xts

I would expect cbind.xts and do.call(cbind.xts) to perform with similar elapsed time. That was true for R2.11, R2.14. For R2.15.2 and xts 0.8-8, the do.call(cbind.xts,...) variant performs drastically slower , which effectively breaks my previous codes. As Josh Ulrich notes in a comment below, the xts package maintainers are aware of this problem. In the meantime, is there a convenient work around? Reproducible example: library(xts) secs <- function (rows, from = as.character(Sys.time()), cols = 1, by = 1) { deltas <- seq(from = 0, by = by, length.out = rows) nacol <- matrix(data = NA, ncol =

I want to create an xts object in R, which I then want to decompose to seasonal and trend. > require(xts) > require(lubridate) > chicos$date <- ymd(chicos$date) > ctr.ts <- xts(chicos[, 7], order.by = chicos[, 8], frequency = 365) > plot(ctr.ts, main="Meaningful title") When I try to decompose it, I get this error message.. > plot(decompose(ctr.ts)) Error in decompose(ctr.ts) : time series has no or less than 2 periods My observations are daily, and span from 2014-12-01 to 2015-02-25. Did I set the wrong frequency ? For the frequency of the time series of type xts: By default xts has a daily

I am using intra-day data that starts at 9:50am and would like to convert it into 20 minute time intervals so the first period would be from 09:50 to 10:09:59 and the second time period would be from 10:10 to 10:29:59 etc. However to.minutes() from the xts package seems to fix it onto the hours and the has time bars at 09:59:59 and 10:19:59 etc...i.e. it is 10 minutes out... i know its probably not a regular request...but is there anyway of doing this so that it has the correct endpoints, i.e. basing it upon the frist timestamp? and for bonus points...is there a way to do do it based on the

I want to aggregate data by hourly mean. Daily is very easy: apply.daily(X2,mean) Why is there no function for hourly? I tried hr.means <- aggregate(X2, format(X2["timestamp"],"%Y-%m-%d %H")) and got always error with trim argument. Is there an easier function similar to apply.daily? What if I want to aggregate the mean of 5 minutes. Data are values per minute: "timestamp", value "2012-04-09 05:03:00",2 "2012-04-09 05:04:00",4 "2012-04-09 05:05:00",5 "2012-04-09 05:06:00",0 "2012-04-09 05:07:00",0 "2012-04-09 05:08:00",3 "2012-04-09 05:09:00",0 "2012-04-09 05:10:00",1 I am using xts and zoo.

I have a dataset containing 10 events occuring at a certain time on a given day, with corresponding value for each event: d1 <- data.frame(date = as.POSIXct(c("21/05/2010 19:59:37", "21/05/2010 08:40:30", "21/05/2010 09:21:00", "21/05/2010 22:29:50", "21/05/2010 11:27:34", "21/05/2010 18:25:14", "21/05/2010 15:16:01", "21/05/2010 09:41:53", "21/05/2010 15:01:29", "21/05/2010 09:02:06"), format ="%d/%m/%Y %H:%M:%S"), value = c(11313,42423,64645,643426,1313313,1313,3535,6476,11313,9875)) I want to aggregate the results every 3 minutes, in a standard dataframe format (from "21/05/2010 00:00:00"