Hi again,
Didn't realize that the example didn't even span a full day.
ssdf<-read.table(text="date_POSIX time_POSIX vmax
2018-02-01 00:00:00 27
2018-02-01 00:10:00 41
2018-02-01 00:20:00 46
2018-02-01 00:30:00 39
2018-02-01 00:40:00 34
2018-02-01 00:50:00 32
2018-02-01 01:00:00 37
2018-02-01 01:10:00 31
2018-02-01 01:20:00 26
2018-02-01 01:30:00 29
2018-02-01 01:40:00 24
2018-02-01 01:50:00 35",
header=TRUE,stringsAsFactors=FALSE)
# extract the hour
ssdf$hour<-
as.numeric(unlist(lapply(strsplit(ssdf$time_POSIX,":"),"[",1)))
# get the time of day as seconds from the time field
ssdf$mins<-
as.numeric(unlist(lapply(strsplit(ssdf$time_POSIX,":"),"[",2)))
# create an AM/PM variable
ssdf$ampm<-ifelse(ssdf$mins > 0 & ssdf$mins <= 30,"am","pm")
# drop first row
ssdf<-ssdf[-1,]
means<-aggregate(vmax~hour+ampm,ssdf,mean)
This does a full day. To do more, add the date_POSIX field to the
aggregate command. If you have the date and time in one field you'll
have to split that. That will distinguish the AM/PM means in each day
as well as hour.
Jim
On Thu, Dec 3, 2020 at 2:10 PM Jim Lemon <drjimle...@gmail.com> wrote:
>
> Hi Stefano,
> I read in your date-time as two separate fields for convenience. You
> can split your single field at the space to get the same result.
>
> ssdf<-read.table(text="date_POSIX time_POSIX vmax
> 2018-02-01 00:00:00 27
> 2018-02-01 00:10:00 41
> 2018-02-01 00:20:00 46
> 2018-02-01 00:30:00 39
> 2018-02-01 00:40:00 34
> 2018-02-01 00:50:00 32",
> header=TRUE,stringsAsFactors=FALSE)
> # get the time of day as seconds from the time field
> ssdf$seconds<-as.numeric(strptime(ssdf$time_POSIX,"%H:%M:%S"))
> # subtract whatever current date strptime guesses for the date
> ssdf$seconds<-ssdf$seconds-min(ssdf$seconds)
> # create an AM/PM variable
> ssdf$ampm<-ifelse(ssdf$seconds > 0 & ssdf$seconds <= 1800,"am","pm")
> means<-aggregate(vmax~ampm,ssdf,mean)
>
> Jim
>
> On Thu, Dec 3, 2020 at 4:55 AM Stefano Sofia
> <stefano.so...@regione.marche.it> wrote:
> >
> > Dear list users,
> > I have wind data with frequency of 10 minutes (three years data). For
> > simplicity let me use only max wind speed.
> > I need to reduce the frequency to 30 minutes, at 00 (taking the mean of
> > data at 40, 50 and 00 minutes) and at 30 (taking the mean of data at 10, 20
> > and 30 minutes) of each hour.
> >
> > The simple code here reported works well, but the column "interval" groups
> > data forward, not backward:
> >
> > init_day <- as.POSIXct("2018-02-01-00-00", format="%Y-%m-%d-%H-%M",
> > tz="Etc/GMT-1")
> > fin_day <- as.POSIXct("2018-02-01-02-00", format="%Y-%m-%d-%H-%M",
> > tz="Etc/GMT-1")
> > mydf <- data.frame(data_POSIX=seq(init_day, fin_day, by="10 mins"))
> > mydf$vmax <- round(rnorm(13, 35, 10))
> > mydf$interval <- cut(mydf$data_POSIX, , breaks="30 min")
> > means <- aggregate(vmax ~ interval, mydf, mean)
> >
> > data_POSIX vmax interval
> > 1 2018-02-01 00:00:00 27 2018-02-01 00:00:00
> > 2 2018-02-01 00:10:00 41 2018-02-01 00:00:00
> > 3 2018-02-01 00:20:00 46 2018-02-01 00:00:00
> > 4 2018-02-01 00:30:00 39 2018-02-01 00:30:00
> > 5 2018-02-01 00:40:00 34 2018-02-01 00:30:00
> > 6 2018-02-01 00:50:00 32 2018-02-01 00:30:00
> > ...
> >
> > I should work with
> >
> > data_POSIX vmax interval
> > 1 2018-02-01 00:00:00 27 2018-02-01 00:00:00
> > 2 2018-02-01 00:10:00 41 2018-02-01 00:30:00
> > 3 2018-02-01 00:20:00 46 2018-02-01 00:30:00
> > 4 2018-02-01 00:30:00 39 2018-02-01 00:30:00
> > 5 2018-02-01 00:40:00 34 2018-02-01 00:00:00
> > 6 2018-02-01 00:50:00 32 2018-02-01 00:00:00
> > ...
> >
> >
> > Is there a way to modify this code to groupp data correctly? (I would
> > prefer using only the base package)
> >
> > Thank you for your help
> > Stefano
> >
> >
> >
> > (oo)
> > --oOO--( )--OOo----------------
> > Stefano Sofia PhD
> > Civil Protection - Marche Region
> > Meteo Section
> > Snow Section
> > Via del Colle Ameno 5
> > 60126 Torrette di Ancona, Ancona
> > Uff: 071 806 7743
> > E-mail: stefano.so...@regione.marche.it
> > ---Oo---------oO----------------
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