similar to: Moving average

Hello all, Have some time series data stored in a data.frame, and am plotting it with ggplot2 (which is totally awesome). I have explored the documentation and mailing list archives, and I can't see any way to plot a 'smoother' that is just the K-step moving average. For example, imagine I had a data.frame called 'sleep' with 'date' as the date (from as.Date()) and

Hi all R users, Can anyone please let me know how to do the moving average with R? With regards, Abu _________________________________________________________________ Free games, great prizes - get gaming at Gamesbox.

> 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

Hi, I'm trying to figure out common approach on calculating MA on a dataset that contains column "time". After digging around, I believe functions rollmean and rollaply should be used. However I don't quite understand the requirements for the underlying data. Should it be zoo object type? Formatted in a special way? As an example, I'm looking to get calculated

I am looking for some help at removing low-frequency components from a signal, through Moving Average on a sliding window. I understand thiis is a smoothing procedure that I never done in my life before .. sigh. I searched R archives and found "rollmean", "MovingAverages {TTR}", "SymmetricMA". None of the above mantioned functions seems to accept the smoothing

Dear List, I have a data frame with approximately 500000 rows that looks like this: ?Date??? time??? value ? 19.07.1956????????? 12:00:00?????????????? 4.84 19.07.1956????????? 13:00:00?????????????? 4.85 19.07.1956????????? 14:00:00?????????????? 4.89 19.07.1956????????? 15:00:00?????????????? 4.94 19.07.1956????????? 16:00:00?????????????? 4.99 19.07.1956????????? 17:00:00?????????????? 5.01

Dear R People: I'm having trouble with something that should be very simple. I'm setting up a matrix outside of a loop and writing items into it during the loop. Here is the output: > glob3b("sites.info") dim 27 3 [1] "/raid1/osg-app" Error in xy[i, ] : incorrect number of dimensions Here is the function: > glob3b function(xx) { x.df <-

Hello Rers: Let's say I have a column that looks like this: Column 167.8 292.8 363.3 1.9 115.25 I want to manipulate the above data such that I only end up with this same column but only with its decimals and not its whole numbers. So the new column would look like this (but the real column is VERY long): Column .8 .8 .3 .9 .25 Any suggestions about how I could do this? -- Do all you

All, I have been just recently working with zoo objects for trading systems. Can someone please help with these basic questions? Given a daily time series downloaded using get.hist.quote() from the tseries package, ie...... startDate= as.Date("2000-01-01") endDate= as.Date("2011-01-29") frequency= 'd' s= get.hist.quote('IWF', start= startDate, end=

Hi, Can anyone suggest a clever way to compute a rolling weekly average of the columns in a matrix? The column bit is straightforward – use apply given a function which does what you want on a column. With regard to a particular column, the obvious way is to run a for loop indexing the last 7 days and computing the average . I simply would like to know if there is a better / quicker way.

Running R.app on Mac OS X 10.4 > version _ platform powerpc-apple-darwin8.6.0 arch powerpc os darwin8.6.0 system powerpc, darwin8.6.0 status major 2 minor 3.1 year 2006

Running R.app on Mac OS X 10.4 > version _ platform powerpc-apple-darwin8.6.0 arch powerpc os darwin8.6.0 system powerpc, darwin8.6.0 status major 2 minor 3.1 year 2006

R2.2, WinXP: I am using xyplot( ) to generate time plots of plasma concentration data. The following is an edited version of my code: xyplot(log.conc ~ time| group, data = foo, groups = subject, panel = function(x, y, panel.number, ...) { panel.superpose(x, y, subscripts = TRUE, groups = foo$group, type = 'l', col

A young colleague (Matthew Keller) who is an ardent fan of R is teaching me much about R and discussions surrounding its use. He recently showed me some of the sometimes heated discussions about Type I and Type III errors that have taken place over the years on this listserve. I'm presumptive enough to believe I might add a little clarity. I write this from the perspective of someone old

Hi R users, I have a time series variable that is only available at a monthly level for 1 years that I need to decompose to a weekly time series level - can anyone recommend a R function that I can use to decompose this series? eg. if month1 = 1200 I would to decompose so that the sum of the weeks for month1 equals 1200, etc.. Many thanks in advance for any help. -- View this message in

Is there a way of aggregating 'zoo' daily data according to day of week? eg all Thursdays I came across the 'nextfri' function in the documentation but am unsure how to change this so any day of week can be aggregated. I have used POSIX to arrange the data (not as 'zoo' series) according to day of week, but am curious if I've missed if a similar option available

Folks, Normally, in a data frame, one observation counts as one observation of the distribution. Thus one can easily produce a CDF and (in Splus atleast) use cdf.compare to compare the CDF (BTW: what is the R equivalent of the SPlus cdf.compare() function, if any?) However, if each point should not count equally, how can I weight the points before comparing the distributions? I was thinking of

Hello, I am trying to calculate a 31 day running mean in some temperature data along ROWS. Rollmean() works great along columns, but how do I perform this same action on my rows? The data is a matrix of 365 columns (days of the year) by 5,000 rows (lat/long coordinates). I would like to perform a 31 day running mean along the 365 days. I am new to R so any help would be greatly appreciated!

Hi Duncan I tried Ecdf and/or wtd.quantile from Hmisc and it is working (probably). Ecdf(x, q=.5) Ecdf(x, weights=xw,col=2, add=T, q=.5) wtd.quantile(x) 0% 25% 50% 75% 100% 10 10 10 100 300 wtd.quantile(x, weights=xw, type="i/n") 0% 25% 50% 75% 100% 10.0000 138.8667 192.5778 246.2889 300.0000 But could you please be more specific in this? >

On 24/11/2017 6:27 AM, PIKAL Petr wrote: > Dear all > > Strictly speaking it is not R question but as you are the most capable persons I know I give it a try. > > I am strugling with recalculation of number weighted to volume weighted distribution. > > Suppose I have objects (cubes) with size > > x<- c(rep(10,20), rep(100, 10), rep(300,5)) > I can get >