R help - Oct 2010 - doubt in climate variability analysis in R!

Hello all,

I  am trying to use "clim.pact" package for my work, but since this is
the  beginning for me to use gridded datasets in "R", I am having some
trouble.

I want to do seasonal analyses like   trends, anomalies, variograms, EOF and
probably kriging too to   downscale my 1 degree gridded data to 0.5.  So, as a
first step, I   compiled my entire dataset (with 25 yeears of daily dataset
which were   present as 25 files) into a single netcdf file.

Then, I downloaded clim.pact to do further analysis, which works but  seems  to
change dataset's original dimensions' order for  "retrieve.nc"
function (i.e. original lon, lat, time order was changed  to time,  lat,  lon
after using this function to get a subset). I am not  sure as  to why  this
happened and not able to get any plots such as box  plot  (showing  trend in
"lon", "lat", "time"), variogram (or variance),  
correlation  analysis done because of this conversion problem.

Further, basic "R"  functions seem to work well with objects such as  
dataframe, matrix ..etc  with time in a separate column, and the data   values
(precipitation, or  temperature) in a separate coulmn with   corresponding
station values  (lon/lat). So, now I have very little idea   about what I have
to do. Can anyone suggest me a better (probably  more refined way) way than what
I am currently doing to analyze these  data?

  

-- 
Regards,
Mahalakshmi
Graduate Student
#20, Department of Geography
Michigan State University
East Lansing, MI 48824
	[[alternative HTML version deleted]]

> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at
r-project.org]
> On Behalf Of govindas at msu.edu
> Sent: Friday, October 29, 2010 8:33 AM
> To: r-help at r-project.org
> Subject: [R] doubt in climate variability analysis in R!
> 
> 
> 
> Hello all,
> 
> I  am trying to use "clim.pact" package for my work, but since
this is
> the  beginning for me to use gridded datasets in "R", I am having
some
> trouble.
> 
> I want to do seasonal analyses like   trends, anomalies, variograms, EOF
> and probably kriging too to   downscale my 1 degree gridded data to 0.5.
> So, as a first step, I   compiled my entire dataset (with 25 yeears of
> daily dataset which were   present as 25 files) into a single netcdf file.
> 
> Then, I downloaded clim.pact to do further analysis, which works but
> seems  to change dataset's original dimensions' order for 
"retrieve.nc"
> function (i.e. original lon, lat, time order was changed  to time,  lat,
> lon after using this function to get a subset). I am not  sure as  to why
> this happened and not able to get any plots such as box  plot  (showing
> trend in "lon", "lat", "time"), variogram (or
variance),   correlation
> analysis done because of this conversion problem.
> 
> Further, basic "R"  functions seem to work well with objects such
as
> dataframe, matrix ..etc  with time in a separate column, and the data
> values (precipitation, or  temperature) in a separate coulmn with
> corresponding station values  (lon/lat). So, now I have very little idea
> about what I have to do. Can anyone suggest me a better (probably  more
> refined way) way than what I am currently doing to analyze these  data?
> 
> 
> 
> --
> Regards,
> Mahalakshmi
> Graduate Student
> #20, Department of Geography
> Michigan State University
> East Lansing, MI 48824
> 	[[alternative HTML version deleted]]
You should read the posing guide referenced at the bottom of every email.  It
will tell you how to ask questions and what information you need to provide in
order to get useful help.  At a minimum, we need a self-contained reproducible
example that demonstrates the problem you are having.  Provide say 10
observations and the code you used to subset the data, and maybe someone can
help you with a solution.

Dan

Daniel Nordlund
Bothell, WA USA

the following code was used .... 

library(akima)
library(clim.pact)
nc.1 <- "RF_80-05.nc"
nc.rf.in <- open.ncdf(nc.1)

x1 <- retrieve.nc(nc.1, v.nam="Rainfall",l.scale=FALSE, 
x.rng=c(70, 80), y.rng=c(10, 13.5))

#dimension is checked for the subset. (lon, lat, time) is changed as (time, lat,
lon)
>dim(x1$dat)
#[1] 2192    8   20

My question is - how can i convert this array into a dataframe so that i have
"lat", "lon", "precipitation values" in 3
different columns (note, I will have it for just a single day). So, my expected
dataframe will have rainfall values for each given pair of "lon" and
"lat".

Or is there any other better way to do my spatial variogram analysis for a
single day given the above dataset?

here is the link for the dataset.
HTTP://WWW.4SHARED.COM/FILE/4ZV0G3JR/RF_80-85.HTML

-- 
Regards,
Mahalakshmi
Graduate Student
#20, Department of Geography
Michigan State University
East Lansing, MI 48824 Quoting govindas@msu.edu:
>
>
> Hello all,
>
> I  am trying to use "clim.pact" package for my work, but since
this
> is  the  beginning for me to use gridded datasets in "R", I am
having
> some   trouble.
>
> I want to do seasonal analyses like   trends, anomalies, variograms, 
> EOF and probably kriging too to   downscale my 1 degree gridded data 
> to 0.5.  So, as a first step, I   compiled my entire dataset (with 25 
> yeears of daily dataset which were   present as 25 files) into a 
> single netcdf file.
>
> Then, I downloaded clim.pact to do further analysis, which works but  
> seems  to change dataset's original dimensions' order for  
> "retrieve.nc"   function (i.e. original lon, lat, time order was 
> changed  to time,  lat,  lon after using this function to get a 
> subset). I am not  sure as  to why  this happened and not able to get 
> any plots such as box  plot  (showing  trend in "lon",
"lat",
> "time"), variogram (or variance),   correlation  analysis done 
> because of this conversion problem.
>
> Further, basic "R"  functions seem to work well with objects such
as 
>  dataframe, matrix ..etc  with time in a separate column, and the 
> data   values (precipitation, or  temperature) in a separate coulmn 
> with   corresponding station values  (lon/lat). So, now I have very 
> little idea   about what I have to do. Can anyone suggest me a better 
> (probably  more refined way) way than what I am currently doing to 
> analyze these  data?
>
>  
>
> --
> Regards,
> Mahalakshmi
> Graduate Student
> #20, Department of Geography
> Michigan State University
> East Lansing, MI 48824
	[[alternative HTML version deleted]]