Gabor,
That does it! I can't thank you enoughÂ…
Many thanks,
Tom
On Mon, Mar 26, 2012 at 7:22 AM, Gabor Grothendieck <ggrothendi...@gmail.com
> wrote:
> On Sun, Mar 25, 2012 at 10:20 PM, Thomas Adams <thomas.ad...@noaa.gov>
> wrote:
> > Gabor,
> >
> > Thank you for your help -- it did help me a lot. However, with my data:
> >
> > lead_time cycle r_squared fcst_date
> > 1 6 0 5.405095e-02 07/31/2010
> > 2 12 0 5.521620e-06 07/31/2010
> > 3 18 0 1.565910e-04 07/31/2010
> > 4 24 0 8.646822e-02 07/31/2010
> > 5 30 0 1.719604e-02 07/31/2010
> > 6 36 0 5.768113e-04 07/31/2010
> > 7 42 0 2.501269e-06 07/31/2010
> > 8 48 0 6.451727e-02 07/31/2010
> > 9 6 12 2.857931e-01 07/31/2010
> > 10 12 12 1.138635e-01 07/31/2010
> > 11 18 12 2.225503e-02 07/31/2010
> > 12 24 12 1.182031e-03 07/31/2010
> > 13 30 12 8.841142e-04 07/31/2010
> > 14 36 12 1.082490e-01 07/31/2010
> > 15 42 12 1.502887e-05 07/31/2010
> > 17 6 0 8.689588e-02 08/01/2010
> > 18 12 0 5.884336e-04 08/01/2010
> > 19 18 0 2.219316e-07 08/01/2010
> > 20 24 0 3.960752e-02 08/01/2010
> > 21 30 0 1.087413e-04 08/01/2010
> > 23 42 0 3.583030e-07 08/01/2010
> > 24 48 0 2.907109e-05 08/01/2010
> > 25 6 12 8.693451e-02 08/01/2010
> > 26 12 12 3.208215e-02 08/01/2010
> > 27 18 12 0.000000e+00 08/01/2010
> > 28 6 0 2.962669e-02 08/02/2010
> > 29 6 12 2.363506e-05 08/02/2010
> > 30 12 12 9.050178e-03 08/02/2010
> >
> > from:
> >
> >> z <- read.zoo(q,index = 4, FUN = as.yearmon, format =
> "%m/%d/%Y",aggregate
> >> = mean)
> >
> > I get:
> >> z
> > lead_time cycle r_squared
> > Jul 2010 25.60000 5.600000 0.05034771
> > Aug 2010 18.46154 4.615385 0.02191903
> >
> > what I need is to NOT have the lead_time and cycle averaged, but only
> have
> > the r_squared values averaged by lead_time and cycle. I can not seem to
> > figure out the correct syntax to do this. I assume I use something like:
> >
> > q_agg<-aggregate(q,by=list(q$lead_time,q$cycle),index = 4, FUN =
> as.yearmon,
> > format = "%m/%d/%Y")
> >
> > but I get errors or nonsense when I follow with...
> >
> > z <- read.zoo(q_agg,index = 4, FUN = as.yearmon, format =
> > "%m/%d/%Y",aggregate = mean)
> >
> > or some variation of this.
> >
> > Regards,
> > Tom
> >
> >
> >
> > On Sat, Mar 24, 2012 at 10:58 PM, Gabor Grothendieck
> > <ggrothendi...@gmail.com> wrote:
> >>
> >> On Sat, Mar 24, 2012 at 10:44 PM, Thomas Adams <thomas.ad...@noaa.gov>
> >> wrote:
> >> > All:
> >> >
> >> > I have a SQlite database where I have stored some verification data by
> >> > date
> >> > & time (cycle Z/UTC), lead_time as well as type, duration, etc. I
> would
> >> > like to analyze & plot the data as monthly averages. I have looked at
> a
> >> > bunch of examples which use some combination of zoo and aggregate,
> but I
> >> > have not been able to successfully apply bits and pieces from the
> >> > examples
> >> > I have found. Any help is appreciated. BTW, I calculate mae (mean
> >> > absolute
> >> > error), mse (mean squared error), me (mean error), and other measures
> >> > obtained by using the R verification package.
> >> >
> >> > The example below is limited to 20 records and shows lead_time,
> >> > r_squared,
> >> > (forecast) cycle, fcst_date (forecast date) -- the full data set is
> just
> >> > over 2 years of daily data with 3 forecast cycles (00Z, 12Z, and 18Z)
> >> > daily.
> >> >
> >> > >From my query, below) how do I construct an appropriate data
> structure
> >> > to
> >> > analyze & plot the data as monthly averages?
