sDaply2(X, X$ID)
# list is not a timeSeries object
str(cbind(t(res)))
res <- as.timeSeries(cbind(t(res)))
-Original Message-
From: h.wick...@gmail.com [mailto:h.wick...@gmail.com] On Behalf Of Hadley
Wickham
Sent: 14 March 2011 15:07
To: Daniele Amberti
Cc: r-help@r-project.org
Subjec
> res <- daply(X, "ID", buildTimeSeriesFromDataFrame2, .parallel = FALSE)
> return(res)
> }
> # tsDaply2 .parallel = FALSE work but list discart timeSeries class
>
> # bind after ts creation
> res <- tsDaply2(X, X$ID)
> # list is not a timeSeries object
&g
orkers(w)
-----Original Message-
From: h.wick...@gmail.com [mailto:h.wick...@gmail.com] On Behalf Of Hadley
Wickham
Sent: 14 March 2011 12:48
To: Daniele Amberti
Cc: r-help@r-project.org
Subject: Re: [R] dataframe to a timeseries object - [ ] Message is from an
unknown sender
Well, I'd start
Well, I'd start by removing all explicit use of environments, which
makes you code very hard to follow.
Hadley
On Monday, March 14, 2011, Daniele Amberti wrote:
> I found that plyr:::daply is more efficient than base:::by (am I doing
> something wrong?), below updated code for comparison (I als
I found that plyr:::daply is more efficient than base:::by (am I doing
something wrong?), below updated code for comparison (I also fixed a couple
things).
Function daply from plyr package has also a .parallel argument and I wonder if
creating timeseries objects in parallel and then combining th
I’m wondering which is the most efficient (time, than memory usage) way to
obtain a multivariate time series object from a data frame (the easiest data
structure to get data from a database trough RODBC).
I have a starting point using timeSeries or xts library (these libraries can
handle time zo
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