similar to: Problems plotting a sparse time series in R

Dear R users, I have a dataframe where column is has countries, column 2 is dates (monthly) for each countrly, the next 10 columns are my factors where I have measurements for each country and for each date. I have attached a sample of the data in csv format with the data for 3 countries. I would like to convert my monthly data into quarterly data, finding the mean over 3 month periods for

Hi folks. This set of code used to work, but after upgrading to the latest version of Rmetrics it no longer does. Any ideas? SP500<-read.table("SP500.csv",header=TRUE,sep=",") > head(SP500) Date Open High Low Close Volume Close2 1 8/4/2006 1280.26 1292.92 1273.82 1279.40 2530970112 1279.40 2 8/3/2006 1278.22 1283.96 1271.25 1280.27

Dear R Users and maintainers of packages zoo and timeSeries, I believe there is a recently introduced inconsistency between timeSeries and zoo which is causing a problem with rbind. I had previously reported that I was having problems with rbind in the following code: library(zoo) foo<-zoo(1,order.by=as.Date("2007-10-09")) bar<-zoo(2,order.by=as.Date("2007-10-10"))

When I try to convert the zoo object to a timeSeries object, which would allow me to utilize Rmetrics packages, I get an error message. > Data<-read.zoo("c:\\DOWUBSPRICING.txt,na.strings="NA",sep="\t",header=T) > is(Data) "zoo" > as.timeSeries.zoo(Data) Error in .local (.Object, . ) Is this happening because I am using daily data?

Hi, I am trying to get the following two timeseries (these are small subsets of the whole thing) into R so I can merge them using zoo. Timeseries 1=[ Date Count 9/28/2003 1505 10/5/2003 1535

Greetings -- I've got some sensor data of the form t1_1, t1_2 t2_1, t2_2 ... tN_1,tN_2 -- time intervals measuring starts and stops of sensor activity. I'd like to see whether there's any regularity in it. Seems natural to consider these data timeseries -- except most of the timeseries packages and models assume regular ones, with a fixed frequency. I wonder what's a

Hello Let's say I have a multivariate zoo timeseries (synchronised automatic loggers at different places): library(zoo) library(lattice) library(latticeExtra) x<-zoo(data.frame(a=rnorm(100), c=rnorm(100), b=rnorm(100)), seq(from=as.Date("2010-01-01"), by="day", length.out=100)) and a dataframe with manual control points at variable dates:

dear r-list I have a zoo object with 2 objects and time: looks like: 2005-12-31 12:00:00 NA NaN 2005-12-31 13:00:00 NA NaN 2005-12-31 14:00:00 NA NaN 2005-12-31 15:00:00 NA NaN 2005-12-31 16:00:00 NA NaN 2005-12-31 18:00:00 NA NaN 2005-12-31 19:00:00 NA NaN 2005-12-31 20:00:00 NA NaN 2005-12-31 21:00:00 NA

Is there a function in R that is equivalent to S-PLUS's seriesMerge(x1, x2, pos="union") where x1, and x2 are of class timeSeries seriesMerge is in S-PLUS's finmetrics. I looked into R's mergeSeries (in fSeries part of Rmetrics) but I could not make it behave quite the same. In R it expected a timeSeries object and a matrix of the same row count. In S-PLUS when using the

i have time series of momentum signal. I want to get the date of each of the "-1" signal period. for example , the first period of -1 signal begins on 2005-9-21 and ends on 2005-9-28. 2nd period of -1 signal begins on 2005-09-30 and ends on 2005-10-28. Thx Cameron date Px 200MA Signals 2005-09-15 26.27 25.83865 1 2005-09-16 26.07 25.83275 1

Hello there, I am trying to compute the 3 months return momentum with the timeSeries x.ts, which is just a subset of simple returns from a much bigger series, > class(x.ts) [1] "timeSeries" attr(,"package") [1] "timeSeries" > dim(x.ts) [1] 20 3 > x.ts[1:8,] GMT MS.US AAPL.US CA.FP 1996-01-31 0.15159065 -0.133391894

I retrieve for a few hundred times a group of time series (10-15 ts with 10000 values each), on every group I do some calculation, graphs etc. I wonder if there is a faster method than what presented below to get an appropriate timeseries object. Making a query with RODBC for every group I get a data frame like this: > X ID DATE VALUE 14 3 2000-01-01 00:00:03 0.5726334

Hi, I have an intraday timeseries of financial data (see below) which has gaps due to market opening and closing hours. I am trying to plot it, but the time gap is always visible in the plot. I tried converting data to xts, zoo, timeSeries and plotting it with different functions i.e. plot.xts, plot.zoo. The only way to make it work was with function 'chartSeries' in the quantmod package

You can just use the same code that I provided before but now use your dataset. Like this df <- read.csv(file="data2.csv",header=TRUE) dates <- as.Date(paste(df$year,"-01-01",sep="")) myXts <- xts(df,order.by=dates) head(myXts) #The last command "head(myXts)" shows you the first few rows of the xts object year cnsm incm wlth

> On 15 Sep 2017, at 11:38, yadav neog <yadavneog at gmail.com> wrote: > > hello to all. I am working on macroeconomic data series of India, which in > a yearly basis. I am unable to convert my data frame into time series. > kindly help me. > also using zoo and xts packages. but they take only monthly observations. > > 'data.frame': 30 obs. of 4 variables:

Hi, I'm new to R and tried a search but couldn't find what I was looking for. I have some data as a csv file with columns:- longditude latitude year month rainfall region What I need to do is produce a monthly time series for each region, where region is an integer id and where each time point in the series is the monthly average of rainfall for each location in that region. Basically

Hello, Sorry if anyone gets this message twice, as my mailserver may not be working. Thanks for your response. Your idea makes a lot of sense to me, but I've been unable to get seconds to work. I ended up with this format finally: "2007-10-31_16:20:22" Problem is I am unable to get it recognized as a date using timeDate(): R>

oky.. thank you very much to all of you On Sat, Sep 16, 2017 at 2:06 PM, Eric Berger <ericjberger at gmail.com> wrote: > You can just use the same code that I provided before but now use your > dataset. Like this > > df <- read.csv(file="data2.csv",header=TRUE) > dates <- as.Date(paste(df$year,"-01-01",sep="")) > myXts <-

Hi, I have a csv file with different datatypes: 2009-01-01, character1, 10, 20.1 2009-01-02, character2, 11, 21.1 (I have attached the file to this post) I read this file with read.zoo as I want a zoo/xts timeseries: > t = read.zoo("./data.txt", sep=",", dec = ".", header=FALSE) If I look at the zoo data all integer/numeric columns are read as character: >

Hi all I have some tick-by-tick data and I have calculated the intraday returns. I want to sum up the intraday squared returns to calculate the daily volatility(or daily variance). I know that the s-plus FinMerics has the function aggregateSeries function that can be apply to daily data: aggregateSeries(x, Fun, by="daily"), but the counterpart function in R:applySeries can not be apply