cumulative data monthly

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cumulative data monthly

Diego Avesani
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/m²,  %,-
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]]

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Re: cumulative data monthly

Jeff Newmiller
Are you looking for a plot where each point represents a month? Or a plot  where each point represents the accumulated precipitation so far that month? The latter seems closer to your computations so far, but doesn't seem like a typical way to present precipitation data...

On January 27, 2019 7:25:17 AM PST, Diego Avesani <[hidden email]> wrote:

>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/m²,  %,-
>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]]
>
>______________________________________________
>[hidden email] 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.

______________________________________________
[hidden email] 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.
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Re: cumulative data monthly

Rui Barradas
In reply to this post by Diego Avesani
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

Às 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/m²,  %,-
> 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]]
>
> ______________________________________________
> [hidden email] 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.
>

______________________________________________
[hidden email] 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.
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Re: cumulative data monthly

Jeff Newmiller
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 <[hidden email]> 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$DATAORA, PMES)
>
>
>Hope this helps,
>
>Rui Barradas
>
>Às 15:25 de 27/01/2019, Diego Avesani escreveu:
>> Dear all,
>>
>> I have a set of data with has hourly value:
>>
>> # ID
>> # Lo
>> # L
>> # Q
>> Time,    T, RH,PSFC,DIR,VEL10, PREC, RAD, CC,FOG
>> yyyy-mm-dd hh:mm,   °C,  %, hPa, °N,  m/s, mm/h,W/m²,  %,-
>> 2012-01-01 06:00, -0.1,100, 815,313,  2.6,  0.0,   0,  0,0
>> 2012-01-01 07:00, -1.2, 93, 814,314,  4.8,  0.0,   0,  0,0
>> 2012-01-01 08:00,  1.7, 68, 815,308,  7.5,  0.0,  41, 11,0
>> 2012-01-01 09:00,  2.4, 65, 815,308,  7.4,  0.0, 150, 33,0
>> .....
>> .....
>>
>> I was able to read it,  create my-own data frame and to plot the
>total
>> cumulative function.
>> This is basically what I have done:
>>
>> dati <- read.csv(file="116.txt", header=FALSE, sep="," ,
>> na.strings="-999",skip = 6)
>> colnames(dati)=c("DATAORA","T", "RH","PSFC","DIR","VEL10", "PREC",
>"RAD",
>> "CC","FOG")
>>
>> dati$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]]
>>
>> ______________________________________________
>> [hidden email] 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.
>>
>
>______________________________________________
>[hidden email] 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.

______________________________________________
[hidden email] 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.
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Re: cumulative data monthly

Diego Avesani
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 <[hidden email]>
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 <[hidden email]>
> 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$DATAORA, PMES)
> >
> >
> >Hope this helps,
> >
> >Rui Barradas
> >
> >Às 15:25 de 27/01/2019, Diego Avesani escreveu:
> >> Dear all,
> >>
> >> I have a set of data with has hourly value:
> >>
> >> # ID
> >> # Lo
> >> # L
> >> # Q
> >> Time,    T, RH,PSFC,DIR,VEL10, PREC, RAD, CC,FOG
> >> yyyy-mm-dd hh:mm,   °C,  %, hPa, °N,  m/s, mm/h,W/m²,  %,-
> >> 2012-01-01 06:00, -0.1,100, 815,313,  2.6,  0.0,   0,  0,0
> >> 2012-01-01 07:00, -1.2, 93, 814,314,  4.8,  0.0,   0,  0,0
> >> 2012-01-01 08:00,  1.7, 68, 815,308,  7.5,  0.0,  41, 11,0
> >> 2012-01-01 09:00,  2.4, 65, 815,308,  7.4,  0.0, 150, 33,0
> >> .....
> >> .....
> >>
> >> I was able to read it,  create my-own data frame and to plot the
> >total
> >> cumulative function.
> >> This is basically what I have done:
> >>
> >> dati <- read.csv(file="116.txt", header=FALSE, sep="," ,
> >> na.strings="-999",skip = 6)
> >> colnames(dati)=c("DATAORA","T", "RH","PSFC","DIR","VEL10", "PREC",
> >"RAD",
> >> "CC","FOG")
> >>
> >> dati$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]]
> >>
> >> ______________________________________________
> >> [hidden email] 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.
> >>
> >
> >______________________________________________
> >[hidden email] 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.
>

        [[alternative HTML version deleted]]

______________________________________________
[hidden email] 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.
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Re: cumulative data monthly

