On Thu, Mar 9, 2017 at 3:31 PM, Waichler, Scott R
<scott.waich...@pnnl.gov> wrote:
> Hi,
>
> I found that apply.monthly() in xts does not work as I expected in the case
> of a sparse timeseries:
>
> my.dates <- as.Date(c("1992-06-01", "1992-06-24", "1992-06-30", "1993-06-22",
> "1994-06-07", "1995-06-08"))
> my.xts <- xts(1:6, my.dates)
> start(my.xts) # "1992-06-24"
> end(my.xts) # "1995-06-08"
> apply.monthly(my.xts, mean)
> # [,1]
> # 1995-06-08 3.5
>
> The endpoints it chooses are based on looking at the month (June) alone. I
> was able to get a value for each (month, year) in the timeseries with the
> following use of aggregate():
>
Thanks for the minimal, reproducible example! This is clearly a bug.
> my.months <- months(my.dates)
> my.years <- years(my.dates)
> df1 <- data.frame(x = coredata(my.xts), dates = my.dates, months = my.months,
> years = my.years)
> df2 <- aggregate(df1[-c(3,4)], df1[c("months", "years")], mean)
> xts(df2$x, df2$dates)
> # [,1]
> # 1992-06-18 2
> # 1993-06-22 4
> # 1994-06-07 5
> # 1995-06-08 6
>
> Two questions:
> 1) Is there a more elegant way to do this?
Create your own endpoints until endpoints() is fixed. Here's a quick
hack, off the top of my head:
endpointsMonthHack <- function(x, on = "months", k = 1) {
# yearmon index
ymIndex <- as.yearmon(index(x))
# month changes
monthDiff <- c(0, diff(ymIndex))
# locations in index
locations <- which(monthDiff != 0)
ep <- c(0, locations, nrow(x))
unique(ep)
}
> 2) Shouldn't the xts documentation discuss the problem of sparse data?
No, because it shouldn't be a problem. :)
>
> Regards,
> Scott Waichler
> Pacific Northwest National Laboratory
> Richland, WA USA
>
> ______________________________________________
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--
Joshua Ulrich | about.me/joshuaulrich
FOSS Trading | www.fosstrading.com
R/Finance 2017 | www.rinfinance.com
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