Peter,
Thank you very much for your help.
On Fri, Oct 4, 2024 at 9:19 PM Petr Pikal wrote:
> Hallo
>
> you can extract POSIX object
>
> tv <- as.POSIXct(index(dt_train))
>
> and use cut together with aggregate
> cut(tv, "hour")
>
> aggregate(dt_train, list(cut(tv, "hour")), mean)
>
> 2014-10-06
Hallo
you can extract POSIX object
tv <- as.POSIXct(index(dt_train))
and use cut together with aggregate
cut(tv, "hour")
aggregate(dt_train, list(cut(tv, "hour")), mean)
2014-10-06 21:00:00 9.807692
2014-10-06 22:00:00 8.67
Cheers.
Petr
čt 3. 10. 2024 v 17:25 odesílatel roslinazairimah
Hi
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of Kapil Shukla
> Sent: Saturday, March 01, 2014 6:23 PM
> To: r-help@r-project.org
> Subject: [R] Time Series Data Analysis
>
> Hi All
>
> I am totally new to R so this question
On 26.08.2011 17:43, Simmons, Ryan wrote:
I am working with data from the USGS with data every 30 minutes from
4/27/2011 to 8/25/2011.
I am having trouble with setting the frequency.
My R script is below:
shavers=read.csv("shavers.csv")
names(shavers)
[1] "agency_cd""site_no"
Hi:
Perhaps something like this, assuming DATE is a Date object (try str(plcm60)
to check) - if not, you need to use as.Date() to convert.
jandays <- data.frame(DATE = seq(as.Date('2003-01-01'), by = 'days', length
= 23))
merge(jandays, plcm60, by = 'DATE', all.x = TRUE)
HTH,
Dennis
On Wed, Nov
> From: ggrothendi...@gmail.com
> Date: Tue, 26 Oct 2010 00:37:05 -0400
> To: flym...@gmail.com
> CC: r-help@r-project.org
> Subject: Re: [R] Time series data with dropouts/gaps
>
> On Tue, Oct 26, 2010 at 12:28 AM, Bob Cunningh
On 10/25/2010 09:37 PM, Gabor Grothendieck wrote:
On Tue, Oct 26, 2010 at 12:28 AM, Bob Cunningham wrote:
I have time-series data from a pair of inexpensive self-logging 3-axis
accelerometers (http://www.gcdataconcepts.com/xlr8r-1.html). Since I'm not
sure of the vibration/shock spectrum I
On Tue, Oct 26, 2010 at 12:28 AM, Bob Cunningham wrote:
> I have time-series data from a pair of inexpensive self-logging 3-axis
> accelerometers (http://www.gcdataconcepts.com/xlr8r-1.html). Since I'm not
> sure of the vibration/shock spectrum I'm measuring, for my initial sensor
> characterizat
On Mar 20, 2010, at 1:17 PM, Gaathier Mahed wrote:
Dear Sir/madam
I am a new user to R. I have no background in coding or even
scripting. It seems as if R would be the best tool in order to analyse
large sets of data. I need to sum hourly readings for the day and then
sum daily into monthly re
ciated,
Chris
> CC: r-help@r-project.org
> From: dwinsem...@comcast.net
> To: w_chris_carle...@hotmail.com
> Subject: Re: [R] Time Series Data
> Date: Fri, 27 Nov 2009 10:10:36 -0500
>
>
> On Nov 27, 2009, at 9:55 AM, chris carleton wrote:
>
> >
> > Hi Al
On Nov 27, 2009, at 9:55 AM, chris carleton wrote:
Hi All,
I'm trying to analyze some time series data and I have run into
difficulty. I have decadal sun spot data and I want to separate the
very regular periodic function from the trend and noise. I looked
into using stl(), but the freq
If you convert your dates to an object d of Date class then
as.numeric(d) will be the number of days since the Epoch. See R News
4/1.
On Mon, Oct 19, 2009 at 2:20 PM, Marlin Keith Cox wrote:
> Hi all and thanks in advance.
>
> I am regressing Time and Weight, and then predicting Weight at
> diff
jullian day?
On Mon, Oct 19, 2009 at 1:20 PM, Marlin Keith Cox wrote:
> Hi all and thanks in advance.
>
> I am regressing Time and Weight, and then predicting Weight at
> different Time. The format of the Time data is day/month/year. How
> can I get R to use time series data such as this?
>
> K
Check out na.locf in the zoo package. Here we fill in
NAs going forward and just in case there were NAs
right at the beginning we fill them in backward as well.
library(zoo)
x <- as.Date(c(NA, "2000-01-01", NA))
x2 <- na.locf(x, na.rm = FALSE)
x2 <- na.locf(x2, fromLast = TRUE, na.rm = FALSE)
gi
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