similar to: Convert daily to weekly ts ?

#this is a daily series of precipitation data. I would like to condense it into weekly means. How can I do this #as a side note I would like to do this same thing to two years worth of fifteen minute interval data and make it into #a series of daily averages (there are 96 readings per day) #is aggregate the right function? or... y <- c(1.23, 0, 0, 0, 0, 0, 0, 0, 0, 0.27, 0, 0.29, 0, 0, 0,

Dear All, Monthly and Quarterly ts obj. is easy to understand. But I couldn't find an example in R manual how to create daily or weekly ts object. Could you please shed some light on it? I really appreciate it.

Hi, I have a ts object with a frequency of 4, i.e., quarterly data, and I would like to calculate the mean for each quarter. So for example: > ts.data=ts(1:20,start=c(1984,2),frequency=4) > ts.data Qtr1 Qtr2 Qtr3 Qtr4 1984 1 2 3 1985 4 5 6 7 1986 8 9 10 11 1987 12 13 14 15 1988 16 17 18 19 1989 20 If I do this manually, the mean

Hi everyone Please, I need one more help you I have some data that need to make a forecast. I´m using the command: Yst<-print(ts<-ts(Y,start=c(1,5),freq=7),calendar=T) to create the dayly time series by the dataframe Y. My data Y means: Y=measure dayly , start at 01/jul/2010 (thursday), for this reason that I use c(1,5). I need to create the time series aggregate by weakly (and not dayly as

I've been using R for a while, but now find myself needing to understand time-series objects for a short course I am teaching. I am putting together some daily financial datasets for illustration, but having some trouble in aggregating the data to months or weeks. I am getting a "cannot change frequency from 1 to 12" message. I am not sure that aggregation is where I want to go

Dear R People: I'm trying to convert a daily zoo object to a weekly zoo object: xdate <- seq(as.Date("2002-01-01"),as.Date("2010-07-10"),by="day") library(zoo) length(xdate) xt <- zoo(rnorm(3113),order=xdate) xdat2 <- seq(index(xt)[1],index(xt)[3113],by="week") xt.w <- aggregate(xt,by=xdat2,mean) Error: length(time(x)) ==

I'm having some problems with aggregate.ts, e.g. R> x <- ts(1:20) R> frequency(x) [1] 1 R> aggregate(x, nfreq=1/3) Error in aggregate.ts(x, nfreq = 1/3) : cannot change frequency from 1 to 0.333333333333333 In fact aggregate.ts only accepts a new frequency that is a negative power of two in this example. The problem with the current test for compatible frequencies if

Dear Lest, I have a two-variable data frame as follows (the time peirod of the actual data set is 10 years): Date Amount 1 6/1/2007 1 2 6/1/2007 1 3 6/4/2007 2 4 6/5/2007 2 5 6/11/2007 3 6 6/12/2007 3 7 6/12/2007 3 8 6/13/2007 3 9 6/13/2007 3 10 6/18/2007 4 11 6/18/2007 4 12 6/25/2007 5 13 6/28/2007 5

Hi, Is it possible to create a ts() object, whose data is daily based BUT measured only on working days? In other words, suppose I have a data set with 255 observations, measured from 29 June 1959 to 30 June 1960. How would I create such a data? I tried something like: ts(c(...), start(1959, 180)) but I'm not sure what to use for frequency. In other words I don't know how to

Dear all, Currently, a ts object behaves like an array, and it would be very useful to have a similar object, which would behave like a data.frame, i.e. it could be indexed, named, etc. like a data.frame. What would be the most efficient way to construct such an object? I have tried to make one on my own following the directions of class design from "S Programming" (2000) as a

Hi, I have a dataframe (try.1) with  date/time and temperature columns, and the date/time is in POSIXct fomat. Sample included below. I would like to to try decompose () or stl() to look at the trends and seasonality in my data, eventually so that  I can  look at autocorrelation.  The series is 3 years of water temperature with clearly visible seasonal periods. Right now, if I try decompose,

Dear all, I have daily data in wide-format from 01/01/1986 to 31/12/2016 by ID. I would like to convert this to weekly average data. The data has been generated by an algorithm. I know that I can use the lubridate package but that would require me to first convert the data to long-form (which is what I want). I am at a bit of loss of how to extract the date from the variable names and then

Hi All, I have a data set of daily measurements of river flow. I would like to create a "ts" object from this data. Here's a sample data set: date <- as.Date(c(1:300), format="%Y") year=as.numeric(format(date, format = "%Y")) month=as.numeric(format(date, format = "%m")) julianday=as.numeric(format(date, format = "%j"))

Hi R-users: How can I format a daily time serie with ts function so the plot of the time shows the date right (dd/mm/yy) or yy.xxxx ? Excerp of the database: FECHA TRM 1 01/01/2000 1873.77 2 02/01/2000 1873.77 3 03/01/2000 1873.77 4 04/01/2000 1874.35 5 05/01/2000 1895.97 . . . 1397 10/11/2003 2843.82 1398 11/11/2003 2840.41 1399 12/11/2003 2840.41 1400 13/11/2003

I have defined a daily timeseries for the 365 days of 2003 issuing: myts = ts(dati[,2:10],frequency=365,) > myts Time Series: Start = c(1, 1) End = c(1, 365) Frequency = 365 and mytime = as.POSIXct(strptime(as.character(dati[,1]),format="%Y-%m-%d")) contains the dates from "2003-01-01" to "2003-12-31" How can I combine mytime and myts in order to list

Hi Based on your explanation I would advice to use ?cut.POSIXt with breaks "week". However your data are rather strange, you have data frame with names which looks like dates names(test) [1] "X1986.01.01.10.30.00" "X1986.01.02.10.30.00" "X1986.01.03.10.30.00" [4] "X1986.01.04.10.30.00" "X1986.01.05.10.30.00"

> Hello, > > I have lots of data in zoo format and would like to do some time > series analysis. (using library(zoo), library(ts) ) > > My data is usually from one year, and I try for example stl() to find > some seasonalities or trends. > > I have now accepted, that I might have to convert my series into ts() > but still I am not able to execute the comand since

I have some daily data and would like to apply some of the time series functions to it. I have read various notes in the help archive on this, the latest I found suggested that I need to use the irts class (Irregularly spaced time series) for daily data since a year does not divide into an integer number of days. I see why I would have to do that if I have gaps (e.g. only data on weekdays) but

I have a time series vector (not necessarily ts class) that has NAs in it. How can I omit the NAs when using aggregate.ts() to compute a function on each window? If there is at least one non-NA value in each window, I'd like to proceed with evaluating the function; otherwise, I would like NA returned. I'm not wedded to aggregate.ts and don't need ts class if there is another fast,

Forgot the year if you also want to summarise by that. > x <- structure(list(X1986.01.01.10.30.00 = c(16.8181762695312, 16.8181762695312, + 18.8294372558594, 16 .... [TRUNCATED] > library(tidyverse) > library(lubridate) > # convert to long form > x_long <- gather(x, key = 'date', value = "value", -ID) > #