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():
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?
2) Shouldn't the xts documentation discuss the problem of sparse data?
Regards,
Scott Waichler
Pacific Northwest National Laboratory
Richland, WA USA
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