[R] cumulative data monthly

Diego Avesani d|ego@@ve@@n| @end|ng |rom gm@||@com
Mon Jan 28 09:25:21 CET 2019


Dear Jeff, Dear Rui, Dear all,

Forget about the monthly things. I was trying to do two things at the same
time.
I try to explain myself. Thanks for your time and I really appreciate your
help.

I have  a long file with hourly precipitation from 2000 to 2018. I would
like to select only on e year or even half of a year and plot the
cumulative precipitation of it in order to compare it with the simulation
data that I have.

So far I was able only to read all the file:
dati <- read.csv(file="116.txt", header=FALSE, sep="," ,
na.strings="-999",skip = 6)

and to plot the entire cumulative:
P <- cumsum(dati$PREC)
plot(dati$DATAORA, P)

How can I choose only, for example, 2013 in order to have P?
thanks again


Diego



On Mon, 28 Jan 2019 at 02:36, Jeff Newmiller <jdnewmil using dcn.davis.ca.us>
wrote:

> I have no idea what you mean when you say "select starting date and ending
> date properly form [sic] datai$DATA". For one thing there is no column
> called DATA, and for another I don't know what starting dates and ending
> dates you might be interested in. If you need help to subset by time,
> perhaps you should ask a question about that instead.
>
> Here is a reproducible example of making monthly data and manipulating it
> using artificial data:
>
> ###############
> library(zoo)
> Sys.setenv( TZ = "GMT" )
> set.seed(42)
> dati <- data.frame( DATAORA = as.POSIXct( "2012-01-01" )
>                              + as.difftime( seq( 0, 365*3*24
>                                           ), units="hours" )
>                    )
> # terrible simulation of precipitation
> dati$PREC <- 0.1 * trunc( 50 * rbeta( nrow( dati ), 1, 80 ) )
> dati$ym <- as.yearmon( dati$DATAORA )
> # aggregate usually reduces the number of rows given to it
> datim <- aggregate( list( PREC = dati$PREC ) # data to summarize
>                    , dati[ , "ym", drop=FALSE ] # columns to group on
>                    , FUN = sum  # calculation on data
>                    )
> plot(PREC ~ ym, data=datim) # This is how I would usually look at it
> as.year <- function(x) floor( as.numeric( x ) ) # from help file on
> as.yearmon
> datim$y <- as.year( datim$ym )
> # ave typically does not change the number of rows given to it
> datim$PMES <- ave( datim$PREC, datim$y, FUN = cumsum)
> plot(PMES ~ ym, data=datim) # My guess as to what you asked for?
> ###############
>
> On Sun, 27 Jan 2019, Diego Avesani wrote:
>
> > Dear  Jeff, Dear Rui, Dear all,
> >
> > I will try Rui's solution as soon as possible.
> > If I could ask:
> > As a first step, I would like to follow Jeff's suggestion. I will
> represent the precipitation data with a cumulative
> > distribution, one for each year.
> > This follow that I would like to select the starting date and the ending
> date properly form dati$DATA in order to
> > perform the cumulative function.
> >
> > Could you help me on that.
> >
> > Again, really really thanks
> >
> > Diego
> >
> >
> >
> > On Sun, 27 Jan 2019 at 21:37, Jeff Newmiller <jdnewmil using dcn.davis.ca.us>
> wrote:
> >       Very succinct, Rui!
> >
> >       One warning to Diego.... automatic data recorders tend to use the
> local standard timezone year-round. R by
> >       default assumes that timestamps converted from character to
> POSIXct using the current timezone on your
> >       computer... which may not be in the same zone that the logger was
> in but even more commonly the computer
> >       follows daylight savings time. This leads to NAs showing up in
> your converted timestamps in spring and
> >       duplicated values in autumn as the data are misinterpreted. The
> easiest solution can be to use
> >
> >       Sys.setenv( TZ="GMT" )
> >