> >> >
> >> > Regards,
> >> > Tom
> >> >
> >> >> q<-dbGetQuery(con,"select lead_time,r_squared,cycle,fcst_date from
> >> > verify_table where duration=6 limit 20")
> >> >> q
> >> > lead_time r_squared cycle fcst_date
> >> > 1 6 5.405095e-02 00 07/31/2010
> >> > 2 12 5.521620e-06 00 07/31/2010
> >> > 3 18 1.565910e-04 00 07/31/2010
> >> > 4 24 8.646822e-02 00 07/31/2010
> >> > 5 30 1.719604e-02 00 07/31/2010
> >> > 6 36 5.768113e-04 00 07/31/2010
> >> > 7 42 2.501269e-06 00 07/31/2010
> >> > 8 48 6.451727e-02 00 07/31/2010
> >> > 9 6 2.857931e-01 12 07/31/2010
> >> > 10 12 1.138635e-01 12 07/31/2010
> >> > 11 18 2.225503e-02 12 07/31/2010
> >> > 12 24 1.182031e-03 12 07/31/2010
> >> > 13 30 8.841142e-04 12 07/31/2010
> >> > 14 36 1.082490e-01 12 07/31/2010
> >> > 15 42 1.502887e-05 12 07/31/2010
> >> > 16 48 NA 12 07/31/2010
> >> > 17 6 8.689588e-02 00 08/01/2010
> >> > 18 12 5.884336e-04 00 08/01/2010
> >> > 19 18 2.219316e-07 00 08/01/2010
> >> > 20 24 3.960752e-02 00 08/01/2010
> >> >
> >>
> >> Try this:
> >>
> >> Lines <- "lead_time r_squared cycle fcst_date
> >> 1 6 5.405095e-02 00 07/31/2010
> >> 2 12 5.521620e-06 00 07/31/2010
> >> 3 18 1.565910e-04 00 07/31/2010
> >> 4 24 8.646822e-02 00 07/31/2010
> >> 5 30 1.719604e-02 00 07/31/2010
> >> 6 36 5.768113e-04 00 07/31/2010
> >> 7 42 2.501269e-06 00 07/31/2010
> >> 8 48 6.451727e-02 00 07/31/2010
> >> 9 6 2.857931e-01 12 07/31/2010
> >> 10 12 1.138635e-01 12 07/31/2010
> >> 11 18 2.225503e-02 12 07/31/2010
> >> 12 24 1.182031e-03 12 07/31/2010
> >> 13 30 8.841142e-04 12 07/31/2010
> >> 14 36 1.082490e-01 12 07/31/2010"
> >>
> >> library(zoo)
> >> q <- read.table(text = Lines)
> >>
> >> z <- read.zoo(q, index = 4, FUN = as.yearmon, format = "%m/%d/%Y",
> >> aggregate = mean)
> >> plot(z)
> >>
> >> See the 5 vignettes that come with zoo as well as ?read.zoo, ?plot.zoo
> >> and ?xyplot.zoo
> >>
> >>
> >> --
> >> Statistics & Software Consulting
> >> GKX Group, GKX Associates Inc.
> >> tel: 1-877-GKX-GROUP
> >> email: ggrothendieck at gmail.com
> >
> >
> >
> >
> > --
> >
> > Thomas E Adams
> > National Weather Service
> > Ohio River Forecast Center
> > 1901 South State Route 134
> > Wilmington, OH 45177
> >
> > EMAIL: thomas.ad...@noaa.gov
> >
> > VOICE: 937-383-0528
> > FAX: 937-383-0033
> >
> >
>
> Regarding the revised question:
>
> library(zoo) # yearmon
> library(chron) # chron
> library(ggplot2) # qplot
>
> q <- read.table(text = Lines, as.is = TRUE)
>
> # aggregate by lead_time, cycle and year/month
> q.ag <- aggregate(r_squared ~.,
> transform(q, fcst_date =
> as.Date(as.yearmon(chron(fcst_date)))),
> mean)
>
> # plot in cycle by lead_time grid
> qplot(fcst_date, r_squared, data = q.ag) + facet_grid(cycle ~ lead_time)
>
> See ?aggregate and the ggplot2 package documentation .
>
> --
> Statistics & Software Consulting
> GKX Group, GKX Associates Inc.
> tel: 1-877-GKX-GROUP
> email: ggrothendieck at gmail.com
>
--
Thomas E Adams
National Weather Service
Ohio River Forecast Center
1901 South State Route 134
Wilmington, OH 45177
EMAIL: thomas.ad...@noaa.gov
VOICE: 937-383-0528
FAX: 937-383-0033
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