Hi Gabor-

Your suggestion did work. However, maybe I should explain more what I am trying to do. Once I have dataset (z) condensed to an hourly format I will compare it with another dataset (lets call it y). Here's a tidbit of dataset y:

doy yr mon day hr hgt1 hgt2 hgt3 co21 co22 co23 sig1 sig2 sig3 dif flag

244.02083 2005 09 01 00 2.5 5.8 9.1 -999.99 -999.99 -999.99 -999.99 -999.99 -999.99 -999.99 PRE 244.0625 2005 09 01 01 2.5 5.8 9.1 -999.99 -999.99 -999.99 -999.99 -999.99 -999.99 -999.99 PRE 244.10417 2005 09 01 02 2.5 5.8 9.1 -999.99 -999.99 -999.99 -999.99 -999.99 -999.99 -999.99 PRE 244.14583 2005 09 01 03 2.5 5.8 9.1 -999.99 -999.99 -999.99 -999.99 -999.99 -999.99 -999.99 PRE 244.1875 2005 09 01 04 2.5 5.8 9.1 -999.99 -999.99 -999.99 -999.99 -999.99 -999.99 -999.99 PRE 244.22917 2005 09 01 05 2.5 5.8 9.1 -999.99 -999.99 -999.99 -999.99 -999.99 -999.99 -999.99 PRE 244.27083 2005 09 01 06 2.5 5.8 9.1 -999.99 -999.99 -999.99 -999.99 -999.99 -999.99 -999.99 PRE

I need to match up the date/time of the datasets and then invoke a conditional statement, such as: if z$mph is >= 12 then keep y$co23 for the corresponding time/date stamp. But, when I convert "

(09/01/05 00:00:00) (09/01/05 01:00:00) (09/01/05 02:00:00)"
           9.27500            10.08333             9.20000

to a matrix to invoke this conditional statement, the time/date stamp doesn't appear. Does it have to do with the parentheses?

thanks for your time and help and happy new year:

sherri heck

Gabor Grothendieck wrote:
Try this:

aggregate(z$mph, trunc(time(z), "hour"), mean)
(09/01/05 00:00:00) (09/01/05 01:00:00) (09/01/05 02:00:00)
            9.27500            10.08333             9.20000

On Tue, Dec 30, 2008 at 6:30 PM, Sherri Heck <sh...@ucar.edu> wrote:
Dear All-

I have a dataset that is comprised of the following (LST = yymmddhhMM):



   LST   in     mph    Deg   DegF  DegF2    %    volts   Deg    mph2   w/m2
0509010000   0.00    7.8  216.9   45.1   -999   24.4   -999   -999   10.6
 0.2
0509010005   0.00    8.6  206.6   45.1   -999   25.2   -999   -999   11.7
 0.2
0509010010   0.00    7.8  199.2   44.9   -999   25.4   -999   -999   12.8
 0.2
0509010015   0.00    7.7  197.4   44.8   -999   25.4   -999   -999   10.4
 0.2
0509010020   0.00    7.6  203.9   44.8   -999   25.3   -999   -999   10.0
 0.2
0509010025   0.00    9.3  200.9   44.9   -999   25.3   -999   -999   11.8
 0.2
0509010030   0.00    9.4  200.3   44.7   -999   25.5   -999   -999   12.2
 0.2
0509010035   0.00   10.0  199.2   44.6   -999   25.9   -999   -999   13.0
 0.2
0509010040   0.00    9.5  201.5   44.5   -999   25.9   -999   -999   13.3
 0.2
0509010045   0.00   10.8  200.4   44.5   -999   26.1   -999   -999   13.0
 0.2
0509010050   0.00   11.8  198.4   44.5   -999   26.1   -999   -999   13.3
 0.2
0509010055   0.00   11.0  197.4   44.5   -999   25.5   -999   -999   13.3
 0.2
0509010100   0.00    9.7  202.0   44.6   -999   25.1   -999   -999   13.0
 0.2
0509010105   0.00    9.0  215.1   44.7   -999   24.9   -999   -999   12.2
 0.2
0509010110   0.00   10.1  223.1   44.6   -999   25.1   -999   -999   13.2
 0.2
0509010115   0.00   10.4  231.2   44.5   -999   25.5   -999   -999   12.0
 0.2
0509010120   0.00   11.0  237.4   44.2   -999   25.9   -999   -999   11.7
 0.2
0509010125   0.00   10.6  241.0   44.2   -999   26.0   -999   -999   11.8
 0.2
0509010130   0.00   11.1  242.2   44.1   -999   26.2   -999   -999   12.2
 0.2
0509010135   0.00   10.6  240.0   44.0   -999   26.5   -999   -999   11.5
 0.2
0509010140   0.00   10.1  241.0   44.0   -999   26.4   -999   -999   11.5
 0.2
0509010145   0.00    9.8  243.2   44.0   -999   26.6   -999   -999   10.7
 0.2
0509010150   0.00    9.3  240.3   43.9   -999   27.0   -999   -999   10.0
 0.2
0509010155   0.00    9.3  239.2   43.8   -999   26.8   -999   -999   10.0
 0.2
0509010200   0.00    9.2  240.1   43.8   -999   26.6   -999   -999    9.8
 0.2
0509010205   0.00    9.0  240.0   43.8   -999   26.6   -999   -999    9.4
 0.2
0509010210   0.00    9.2  245.0   43.9   -999   26.3   -999   -999    9.8
 0.2
0509010215   0.00    9.4  253.2   44.1   -999   26.4   -999   -999   10.6
 0.2

The data are recorded in 5 minute intervals and I would like to condense it
into hourly means for "mph". For example,  I would like the hourly avg of
mph so that the output would be as follows:

Year Month Day Hour mph
2005 1 1 0 12
2005 1 1 1 7
2005 1 1 2 11, etc.


It seems I am able to get the averages but not output the corresponding
date/time stamp.  From looking at previous help questions, I think I need to
us "ts" and "aggregate".   Gabor taught me how to convert the date/time
stamp to an easier to manage format (his help is shown below).  This is what
I have so far.
library(zoo)
library(chron)

z <- read.zoo("SPL 2005 2008 met data 5 min wout full hdr.txt", header =
TRUE, na.strings = -999,
format = "%y%m%d%H%M", FUN = as.chron,
colClasses = c("character", rep("numeric", 10)))
z.ts <- ts(z, frequency=12)    #avging 5 min intervals to get hourly avg.
  ww <- matrix(aggregate(z.ts[,2], FUN=mean))

any thoughts as to how to add the time stamp is greatly welcomed!

sherri heck

______________________________________________
R-help@r-project.org mailing list
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@r-project.org mailing list
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.

Reply via email to