[R] loop is going to take 26 hours - needs to be quicker!

[Jenny Barnes]

Dear R-help,

I forgot to mention that I need the array in that format because I am going to 
do the same thing for another dataset of precipitation (ncep.data2) so they are 
both arrays of dimensions [144,72,46] so that I can correlate them globally and 
plot a visual image of the global correlations between the 2 datasets.... One of 
the datasets has a land mask applied to it already so it should be clear to see 
the land and pick ot the locations (i.e.over Europe) where there is strongest 
and weakest correlation.....that is the ultimate goal.

Following Rainer's response I should also point out that the columns in 
gpcc.data2 (with dimensions dim(gpcc.data2) = [476928,5]) are:

[,1]="Year", [,2]="month" (which is just january so always 1), [,3]="latitude", 
[,4]="longitude" and [,5]="data". All I want in the gpcc.array is the data not 
the longitudes and latitude values...hope that helps clear it up a bit!

I look forward to hearing any more ideas, thanks again for your time in reading 
this,

Jenny Barnes

>
>Jenny Barnes wrote:
>> Dear R-help,
>> 
>> I have a loop, which is set to take about 26 hours to run at the rate it's 
going 
>> - this is ridiculous and I really need your help to find a more efficient way 
of 
>> loading up my array gpcc.array:
>> 
>> #My data is stored in a table format with all the data in one long column 
>> #running though every longitute, for every latitude, for every year. The 
>> #original data is sotred as gpcc.data2 where dim(gpcc.data2) = [476928,5] 
where 
>> #the 5th column is the data:
>> 
>> #make the array in the format I need [longitude,latitude,years]
>> 
>> gpcc.array <- array(NA, c(144,72,46)) 
>> 
>> n=0
>> for(k in 1:46){
>> for(j in 1:72){
>> for(i in 1:144){
>> n <- n+1
>> gpcc.array[i,j,k] <- gpcc.data2[n,5]
>> print(j)
>> }
>> }
>> }
>
>I don't know if it is faster - but adding three columns to qpcc.data, 
>one for longitude, one for lattitude and one for year (using rep() as 
>they are in sequence) and the using reshape() might be faster?
>
>
>> 
>> So it runs through all the longs for every lat for every year - which is the 
>> order the data is running down the column in gpcc.data2 so n increses by 1 
each 
>> time and each data point is pulled off....
>> 
>> It needs to be a lot quicker, I'd appreciate any ideas!
>> 
>> Many thanks for taking time to read this,
>> 
>> Jenny Barnes
>> 
>> ~~~~~~~~~~~~~~~~~~
>> Jennifer Barnes
>> PhD student - long range drought prediction
>> Climate Extremes
>> Department of Space and Climate Physics
>> University College London
>> Holmbury St Mary, Dorking
>> Surrey
>> RH5 6NT
>> 01483 204149
>> 07916 139187
>> Web: http://climate.mssl.ucl.ac.uk
>> 
>> ______________________________________________
>> R-help at stat.math.ethz.ch 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.
>
>
>-- 
>Rainer M. Krug, Dipl. Phys. (Germany), MSc Conservation
>Biology (UCT)
>
>Department of Conservation Ecology and Entomology
>University of Stellenbosch
>Matieland 7602
>South Africa
>
>Tel:		+27 - (0)72 808 2975 (w)
>Fax:		+27 - (0)86 516 2782
>Fax:		+27 - (0)21 808 3304 (w)
>Cell:		+27 - (0)83 9479 042
>
>email:	RKrug at sun.ac.za
>       	Rainer at krugs.de

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Jennifer Barnes
PhD student - long range drought prediction
Climate Extremes
Department of Space and Climate Physics
University College London
Holmbury St Mary, Dorking
Surrey
RH5 6NT
01483 204149
07916 139187
Web: http://climate.mssl.ucl.ac.uk