> >       though if you need the actual timezone you can use a zone name of
> the form "Etc/GMT+5" (5 hrs west of GMT).
> >
> >       Note that Rui's solution will only work correctly near the month
> transition if you pretend the data timezone
> >       is GMT or UTC. (Technically these are different so your mileage
> may vary but most implementations treat them
> >       as identical and I have not encountered any cases where they
> differ.)
> >
> >       On January 27, 2019 10:03:44 AM PST, Rui Barradas <
> ruipbarradas using sapo.pt> wrote:
> >       >Hello,
> >       >
> >       >See if the following can get you started.
> >       >It uses package CRAN zoo, function as.yearmon.
> >       >
> >       >dati$MES <- zoo::as.yearmon(dati$DATAORA)
> >       >PMES <- ave(dati$PREC, dati$MES, FUN = cumsum)
> >       >
> >       >plot(dati$DATAORA, PMES)
> >       >
> >       >
> >       >Hope this helps,
> >       >
> >       >Rui Barradas
> >       >
> >       >?s 15:25 de 27/01/2019, Diego Avesani escreveu:
> >       >> Dear all,
> >       >>
> >       >> I have a set of data with has hourly value:
> >       >>
> >       >> # ID
> >       >> # Lo
> >       >> # L
> >       >> # Q
> >       >> Time,    T, RH,PSFC,DIR,VEL10, PREC, RAD, CC,FOG
> >       >> yyyy-mm-dd hh:mm,   ?C,  %, hPa, ?N,  m/s, mm/h,W/m?,  %,-
> >       >> 2012-01-01 06:00, -0.1,100, 815,313,  2.6,  0.0,   0,  0,0
> >       >> 2012-01-01 07:00, -1.2, 93, 814,314,  4.8,  0.0,   0,  0,0
> >       >> 2012-01-01 08:00,  1.7, 68, 815,308,  7.5,  0.0,  41, 11,0
> >       >> 2012-01-01 09:00,  2.4, 65, 815,308,  7.4,  0.0, 150, 33,0
> >       >> .....
> >       >> .....
> >       >>
> >       >> I was able to read it,  create my-own data frame and to plot the
> >       >total
> >       >> cumulative function.
> >       >> This is basically what I have done:
> >       >>
> >       >> dati <- read.csv(file="116.txt", header=FALSE, sep="," ,
> >       >> na.strings="-999",skip = 6)
> >       >> colnames(dati)=c("DATAORA","T", "RH","PSFC","DIR","VEL10",
> "PREC",
> >       >"RAD",
> >       >> "CC","FOG")
> >       >>
> >       >> dati$DATAORA<-as.POSIXct(strptime(dati$DATAORA,format="%Y-%m-%d
> >       >%H:%M"))
> >       >>
> >       >>
> >       >> P <- cumsum(dati$PREC)
> >       >> plot(dati$DATAORA, P)
> >       >>
> >       >> I would like to select the data according to an starting and
> ending
> >       >date.
> >       >> In addition, I would like to plot the monthly and not the total
> one.
> >       >> I mean, I would like to have a cumulative plot for each month
> of the
> >       >> selected year.
> >       >>
> >       >> I am struggling with "ddply" but probably it is the wrong way.
> >       >>
> >       >> Could someone help me?  Really Really thanks,
> >       >>
> >       >>
> >       >> Diego
> >       >>
> >       >>      [[alternative HTML version deleted]]
> >       >>
> >       >> ______________________________________________
> >       >> R-help using r-project.org mailing list -- To UNSUBSCRIBE and more,
> see
> >       >> https://stat.ethz.ch/mailman/listinfo/r-help
> >       >> PLEASE do read the posting guide
> >       >http://www.R-project.org/posting-guide.html
> >       >> and provide commented, minimal, self-contained, reproducible
> code.
> >       >>
> >       >
> >       >______________________________________________
> >       >R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
> >       >https://stat.ethz.ch/mailman/listinfo/r-help
> >       >PLEASE do read the posting guide
> >       >http://www.R-project.org/posting-guide.html
> >       >and provide commented, minimal, self-contained, reproducible code.
> >
> >       --
> >       Sent from my phone. Please excuse my brevity.
> >
> >
> >
>
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