = "PX_LAST",
+ startdate = "20110101")
R version 3.1.2 (2014-10-31)
rJava Version 0.9-6
Rbbg Version 0.5.3
Java environment initialized successfully.
Looking for most recent blpapi3.jar file...
Adding C:\blp\API\APIv3\JavaAPI\v3.7.1.1\lib\blpapi3.jar to Java classpath
Bloomberg API Version 3.7.1.1
> class(prices)
[1] "xts" "zoo"
> head(prices)
AUDUSD EURUSD
2011-01-03 1.0168 1.3361
2011-01-04 1.0051 1.3308
2011-01-05 0.9995 1.3149
2011-01-06 0.9944 1.3003
2011-01-07 0.9959 1.2907
2011-01-10 0.9956 1.2951
> tail(prices)
AUDUSD EURUSD
2015-01-26 0.7925 1.1238
2015-01-27 0.7937 1.1381
2015-01-28 0.7889 1.1287
2015-01-29 0.7762 1.1320
2015-01-30 0.7762 1.1291
2015-02-02 0.7806 1.1351

Answer 3

rbbg's blh (now bdh) is dumb. this outputs time series correctly.
bdhx <- function(conn,securities,start_date,end_date=NULL,fields="PX_LAST",override_fields = NULL,overrides = NULL) {
temp <- bdh(conn=conn,securities=securities,fields=fields,start_date=start_date,end_date=end_date,override_fields=override_fields)
if (colnames(temp)[1]=="date")
{temp <- as.xts(temp)[,-1];colnames(temp) <- securities;res <- temp;}
else
{cn <- unique(temp[,1]);fil <- temp[,1]==cn[1];
res <- xts(temp[fil,3],as.Date(temp[fil,2]));colnames(res) <- securities[1];
for (i in 4:(length(cn)+2)){
fil <- temp[,1]==cn[i-2]
temp2 <- xts(temp[fil,3],as.Date(temp[fil,2]));colnames(temp2) <- securities[i-2];
res <- merge.xts(res,temp2)}
}
res}