Jeff Newmiller
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$DATA in order to
> perform the cumulative function.
>
> Could you help me on that.
>
> Again, really really thanks
>
> Diego
>
>
>
> On Sun, 27 Jan 2019 at 21:37, Jeff Newmiller <[hidden email]> wrote:
>       Very succinct, Rui!
>
>       One warning to Diego.... automatic data recorders tend to use the local standard timezone year-round. R by
>       default assumes that timestamps converted from character to POSIXct using the current timezone on your
>       computer... which may not be in the same zone that the logger was in but even more commonly the computer
>       follows daylight savings time. This leads to NAs showing up in your converted timestamps in spring and
>       duplicated values in autumn as the data are misinterpreted. The easiest solution can be to use
>
>       Sys.setenv( TZ="GMT" )
>
>       though if you need the actual timezone you can use a zone name of the form "Etc/GMT+5" (5 hrs west of GMT).
>
>       Note that Rui's solution will only work correctly near the month transition if you pretend the data timezone
>       is GMT or UTC. (Technically these are different so your mileage may vary but most implementations treat them
>       as identical and I have not encountered any cases where they differ.)
>
>       On January 27, 2019 10:03:44 AM PST, Rui Barradas <[hidden email]> wrote:
>       >Hello,
>       >
>       >See if the following can get you started.
>       >It uses package CRAN zoo, function as.yearmon.
>       >
>       >dati$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]]
>       >>
>       >> ______________________________________________
>       >> [hidden email] 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.
>       >>
>       >
>       >______________________________________________
>       >[hidden email] 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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Re: cumulative data monthly

Diego Avesani
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 <[hidden email]>
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$DATA in order to
> > perform the cumulative function.
> >
> > Could you help me on that.
> >
> > Again, really really thanks
> >
> > Diego
> >
> >
> >
> > On Sun, 27 Jan 2019 at 21:37, Jeff Newmiller <[hidden email]>
> wrote:
> >       Very succinct, Rui!
> >
> >       One warning to Diego.... automatic data recorders tend to use the
> local standard timezone year-round. R by
> >       default assumes that timestamps converted from character to
> POSIXct using the current timezone on your
> >       computer... which may not be in the same zone that the logger was
> in but even more commonly the computer
> >       follows daylight savings time. This leads to NAs showing up in
> your converted timestamps in spring and
> >       duplicated values in autumn as the data are misinterpreted. The
> easiest solution can be to use
> >
> >       Sys.setenv( TZ="GMT" )
> >
> >       though if you need the actual timezone you can use a zone name of
> the form "Etc/GMT+5" (5 hrs west of GMT).
> >
> >       Note that Rui's solution will only work correctly near the month
> transition if you pretend the data timezone
> >       is GMT or UTC. (Technically these are different so your mileage
> may vary but most implementations treat them
> >       as identical and I have not encountered any cases where they
> differ.)
> >
> >       On January 27, 2019 10:03:44 AM PST, Rui Barradas <
> [hidden email]> wrote:
> >       >Hello,
> >       >
> >       >See if the following can get you started.
> >       >It uses package CRAN zoo, function as.yearmon.
> >       >
> >       >dati$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]]
> >       >>
> >       >> ______________________________________________
> >       >> [hidden email] 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.
> >       >>
> >       >
> >       >______________________________________________
> >       >[hidden email] 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.
> >
> >
> >
>
> ---------------------------------------------------------------------------
> Jeff Newmiller                        The     .....       .....  Go Live...
> DCN:<[hidden email]>        Basics: ##.#.       ##.#.  Live
> Go...
>                                        Live:   OO#.. Dead: OO#..  Playing
> Research Engineer (Solar/Batteries            O.O#.       #.O#.  with
> /Software/Embedded Controllers)               .OO#.       .OO#.  rocks...1k
> ---------------------------------------------------------------------------

        [[alternative HTML version deleted]]

______________________________________________
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Re: cumulative data monthly

Rui Barradas
Hello,

With on«bjects of class "Date" or "POSIXt", POSIXct" you can do

lubridate::year(date_obj)

to extract the year. Then aggregate by it.

