zoo

I have a daily zoo (xts) with a few decades of data in the following format: head(almorol) 1973-10-02 1973-10-03 1973-10-04 1973-10-05 1973-10-06 1973-10-07 183.9 208.2 153.7 84.8 52.5 35.5 and I would like to plot just winter data (the full months of December, January and February). I found the subsetting for xts so I thought I could extract all the Decembers using: x<-apply.yearly(almorol, FUN=last(almorol, "1 month")) and then do something similar for Jan and Feb, but I get the following error: Error in get(as.character(FUN), mode = "function", envir = envir) : object 'FUN' of mode

I have a dataframe of "date", "company" and "return", reproducible by the code below: library(dplyr) n.dates <- 60 n.stocks <- 2 date <- seq(as.Date("2011-07-01"), by=1, len=n.dates) symbol <- replicate(n.stocks, paste0(sample(LETTERS, 5), collapse = "")) x <- expand.grid(date, symbol) x$return <- rnorm(n.dates*n.stocks, 0, sd = 0.05) names(x) <- c("date", "company", "return") With this dataframe, I can calculate the daily market average return and add that result into a new column "market.ret". x <- group_by(x, date) x <- mutate(x, market.ret = mean(x$return, na.rm = TRUE)) Now I want to

Guys, normally when you do something like: tmp = zoo(rnorm(100), 1:100) rollapply(tmp, 10, function(x) quantile(x, 0.05), align="right") Quite rightly rollapply will start calculating the value from the moment 10 elements are available. Unfortunately I need something that uses as much data as possible for the fist 10 observations, essentially a growing window of data till there is enough data to use a sliding window, e.g. 1, 1:2, 1:3, 1:4, etc. till we have at least 10 elements and then slide the window as usual. Is there a better way to do this than an ugly for loop? Why not just pad the

I have a .csv file containing 4 columns of data against a column of dates/times at one-minute intervals. Some timestamps are missing, so I'm trying to generate the missing dates/times and assign them NA values in the Y columns. I have previously done this with other .csv files with exactly the same formatting, with no issues. The code is: # read the csv file har10 = read.csv(fpath, header=TRUE); # set date har10$HAR.TS<-as.POSIXct(har10$HAR.TS,format="%y/%m/%d %H:%M") # convert to zoo df1.zoo<-zoo(har10[,-1],har10[,1]) #set date to Index # merge and generate NAs df2 <- merge(df1.zoo,zoo(,seq

I have zoo object with 10000+ rows. > head(tt) A B 2007-01-04 0.005945924 0.0021167475 2007-01-05 -0.004201991 -0.0080020024 2007-01-08 0.001740897 0.0045804104 2007-01-09 0.000000000 -0.0008163931 2007-01-10 -0.004503531 0.0032615812 2007-01-11 -0.005841138 0.0043863282 I have tried variations of the following line, but to no avail. rollapply(tt, 21, function(x) cor(x[,1],x[,2])) Every entry gave correlation of 1, looks like it's picking up the 1 off the diagonal of the correlation matrix. 2013-11-25 1 1 2013-11-26 1 1 2013-11-27 1 1 2013-11-29 1 1 2013-12-02 1 1 2013-12-03 1 1 What I really