Hope this helps,

Rui Barradas

Às 08:25 de 28/01/2019, Diego Avesani escreveu:

> 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 <[hidden email]
> <mailto:[hidden email]>> 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$DATA in order to
>      > perform the cumulative function.
>      >
>      > Could you help me on that.
>      >
>      > Again, really really thanks
>      >
>      > Diego
>      >
>      >
>      >
>      > On Sun, 27 Jan 2019 at 21:37, Jeff Newmiller
>     <[hidden email] <mailto:[hidden email]>> wrote:
>      >       Very succinct, Rui!
>      >
>      >       One warning to Diego.... automatic data recorders tend to
>     use the local standard timezone year-round. R by
>      >       default assumes that timestamps converted from character to
>     POSIXct using the current timezone on your
>      >       computer... which may not be in the same zone that the
>     logger was in but even more commonly the computer
>      >       follows daylight savings time. This leads to NAs showing up
>     in your converted timestamps in spring and
>      >       duplicated values in autumn as the data are misinterpreted.
>     The easiest solution can be to use
>      >
>      >       Sys.setenv( TZ="GMT" )
>      >
>      >       though if you need the actual timezone you can use a zone
>     name of the form "Etc/GMT+5" (5 hrs west of GMT).
>      >
>      >       Note that Rui's solution will only work correctly near the
>     month transition if you pretend the data timezone
>      >       is GMT or UTC. (Technically these are different so your
>     mileage may vary but most implementations treat them
>      >       as identical and I have not encountered any cases where
>     they differ.)
>      >
>      >       On January 27, 2019 10:03:44 AM PST, Rui Barradas
>     <[hidden email] <mailto:[hidden email]>> wrote:
>      >       >Hello,
>      >       >
>      >       >See if the following can get you started.
>      >       >It uses package CRAN zoo, function as.yearmon.
>      >       >
>      >       >dati$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]]
>      >       >>
>      >       >> ______________________________________________
>      >       >> [hidden email] <mailto:[hidden email]>
>     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.
>      >       >>
>      >       >
>      >       >______________________________________________
>      >       >[hidden email] <mailto:[hidden email]> 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.
>      >
>      >
>      >
>
>     ---------------------------------------------------------------------------
>     Jeff Newmiller                        The     .....       .....  Go
>     Live...
>     DCN:<[hidden email] <mailto:[hidden email]>>  
>          Basics: ##.#.       ##.#.  Live Go...
>                                             Live:   OO#.. Dead: OO#..
>     Playing
>     Research Engineer (Solar/Batteries            O.O#.       #.O#.  with
>     /Software/Embedded Controllers)               .OO#.       .OO#.
>     rocks...1k
>     ---------------------------------------------------------------------------
>

______________________________________________
[hidden email] 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
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Re: cumulative data monthly

Rui Barradas
Hello,

Please click <reply all> to keep this threaded.

What I was trying to say is to do something along the lines of

Y <- lubridate::year(dati$DATAORA)
Y2013 <- Y[Y == 2013]
PY2013 <- ave(dati$PREC, Y2013, FUN = cumsum)

plot(dati$DATAORA, PY2013)


Hope this helps,

Rui Barradas

Às 08:57 de 28/01/2019, Diego Avesani escreveu:

> Dear Rui,
>
> thanks a lot but I am quite new with R
>
> I have done this:
> dati$DATAORA<-as.POSIXct(strptime(dati$DATAORA,format="%Y-%m-%d %H:%M"))
>
> Could you please specify what I have to do with lubridate?
> Really Really thanks,
>
> Diego
>
>
>
> On Mon, 28 Jan 2019 at 09:33, Rui Barradas <[hidden email]
> <mailto:[hidden email]>> wrote:
>
>     Hello,
>
>     With on«bjects of class "Date" or "POSIXt", POSIXct" you can do
>
>     lubridate::year(date_obj)
>
>     to extract the year. Then aggregate by it.
>
>     Hope this helps,
>
>     Rui Barradas
>
>     Às 08:25 de 28/01/2019, Diego Avesani escreveu:
>      > 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
>     <[hidden email] <mailto:[hidden email]>
>      > <mailto:[hidden email]
>     <mailto:[hidden email]>>> 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
>      >     <[hidden email] <mailto:[hidden email]>
>     <mailto:[hidden email] <mailto:[hidden email]>>>
>     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
>      >     <[hidden email] <mailto:[hidden email]>
>     <mailto:[hidden email] <mailto:[hidden email]>>> 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]]
>      >      >       >>
>      >      >       >> ______________________________________________
>      >      >       >> [hidden email]
>     <mailto:[hidden email]> <mailto:[hidden email]
>     <mailto:[hidden email]>>
>      >     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.
>      >      >       >>
>      >      >       >
>      >      >       >______________________________________________
>      >      >       >[hidden email] <mailto:[hidden email]>
>     <mailto:[hidden email] <mailto:[hidden email]>> 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.
>      >      >
>      >      >
>      >      >
>      >
>      >  
>       ---------------------------------------------------------------------------
>      >     Jeff Newmiller                        The     .....    
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>     <mailto:[hidden email]> <mailto:[hidden email]
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